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Microclimate and building energy performance

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Microclimate and building energy performance

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Content MICROCLIMATE AND BUILDING ENERGY PERFORMANCE The Potential Impact of Trees on a Redlined Neighborhood by Kalsank Krupa Pai A Thesis Presented to the FACULTY OF THE USC SCHOOL OF ARCHITECTURE UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfilment of the Requirements for the Degree MASTER OF BUILDING SCIENCE August 2022 Copyright 2022 Kalsank Krupa Pai ii ACKNOWLEDGEMENTS I would like to express my gratitude to Karen M Kensek for the academic support and for inspiring this thesis. I would like to thank Marc Schiler for providing guidance and for his technical expertise in the subject. I would also like to thank Kyle Konis and Douglas Noble for their valuable feedback. I am grateful for the resources provided by landscape architect Esther Margulies while getting started with my thesis. My deepest appreciation goes to my family and my friends who are a constant source of support and laughter to me and I cannot thank them enough. Chair Karen M. Kensek, DPACSA, LEED BD+C Professor of Practice in Architecture, School of Architecture, University of Southern California kensek@usc.edu Committee members Marc Schiler, FASES Professor, School of Architecture, University of Southern California marcs@usc.edu Kyle Konis, Ph.D., AIA, Associate Professor, School of Architecture, University of Southern California kkonis@usc.edu iii Table of Contents ACKNOWLEDGEMENTS ................................................................................................ii LIST OF TABLES ........................................................................................................... vii LIST OF FIGURES ........................................................................................................ viii ABSTRACT ................................................................................................................... xiii 1. INTRODUCTION: TREES FOR MITIGATING EXTREME HEAT IN URBAN AREAS . 2 1.1 Mediterranean climate type: Los Angeles ............................................................. 4 1.2 Microclimate ........................................................................................................ 11 1.2.1 UHI and heat vulnerability in redlined neighborhoods .................................. 13 1.2.2 Trees- Mechanisms of heat mitigation ......................................................... 18 1.2.3 Computational fluid dynamics (CFD) and microclimate simulations ............. 20 1.2.4 Outdoor thermal comfort metrics .................................................................. 22 1.3 Real trees ............................................................................................................ 24 1.3.1 Tree characteristics that maximise cooling .................................................. 28 1.3.2 Leaf area density (LAD) ............................................................................... 29 1.3.3 Trees in simulations ..................................................................................... 30 1.4 Building Energy Modelling-The impact of trees on cooling loads ........................ 32 1.5 Summary ............................................................................................................. 32 2. IMPACT OF TREES ON COMFORT AND ENERGY ............................................... 34 2.1 Impact of trees on microclimate variables ........................................................... 34 2.2 Impact of trees on heat fluxes ............................................................................. 36 2.3 ENVI-met ............................................................................................................ 37 2.3.1 Albero- The vegetation modelling application in ENVI-met .......................... 40 2.3.2 Model area and grid settings ........................................................................ 42 iv 2.3.3 Output variables ........................................................................................... 45 2.3.4 A comparison of popular CFD tools used for microclimate simulations ....... 48 2.4 Coupling CFD and Building energy simulations .................................................. 51 2.4.1 Case study - CFD and weather data morphing ............................................ 56 2.4.2 Air temperature trends due to vegetation ..................................................... 61 2.5 Summary ............................................................................................................. 69 3. PRELIMINARY STUDIES AND SIMULATIONS ........................................................ 71 3.1 ENVI-met studies- Pathological test cases ......................................................... 71 3.2 Verification study: Harris Hall courtyard simulation ............................................. 83 3.3 ENVI-met: Strengths and limitations ................................................................... 90 3.4 Weather data and analysis period ....................................................................... 91 3.5 Coupling radiation: ENVI-met and the Ladybug tools.......................................... 94 3.5.1 The tree model in CFD (ENVI-met) and in BEM (the Ladybug tools) ........... 95 3.5.2 Obtaining canopy transmission for shade trees and gap fraction for BEM ... 96 3.6 Summary ............................................................................................................. 97 4. METHODOLOGY .................................................................................................... 100 4.1 CFD model ........................................................................................................ 103 4.1.1 Base case CFD model ............................................................................... 103 4.1.2 The site with shade trees ........................................................................... 106 4.2 CFD Simulation ................................................................................................. 111 4.2.1 Typical hot day ........................................................................................... 111 4.2.2 Typical cold day ......................................................................................... 112 4.2.3 Average/ Mild Day ...................................................................................... 113 4.3 CFD Output ....................................................................................................... 115 4.3.1 Output variables ......................................................................................... 115 4.3.2 Outdoor heat stress analysis ..................................................................... 116 4.4 Building energy simulations .............................................................................. 117 v 4.4.1 Weather file modification ............................................................................ 117 4.4.2 BEM ........................................................................................................... 119 4.4.3 Heating and cooling loads .......................................................................... 120 4.5 Summary ........................................................................................................... 121 5: DATA AND SUMMARY OF RESULTS ................................................................... 122 5.1 CFD output ........................................................................................................ 123 5.1.1 Typical hot day results ............................................................................... 124 5.1.2 Typical cold day results .............................................................................. 127 5.2 Building boundary conditions ............................................................................ 134 5.2.1 Typical hot day results ............................................................................... 134 5.2.2 Typical cold day results .............................................................................. 141 5.2.3 Mild day results .......................................................................................... 147 5.3 Weather data modification ................................................................................ 153 5.3.1 Typical hot day micrometeorology ............................................................. 154 5.3.2 Typical Cold day micrometeorology ........................................................... 156 5.3.3 Mild day micrometeorology ........................................................................ 158 5.4 Building energy modelling ................................................................................. 163 5.4.1 Baseline Building loads .............................................................................. 166 5.4.2 Shaded building loads ................................................................................ 171 5.5 Summary ........................................................................................................... 177 6. RESULTS AND DISCUSSION ................................................................................ 181 6.1 The impact of shade trees on ambient air cooling and outdoor heat stress ...... 181 6.1.1 Outdoor heat stress ................................................................................... 181 6.2 Accounting for the impact of trees on building energy performance ................. 195 6.2.1 Typical hot period heating and cooling loads ............................................. 200 6.2.2 Typical cold period heating and cooling loads ........................................... 203 6.2.3 Mild day heating and cooling loads ............................................................ 206 vi 6.3 Summary ........................................................................................................... 209 7. DISCUSSION AND FUTURE WORK ...................................................................... 215 7.1 Discussion ......................................................................................................... 215 7.2 Future work ....................................................................................................... 227 7.2.1 Short term considerations .......................................................................... 227 7.2.2 Long term research .................................................................................... 232 7.3 Conclusion ........................................................................................................ 234 REFERENCES ............................................................................................................ 236 APPENDICES ............................................................................................................. 239 vii LIST OF TABLES Table 1.1: Nine weather data fields in a typical .epw file used in EnergyPlus calculations ................................................................................................................ 8 Table 1.11: EnergyPlus weather data fields- Used and unused fields ..................... 10 Table 1.2.1: Local effort in increasing street tree cover (Margulies et al.) ................ 16 Table 2.3: ENVI-met calculations for a wall profile of more than 3 materials ............ 39 Table 2.4.21: Model and meteorological settings (Baghaeipoor and Nasrollahi 2019) ........................................................................................................................ 64 Table 2.4.22: EPW file settings for October 1 (Ayyad and S Sharples 2019) ........... 66 Table 2.4.23: Summary of the results (Ayyad and S Sharples 2019) ....................... 68 Table 3.1: Errors in output of a model with different grid resolutions ........................ 75 Table 3.3: Strengths and limitations of ENVI-met ..................................................... 91 Table 3.4: Increase in temperatures in LA ................................................................ 94 Table 3.6: Summary of preliminary study results ..................................................... 97 Table 4.1.1 Model domain and settings.................................................................. 104 Table 4.1.2: New trees modelled in the Albero application ..................................... 110 Table 4.3.1: Data collection and analysis of CFD output ........................................ 115 Table 4.4.3: Summary of simulations ..................................................................... 120 Table 5.2.1: Building boundary layer conditions in the baseline site ...................... 136 Table 5.2.11: Building boundary layer conditions in the shaded site ...................... 138 Table 5.2.2: Building boundary layer conditions in the baseline site ...................... 142 Table 5.2.21: Building boundary layer conditions in the baseline site .................... 144 Table 5.2.3: Building boundary layer conditions in the baseline site ...................... 148 Table 5.2.31: Building boundary layer conditions in the shaded site ...................... 151 Table 5.3.1: Updated weather data for the baseline and shaded building .............. 154 Table 5.3.2: Updated weather data for the baseline and shaded building .............. 157 Table 5.3.3: Updated weather data for the baseline and shaded building .............. 159 Table 5.4: Zone boundary layer type and construction set ..................................... 165 Table 5.41: Loads by program ............................................................................... 166 Table 5.4.1: Hourly Baseline cooling loads ............................................................ 166 Table 5.4.2: Hourly Baseline heating loads ............................................................ 169 Table 5.4.2: Hourly Shaded building cooling loads ................................................ 172 Table 5.4.21: Hourly Shaded building heating loads .............................................. 175 Table 5.3: Summary of heat stress temperatures and categories (at Sensor grid 01) .......................................................................................................................... 178 Table 5.31: Total building heating and cooling loads ............................................. 180 Table 6.2: Annual radiant heat gain and building heating and cooling loads .......... 197 Table 6.2.2: Heating and cooling loads on a typical cold day in LA ........................ 204 viii LIST OF FIGURES Figure 1: Locations classified by the Koppen-Geiger system and the classification legend. ....................................................................................................................... 4 Figure 1.1: Visualizing annual wind data from TMY3 weather data for Los Angeles; generated using the Ladybug tools for Grasshopper ................................................. 6 Figure 1.12: Annual dry bulb temperature (DBT) map of diurnal and seasonal temperature variations; generated using the Ladybug tools for Grasshopper ............ 7 Figure 1.13: Sun path diagram- each sphere represents solar position at a particular hour of year (HOY) and set to reflect DBT at that HOY; generated using the Ladybug tools for Grasshopper ............................................................................ 7 Figure 1.2: Microclimate spatial and temporal resolution (Blanken and Barry, 1970) ........................................................................................................................ 12 Figure 1.2.1 a) National-scale Land Surface Temperature Anomalies by HOLC security rating ........................................................................................................... 14 b) National-scale averages of underlying percent developed impervious surface .... 14 c) National-scale averages of percent tree canopy [35,36] by HOLC security rating (Green, ..................................................................................................................... 14 “Best,” A; Blue, “Still Desirable,” B; Yellow, “Definitely Declining,” C; Red, “Hazardous) (Hoffman et al. 2020) ........................................................................... 14 Figure 1.2.12: East Los Angeles locations chosen for the tree planting initiative (Margulies et al. 2021). ............................................................................................. 18 Figure 1.2.2: Impact of trees on micrometeorological conditions (Palme and Salvati, 2021) ........................................................................................................... 19 Figure 1.2.21: Trees in two placement scenarios- Parkway and private (https://pw.lacounty.gov/rmd/parkwaytrees/sidewalkrepairparkwaytrees.aspx) ....... 20 Figure 1.2.4: UTCI and the heat stress categories .................................................. 23 Figure 1.3: From top to bottom- C3 (African sumac tree) (https://rosevilletree.org/selecting-trees/african-sumac/) .......................................... 25 C4 plants- Sugarcane, maize, sorghum (http://bio1151b.nicerweb.com/Locked/media/ch10/c4-plants.html) ; ....................... 25 CAM- Crassulaceae family (https://www.khanacademy.org/science/biology/photosynthesis-in- plants/photorespiration--c3-c4-cam-plants/a/c3-c4-and-cam-plants-agriculture) ...... 25 Figure 1.31: Sunset climate zones; 22- Southern California (https://www.sunset.com/garden/climate-zones/sunset-climate-zone-los-angeles- area) ......................................................................................................................... 27 Figure 1.3.1.1: Relationship between LAI and LAD (Li et al. 2020) .......................... 28 Figure 1.3.2: Sparse Eucalyptus (left); dense Ficus(right) that can represent a shade tree ................................................................................................................ 29 Figure 1.3.3: Simulating trees in SimScale (How to Model Trees with Porous Media — Pedestrian Wind Comfort (slideshare.net) ................................................ 30 Figure 1.3.31: ENVI-met simulations account for evapotranspiration (Trees and Vegetation - ENVI-met) ............................................................................................ 31 ix Figure 2.3: A wall of 5 materials; The specific heat capacity of 2, 3 and 4 are recalculated to form one (Material 3) ........................................................................ 39 Figure 2.3.1: Aristid Lindenmayer and the L-systems fractal branching sequence in ENVI-met 5 for generating procedural trees (trees as fractal based systems rather than foliage floating in space). ....................................................................... 41 Figure 2.3.2: Grid-based modelling in the Spaces application of ENVI-met ............ 43 Figure 2.3.4: CFD output for 4 popular tools a) Butterfly, b) BlueCFD, c) ENVI- met and d) SimScale (Brook-Lawson and Holz 2020). ............................................. 50 Figure 2.4- Diurnal air temperature profiles measured at a residential site and the nearby airport (Sailor and Akbari 1992) .................................................................... 52 ................................................................................................................................. 52 Figure 2.41- AC Energy use for a typical summer day using various temperature profiles (Sailor and Akbari 1992). ............................................................................. 52 Figure 2.42: Strong(onion) and weak(ping-pong) coupling of air-flow and thermal equations (Hensen 1999) ......................................................................................... 54 Figure 2.4.1: Butterfly CFD for different wind directions, to obtain wind factors (Mackey et al. 2017) ................................................................................................ 57 Figure 2.4.11: Morphing the Meteorological file to create urban weather data for UTCI calculations at site (Mackey et. Al 2017). ........................................................ 59 Figure 2.4.12: Maps of Average UTCI over the Singapore Cold Week (a), Typical Week (b), and Hot Week (c). (Mackey et. Al 2017). ................................................. 60 Figure 2.4.21: ENVI-met model of the Atisaz complex ............................................. 63 Figure 2.4.22: Left: Simulated vs measured air temperature values; Right: RMSE of the deviation ......................................................................................................... 65 Figure 2.4.23: Site for validation of air temperature simulations for two locations- A and B that are under trees; A is under a larger and thicker canopy (Ayyad and S Sharples 2019) ..................................................................................................... 65 Figure 2.4.24: Temperature comparison for Location A (Ayyad and S Sharples 2019) ........................................................................................................................ 67 ................................................................................................................................. 67 Figure 2.4.25- Temperature comparison for Location B (Ayyad and S Sharples 2019) ........................................................................................................................ 67 Figure 3.1: Same model areas with different grid resolutions ................................... 73 Figure 3.11: Top: Surface temperatures: Coarse grid resolution Bottom: Fine grid resolution .................................................................................................................. 74 Figure 3.12: Setting up two different types of materials and wind settings ............... 76 Figure 3.13: Buoyant air flow .................................................................................... 77 Figure 3.14: Air and heat flow due to wind; wind speed at inflow= 2.1 m/s .............. 78 Figure 3.15: A test to determine surface temperatures of the baseline unshaded, model shaded by opaque wall and model shaded by a translucent material. ........... 79 Figure 3.16: A test for surface temperatures of the unshaded baseline, model shaded by a sparse tree (low LAD) and model shaded by a denser tree (greater LAD). ........................................................................................................................ 80 Figure 3.17: Surface temperatures of different facades in LEONARDO................... 80 Figure 3.18: Surface temperatures of the base case and shaded facades. ............. 81 x Figure 3.19: Surface temperatures of the base case and the façades shaded by trees ......................................................................................................................... 82 Figure 3.20: Surface temperatures of the shaded facades ....................................... 83 Figure 3.2 : Harris Hall courtyard model ................................................................... 84 Figure 3.21: Temperature readings by the FLIR E8 camera .................................... 85 Figure 3.22: Material and wall profile of the Harris Hall building ............................... 86 Figure 3.23: Albero model of the Ficus tree crown and the final ENVI-met model ... 86 Figure 3.24: Areas for comparison with their respective grids in the IR image. ........ 87 Figure 3.25: High degree of agreement between the simulated and measured values of the building wall. ....................................................................................... 88 Figure 3.26: High degree of agreement between the simulated and measured values of the single wall. .......................................................................................... 89 Figure 3.27: Thermal lag of the thermal mass .......................................................... 90 Figure 3.4: LA TMY3 file air temperatures in the period 1960-1990 when they were collected and processed; ................................................................................. 92 Generated using Climate Consultant 6.0 .................................................................. 92 Figure 3.41: Current weather data vs. historical weather ......................................... 93 Figure 4: Model area for analysis; .......................................................................... 101 Figure 4.1: The methodology diagram.................................................................... 102 Figure 4.1: Base case scenario; ENVI-met Spaces ............................................... 106 Figure 4.1.2: Additional shade trees proposed for planting along the south and west of facades and walkways (Margulies et al. 2021)........................................... 107 Figure 4.1.21: Site with a 105 additional shade trees and 28 existing trees (18% new tree canopy) - placement along the S and W facades and walkways ............. 108 Figure 4.1.22: List of approved trees by the City of LA (storymaps.arcgis.com) .... 109 Figure 4.2.1: Meteorology on the 21 st of September- Peak cooling day; from the LA-2020 morphed TMY3 file .................................................................................. 112 Figure 4.2.2: Meteorology on the 4 th of January- Typical cold day; from the morphed LA-2020 TMY3 file .................................................................................. 113 Figure 4.2.3: Mild day meteorological settings; from the LA-2020 morphed TMY3 file........................................................................................................................... 114 Figure 5.1: Analysis point 01 in the shaded Building canyon ................................. 123 Figure 5.1.1: Building canyon baseline-Hourly values on the 21st of September ... 124 Figure 5.1.12: Building canyon in the site with shade trees-Hourly values on the 21st of September .................................................................................................. 125 Figure 5.1.13: Baseline Hourly MRT and UTCI heatmap values on the 21st of September .............................................................................................................. 126 Figure 5.1.14: Site with shade trees- Hourly MRT and UTCI heatmap values on the 21st of September ............................................................................................ 127 Figure 5.1.2: Building canyon-Hourly values on the 4th of Jan .............................. 128 Figure 5.1.21: Building canyon-Hourly values on the 4th of Jan ............................ 129 Figure 5.1.23: Baseline conditions ......................................................................... 130 Figure 5.1.24:: Heatmaps of the shaded site .......................................................... 131 Figure 5.1.3: Building canyon baseline-Hourly values on the 21st of August ......... 132 Figure 5.1.31: Building canyon in the site with shade trees-Hourly values on the 21st of August ........................................................................................................ 132 xi Figure 5.1.32: Baseline Hourly MRT and UTCI heatmap values on the 21st of August .................................................................................................................... 133 Figure 5.1.33: Site with shade trees- Hourly MRT and UTCI heatmap values on the 21st of August .................................................................................................. 134 Figure 5.2.1: Difference in air temperatures at the north west facade .................... 135 Figure 5.2.2: Typical Cold day heat map at 2 pm at 3m section cut ....................... 141 Figure 5.2.3: Mild day heat map at 9AM at 3m section cut ..................................... 148 Figure 5.3.3: Replacing airport data with baseline wind speed and direction in the base scenario EPW file; a capture of weather data modification for the mild day, using the Elements application ............................................................................... 161 Figure 5.3.31: Verifying the psychrometric relationship between variables added in Elements; Ladybug tools (LBT) .......................................................................... 162 Figure 5.3.31:: Variables added in Elements have correct psychometric relationships (values in the blue panels in LBT match values highlighted in blue) RH doesn’t allow decimals in Elements .................................................................. 163 Figure 5.4: Top- Baseline model; bottom-shaded building model .......................... 164 Figure 5.31– UTCI and the heat stress categories; legend for the colours in the previous table ......................................................................................................... 179 Figure 6.1.1: Grid cell location 01-shaded; 02-unshaded parking lot with maximum MRT ....................................................................................................... 182 Figure 6.1.11: Peak UTCI at 12 noon on September 21st ...................................... 184 Figure 6.1.12: No sensor grids fall in the no heat stress range in the site with added shade trees.................................................................................................. 185 Figure 6.1.13: Sensor grids in the strong heat stress in the existing conditions and scenario with trees .......................................................................................... 186 Figure 6.1.14: UTCI Improvement on 21 st September ............................................ 187 Figure 6.1.15: UTCI Improvement on 4 th January .................................................. 188 Figure 6.1.16: Improvement in the areas that were added to the no heat stress zone (coloured green in the UTCI plots) ................................................................. 189 Figure 6.1.17: Area of the site within the no heat stress zone improved by 15.6% 190 Figure 6.1.18: UTCI Improvement on 21 st August .................................................. 191 Figure 6.1.19: left: Ambient air temperature reduction is between 0.1 and 0.4 o C; right- MRT reduced by as much as 40 o C in shade ................................................. 192 Figure 6.1.2: Left- Wind flow and direction on a typical hot day; right- Ambient air reduction is greater in areas of lower wind speed .................................................. 193 Figure 6.1.21: Jan 4th- Difference in air temperatures between the two scenarios at 2PM .................................................................................................................... 194 Figure 6.1.22: August 21st- Difference in air temperatures between the two scenarios at 9AM and 1PM respectively ................................................................ 195 Figure 6.2: Zone transmitted solar radiation- No trees scenario ............................. 196 Figure 6.21: Zone transmitted solar radiation- Shaded scenario ............................ 197 Figure 6.22: Net annual heating and cooling loads ................................................ 198 Figure 6.24: Site map and the building of interest .................................................. 199 Figure 6.2.1: Net heating and cooling loads for September 21-22nd ..................... 200 Figure 6.2.11: Heating and cooling load profile for September 21-22nd ................ 201 Figure 6.2.12: Indoor temperature and comfort hours profile during the hot period 203 xii Figure 6.2.2: Heating load profile ........................................................................... 205 Figure 6.2.21: Indoor temperature and comfort hours profile during the cold period ..................................................................................................................... 206 ............................................................................................................................... 207 Figure 6.2.3: Net loads on the mild day .................................................................. 207 Figure 6.2.31: Cooling load profile ......................................................................... 208 Figure 6.2.32: Comfort hours profile ....................................................................... 209 Figure 7.1: A proposed increase in tree canopy cover in a Ramona Gardens ....... 216 Figure 7.11: Harris Courtyard simulation results .................................................... 217 Figure 7.12: The CFD-BEM coupling methodology ................................................ 218 Figure 7.13: Heat stress was significantly reduced under shade trees .................. 219 Figure 7.14: Strong and heat moderate stress on site............................................ 220 Figure 7.15: High ambient air temperatures in August and September .................. 221 Figure 7.16:: Maximum ambient air temperature reductions on Jan 4 th at 2PM, and August 21 st at 9 AM and 1 PM respectively ..................................................... 222 Figure 7.17: Reduction in ambient air temperatures is limited when compared to MRT under the canopy; on a typical hot day .......................................................... 223 Figure 7.18: Cooling load reductions on a typical hot day ...................................... 224 Figure 7.19: An increase in comfort hours during the day ...................................... 225 Figure 7.2.1: Studying the impact of deciduous trees on building heating and cooling loads for different wall orientations and climate types ................................ 228 Figure 7.2.11:: Studying façade greening that can naturally cool indoor spaces and enhance outdoor thermal comfort; ENVI-met Database Manager ................... 229 Figure 7.2.12: Ambiguity in determining heat mitigation targets (https://plan.lamayor.org/) ...................................................................................... 230 Figure 7.2.13: LCZ scheme (Stewart and Oke 2012) ............................................. 231 xiii ABSTRACT The type of building and paving materials, the amount of shade or tree canopy cover, and the local climate zone typology contribute to the formation of a microclimate that is distinct from the general climate of an area. Urban heat mitigation measures largely involve manipulating one or a combination of the above characteristics to improve thermal comfort. For example, an effective heat mitigation strategy for a redlined neighborhood characterized by a lack of shade trees and extensive paved surfaces could be to plant more shade trees. Trees provide cooling by shading and evapotranspiration, and sometimes by wind sheltering effects. They contribute to changes in the microclimate boundary conditions around buildings in terms of the surface temperatures, ambient air temperatures, wind speed, direction and relative humidity. Modelling the performance aspects of trees and trees as porous media, and simulating the cooling by evapotranspiration due to trees have presented major challenges in research, and a workflow that incorporates the cooling effect of trees on building heating and cooling loads has largely been non-existent. The heat mitigation due to a tree planting initiative was quantified for the outdoor environment in terms of the universal thermal climate index (UTCI) and for building heating and cooling loads, for the base case scenario with a 6.6% tree canopy cover and the site with added shade trees. To find the impact of a 12% increase in tree canopy cover on outdoor thermal stress, and on the micro-climate variables required for building energy simulations (BES), computational fluid dynamics (CFD) simulations were carried out xiv using the software ENVI-met. For outdoor heat stress analysis, ENVI-met was used to model and simulate trees as porous media, which is required to account for ray transfer through canopy and represent shade and MRT reduction appropriately. A morphed TMY3 weather file for Los Angeles was used to represent current air temperatures to obtain appropriate comfort and energy outcomes. High MRT due to shortwave radiation in the absence of shade was the peak driver of heat stress in the mild and hot periods and the peak time of heat stress was at noon at peak radiation. In shade, heat stress was eliminated in the mild period, which means that areas under shade were brought to within the no heat stress zone, but was only mitigated in the hot period (strong heat stress to moderate heat stress). In the hot period, high ambient air temperatures caused thermal stress and due to limited ambient air cooling by trees, UTCI improvements were largely due to a significant drop in MRT in shade. The impact of the changes in microclimate variables on building heating and cooling loads were simulated using a modified EPW data file that included CFD results of hourly air temperature, wind speed, direction and relative humidity values at the building boundary layer. The results showed a decrease in ambient air temperatures by a maximum of 0.4 C in the typical hot period, increase in RH during daytime and decrease in wind speeds proportional to the magnitude in the base case scenario, at the building boundary layer. There was a significant decrease in cooling loads on the mild and hot days. There was a significant energy penalty at night on the typical hot days in September. The energy penalty on a typical cold day in January was not significant when compared to the total loads that day. The reduction in MRT due to shade was the most significant contributor xv to decreasing outdoor heat stress in the site. The study showed that ambient air cooling by evapotranspiration is not a predictable outcome and is limited when compared to the contribution of shade to improving the outdoor thermal environment. Keywords- Microclimate, ENVI-met, Coupling strategies. Hypothesis Increasing shade tree canopy in a formerly redlined neighborhood with a low tree canopy cover would mitigate outdoor thermal stress and decrease building heating and cooling loads. Research objectives 1. To compare surface temperatures (T surface) measured using the FLIR IR camera and the simulated surface temperature of a 1.5X1.5 m wall area of the south-east façade in Harris Hall courtyard. 2. To quantify and compare the potential heat mitigation due to increased shade tree canopy cover in a neighborhood in Los Angeles, in terms of the ambient air temperatures and the universal thermal climate index (UTCI) as metrics, with that of the base case scenario. 3. To quantify and compare the impact of potential heat mitigation due to increased shade tree canopy cover on residential heating and cooling loads, with that of the base case scenario. 2 1. INTRODUCTION: TREES FOR MITIGATING EXTREME HEAT IN URBAN AREAS Extreme temperatures in cities are exacerbated by climate change, anthropogenic sources of heat, the lack of vegetation cover, diminished airflow, and the geometry and materiality of urban surfaces that trap heat without radiating freely back to the sky (Palme and Salvati, 2021). Heat islands are distinct areas within the city that are hotter than their surroundings owing to the contribution of the above factors on trapping and retaining heat. Urban heat islands in cities can also result from land-use and zoning policies. For example, the historic redlining policies in the U.S resulted in hot neighborhoods characterized by extensive paved surfaces and a lack of shade trees. Urban heat islands impact occupant thermal comfort and the building heating and cooling loads. A lack of street shade leads to thermal discomfort not only from direct exposure to short-wave radiation but also indirectly due to elevated land surface temperatures (LSTs) that in turn raise the surrounding ambient air temperatures due to heat transfer by conduction and convection. Elevated LST is the most common indicator/characteristic of an urban heat island. Studies have shown an increase of up to 500% in building cooling energy needs by mid-century (Santamouris 2014). Furthermore, there is a greater annual energy demand due to the urban heat island (UHI) effect causing an “energy penalty” on urban buildings (Santamouris 2020). Heat mitigation strategies are an emerging area of research. A contextual understanding of the local climate zone shed light on the climate variables and comfort conditions of the study area of interest. To study the UHI effect and the heat mitigation by adding shade trees, it is important to study the various urban characteristics that result in the UHI effect, the heat transfer mechanisms that contribute to the effect and the software and workflow used to simulate the UHI and mitigation 3 scenarios. The chapter draws on the local climate in hot arid climate zone, microclimate in UHIs, urban trees and their impact on cooling loads of buildings in a UHI. The Koppen-Geiger climate classification is widely used to characterize the general climate of a location (Palme and Salvati 2021). It is used to categorize the different parts of the world into five broad climate types, each subdivided into more defined climates of smaller regions dictated by topography, local wind patterns and oceanic influence. (https://en.wikipedia.org/wiki/K%C3%B6ppen_climate_classification). The yellow region marked C with Csa, Csb and Csc subtypes represent locations characterized by warm summers and cold winters and blue regions marked as A with Af, Am and Aw/As subtypes depict locations characterized by tropical hot humid climate (Figure 1). 4 Figure 1: Locations classified by the Koppen-Geiger system and the classification legend. 1.1 Mediterranean climate type: Los Angeles The co-ordinates for Los Angeles are 34.05349° N, -118.24532° E. According to the Koppen Geiger weather classification, Los Angeles comprises coastal areas with the Csb climate type and inland areas characterized by Csa type climate (Palme and Salvati 2021). Los Angeles experiences seasonal rainfall. The summers are dry and rainfall occurs in the winter. Los Angeles generally has clear sky conditions for most of the year. Southern California or the inland areas are influenced by the Santa Ana winds, hot gusts of wind that blow from the north east desert regions and the south 5 westerly winds usually in late summer and early fall (Kolokotroni and Salvati, 2021). The climate type is further characterized by large diurnal and seasonal swings in temperature. Therefore, the buildings constructed in LA have both heating and cooling loads during the hot and cold parts of the day and the year respectively (Mediterranean climate - Wikipedia). Heating and cooling loads are determined by the heating (HDD) and cooling degree days (CDD) respectively. Heating and cooling degree days are based on a 65 O F base calculation. A city with an average low of 60 O F in the winter that lasts for 90 days (3 months) has 450 HDDs [(65-60) * 90]. These regions typically have 60-62.4 O F of average temperatures in the cold season and average high temperature greater than 72 O F in the warmest month (NWS JetStream MAX - Addition Köppen Climate Subdivisions (weather.gov)). The wind rose and annual dry bulb temperature maps help visualize the diurnal and seasonal variations. The wind speed is most frequent and at the highest from the WSW direction (210-280 O ) according to TMY3 data for Los Angeles (Figure 1.1). The wind rose does not contain wind temperature information. 6 Figure 1.1: Visualizing annual wind data from TMY3 weather data for Los Angeles; generated using the Ladybug tools for Grasshopper The dry bulb chart shows the highest average daytime temperatures from June to September (Figure 1.1.2). The maximum temperature according to the TMY3 weather data visualized using the chart is 32.2 O C with a minimum of 5.6 O C. The typical days that tend to be used as analysis periods in simulations are the summer solstice (June 21), winter solstice (December 21) and the equinox (March 21). 7 Figure 1.12: Annual dry bulb temperature (DBT) map of diurnal and seasonal temperature variations; generated using the Ladybug tools for Grasshopper The sun path diagram in the Ladybug tools is a useful tool to customize and represent the solar position for different hours of the year (HOYs) as spheres coloured with temperatures from the dry bulb chart. The diagram helps visualize both the solar angles and temperatures (Figure 1.13). Figure 1.13: Sun path diagram- each sphere represents solar position at a particular hour of year (HOY) and set to reflect DBT at that HOY; generated using the Ladybug tools for Grasshopper 8 The weather data used for climate analysis is the Los Angeles Typical Meteorological Year-3 (TMY3) file from the EnergyPlus website that represents climate from 1967-1977. The EnergyPlus weather (.epw) file contains numerous meteorological data required for calculations, in the format that can be read by EnergyPlus, a validated building energy simulation (BES) engine and also a popular opensource BES tool (Table 1.1). Table 1.1: Nine weather data fields in a typical .epw file used in EnergyPlus calculations Among these, the site -specific variables are the dry bulb temperatures, relative humidity, wet bulb temperature, atmospheric pressure, dew point temperature and wind speed. A CFD simulation is required to assess how each variable may undergo changes due to trees when compared to a base case scenario of no tree cover. Typical days may be used as analysis periods to simulate and observe the trends in changes. For example, wind speeds measured at a rural or airport weather station are different from wind speeds in an urban area as a result of the obstructions- buildings and trees. Similarly, 9 dry bulb temperatures would be lower in a neighborhood with trees due to the evapo transpirative cooling effect and shading contribution when compared to a site that is devoid of trees (Palme and Salvati, 2021). The common historical weather data files are the example weather year (EWY) mostly used in the UK, Typical meteorological year (TMY) used in USA, test reference year (TRY), design summer year(DSY) used in Europe, Design reference year (DRY) used in Denmark and some 20 other countries and the International Weather Year for Energy Calculation (IWYEC) used by ASHRAE in the US and the ISHRAE weather data files by the Indian Society of Heating, Refrigerating, and Air-Conditioning Engineers (ISHRAE) used in India (Cox et al. 2015). Each has different parameters, for example, the DRY has 25 weather parameters and each has 8760 hourly timeseries values for each parameter (Cox et al. 2015). Historical weather data is real recorded weather data. The hourly temporal resolution is the required resolution for most BES programs like EnergyPlus. EnergyPlus and its many interfaces such as the Ladybug tools and DesignBuilder also require a special format for input as weather data called the EnergyPlus(.epw) format. The website comprises TMY weather data by location and each location has an EnergyPlus dataset that includes the .epw file, design day file( .ddy) and an EnergyPlus weather data summary report in .stat format (https://bigladdersoftware.com/epx/docs/9-6/auxiliary-programs/energyplus-weather- file-epw-data-dictionary.html ). An epw file comprises many fields; some are used in EnergyPlus for calculations, some are used as descriptors only and others are not used at all. (Table 1.11) (from the bigladder) 10 Table 1.11: EnergyPlus weather data fields- Used and unused fields Sl. No. Description fields Used Unused 1. Year Hour Extra-terrestrial Horizontal Radiation 2. Location Minute Extra-terrestrial Direct Normal Radiation 3. Month Dry Bulb Temperature Global Horizontal Illuminance 4. Day Dew Point Temperature Direct Normal Illuminance 5. Relative Humidity Diffuse Horizontal Illuminance 6. Atmospheric Station Pressure Zenith Luminance 7. Global Horizontal Radiation Visibility 8. Horizontal Infrared Radiation Intensity Ceiling Height 9. Direct Normal Radiation Present Weather Observation 10. Diffuse Horizontal Radiation Present Weather Codes 11. Wind Direction Precipitable Water 12. Wind Speed Aerosol Optical Depth 11 13. Opaque Sky Cover (used if Horizontal Infrared Radiation Intensity is missing. days Since Last Snowfall 14. Snow Depth Albedo 15. Liquid Precipitation Depth Liquid Precipitation Quantity 1.2 Microclimate Local climate comprises weather information of air temperature and humidity measured in a Stevenson screen at 1.5-2 m height, and wind velocity at a mast height of 10m (Blanken and Barry, 1970). A Stevenson screen is a louvered shelter that is used to shield the instruments used for meteorological measurements from direct solar radiation and precipitation while allowing air to circulate around the instruments (Stevenson screen - Wikipedia). The instruments may comprise a thermometer, hygrometer, psychrometer, barometer etc. Near the ground and surfaces and man-made objects like buildings, meteorological conditions are different and constantly changing and therefore cannot be determined by a standard set of temperature, humidity and wind readings. Values that change at spatial and temporal scales that are much smaller comprise the microclimate. Microclimate can be defined by a set of meteorological variables that differentiates a certain area from its surroundings. The microclimate and mesoclimate differ in both temporal and spatial resolutions. Mesoclimate is not only defined by a larger area but also the local climatic phenomena including tornadoes and thunderstorms that emerge from global wind patterns (Barry, 1970) (Figure 1.2). 12 Figure 1.2: Microclimate spatial and temporal resolution (Blanken and Barry, 1970) Microclimate can be defined as a region ranging from 1m (building scale) to 1km (district or neighborhood scale) (Palme and Salvati, 2021). The size of an intervention aimed at climate amelioration would define the bounds of the microclimate study. For example, increasing the street tree cover in a residential neighborhood would directly impact the microclimate of that neighbourhood and have limited influence beyond the site. The UHI effect on the other hand is observed at a mesoscale level and can be simulated using parametric urban climate models (UCMs) such as the urban weather generator (UWG). This can account for urban characteristics but cannot account for the effects of vegetation accurately. Tools that can accurately model and simulate detailed changes on site and vegetation, at the scale of intervention are required. 13 1.2.1 UHI and heat vulnerability in redlined neighborhoods Some communities suffer disproportionately from hotter surroundings of extensive impervious pavements and a lack of greenery and shade (Mitchell and Chakraborty, 2014, 2015). Hoffman et al. have found a correlation between the urban characteristics of hot neighborhoods (low tree canopy cover and extensive impervious surfaces) formed as a result of discriminatory redlining policies, and the elevated land surface temperatures (LSTs) of the neighborhoods they studied in different cities of the US (Figure 1.2.1). 14 a) b) c) Figure 1.2.1 a) National-scale Land Surface Temperature Anomalies by HOLC security rating b) National-scale averages of underlying percent developed impervious surface c) National-scale averages of percent tree canopy [35,36] by HOLC security rating (Green, “Best,” A; Blue, “Still Desirable,” B; Yellow, “Definitely Declining,” C; Red, “Hazardous) (Hoffman et al. 2020) Grades A to D represent the risk rating assigned to neighborhoods by the Home Owners' Loan Corporation (HOLC) -A (minimal risk) to D (hazardous). This resulted in racially segregating black and minority neighborhoods. Areas where the blacks lived were marked D or marked in red in maps for identification by lending institutions to then deny 15 potential customers access to capital investment that could improve and develop the site (Hoffman et al., 2020). The term “redlining” was coined by sociologist John McKnight to describe disinvestment based on the racial makeup of certain neighborhoods and their categorization as “hazardous for investment” by banks (https://en.wikipedia.org/wiki/Redlining). Land-use patterns depicting the location of retail (supermarket, food stores etc.) and healthcare establishments far away from these hazardous areas emerged as a result of redlining practices (https://en.wikipedia.org/wiki/Redlining). Extreme temperatures present a serious and growing public health crisis and can impair the well-being of occupants and even cause premature death (Palme and Salvati, 2021). While cooling by air conditioners (AC) creates a comfortable thermal environment, ACs cannot be afforded by all. In addition, it is also important to limit the need for mechanical cooling to reduce the release of anthropogenic heat into the atmosphere. There are many reasons why redlined communities are more vulnerable to extreme heat. Environmental reasons include the urban heat island (UHI) effect and the occurrence of heat waves, which are sporadic and hinder adaptation to heat and can even cause sudden deaths (Palme and Salvati, 2021). The built environment is also a contributor, where a lack of adequate insulation and poor envelope construction quality fail to provide a barrier against extreme heat and temperature fluctuations in the exterior environment. Other reasons may include an aging population, underlying health conditions such as diabetes, poor access to health-care and proper nutrition that make residents of low- income communities more susceptible to heat-related illness (Gabbe and Pierce, 2020). 16 A lack of investment in improving the thermal condition of these neighborhoods was prevalent for decades but this is slowly changing with the tree planting initiatives by Mayor Garcetti, the USC Urban trees initiative and some organizations in Los Angeles (Table 1.2.1). Table 1.2.1: Local effort in increasing street tree cover (Margulies et al.) Project People/Partie s Involved Location and Status Project Details USC Urban Trees Initiative Website: https://publicex change.usc.ed u/urban-trees- initiative/ USC – led by Prof. John Wilson from the Spatial Sciences Institute at USC The City of LA East side Los Angeles comprising Lincoln Heights, El Sereno, USC Health Sciences Campus and Ramona Gardens Public housing covering approx. 5 sq. miles of area. The goal is to increase the tree coverage in the study area comprising majority underserved communities, determined by how much and what tree species to add. Work in Progress For e.g., in Ramona Gardens public housing complex, there is room for an additional 183 trees and the target locations are to the West and South sides of buildings. A Habitat restoration project Website: https://www.northea sttrees.org/about/ North East Trees Ascot Hills Park- Local Greening project Ongoing An ongoing project in the Urban Trees study area, involving the planting of 950 trees and 5,000 shrubs, and the addition of two vista points and interpretative signage. Lincoln Park Neighbourhood Green Street Network Project Los Angeles Sanitation and Environment City of Los Angeles Recreation and Parks Department Lincoln Park Ongoing A restoration project to enhance and beautify the community and improve water quality and sustainability practices by building a three-mile “Green Street” network that would capture stormwater and connect Lincoln Park 17 with surrounding neighbourhoods. Ramona Gardens Green Connections Project http://bondaccountabilit y.resources.ca.gov/Proj ect.aspx?ProjectPK=18 411&PropositionPK=48 North East Trees Ramona Gardens and the surrounding streets Ongoing A project involving the planting of 65 trees to help improve air quality and shade buildings. An additional 250 trees are planned for the adjacent streets to enhance walkability. Health Sciences Campus project Website: https://hscnews.usc .edu/existing-new- construction- projects-ongoing- on-health-sciences- campus USC Lincoln Heights and Ramona Gardens communities Health Sciences campus and surrounding community areas The plan includes the development of wider sidewalks for more accessible public space, new vegetation including drought tolerant flora, 200 new street trees, the installation of bioswales to infiltrate water from surface parking lots, and the undergrounding of overhead utilities. In the wake of climate change, LA is projected to have an additional 40 extreme heat days above 96 O F annually (Margulies et. al. 2021). Extreme heat days are a period of high heat and humidity when the daily maximum temperature exceeded the 98th percentile threshold (ClimateChangeandExtremeHeatEvents.pdf (cdc.gov)). The body would work extra hard to maintain the core body temperature at 37 O C, which can exert a significant amount of stress and can even lead to death (ClimateChangeandExtremeHeatEvents.pdf (cdc.gov)). A lack of trees means that a site 18 is worse off without the cooling and shade benefits of trees. In East Los Angeles, a number of redlined neighborhoods exist, for example, the neighborhood of Ramona Gardens in East Los Angeles is characterized by a lack of shade trees and extensive paved surfaces (Figure 1.2.12). Ramona Gardens lies in the Redlined area marked D53 in the residential security map drawn by the HOLC (https://joshbegley.com/redlining/maps/Los_Angeles1-hi.jpg). Figure 1.2.11: East Los Angeles locations chosen for the tree planting initiative (Margulies et al. 2021) 1.2.2 Trees- Mechanisms of heat mitigation One of the most effective heat mitigation strategies that is also cost-effective and low- maintenance is increasing the street tree cover (Schiler and Moffat, 1981). Trees also benefit their surroundings in terms of air purification, visual interest and human health. The cooling effect of shade trees on the microclimate of neighborhoods that have low to 19 no shade is enormous, with surface temperatures under tree canopies that are up to 55 o F cooler than paved surfaces (Margulies et al. 2021). Trees cool their surroundings by shading surfaces from direct and diffuse radiation, by evapo-transpiration, and by sheltering from winds (Schiler, 1979) (Palme and Salvati 2021) (Figure 1.2.2). Figure 1.2.2: Impact of trees on micrometeorological conditions (Palme and Salvati, 2021) They impact the surrounding microclimate by changing the ambient air temperatures, surface temperatures, wind speed, direction and relative humidity. The relative impact depends on the species and the tree performance parameters that are discussed in the next section (Gromke et al. 2020). The impact can also vary with the distance of the tree from the building or street, that may be determined by placement scenarios. Placement scenarios determine what surfaces are shaded and the extent of their shading. For example, private shade trees could directly impact building energy by shading and cooling the building envelope in their vicinity. Parkway or street trees could 20 directly improve outdoor thermal comfort along sidewalks where they are planted (Figure 1.2.21). Figure 1.2.21: Trees in two placement scenarios- Parkway and private (https://pw.lacounty.gov/rmd/parkwaytrees/sidewalkrepairparkwaytrees.aspx) 1.2.3 Computational fluid dynamics (CFD) and microclimate simulations Numerical models and physics-based mesoscale and urban canopy modelling have been used for assessing the UHI effect at a large scale and are tabulated and discussed in Chapter 2 (Palme and Salvati, 2021). To assess the UHI effect and mitigation strategies at a smaller scale with high spatio-temporal resolutions, simulation-driven methods need to be used. To find the impact of vegetation on building heating and cooling loads, the combined effect of not only shading but also the airflow and evapo transpirative cooling need to be accounted for. Software would have to account for air and heat flow in an urban environment but BES tools only use the weather file data for 21 calculating heating and cooling loads in buildings and cannot simulate dynamic interactions in the microclimate surrounding buildings. Software would also have to account for evapo-transpirative (ET) cooling due to changes in vegetation cover. The tools that are capable of simulating air-flow and thermodynamic heat transfer comprise computational fluid dynamics software such as OpenFOAM CFD, SimScale, Ansys etc. (Palme and Salvati, 2021). Computational fluid dynamics involves the simulation of fluids by solving numerous mathematical equations that represent the physical phenomenon of fluid flow (https://www.simscale.com/docs/simwiki/cfd-computational-fluid- dynamics/what-is-cfd-computational-fluid-dynamics/). Fluids are shapeless substances like air and liquids that can flow. CFD can simulate the flow of fluids in terms of their physical properties such as velocity, pressure, temperature, density and viscosity (Marshall 2012). Heat transfer due to airflow and evapotranspiration due to trees involve the flow of both fluids and heat exchange between surfaces and the atmosphere (latent heat exchange) and can be simulated using CFD. However, those tools are limited in modelling and simulating the evapo-transpirative cooling by trees or the cooling due to latent heat transfer from plant foliage to the atmosphere. Section 1.3.3 describes the challenges in simulating trees, the popular CFD tools-SimScale and ENVI-met and their limitations. SimScale is a cloud based CFD tool that can simulate vegetation, but does not provide hourly time-series data output that could be used as weather data input for building energy modelling (BEM) to derive building heating and cooling loads. To study the impact of changes in the microclimate on building energy demand, a CFD tool capable of simulating the surface-plant-air interactions is required and also one that can produce hourly microclimate output 22 comprising air temperature, wind speed, direction and humidity changes at the building boundary layer. The new hourly time series data for each microclimate variable can then be used as weather data input in the requisite format to calculate building heating and cooling loads in a BES tool. 1.2.4 Outdoor thermal comfort metrics Outdoor thermal comfort is a result of both environmental and personal parameters. It is a function of environmental variables such as air temperature, radiant temperature which is influenced by short-wave radiation from the sun and long-wave radiation from the ground and building surfaces, humidity, and wind movement (Nikolopoulou et al., 2020). Personal parameters include clothing insulation measured in clo and the metabolic rate due to activity levels measured in met. To maintain a core body temperature of 37 O C, the heat loss must equal heat gains between the human body and the surrounding environment. Then thermal balance is achieved without any heat stress on the body. The ASHRAE defines comfort as the ‘condition of the mind in which satisfaction is expressed with the environment’ (Nikolopoulou et al., 2020). Comfortable outdoor spaces are critical social infrastructure that are lacking in areas without shade and that are exposed to highly radiant surfaces in an UHI. For people with no access to vehicles, walking under direct solar exposure can cause heat stress and dehydration. Shade can provide enormous respite to pedestrians by blocking solar radiation exposure. The outdoor thermal stress and comfort metrics commonly used in comfort studies and that are integrated in ENVI-met are the physiological equivalent temperature (PET), predicted mean vote (PMV)- predicted percentage of dissatisfied (PPD) and the recently 23 introduced universal thermal climate index (UTCI) metric. The PMV-PPD metric is more commonly used for indoor spaces that are mechanically cooled. For outdoor heat stress, the UTCI is a suitable metric and is given by Where 𝑇 𝑎 𝑚𝑏 is the ambient temperature, 𝑇 𝑀𝑅 𝑇 is the mean radiant temperature, 𝑈 𝑊 𝑖 𝑛 𝑑 is the wind velocity, and 𝑝 𝑣 𝑎 𝑝 𝑜 𝑢 𝑟 refers to the vapor pressure, which is a function of the dry-bulb temperature (Kastner et al. 2020). The UTCI can be depicted spatially as a heat map of the study area for a point in time or for hourly time series over an analysis period at a given point in space. The improvement in outdoor thermal comfort due to shade trees is useful to be quantified on a typical hot day. The UTCI scale has the following range of temperatures classified into heat stress categories (Figure 1.2.4). Figure 1.2.4: UTCI and the heat stress categories 24 The values ranging from 9 to 26 O C represent no thermal stress which means that the body is able to achieve heat balance or the core 37 O C body temperature without any strain, when the UTCI is in this range. 1.3 Real trees The impact of trees on the microclimate depends on the species and the type of tree (photosynthetic metabolism) and parameters like the leaf area density (LAD), foliar transmissivity, tree height, and crown radius. The rate of transpiration largely depends on the type of vegetation and is defined by the type of CO2 fixation metabolism and photo-respiration. CO2 fixation is the process by which plants produce organic compounds or their own food by converting CO2 to starch. Photorespiration involves the loss of already fixed CO2 by the same enzyme that fixes CO2 to sugars. This occurs under hot and dry conditions unless the plant is adapted to combat photo-respiration (Photorespiration (article) | Photosynthesis | Khan Academy). The enzyme, Rubisco sometimes uses O2 instead of CO2 from the atmosphere and initiates photorespiration, which decreases sugar synthesis. The CO2 fixation metabolism in plants may comprise the C3 mechanism in trees, C4 in plants that are adapted to minimize photo-respiration, and CAM (crassulacean acid metabolism) plants that comprise a family of plants called Crassulaceae, desert plants such as cacti, and pineapple (https://www.khanacademy.org/science/biology/photosynthesis-in- plants/photorespiration--c3-c4-cam-plants/a/c3-c4-and-cam-plants-agriculture)(Palme and Salvati, 2021) (Figure 1.3). 25 . Figure 1.3: From top to bottom- C3 (African sumac tree) (https://rosevilletree.org/selecting-trees/african-sumac/) C4 plants- Sugarcane, maize, sorghum (http://bio1151b.nicerweb.com/Locked/media/ch10/c4-plants.html) ; CAM- Crassulaceae family (https://www.khanacademy.org/science/biology/photosynthesis-in-plants/photorespiration--c3-c4- cam-plants/a/c3-c4-and-cam-plants-agriculture) C3 trees transpire to regulate their temperatures and as part of photosynthesis in a metabolic pathway called the Calvin-Benson cycle, determined by Melvin Calvin, Andrew Benson and James Bassham 26 (https://en.wikipedia.org/wiki/C3_carbon_fixation). Trees are of the C3 type (in reality, the C4 mechanism is incompatible/not observed in trees) (Blanken and Barry, 2016). C3 plants make up about 85% of all plant species on the planet (Blanken and Barry, 2016). CAM plants like xerophytes or desert plants are adapted to conserve water by various adaptation measures like developing a wax coating over their leaf surface and spines instead of leaves to prevent water loss by transpiration. They also have a mechanism that minimizes photorespiration, similar to the mechanism observed in C4 plants. These plants have high water-use efficiency due to adaptation but do not contribute to ET cooling as they do not transpire significant amounts of water due to their adaptation measures (C4 photosynthesis (video) | Photosynthesis | Khan Academy). Shade trees that are evergreen and broad-leaved provide the most shade benefits (Margulies, 2021). They shade surfaces in a predictable manner allowing minimal transmission of direct radiation through the canopy and onto surfaces. Southern California lies in climate sunset zone-22 (https://www.sunset.com/garden/climate- zones/sunset-climate-zone-los-angeles-area) (Figure 1.3.2). This climate zone is conducive to the growth of shade trees such as African sumac and Australian willow, which provide shade through the year and require a reasonable amount of water to grow and survive in hot conditions (Margulies, 2021) (Figure 1.31). 27 Figure 1.31: Sunset climate zones; 22- Southern California (https://www.sunset.com/garden/climate-zones/sunset-climate- zone-los-angeles-area) 28 1.3.1 Tree characteristics that maximise cooling Evergreen, broad-leaved trees can be categorized as shade trees, and the species may vary with different climate zones. The foliage density of trees is measured in terms of the leaf area density (LAD) which is the one-sided leaf area per unit volume of the tree canopy (m 2 /m 3 ) whereas leaf area index (LAI) is the total projected area of leaves over a unit of land/ground (m 2 /m 2 ) (Li et al. 2020). The LAI does not account for the overlapping of leaves or the percentage of light transmitted by foliage. The LAI is the integral of the LAD for the measured height of a tree canopy (Figure 1.3.1.1). Figure 1.3.1.1: Relationship between LAI and LAD (Li et al. 2020) A higher LAI and LAD indicate dense foliage trees that can block direct solar radiation and cast more uniform shadows as a result. Trees may also be evergreen or deciduous. Planting deciduous trees can also serve as a passive design strategy in the winter or the heating season when defoliation allows solar radiation to strike building surfaces and heat it passively. However, this depends on the climate type and it is important to consider evergreen shade trees over deciduous trees as Southern California remains hot in the leaf-off period of deciduous trees (Margulies, 2021). 29 Tree species that have sparse foliage density do not block solar radiation enough so that it can significantly change the surface temperature of the area under its shade. Due to lower leaf surface areas, the overall volumetric cooling is also reduced without bringing about a significant change in ambient air temperatures when compared to trees that have dense foliage (Gromke et al., 2014). 1.3.2 Leaf area density (LAD) The LAD is the vertical foliage density profile of trees or the one-sided area of leaves per unit volume. Higher LAD trees provide more shading and ambient air cooling due to greater blocking of radiation and higher surface areas for evapo-transpiration respectively (Gromke et al., 2014). Sparse tree species such as the Eucalyptus globulus have a typical LAD profile of 0.5 m 2 /m 3 (Gromke et al., 2014) and the Ficus microcarpa has a high LAD profile of 2 m 2 /m 3 (Figure 1.3.2). For the microclimate study, dense species characterized by medium to high LAD profiles of 1-2 m 2 /m 3 are appropriate and represented parametrically in simulations (Bruse et al., 2020). Figure 1.3.2: Sparse Eucalyptus (left); dense Ficus(right) that can represent a shade tree 30 1.3.3 Trees in simulations Trees are modelled and simulated in different ways in different software. CFD tools simulate trees as porous media with parameters such as the leaf area index (LAI), size, and drag coefficient. Some CFD tools can also simulate wind flow and direction changes due to obstructions such as trees, for example SimScale (Figure 1.3.3). Figure 1.3.3: Simulating trees in SimScale (How to Model Trees with Porous Media — Pedestrian Wind Comfort (slideshare.net) CFD tools are limited in their ability to account for evapo-transpirative cooling. ENVI-met is a CFD tool that can simulate vegetation using different input parameters and export data at a fine spatio-temporal resolution. Correlation between the different tree parameters, the effect of wind on trees due to being sheltered in a courtyard and different outputs such as the latent heat flux due to ET can be analysed (Figure 1.3.31). 31 Figure 1.3.31: ENVI-met simulations account for evapotranspiration (Trees and Vegetation - ENVI-met) In building energy modelling tools, it is common for trees to be modelled as solids or surfaces that cool the building by shading the envelope but does not account for evapo transpirative cooling by trees or the latent heat flux. Furthermore, the foliage material is not represented appropriately and these tools cannot simulate air flow either (Hsieh et al. 2018). In CFD studies of cooling effects and airflow changes due to trees, representing trees as porous media is important to account for the total surface area of leaves that contribute to transpirative cooling and also the airflow through the foliage layers to represent airflow speed and direction change realistically (Gromke et al., 2014). The LAD value thus determines cooling potential. A larger LAD value means more one- sided leaf area in the canopy and a greater net evapo-transpirative cooling (Gromke et al., 2014). In BEM, it is important to account for the diffuse radiation that passes through the foliage layers and impinge on building façade as foliage allows radiation to pass through the canopy in reduced amounts based on leaf overlap or silhouette and the form of diffused light (Schiler,1979). Hence accurate modelling of trees as porous media is important. 32 1.4 Building Energy Modelling-The impact of trees on cooling loads The heating and cooling loads in buildings are obtained by building energy modelling (BEM) software using a weather data file. A weather data file is a set of air temperature, humidity, solar radiation and wind speed and direction values that are commonly recorded at airport or rural weather stations but these are not indicative of the site conditions to be accounted for in BEM. The weather data input for common BEM software such as EnergyPlus is required in hourly time steps for a total of 8760 hours of the year (HOYs). Trees change the surrounding microclimate variables such as air temperatures, surface temperatures, wind speed, direction and the relative humidity. A site-specific microclimate weather data file is required to simulate and obtain the building heating and cooling loads for the baseline building and for the building in its improved site context. Several ways of including microclimate variables in BEM have been researched. A widely used method is to replace the meteorological variables recorded at the weather station with those measured on site or obtained through microclimate simulations conducted using CFD software. Other methods entail the use of coupling modules like the building controls virtual test bed (BCVTB) developed at the Lawrence Berkely National Laboratory (LBNL) (Yang et al., 2012). 1.5 Summary The local climate of a region is determined by its location, precipitation or sky conditions, global wind patterns and the moderating influence of large water bodies such as oceans. The latitude governs sun path and available radiation at the edge of the atmosphere 33 whereas the direct normal is determined by atmospheric extinction and weather conditions (Schiler, 2022). A UHI is a distinct region of hot microclimatic conditions formed as a result of man-made changes such as extensive impervious paving, reducing vegetation cover and building construction using materials with high thermal storage. Some residential neighborhoods have become UHIs due to discriminatory policies such as redlining. Increasing tree cover in such areas has multiple benefits to residents and can improve thermal comfort and passively decrease high building cooling loads that resulted from the UHI effect due to lack of vegetation cover. The cooling energy for buildings is decreased to a great extent when the building envelope is shaded (Wendy, 2020). The selection of appropriate tree species would determine their survival in arid conditions and also their performance aspects like LAD and maximum growth height when fully grown. There is currently a lack of accurate means of modelling vegetation as porous media that accounts for evapotranspiration (ET) in both CFD and BEM. ENVI-met has a dedicated tree modelling UI that is under constant improvement and requires testing by the user to get accustomed to changes in parameters and modelling means within the UI with each development. 34 2. IMPACT OF TREES ON COMFORT AND ENERGY The location and climate zone determine the thermal comfort criteria and heating and cooling loads in buildings resulting from the variations in meteorological (pertaining to the location’s climate) and the micro-meteorological variables. The elements that exert influence on a region’s climate scale are the sky conditions, solar radiation determined by the latitude of the location and moderating influences of water bodies. High latitude locations receive less intense solar radiation on the horizontal and vertical surfaces of a building than lower latitudes, due to solar angles (Palme and Salvati 2021) and increased atmospheric extinction. Seasonal precipitation and cloud cover affect the amount of direct radiation passing through the atmosphere leading to a decrease in T sol, ambient air temperatures and mean radiant temperatures (Palme and Salvati, 2021). It is important to know how trees can change microclimate variables that determine the comfort level for occupants and also the heating and cooling loads of buildings. This chapter discusses research on the impact of trees on microclimate variables, the impact of vegetation on heat fluxes, ENVI-met and coupling strategies for the use of microclimate output as input for building energy simulations (BES). 2.1 Impact of trees on microclimate variables Trees lessen ambient air temperatures and the decrease in ambient temperatures depends on the scale of the area that are vegetated (Rosheidat et al., 2008). In hot arid 35 locations, the local cooling effect is attributed to shade and not cooling by evapotranspiration (Rosheidat et al., 2008). Trees shade surfaces and prevent them from heating and storing heat that would then be reradiated to the sky and onto pedestrians at night as long-wave radiation, thereby increasing the mean radiant temperature (MRT), a variable that impacts thermal comfort (Hoffman, 2000). Trees decrease the surface temperatures (T sol) and MRT by blocking direct and reflected long-wave radiation. The effect of a 1 O F increase in air temperature on the human heat balance can be offset by a decrease of 1.39 O F in MRT (Rosheidat et al. 2008). In hot dry locations, where the relative humidity is low, thermal comfort can be achieved faster by evaporative cooling of sweat which occurs at a greater rate when the relative humidity (RH) of air is low (Rosheidat et al. 2008). The cooling by evapotranspiration depends on the size and leaf area density of trees and their collective cooling impact when planted over a large scale. Researchers found that the temperature in planted urban streets were about 1-3 K cooler than an adjacent street devoid of trees, in a hot arid urban location (Hoffmann et al. 2000). However, the cooling effect may be lessened in the absence of irrigation of the soil as the evapotranspiration rate decreases in this condition (Shashua-Bar and Hoffman, 2000). The cooling by evapotranspiration may be negligible in hot summers as the cooled air surrounding the tree may quickly dissipate due to wind (McPherson and Simpson 1995). In an urban context such as a residential neighborhood, the cooling effect of vegetation correlates with the ratio of planted area to the urban built up area (Rosheidat et al. 2008). 36 2.2 Impact of trees on heat fluxes The effect of foliage on the surrounding microclimate can be essentially categorized as radiative and airflow (Schiler, 1979). The effect of solar radiation on foliage and buildings is important to understand building energy loads. The other heat transfer mechanisms- conduction, convection, and latent heat transfer contribute to building energy loads in varying degrees. Shading of building and glazing surfaces by trees effectively lowers cooling loads as the solar energy is intercepted before reaching these surfaces (Schiler, 1979). Radiation impinging on foliar surfaces may be reflected by foliar surfaces, transmitted through the foliage, absorbed as heat, and re-radiated from the plant matter. Glass does not transmit light of wavelength 3000 microns and greater. Foliar surfaces reflect a maximum of 1% of incident light at 65 O or less to the normal glass surface but since glass can transmit light that is incident at less than 70 O its normal, the heat gain by reflected light is limited to 1% due to reflected light at 65 O or less to the normal of the glass surface (Schiler, 1979). Moreover, reflected light from foliage comprises wavelengths greater than 3000 microns, cannot be transmitted through the glass. Therefore, glass is essentially opaque to reflected light, mostly absorbing it as heat (even this is just the 1% as explained above) and re-radiating the rest away from the interior space, thereby eliminating the heat gain by reflected light (Schiler, 1979). In addition, the wavelength of light re-radiated by foliar 37 surfaces is inversely proportional to the temperature and is given by the following equation. T Wmax = 2880 0 K Where T is the temperature of the radiating source, 0 K Wmax is the wavelength of the maximum flux (in microns) (Schiler, 1979). Due to the regulation of foliage temperature due to transpiration (foliage surface temperatures do not exceed 325K, light reradiated from foliage would be of high wavelength of the order 9000 microns, which glass is opaque to (Schiler, 1979). Trees shelter the building from winds depending on the distance of the building from the tree community. The sheltering effect is measured as a static approximate around the tree crown and is related to the crown radius. Trees decrease the magnitude of heat transfer by convection and infiltration from the exterior to the building interior (Schiler, 1979). Trees also decrease the magnitude of heat flux by radiation and by air-flow. 2.3 ENVI-met ENVI-met is a microclimate simulations tool with the functionality to simulate surface- vegetation-atmosphere interactions at a fine spatial and temporal resolution from 0.5m- 10 m in space, to a typical time frame of 24-48 hours in 1-5 seconds time steps (Enviadmin 2021). The tool is designed to simulate air flow between buildings, heat exchange between walls and surfaces, pollution dispersion, bioclimatology and the impact of vegetation on the microclimate (Enviadmin 2021). 38 It can simulate the effect of vegetation on microclimate variables- the changes in wind speed and direction, air temperature and humidity. It can simulate the latent heat flux due to evaporation of water from soil and transpiration from vegetation by accounting for plant parameters and processes such as photosynthesis (Enviadmin 2021). Changes in wind speed and direction are solved for each grid cell and for each time step using mathematical equations called the Reynold’s-averaged non-hydrostatic Navier-Stokes equations (RANS) (Enviadmin 2021). This accounts for wind velocity, friction between fluid and surfaces, and obstructions by surfaces/objects (turbulence) to airflow. This is considered an accurate solver in computational fluid dynamics but increases computation time (Marshall 1997). Trees are modelled as porous media and their aerodynamic resistance is accounted for in determining the drag force that reduces wind speed on the leeward side of the tree (Enviadmin 2021). ENVI-met can calculate heat flux due to both shortwave and emitted longwave radiation, façade temperatures for each section of the wall profile, the uptake of water by plants from soil and the transpiration from foliage(evapotranspiration), atmospheric pollution simulations, and outdoor thermal comfort using different metrics such as the PMV/PPD, UTCI and PET. 1. Heat fluxes due to direct shortwave radiation and longwave radiation reflected from building and ground surfaces and the vegetation are accounted for (Enviadmin 2021). Heat flux is the measure of the amount of heat transfer that takes place from one material to another per unit area and the unit is W/m2. It takes place in the direction of the temperature gradient. 39 2. Dynamic surface, facade wall and roof temperatures are calculated for three layers of materials and 7 calculation nodes (Enviadmin 2021). If there are more than 3 layers, the intermediate layers would need to be simplified in terms of the specific heat capacity and thickness of the materials in those layers to represent one layer (Figure 2.3) (ENVI_MET Unfolded Part 3: Building Walls - YouTube) (Table 2.3). Figure 2.3: A wall of 5 materials; The specific heat capacity of 2, 3 and 4 are recalculated to form one (Material 3) Table 2.3: ENVI-met calculations for a wall profile of more than 3 materials Material Thickness; t (cm) Specific heat capacity; c J/(kg*K) Weighted Specific heat capacity = (t/ Total thickness) X c 2 10 850 141.67 3 20 1500 500 4 30 1000 500 Total (New material inputs in ENVI-met Wall Profile settings) 60 Material 2,3 and 4 represented as 3 with c= 1141.67 J/(kg*K) 3. Water uptake by plants is considered in the calculation of evapotranspiration rates. A drought study or water stress on plants during heat waves can be studied using ENVI- 40 met by using the appropriate soil settings associated with dry un-irrigated pervious surfaces (Enviadmin 2021). 4. Pollution and contaminant dispersion in the atmosphere and CO2 uptake by trees can be calculated using ENVI-met (Enviadmin 2021). 5. Outdoor thermal comfort outcomes can be calculated using the following metrics- Predicted mean vote/Predicted percentage dissatisfied (PMV/PPD), Physiological equivalent temperature (PET) or Universal thermal climate index (UTCI), using Biomet. Biomet is a postprocessing tool within ENVI-met that calculates the impact of the four variables: air temperature, MRT, wind speed and humidity in terms of the metric selected by the user (Enviadmin 2021). 2.3.1 Albero- The vegetation modelling application in ENVI-met There are two types of vegetation models: simple plants and complex trees. 1. Simple plants include lawn, hedges, and grassy plants like corn (Enviadmin 2021). 2. Complex trees are defined by the parameters of species type and leaf area density (LAD). In ENVI-met, trees are categorized into low, medium and high LAD trees with LAD values of 0.5, 1 and 2 respectively. Trees are represented as L-system trees starting version 5 of ENVI-met to more accurately represent realistic trees by species, branching patterns, leaf size and the LAD by species. L-trees is the fractal branching system developed by botanist Aristid Lindenmayer (Aristid Lindenmayer - Wikipedia) (Figure 2.3.1). 41 Figure 2.3.1: Aristid Lindenmayer and the L-systems fractal branching sequence in ENVI-met 5 for generating procedural trees (trees as fractal based systems rather than foliage floating in space). In L-tree models, the LAD distribution of the foliage density varies depending on the modelling of the tree in the L-systems editor. In parametric trees, each unit volume of the tree foliage can be input a single value to represent the homogeneity of foliage density distribution or different values for different clumps or units of volume in space to represent dense and sparse clumps of foliage distribution within the tree canopy. L- trees can be used to represent variations in tree canopies and to model existing trees. Parametric trees can be used in heat mitigation studies as representative of the performance aspects of shade trees (Figure 2.3.11). 42 Figure 2.3.11- The difference between a 1) Left-parametric tree (A generic tree defined by a large trunk, spherical profile and dense with high LAD- 2 assigned to grid cells) and 2) Right- an L-tree (a middle-aged Japanese Pagoda tree) defined by actual branches and leaves, in ENVI-met. 2.3.2 Model area and grid settings Grid sizes in ENVI-met models can be set from a 0.5 to 10m resolution in space. Smaller grid sizes are used in models where greater levels of detail or accuracy in size or distance are required in modelling and finer spatial resolution output data is required. For example, when windows need to be modelled on the building facade or when trees or single walls need to be placed at a certain distance from each other or a building, the size or distance would to be equal to or a multiple of the chosen grid size. Vegetation such as simple plants, hedges and grass, ground surfaces and buildings are modelled as grid cells. The lowest grid box can be split into 5 sub-cells. This is particularly useful for obtaining microclimate variables at a height less than the z grid 43 cell size, for example, when UTCI values are required at a certain height such as 1.1m, that represents centroid of the human body, at which thermal comfort can be calculated. A study area of 50x 50 metres can be represented by 50x50 grid cells of 1m each or by 25x25 grid cells of 2x2 m in the x and y directions, depending on the size of the site and the desired resolution. A building may be modelled to align with North and then rotated to the respective angle out of north to preserve straight edges. The building adjacent to the straight one has a different orientation and zig-zag edges owing to grid-based modelling (Figure 2.3.2). Figure 2.3.2: Grid-based modelling in the Spaces application of ENVI-met 44 Modelling User-Interface The three ways in which an ENVI-met model can be created are by modelling directly in the inbuilt modelling application called Spaces, by using Monde and by exporting a Rhino model as an area input file that can be read by Spaces. 1. Spaces is the main environment where buildings, ground surfaces and details such as façade or roof greening and windows are added to create the area input file (.inx file) for simulation. The model elements can be created over a reference bitmap of a study area imported as an overlay. 2. Monde is the user interface that allows users to develop buildings and allocate surface materials using polygons, lines and points layers or by using shapefiles obtained from a geographic information systems (GIS) tools such as QGis or ArcGIS. A shapefile is an Environmental Systems Research institute (ESRI) vector data format for storing building footprint data and geographical features (Arcgis.com). Respective building heights are then applied to the shape layers to create a 3d model. The model is then digitized to form a grid-based area input file (.inx extension) model that can then be edited in Spaces. Then details such as façade greening and windows can be added as grid cells, in 3d detail design mode. 3. A Rhino model can be exported as an .inx file using the Dragon-fly legacy version (https://github.com/ladybug-tools/dragonfly-legacy) of the legacy Ladybug tools (the oldest stable version) plugin that comprises ENVI-met components for Grasshopper. The grid settings, model rotation, surface material profiles and building materials are set up using their respective components in the plug-in. 45 2.3.3 Output variables The output variables from ENVI-met are included in 7 output folders. The main site- specific variables that can be used to create a microclimate weather data file are the ambient air temperature, wind speed, direction and relative humidity (Table 2.3.3). Table 2.3.3: ENVI-met output folders and their respective outputs Folder Variables Microclimate variables that may be included in new weather data ATMOSPHERE Objects () Flow U (mis) Flow v (mis) Flow w (mis) Wind Speed (m/s) Wind Speed Change (%} Wind Direction (deg) Pressure Perturbation (Diff} Potential Air Temperature Spec. humidity Relative humidity Air Temperature Delta (K} ('C) Vegetation LAD Direct Sw Radiation Diffuse Sw Radiation Reflected Sw Radiation Temperature Flux Vapour Flux Water on Leaves) Leaf Temperature ('C) Local Mixing Length (m) Mean Radiant Temp. ('C) TKE normalised 1 D Dissipation normalised 1 D () Km normalised 1 D () TKE Mechanical Turbulence Prod. Stomata Resistance (s/m) Wind Speed (m/s) Wind Speed Change (%} (At the receptor or grid in the model to get wind factors) Wind Direction (deg) Potential Air Temperature Relative humidity Mean Radiant Temp. ('C) for UTCI calculations at various grid cells in the model. BUILDING- DYNAMIC FOLDER Wall shading flag Wall: Temperature Node 1/ outside Wall: Temperature Node 2 Wall: Temperature Node 3 Wall: Temperature Node 4 Wall: Temperature Node 5 Wall: Temperature Node 6 Wall: Temperature Node 7/ inside Wall: Wind Speed in front of façade. Wall: Air Temperature in front of façade. 46 Building: Sum Humidly Flux at facade Wall: longwave radiation emitted by Wall: Wind Speed in front of facade Wall: Air Temperature in front of facade Wall: Shortwave radiation received Wall: Absorbed direct shortwave radiation Wall: Incoming longwave radiation Wall: Reflected shortwave radiation from facade Wall: Sensible Heat transmission coefficient Wall: Longwave Energy Balance Wall: Sensible Heat Flux Wall: Latent Heat Flux Greening variables (no greening in project) Substrate variables (no greening in project) RADIATION Q sw Direct Q sw Direct Relative () Q sw Diffuse View factor Up Sky () Viewfactor Up Build1ng Viewfactor Up Vegetation Viewfactor Up soil Viewfactor down sky Viewfactor down buildings Viewfactor down vegetation Viewfactor Down soil Q sw Diffuse without sec source Q sw Diffuse Sec Source Q sw Sum dir+dif+refl SOIL Temperature ( o C) Volumetric Water Content (m 3 H20/m 3 } Relative Soil Wetness related to sat (%} Relative soil Wetness related to av. Field Local RAD (normalized) (m 2 /m 3 ) Local RAD Owner () Root Water Uptake (g H20/m3*1s) SOLAR ACCESS z Topo (m) Buildings (Flag) Building Height (m) Index z node Terrain () Index z node Biomet () z Biomet absolute (m) SkyViewFactor () Sun hours Terrain level (h) Shadow hours Terrain level (h) Sun hours Biomet level (h) SURFACE Index Surface Grid () Soil Profile Type () z Topo (m) Surface Inclination (o) Surface exposition Shadow flag T surface T surface difference T surface change Q surface Uv above surface Sensible heat flux (W/m2) Exchange Coeff. Heat (m2/s) Latent heat flux (W/m2) Soil heat flux (W/m2) 47 Q sw Direct (W/m2) Q sw Direct Horizontal (W/m2) Q sw Diffuse Horizontal (W/m2) Q sw Reflected Lambert Factor () Q_Lw budget (W/m2) Q_Lw Sum all Fluxes (W/m2) Water Flux (g/m2s) Sky-View-Factor () Building Height (m) Surface Albedo () Deposition Speed (mm/s) Mass Deposed (ug/m2) Z node Biomet () Z Biomet (m) T Air Biomet ('C) q Air Biomet (g/kg) TMrt Biomet ('C) Wind Speed Biomet (m/s) Mass Biomet (ug/m3) Receptors () VEGETATION Objects ( ) Plant Index ( ) Plant Type ID () Flow u (m/s) Flow v (m/s) Flow w (m/s) Wind speed at Vegetation (m/s) Wind speed change at Vegetation (o) Wind direction at vegetation (deg) Local drag at vegetation (N) Horizontal drag at vegetation Air temperature at vegetation ( O C) Horizontal Drag al Vegetation (N) Horizontal Drag at Vegetation Spec. Humidity at Vegetation (g/kg) Relative Humidity at Vegetation (%) TKE at Vegetation (m2/m3) Vegetation LAD (m2/m3) Direct Sw Radiation (W/m2) Diffuse Sw Radiation (W/m2) Reflected Sw radiation (W/m2) Mean radiant temperature ( O C) Temperature Flux (K•m/s) Vapour Flux (g/kg•m/s} Water on leaves (g/m2) Leaf temperature ( O C) Aerodynamic Resistance (s/m) Stomata Resistance (s/m) Plant CO2 Flux (mg/ (m 2 *s)) Plant Isoprene Flux (mg/cell*h) PAR (micro mol m -2 s -1 ) 48 2.3.4 A comparison of popular CFD tools used for microclimate simulations CFD tools that are widely used for studying air-flow and heat transfer may have different ways of generating geometry, different user-interfaces, computation times and functionalities including vegetation models coupled with air-flow (Brook-Lawson and Holz, 2020) (Table 2.3.4). 49 Table 2.3.4: Popular CFD tools with different settings, computation times and limitations in vegetation modelling (Brook-Lawson and Holz, 2020). SimScale 2020 version https://www.simscale. com/ ENVI-met 4.4.3 https://www.envi- met.com/ Butterfly 0.0.05 for Ladybug tools https://www.ladybug.to ols/butterfly.html blueCFD-Core, 2017-2 https://joomla.blue cape.com.pt/index. php?option=com_c ontent&task=view& id=74&Itemid=30 Model geometry Free-form geometry Rectangular grid, min. dimension 0.5 meter Defined by the blockMesh and snappyHexMesh function to a high resolution. Defined by the blockMesh and snappyHexMesh function to a high resolution. Vegetation model Trees can be simulated as porous media (expert knowledge necessary) Trees and material can easily be set. Trees are modelled as porous media. Inbuilt vegetation presets and modeler called Albero. Vegetation as Solid objects/ no options for trees as porous media Complex vegetation modelling as porous media. Inbuilt data post processor Yes Yes, called Leonardo Yes No Simulation of evapo transpiratio n No Yes, with a dedicated vegetation model UI No Yes, but complex to set up the plant process, no dedicated vegetation model or UI. Settings used for the model in Figures 10x10x10 for Blockmesh with 1.25 m refinement resolution for HexMesh 3m x 3m x 3m 10x10x10 for Blockmesh with 1.25 m refinement resolution for HexMesh 10x10x10 for Blockmesh with 1.25 m refinement resolution for HexMesh Computatio n time* Mesh: 27 min Sim.: 200 min Mesh: 25 min Sim.: 1820 min Mesh: 18 min Sim: 124 min Mesh: 9 min Sim: 127 min Cloud- based Yes, 3000 hours free for academic use No No No *Computation time based on simulations (except SimScale which runs on the cloud) run on the same computer with specifications of CPU: 2.90GHz Intel Core i9-8950HK (hexa-core, 12MB cache, up to 4.8GHz); Intel UHD Graphics 630; RAM: 32.0 GB DDR4 SDRAM; ENVI-met version used was the Full science version (Brook-Lawson and Holz 2020) 50 These tools simulate the same model area faster than ENVI-met. ENVI-met produces a large amount of data output at the input spatial resolution and desired timesteps (mainly hourly). As simulation time increases significantly with the increase in the number of grid cells, a 2-3 m grid resolution is often used for a simulation of the domain area of 840m x 840 m (Brook-Lawson and Holz, 2020) (Figure 2.3.4). Furthermore, grid-based modelling in ENVI-met results in simplification of the original design. Figure 2.3.4: CFD output for 4 popular tools a) Butterfly, b) BlueCFD, c) ENVI-met and d) SimScale (Brook-Lawson and Holz, 2020). 51 2.4 Coupling CFD and Building energy simulations CFD simulations have been used to inform the input variable values for building energy simulations to assess the impact of site morphology or context on the building heating and cooling loads (Sailor and Akbari 1992). In their research of a heat island study in Sacramento, Sailor and Akbari collected the dry bulb temperatures from four airports and fifteen residential sites and compared the airport and site temperature data. They found a difference of 2-4 O C between sites and as much as 7-8 O C between some of the airport and site temperatures. There was a significant temperature difference even for sites that were close to the airport. It was clear that airport data was not appropriate for use in building energy simulations as the data did not represent meteorological conditions at the site. Meteorological modelling was conducted to estimate the impact of variable surface properties on local air temperatures. The simulations showed a difference of 2-3 O C between airport and building sites, the latter being cooler (Figure 2.4) (Sailor and Akbari, 1992). A one-dimensional simulation was used to provide a quick estimate of the impacts of the difference in airport and site surface characteristics on the temperature at the respective sites. To account for more urban characteristics, 3-d simulation models are required (Sailor and Akbari, 1992). 52 Figure 2.4- Diurnal air temperature profiles measured at a residential site and the nearby airport (Sailor and Akbari 1992) The modified site air temperature data was then used to calculate air conditioner (AC) energy use for a building for different temperature profiles (Figure 2.41) (Sailor and Akbari, 1992). There is no indication of software that was used for the building energy simulations. Also, the simulations were conducted for the energy use for one day- a typical summer day. Figure 2.41: AC Energy use for a typical summer day using various temperature profiles (Sailor and Akbari 1992). 53 The difference in simulated energy use for different temperatures show that airport temperatures produced about 50% error in simulations when compared to using site weather. The error reduced to 29% when temperatures obtained from 1-d simulations were used (Sailor and Akbari, 1992). CFD tools have developed since then but only some can simulate airflow and vegetation models as discussed in section 2.2: A comparison of other CFD tools used for microclimate simulations. Coupling is the use of output from one software to use as the input for another to include variables that the latter is not programmed to simulate. Coupling strategies and output variables chosen depend on what needs to calculated using another tool and the software that can receive the output in some format. The important microclimate variables that can be simulated using CFD tools like ENVI- met and be used as input by the BEM are the variables such as air temperature, wind speed, direction and relative humidity; variables that BEM software are not programmed to calculate (Palme and Salvati, 2021). The radiant heat gain and thermal conduction through walls need not be obtained from CFD simulation output as BEM simulations account for this in the same way (Palme and Salvati, 2021). The difference is that BEM cannot account for heat exchange related to airflow in the building surroundings, cooling effect by evapotranspiration by trees and the radiation attenuation by trees represented as porous media. Therefore, these need to be accounted for using a specialized CFD tool first and then brought in as input for BEM. Three coupling strategies are strong, weak and chain coupling. 54 1. Strong (onion) coupling is a combination of two models that calculate air flow and heat flux at the same time for each time step (Palme and Salvati, 2021). An example is the simultaneous coupling of airflow and heat transfer for natural ventilation in building energy simulations (Hensen 1999). This was a major limitation in BEM tools but this can now be seen within a single software such as in IES-VE. 2. Weak (ping-pong) coupling is when the time step iterations are run in a sequence for each of the two models and the output of the previous time step from one model is used as the input for the other (Palme and Salvati 2021). In this method, air flow and thermodynamic equations are solved in a sequence and the output from the flow equations from a certain time step is integrated with the heat transfer equations in the next step (Figure 2.42). Figure 2.42: Strong(onion) and weak(ping-pong) coupling of air-flow and thermal equations (Hensen 1999) 55 3. Chain coupling is when the first model CFD is run for the entire analysis period and then the outputs are used directly as input for the second model to be solved at the respective timestep for which the output was obtained. This is a one-way link (Palme and Salvati, 2021). This is the most commonly used linking strategy for microclimate CFD models and BEMs (Palme and Salvati 2021) (Figure 2.43). This method will be explored in the research. Figure 2.43: Coupling of microclimate modelling tools (left) and BEM tools(right) (Palme and Salvati 2021) The EnergyPlus software has been linked with ENVI-met outputs by developing modules such as the E-E model using the building controls virtual test bed (BCVTB) developed by the Lawrence Berkeley National Laboratory. This requires advanced knowledge of overriding EnergyPlus settings with ENVI-met heat flux outputs (Yang et al. 2012). The Ladybug tools run on the validated EnergyPlus engine and calculate in the same way including the air flow network used for natural ventilation calculations. The Ladybug tools 56 are integrated with the popular visual scripting tool Grasshopper for Rhino and are therefore considered apt for linking with ENVI-met, which also has its own Grasshopper components. Moreover, the Ladybug plug-in can read weather data files in EnergyPlus epw format. Therefore, the weather data file can be morphed to replace TMY data with site specific variables- wind speed, direction, humidity and air temperature obtained from a CFD simulation. 2.4.1 Case study - CFD and weather data morphing It is important to understand the different ways in which site variables can be obtained from CFD simulations to then be used to simulate thermal comfort and building loads. The changed values may be used in place of their respective values in a TMY file or input as time series in a component that calculates UTCI from a collection of time series data for MRT, Wind speed and relative humidity. The former is called weather data file morphing. For urban thermal comfort UTCI values, the urban variables required are wind speed, humidity, air temperature and MRT. MORPHING WIND VALUES CFD tool, Butterfly from the Ladybug tools package has been used to obtain wind speeds and the wind factor for 36 wind directions (at 10-degree intervals- high resolution) to obtain 8760 urban wind values needed to calculate the UTCI (Mackey et al. 2017)(Figure 2.4.1). 57 Figure 2.4.1: Butterfly CFD for different wind directions, to obtain wind factors (Mackey et al. 2017) For each of the 36 simulations, wind velocity plots at pedestrian height (1.1m) were generated and used to produce wind factors (WF) or wind coefficients by dividing the simulated wind velocities at the height with the meteorological wind speed found in the Singapore weather file. These WF were used to scale the TMY wind speeds for their respective directions to derive hourly wind velocities within the area of interest. The computation intensive workflow for 36 wind directions is apt for informing an iterative process if the computation speed is fast and not suitable to be conducted by ENVI-met that takes days to simulate for a short analysis period. (Mackey et. al 2017). The use of 58 only two CFD studies from opposite directions, north and south were sufficient to produce results while reducing the error involved in simplification of wind (Mackey et. al 2017). MORPHING AIR TEMPERATURE Air temperature values were obtained from the urban weather generator (UWG) integrated in Grasshopper for Rhino. It generates a morphed weather data file by simulating urban parameters associated with geometric variables such as building height, site coverage ratio, facade-to-site ratio, and road/roof/wall/window constructions and material variables (Mackey et. al 2017). This tool cannot model the effect of vegetation on evapo transpirative cooling, airflow and shading as porous media (From the comparison of 4 CFD tools). MORPHING RELATIVE HUMIDITY The hourly relative humidity values were not modified. The chart shows the variables that were modified- urban air speed and air temperature (Mackey et. Al 2017) (Figure 2.4.11). 59 Figure 2.4.11: Morphing the Meteorological file to create urban weather data for UTCI calculations at site (Mackey et. Al 2017). OBTAINING MEAN RADIANT TEMPERATURE (MRT) The urban MRT for UTCI calculation was obtained directly from the EnergyPlus simulations in Ladybug tools taking into account direct shortwave solar radiation and long- wave radiation from the sky and urban surfaces (building and ground) (Mackey et. Al 2017). The resulting morphed weather data was used to obtain UTCI maps for the analysis periods of –a typical week (7/30-8/5), cold week (12/10-12/16) and a hot week (6/4-6/10) (Figure 2.4.12). 60 Figure 2.4.12: Maps of Average UTCI over the Singapore Cold Week (a), Typical Week (b), and Hot Week (c). (Mackey et. Al 2017). The case study presented the wind factor method for determining hourly wind speeds in an urban site. Air temperature values were obtained from UWG as Butterfly cannot produce air temperature changes. Heat fluxes were calculated in EnergyPlus itself without the need for coupling; the short-wave and long-wave radiation for MRT was accounted for using the Ladybug tools. The maps also show the heat stress in Singapore in the cold week. The scale starts at 28 O C which is in range of the UTCI moderate heat stress category that ranges from 26-32 O C. The area in between the building canyons is shaded, with a reduction in UTCI temperatures by a class index of 1-2 O C, indicating the importance of shade in reducing heat stress on the human body. Being a holistic microclimate simulations tool, ENVI-met can produce hourly values of air temperature, humidity and wind speed changes due to vegetation but at the cost of long computation times (ENVI-met, 2020). ENVI-met can also generate UTCI heat maps like the one above but for high resolution maps the computation time involved is high. The computation time limits the analysis period to typical days and can take months if a year’s worth of hourly data is required. The hourly values from typical days can be used to inform 61 modifications in the weather file (Palme and Salvati 2021). The morphed weather file can be used in both human thermal comfort calculations using the UTCI metric and to determine building heating and cooling loads as both are a function of the common variables like air temperature, wind speed and humidity that are part of an epw file. 2.4.2 Air temperature trends due to vegetation Increasing the tree canopy cover can decrease the ambient air temperatures in their surroundings by shading and evapo transpirative cooling (Palme and Salvati, 2021). The decrease in ambient air temperatures is directly proportional to the area of the added tree canopy and both the time of day and season when transpiration peaks around the morning and afternoon time and when seasonal changes such as defoliation may occur, respectively (McPherson et al. 1996) (Figure 2.4.2). The figure shows the diurnal trend of observed ambient air temperatures associated with an increase of 10% in tree canopy cover in the neighborhood. Figure 2.4.2: Observed Air temperature decrease during June 1 and 2 (peak air conditioning energy/cooling demand was on June 1 at 4 PM in the BES model) (McPherson et al. 1996) 62 Other studies shed light on the extent of air temperature decreases associated with tree canopy cover (Meili et al. 2021) (Table- 2.4.2). Table 2.4.2: Air temperature changes reported by different researchers (Meilli et al. 2021) It can therefore be concluded that trees contribute to lessened ambient air temperatures which vary diurnally and seasonally and with scale- a cluster of trees or a tree community compared with a single tree. It is impossible to measure air temperature changes for a site without trees and then with trees, in reality. It is important to validate the software by comparing measured and simulated air temperatures and determining the degree of agreement of the values. If the values are in good agreement, then ENVI-met may be 63 considered apt to determine air temperature changes for different scenarios such as heat mitigation scenarios. VALIDATION OF ENVI-MET IN TERMS OF SIMULATING AIR TEMPERATURES STUDY 1: Simulated and on-site measured values Baghaeipoor and Nasrollahi, 2019 validated ENVI-met by comparing simulated and measured air temperatures using the BENETECH GM 1365 data logger. The site that was modeled was a high-rise complex in Atizas, Tehran (Figure 2.4.21). Figure 2.4.21: ENVI-met model of the Atisaz complex 64 The climate is characterized by hot summers and cold winters. The simulation was conducted for 2 days starting at 4 AM with the initial meteorological settings obtained from the Shemiran weather station and based on temperatures averaged over the recent 30 years (Baghaeipoor and Nasrollahi, 2019). The site was modelled in ENVI-met using the appropriate settings and simulated using the meteorological conditions for the coldest day and the hottest day (Table 2.4.21). Table 2.4.21: Model and meteorological settings (Baghaeipoor and Nasrollahi 2019) 65 They found an acceptable level of accuracy between measured and simulated values (RMSE= 0.9141)(Figure 2.4.22). It is to be noted that recent versions of ENVI-met calculate nesting grids internally, without requiring user input. Figure 2.4.22: Left: Simulated vs measured air temperature values; Right: RMSE of the deviation STUDY 2: Simulations vs measured values under tree canopy ENVI-met simulation results were compared with field measurements taken under the tree canopies at two locations- A and B (Ayyad and S Sharples, 2019)( Figure 2.4.23). Figure 2.4.23: Site for validation of air temperature simulations for two locations- A and B that are under trees; A is under a larger and thicker canopy (Ayyad and S Sharples 2019) 66 Climate type: Hot arid (Koppen Bwk-hot arid desert and Bwh-cold arid desert) with a maximum temperature 41.5°C in August of and a minimum -4.5°C in February. Location: Al Ahliyya Amman University in Jordan Site trees: conifers- Pinus halepensis, Mediterranean cypress (Cupressaceae), Phoenix dactylifera and Cupressus macrocarpa ; deciduous trees- Populus nigra Trees modeled in ENVI-met: Trees were modelled from the existing library in Albero with a few changes made(not mentioned). ENVI-met analysis period: Mild/average day- October 1 Meteorological settings for the simulation comprise inflow air temperature and relative humidity from the weather file(EPW) for the 1 st of October (Table 2.4.22). Table 2.4.22: EPW file settings for October 1 (Ayyad and S Sharples 2019) Measurement data loggers: Two Kestrel 5400 Heat Stress Trackers; The sensor is a portable WBGT (Wet Bulb Globe Temperature) logger and weather station with accuracies of readings of ±3% for wind speed; ±0.5°C for air temperature; ±2% for relative humidity (Ayyad and S Sharples, 2019). 67 The measured and simulated air temperatures were plotted and compared using a line chart (Figure 2.4.24) for Location A. Figure 2.4.24: Temperature comparison for Location A (Ayyad and S Sharples 2019) The measured and simulated air temperatures were plotted and compared using a line chart (Figure 2.4.25) for Location B. Figure 2.4.25- Temperature comparison for Location B (Ayyad and S Sharples 2019) The validation results for Location A and B comprise the index of agreement and RMSE (Ayyad and S Sharples 2019) (Table 2.4.23). 68 Table 2.4.23: Summary of the results (Ayyad and S Sharples 2019) Location A- Location B- The results of ENVI-met showed a large deviation from the values measured from 9 to 19:00 hours. The values simulated by ENVI-met were lower by an average value of 2.8 O C for Location A in this time duration and by 2.9 O C for Location B. The results showed a high index of agreement in both cases (Ayyad and S Sharples 2019). The study was conducted in 2019. Notable improvements in ENVI-met calculations with version 5 include the advanced canopy ray transfer(ACRT) scheme and the Index view sphere(IVS) calculations (ENVI-admin 2021) (https://www.youtube.com/watch?v=vLdCnZbDRAA) . The developers claim that this would make a huge improvement in the accuracy of simulated air temperature values as the diffused light passing through the foliage layers is accounted for (Bruse et al. 2020). 69 2.5 Summary Trees moderate heat exchanges that occur in the form of radiation, convection and infiltration. The cooling effect of trees depends on the size of the tree and the leaf area of the foliage as well as the soil conditions in terms of irrigation/ water availability. The meteorological weather data measured at airport or rural weather stations does not represent the microclimate at a site and is therefore not suitable for calculating thermal comfort and energy simulations at that site. To obtain the microclimate variables that reflect the effect of trees on thermal comfort and building heating and cooling loads, a microclimate modelling tool that can simulate heat, airflow and vegetation models is required. ENVI-met is a microclimate simulation tool that can not only simulate radiative fluxes but also cooling due to the latent heat exchange from evapotranspiration by trees. The output of a decrease in ambient air temperatures due to the combined effects of evapotranspiration, wind sheltering and shading can be used to assess the improvement in outdoor thermal comfort and building heating and cooling loads. The other variables that can be accounted for using ENVI-met are the changes in wind speed and relative humidity. The hourly changes in air temperature, wind and relative humidity can be included in a site weather file by modifying the respective airport or rural TMY file. The weather file can then be used as input for a BEM tool to simulate building heating and cooling loads. Using the output from one tool as the input for another is called chain coupling. This process helps account for changes in variables that the BEM tool cannot simulate, for example, the change in external air temperatures due to the cooling effect of trees. CFD simulation 70 results can be used to inform the trends in changes in variables as obtaining all 8760 values as output is not feasible due to large computation time in ENVI-met. An average day, a hot day with peak cooling loads (maximum CDD hours) and the coldest day (maximum number of HDD hours) can be used to study the performance outcomes during the worst and average conditions. An existing meteorological file can be modified for each site-specific variable by replacing the original values. It is important to preserve the relationship between solar and psychrometric variables such as relative humidity and the wet bulb and dew point temperatures. Studies have shown that modifying relative humidity may be required in the case of hot humid climates as this may increase latent heat loads in buildings (Yang et al. 2012). For hot arid locations, the changes in RH due to vegetation cover at a neighborhood scale may not be significant due to dissipation and because dry air can hold more moisture without undergoing a significant change in RH. 71 3. PRELIMINARY STUDIES AND SIMULATIONS Preliminary studies were conducted to test ENVI-met parameters and to inform a coupling workflow between ENVI-met (CFD) and the Ladybug tools (BEM). For a coupling workflow, it is important to know how output from CFD can be incorporated in BEM to account for the impact of microclimate on outdoor thermal comfort and building heating and cooling loads. The main outputs required for coupling between the two software comprise tree canopy transmission, ambient air temperatures, wind and RH resulting from increased tree coverage on site. Weather data files that represent the current environmental conditions are important to represent the present-day thermal environment appropriately. This chapter deals with pathological test case scenarios, a verification study, tree models in ENVI-met and BEM, weather data files that can represent current heat stress and simulations of a shoebox model as an example workflow for obtaining building cooling loads. 3.1 ENVI-met studies- Pathological test cases ENVI-met is a grid-based modelling tool and studies have shown errors associated with different grid resolutions (Ban-Weiss, 2012) but the relative differences between the baseline and a scenario for comparison using a particular grid resolution is likely the same (Schiler, 2021). The grid size would have to be chosen appropriately, based on the dimension of the important elements to include in the model, for example, the spacing of trees or the width of a concrete pavement. Choosing the appropriate grid setting also helps save on computation time. Pathological cases comprise testing the software to 72 simulate cases where the outcomes were known. This was done to determine if the software models parameters correctly within a known range of outcomes, before using it to simulate scenarios for which the solution cannot be determined beforehand (Schiler, 2021). TEST CASE 1: TESTING GRID RESOLUTION A test case with a finer grid and one with a coarser grid resolution were simulated (Figure 3.1) and their outputs: T air and surface temperature were compared. Asphalt and dark concrete become the hottest due to high absorption and low albedo and this was expected to reflect in the surface temperatures of the simulation results. 73 Figure 3.1: Same model areas with different grid resolutions TEST CASE 1: Results The simulation output maps are generated and analysed for 11 AM on the 21 st of September. The hottest surfaces are made of asphalt and dark concrete. The coolest surface is that of sandy loam, a natural material. The surface temperature of grey concrete is lesser than dark concrete as the former has higher albedo (0.5 whereas dark concrete 74 has an albedo of 0.2). However, the minimum and maximum temperatures varied with different grid resolutions (Figure 3.11). Figure 3.11: Top: Surface temperatures: Coarse grid resolution Bottom: Fine grid resolution There were errors in the outputs of the same model modelled with different grid resolutions. The error was larger for surface temperatures. The air temperature values 75 for the two models were almost the same, with a difference of 0.33 O C between the maximum temperatures (Table 3.1). Table 3.1: Errors in output of a model with different grid resolutions Variable Fine grid resolution ( O C) Coarse grid resolution ( O C) T( O C) Surface temperature Min: 24.14 Min: 25.07 0.93 Max- 32.98 Max- 33.82 0.84 Ambient air temperature Min- 23.56 Min- 23.56 0 Max- 24.88 Max- 24.55 0.33 The results showed errors for the same model but different grid resolutions. The maximum and minimum temperatures must be equal regardless of the grid resolution. A study by Ban-Weiss et al. 2018 on the grid independence of ENVI-met on its outputs produced similar errors. With further tests, they concluded that the relative magnitude of error between any two different grid resolutions was less than the modelled differences of heat mitigation strategies they tested. To avoid errors, all scenarios may be modelled with the same grid resolution. This is important as different output variables show different errors and ENVI-met produces around 100 output variables. Lastly, it is important to note that ENVI-met simulated surface temperatures of different materials appropriately. As expected, dark concrete showed the highest surface temperatures and the unsealed natural sandy loam surface, the lowest surface temperatures. The material properties in the material database are set-up accurately and represent real materials appropriately. 76 TEST CASE 2: TESTING MATERIALS AND AIRFLOW ENVI-met was further tested using cases with known outcomes. A test case was set up to test buoyant and wind-driven simulations in ENVI-met to see how ENVI-met simulates wind speed and direction changes on a model area comprising a strip of impervious material and a strip of pervious material next to it. As impervious surfaces retain heat and undergo a greater increase in surface temperatures, the difference between the surface temperatures of the two areas would serve as an important indicator of ENVI-met’s capability of simulating materials reasonably well. As ENVI-met requires dynamic inflow conditions, which means that wind speed cannot be set to 0, the wind speed for buoyant conditions was set to the least number that could be input, which was 0.1 m/s and for wind-driven inflow conditions, wind speed was set at 2.1 m/s. The known outcome was that in the buoyant condition, air movement would be in the direction of the temperature gradient or from the hotter area(impervious) to the colder area (sandy loam) (Figure 3.12). Also, the degree of air temperature distribution in the case with a larger wind speed would be higher than in the buoyant flow case. Figure 3.12: Setting up two different types of materials and wind settings TEST CASE 2: Results 77 There were notable differences between the direction and rate at which heat distribution took place in the two cases. Advection can be observed in the direction of inflow wind. The rate of air and heat flow was slower than when the inflow wind was of a greater speed (Figure 3.13). Figure 3.13: Buoyant air flow Wind speed affects the rate of dissipation of heat and air temperature distribution over a certain area. A greater wind speed means that air temperature distribution over a certain area would occur faster. ENVI-met was able to simulate wind-driven heat flow (Figure 3.14). 78 Figure 3.14: Air and heat flow due to wind; wind speed at inflow= 2.1 m/s The model performed as expected. Air temperature distribution was slower in the case of buoyant air flow when compared with the model set up with greater wind speeds. ENVI- met can simulate dynamic (air flow) changes with thermodynamic exchange. It also simulated inflow conditions appropriately. TEST CASE 3: TESTING FAÇADE TEMPERATURE CHANGES AS A RESULT OF SHADING Radiative fluxes are one of the most important determinants of microclimate variables such as surface and air temperatures. There should be a reasonable difference between the temperature of a shaded wall and that of an unshaded wall. Hence, test cases were set up to compare a baseline unshaded model with a model shaded by a translucent 79 material and a model shaded by an opaque material. It can be predicted that the façade temperature of the south wall shaded by a translucent shade would lie between the temperature of the unshaded south façade and that of the façade completely shaded by an opaque wall (Figure 3.15). Figure 3.15: A test to determine surface temperatures of the baseline unshaded, model shaded by opaque wall and model shaded by a translucent material. The test case was changed to simulate a similar pattern with trees instead of an opaque and translucent material. Since ENVI-met simulates trees as porous material and accounts for direct and diffused light transmission through foliage layers, trees were expected to act as translucent screens that transmit light and the transmissivity depends on LAD. A sparse canopy would transmit more light. This would result in greater irradiation of the façade when compared to irradiation in the partial shade of a denser tree. It was predicted that the surface temperature of the façade shaded by a tree of lower LAD lie between that of an unshaded facade and that of a facade shaded by a denser tree of higher LAD (Figure 3.16). 80 Figure 3.16: A test for surface temperatures of the unshaded baseline, model shaded by a sparse tree (low LAD) and model shaded by a denser tree (greater LAD). TEST CASE 3: Results The ENVI-met façade heat map output depicts surface temperatures for different scenarios (Figure 3.17). Figure 3.17: Surface temperatures of different facades in LEONARDO 81 The surface temperature of the facade shaded by a translucent screen lies between the surface temperature of the unshaded façade and the façade shaded by an opaque screen (Figure 3.18). Figure 3.18: Surface temperatures of the base case and shaded facades. The results are as expected. There is a clear correlation between irradiation/ no irradiation and surface temperatures. The surface that was shaded by an opaque screen showed temperatures that were much lower than the unshaded base case model. The surface temperatures of the facade shaded by a low LAD tree lie between the surface temperature of the unshaded façade and the façade shaded by a tree of greater LAD (Figure 3.19). 82 Figure 3.19: Surface temperatures of the base case and the façades shaded by trees The air temperature values obtained for the same grids in front of façade where the surface temperatures were compared showed negligible difference between shaded and unshaded scenarios. The minimum air temperature value was for the façade that was completely shaded by the opaque brick wall. The minimum surface temperature was also for the façade shaded by the brick wall, as was expected. ENVI-met can simulate surface temperatures resulting from varying radiation transmission through different materials and trees. Furthermore, the radiation output can be used to obtain canopy transmission as the ratio of radiation(W/m 2 ) in the shaded flag of the façade (the flag depicts shading on the grid) and the global radiation(W/m 2 ) (the total shortwave radiation, a sum of the direct and diffuse solar radiation) (Takacs et al. 2015). This was explored as part of the coupling process and is explained in section 3.5: Coupling radiation. 83 Comparing the surface temperature values under different shade parameters, it can be deduced that canopy transmissivity lies between 0 and 60% during the daylit hours (Figure 3.1.10). It is important to note that the transmission through a tree canopy is dynamic and changes with time (during sunlight hours), with changing sun angles in relation to foliage orientation and distribution across the canopy. The translucent screen has fixed transmission. Figure 3.20: Surface temperatures of the shaded facades 3.2 Verification study: Harris Hall courtyard simulation Harris Hall courtyard was chosen as the site to verify ENVI-met by comparing the simulated surface temperature values of a south-east facing wall within the courtyard with temperature readings taken with a FLIR E8 infrared gun. Harris Courtyard was modelled 84 in Rhino and exported to the Spaces application in ENVI-met via the DragonFly legacy Plugin for Grasshopper (Figure 3.2). Figure 3.2: Harris Hall courtyard model Objective: The objective was to simulate the impact of tree shade on surface temperatures of a building facade and track how the measured and simulated surface temperatures changed and compared with each other with time (different solar angles and radiation). ENVI-met results were also checked to determine if the software can model thermal lag in the concrete wall (thermal mass). Hourly façade temperature readings of the south- east facing walls were collected on October 15 th , 2021(from 11AM to 5 PM), a day with clear sky conditions and high ambient air temperatures as reported by a meteorological station (Figure 3.21). 85 Figure 3.21: Temperature readings by the FLIR E8 camera Model settings: The wall material and the tree LAD, height and crown radii are important parameters. The important wall material settings are albedo, reflectivity and specific heat capacity. A new material was created in the materials database for the 30 cm thick tilt up concrete wall painted white (Figure 3.22). The material profile for the free-standing parapet wall (single wall in ENVI-met) comprises a 20cm thick concrete wall with the same material properties as the building walls. 86 Figure 3.22: Material and wall profile of the Harris Hall building The tree LAD determines the transmission of light through the foliage and the amount of direct and diffuse radiation reaching various parts of the walls. There is no appropriate tree model for a Ficus tree in the Albero library. A parametric tree model was used to represent the tree by adding grid cells of LAD= 2 m 2 /m 3 and 1 m 2 /m 3 alternating for each volume of the foliage by observing the profile of the tree and adding cells at various section cuts (Figure 3.23). Figure 3.23: Albero model of the Ficus tree crown and the final ENVI-met model 87 The tree LAD value of 2 m 2 /m 3 was obtained from literature (Gromke et al. 2020). It is also important to note that dense tree species such as the Ficus pack more foliage at the periphery of their crown, optimized for maximum solar exposure and less foliage in the lower to middle sections of the vertical profile (Schiler, 2021). In the lower sections of the tree profile, grid cells of 0.5 m 2 /m 3 were added according to observation. The shading profile depends on the grid settings. The wall surface temperatures are reported for grids on the façade. The FLIR thermal studio suite can be used to report temperatures of pixels in the captured IR images as well as average temperatures of facade areas (area for average temperature can be adjusted in FLIR thermal suite). A grid resolution was chosen to match the reference size of the step in the staggered parapet wall. The area corresponding to the respective ENVI-met grid would be compared by setting up an area equal to the dimensions of the step, in the FLIR thermal suite. The areas for comparison are marked in orange for the single wall and in red for the building wall and their surface temperatures are compared with their respective grid cells in the model (Figure 3.24). Figure 3.24: Areas for comparison with their respective grids in the IR image. 88 RESULTS: The simulated and measured surface temperatures were in close agreement with one another (Figure 3.25). Figure 3.25: High degree of agreement between the simulated and measured values of the building wall. For temperature readings by the FLIR E8 camera, it can be observed that there was a sharp increase in the surface temperature of the single wall at 5PM. This was captured in the ENVI-met simulation and can be seen in the results (Figure 3.26). 89 Figure 3.26: High degree of agreement between the simulated and measured values of the single wall. There was a delay in heat transfer from node 1 to node 7(thermal lag). This can be observed in the temperatures of the inner nodes 3-7 that increase gradually with time. The trend of decrease in node 1 temperatures closely follows that of air temperature outdoors (Figure 3.27). 90 Figure 3.27: Thermal lag of the thermal mass The surface is largely cooled by conduction and convective heat loss at the air-surface layer and at a larger rate with increasing temperature differences between surface and air, later in the day. The inner layers retained heat with a gradual rise in temperature during the latter part of the day despite decrease in outdoor temperatures at that time (stable temperatures of thermal mass). From the chart it can be concluded that ENVI-met could model thermal lag in the concrete wall of a building. ENVI-met simulated changes in external wall surface temperatures in relation to air temperature changes. 3.3 ENVI-met: Strengths and limitations The strengths and limitations of ENVI-met were identified from literature review and 91 experiments (Table 3.3). Table 3.3: Strengths and limitations of ENVI-met STRENGTHS LIMITATIONS 1. ENVI-met can simulate ambient air cooling due to ET by trees. The 4.4.6 version did not simulate this. This was perhaps a bug that was fixed in version 5. A study by Wendy, 2020 also showed higher temperatures for a site with trees when compared to a base case that was devoid of trees. 2. ENVI-met simulated thermal lag in the concrete wall. 3. Diffuse light transmission through canopy is accounted for. 4. ENVI-met models and simulates trees as porous media. 5. The software accounts for direct, diffused and long-wave radiation in the calculation of MRT. It also accounts for the impact of different materials on the radiant environment. 6. ENVI-met simulates changes in wind speed and direction as expected. The results can be obtained and visualized at the tree or façade and interactions between objects are discernible. Does not output wind pressure coefficients at the façade. 3.4 Weather data and analysis period Weather data analysis sheds light on comfort conditions in a particular location. Weather data that can indicate current thermal conditions is required for both CFD and BEM simulations. Typical meteorological year (TMY) files are outdated in terms of representing current weather patterns and trends. The TMY3 file from the EnergyPlus website (https://energyplus.net/weatherregion/north_and_central_america_wmo_region_4/USA/ CA) represents a much milder climate of Los Angeles where the average air temperature 92 values are below the temperatures required for thermal comfort based on the ASHRAE standard 55-2010 adaptive comfort model (Figure 3.4). Figure 3.4: LA TMY3 file air temperatures in the period 1960-1990 when they were collected and processed; Generated using Climate Consultant 6.0 Los Angeles has a mild climate that falls within the comfort range for most parts of the year. The climate of LA has changed in the past couple of decades and much higher temperatures have been observed, especially in the months of September and October (Kensek, 2021). The chosen typical meteorological file represents a warmer Los Angeles and is more suitable for comfort and energy analyses when compared to historical weather data (Figure 3.4.2). 93 Figure 3.41: Current weather data vs. historical weather The warming is tabulated in terms of the increase in average diurnal temperatures between the two TMYs (Table 3.4). The peak cooling and heating days are chosen for the analysis period in the methodology. 94 Table 3.4: Increase in temperatures in LA Month Average diurnal TMY3-historical temperatures over the 1960-1990 period ( o C) Average diurnal TMY3-Morphed to represent 2020 temperatures ( o C) Jan Feb March April May June July Aug September October November December 16.149677 16.201071 16.784194 17.824667 18.674516 19.919333 21.870968 21.490645 21.939333 20.119355 18.679333 17.010968 18.326129 18.065357 18.866452 20.547667 21.034516 22.713 24.569677 24.431935 24.569667 22.80129 20.975 19.313871 3.5 Coupling radiation: ENVI-met and the Ladybug tools Trees are modelled in different ways in CFD and BEM tools. This section is about the tree models in ENVI-met and the Ladybug tools, about using ENVI-met output to obtain canopy transmissivity, and the simulation of a box model in an unshaded and a shaded scenario to quantify a decrease in cooling loads. 95 3.5.1 The tree model in CFD (ENVI-met) and in BEM (the Ladybug tools) Trees are intricately modelled in ENVI-met as porous media. In BEM, trees are modelled simplistically as solids or surfaces that block solar radiation from striking the building façade and consequently reduce the radiant heat gain to the interiors. The tree is custom modelled as a procedural African sumac tree with varying LAD and other tree parameters (Table 3.5.1). Table 3.5.1: Modelling trees in ENVI-met and Grasshopper for Rhino Model Parameters 3D View ENVI-met tree in the Albero application Height: 8m Bole height: 2m L-system Rules: Appendix Width: 3.94m x 4.12m Grid cells: 7 X 7 X 9 in 1m resolution Type: C3, Evergreen LAD per grid: 1m 2 /m 3 96 Rhino 3d- Grasshopper Direct gap transmittance model A random reduce algorithm was applied to the geometry of the tree profile with the same crown radius and height as the ENVI-met tree. The gap fraction will be adjusted to match ENVI- met façade irradiation. 3.5.2 Obtaining canopy transmission for shade trees and gap fraction for BEM Shade trees can be defined as trees with a high shading potential or low transmissivity or solar permeability (Konarska et al. 2014). They allow only a small portion of direct solar radiation to be transmitted through foliage. Canopy transmission for existing trees can be calculated from measurements made using pyranometers in the shade of the canopy (Takacs et al. 2015). Canopy transmissivity may also vary with species. Canopy transmission(T) was calculated using the formula: T= Gtrans / Gact (Takacs et al. 2015) Gtrans (W/m2): is the transmitted solar radiation output in the shade of the selected urban tree Gact; (W/m2): is the value of global radiation free from sky obstruction; the total upper hemispherical shortwave radiation (Takacs et al. 2015). 97 The average transmissivity of the African sumac tree was calculated. The output used was the direct SW radiation (W/m2) from the atmosphere folder for the shaded and baseline unshaded model respectively. T= (130 W/m2)/ (370 W/m2) = 0.35 How much solar energy is transmitted through the ENVI-met shade tree? Façade irradiation is an indicator of how much sun made it through foliage and struck the building façade. The façade irradiation values of the Grasshopper model were matched with the ENVI-met façade irradiation. The gap fraction was adjusted to match irradiation on a façade of the same area in the Ladybug tools. The tree with resulting gap fraction is used as a shade object in BES. 3.6 Summary Urban materials affect their surroundings and human beings that are exposed to both direct radiation from the sun, and reflected and emitted radiation from surfaces. It is important to choose software that can simulate this appropriately to quantify heat mitigation outcomes accurately. Results from preliminary studies show that ENVI-met can simulate materials reasonably accurately (Table 3.6). Table 3.6: Summary of preliminary study results TEST CASES RESULT CONCLUSION ENVI-met grid-dependency on output variables. There was a greater difference in Tsurface values for the same site area modelled in two different grid resolutions, than for Tair values. Scenarios need to be modelled using the same grid resolution to avoid errors associated with different grid sizes. 98 Testing materials and airflow The test case set up with the minimum possible wind speed showed advection and gradual distribution of air temperatures over the model area over time. With larger wind speeds, quick air temperature distribution was observed over the model area. The asphalt area of the model showed higher surface and air temperatures over it when compared with the natural sandy loam surface. ENVI-met generated reasonably accurate outputs for different wind speeds and materials. Testing façade temperatures as a result of shading. The surface temperatures of a shaded and unshaded building wall showed a large expected difference. The surface temperature of a wall shaded by translucent objects such as trees and a translucent screen was somewhere between that of the shaded and unshaded façade temperatures. ENVI-met modelled the effect of radiation and shading on surface temperatures reasonably well. Harris Hall courtyard verification study The simulated T surface-simulated of a portion of the façade shaded by a tree and the T surface-measured using the FLIR E8 IR camera were very close. There was a delay in heat transfer across the nodes of the wall profile from the outside to the inside. The input model material settings and tree parameters generated accurate output that were in good agreement with the measured values. ENVI-met modelled thermal lag of the thermal mass of the concrete wall. A big disadvantage is the lack of wind pressure coefficient output. Wind pressure coefficients better represent microclimatic conditions at different parts of a building when compared to a single value of wind speed in the weather file. Weather data that can represent current climate is important to represent real conditions, problems, and comfort and energy outcomes. CFD using current weather data can generate the impact of surface materials and a lack of vegetation on the microclimate of the neighborhood. The impact of trees on microclimate variables at the building boundary layer- ambient air temperatures, wind speed and direction and relative humidity, can be quantified on the 99 peak heating and cooling days. Since ENVI-met simulates materials and dynamic changes appropriately, the improvement in outdoor thermal environment and heat stress on the human body can be accurately represented using the UTCI metric for the urban heat island and added tree cover scenarios on the hottest and coldest days. To quantify the impact of the changes in microclimate on building heating and cooling loads, CFD outputs can be brought in as BEM input. For example, canopy transmissivity for shade trees on site can be used as a parameter for the tree model in BEM. The tree model with the derived transmissivity (gap fraction) is then used as a shade object that blocks a certain portion of solar radiation and consequently, reduces radiant heat gain indoors and through envelope conduction. The coupling of micrometeorological variables is carried out by replacing weather station data with the site-specific data obtained from ENVI-met. This is discussed in Chapter 4: Methodology. 100 4. METHODOLOGY The research objective is to assess the potential of shade trees in mitigating heat in a neighborhood that has very little tree canopy and shade. The heat mitigation due to increased tree canopy cover in the neighborhood will be quantified using the ambient air temperature and UTCI as metrics. The potential heat mitigation by shade trees will be quantified in terms of the building heating and cooling loads on a mild day, a typical hot day and a typical cold day in Los Angeles. The existing baseline conditions and the potential changes due to added shade trees will then be compared using the above metrics. Ramona Gardens is a public housing neighborhood complex in Los Angeles, California, that covers 32 acres with a hundred 2-story multifamily housing buildings. A 6.9-acre area of the neighborhood has been modelled for quantifying the impact of an increased tree canopy cover on the microclimate (Figure 4). 101 Figure 4: Model area for analysis; 6.9-acre site measuring approximately 215m x 130 m; https://www.bing.com/maps?q=ramona+gardens+los+angeles&FORM=HDRSC4 The ambient air temperature and mean radiant temperature are key performance indicators that are used to quantify the improvement in the surroundings due to the evapo transpirative cooling effect and shade. The workflow for quantifying the improvement in the microclimate of Ramona Gardens with an increased tree canopy coverage entails CFD modelling for obtaining micrometeorological variables from dynamic changes occurring in the plant-surface- 102 atmosphere interface and analysing the variables in terms of the outdoor thermal heat stress and building heating and cooling loads metrics- which are both function of the common microclimate variables- RH, wind speed, direction and air temperature. UTCI is also governed by MRT in addition to the above variables. The trees added in the site were different species of shade trees- trees with low solar permeability that can shade surfaces-ground and buildings effectively. The UTCI was quantified in ENVI-met. Obtaining building heating and cooling loads requires BEM. The weather data file used for BEM was modified by replacing TMY3 airport data with hourly values obtained from the CFD simulation, for both the baseline and the site with added tree canopy, over the analysis period. The UTCI and the building heating and cooling loads were then compared for the two scenarios (Figure 4.1). Figure 4.1: The methodology diagram 103 4.1 CFD model The CFD model for Ramona Gardens comprises built forms, simple vegetation-lawn on pervious surfaces (irrigated sandy loam), complex vegetation- existing and proposed trees and the ground with different material makeup. The model was set up with important grid and telescoping settings and weather data/forcing file for the analysis days. 4.1.1 Base case CFD model The base case scenario is a site with very little tree canopy cover of 6.6 % of the total site area. The existing trees largely comprise low-value shade trees (Margulies et al., 2021). Low-value shade trees are trees that do not contribute significant cooling as they do not shade surfaces effectively; this may be due to the canopy profile, LAD and due to the fact that they are deciduous. Palm trees and sycamores may be low-value shade trees as they do not block significant amounts of direct radiation due to their canopy profile (palm trees have less layers of foliage in the vertical profile) and Sycamore trees shed leaves in the fall and provide little shade when it is needed, for example, in September when it is hot with air temperatures reaching 80-88 o F that exceed the 81 o F threshold for thermal comfort. Heat stress is exacerbated by direct exposure to sun and high MRT in UHIs that lack shade. The other existing species are Ficus and Quercus. Building forms affect the microclimate by changing wind patterns. The building materials determine the heat exchange between the microclimate and the building surface. It is therefore important to model them accurately. For the microclimate study, buildings are exported to ENVI-met from Grasshopper for Rhino 3d, using the Dragon-fly plugin, and 104 for the BEM model, a single building with multiple thermal zones representing the apartment units in the multifamily residential buildings is modelled for a building of interest. The important model elements and settings for the CFD model are the existing trees, buildings, grid settings, soil settings and the forcing file (Table 4.1.1). Table 4.1.1 Model domain and settings The model set-up Settings Area Existing trees Palm, Ficus microcarpa, live oak, Sycamore (Fully grown trees) Tree cover= 1835 sq. m Site canopy %= 6.6 % Building s 6m height 22 building s Material- Uninsulated wood framed brick building 3022 m 2 (10% footprint) 105 Wall profile: Grid resolutio n and model domain x*y*z 2m x 2m x 2m 215M X 130 M Soil /Ground material profile Pervious: Irrigated sandy loam (data.lacounty.gov) 6617 sq. m Impervious walkways: Concrete grey 16682 sq. m Road: Asphalt 4523 sq. m The yellow slice is the building footprint. 106 Forcing file LA TMY3 morphed for 2020 The site with low tree canopy cover comprises old and fully grown low LAI trees or low- value shade trees (Figure 4.1). Figure 4.1: Base case scenario; ENVI-met Spaces 4.1.2 The site with shade trees Additional number of shade trees were proposed for placement along the south and west facades of the buildings (recommendations from the Urban Trees Initiative report) (Margulies et al. 2021) (Figure 4.1.2). 107 Figure 4.1.2: Additional shade trees proposed for planting along the south and west of facades and walkways (Margulies et al. 2021) Spacing was allocated to provide ample space for roots to grow and spread, with some amount of canopy overlap depending on the spacing, which was accounted for in the canopy cover estimation. Canopy cover was estimated as the % of site area covered by trees based on their crown radius when 20 years old, in 2020 (planting intervention was in 2000-assumed) (Figure 4.1.21). 108 Figure 4.1.21: Site with a 105 additional shade trees and 28 existing trees (18% new tree canopy) - placement along the S and W facades and walkways Shade was increased in the site by adding medium LAD shade trees. The existing trees were retained. Lush high LAD trees (like a large Moreton Ficus) provide the maximum shade but may require more frequent irrigation. The new tree inventory comprises evergreen plants approved by the City of LA and trees that grow in climate sunset zone- 22 (https://www.sunset.com/garden/climate-zones/sunset-climate-zone-los-angeles- area). Some are drought resistant and others that were chosen require only a reasonable amount of water for growth (Figure 4.1.22). 109 Figure 4.1.22: List of approved trees by the City of LA (storymaps.arcgis.com) The site with proposed shade trees includes smaller and more compact shade trees such as the African sumac and Australian willow spaced at 4-6m, in rows 3m away from the residences. Oak trees have a larger crown diameter and were provided with more spacing (Table 4.1.2). The proposed shade trees have been modelled in Albero, the tree application in ENVI-met. 110 Table 4.1.2: New trees modelled in the Albero application Tree species (modelled in ENVI- met) Current size (model) Width X width x height ENVI-met tree Spacing African sumac 3.94m x 4.12m x 8m 4m centre to centre Australian willow 4.8m x 5.3m x 7m 4m centre to centre Oak trees (young) 7m x 4m X 8m 8m centre to centre New tree canopy cover 18 % 111 The tree selection process also considered important exclusion criteria to exclude allergy and poisonous trees as well as trees that grow fast but end up weak-wooded like the California pepper wood tree. The modelled trees generally represent medium LAD trees and can be substituted by other trees of similar LAD and canopy profiles. The LAD per grid cell varies with branching patterns dictated by L-system rules that are different for different species and were modelled from observation. The Holly oak (Quercus ilex) tree was added from the existing library in the Albero application of ENVI-met. The L-system sequence can be found in the appendix. 4.2 CFD Simulation The simulations were conducted for typical hot, cold and mild days. The typical hot and cold days are the 21 st of September and 21 st Jan respectively, and a milder condition is simulated for August 21 th- 22 nd . The meteorological conditions are discussed next. 4.2.1 Typical hot day CFD simulation settings for the typical hot day comprise meteorological settings for 20 th - 22 st September (Figure 4.2.1). Forcing was set up in the ENVI-guide application of ENVI-met. 112 Figure 4.2.1: Meteorology on the 21 st of September- Peak cooling day; from the LA-2020 morphed TMY3 file There are maximum temperatures that cross the comfort level threshold of 27 o C and it would be useful to quantify the ambient air cooling due to trees over the entire site and if shade can bring UTCI in the no heat stress category when compared to the base scenario. The simulation period was started at 6:00 AM on 20 th September to provide a day of spin- up prior to the day for which output was required. 4.2.2 Typical cold day CFD simulation settings for the typical cold day comprise meteorological settings for 3 rd - 5 th January (Figure 4.2.2). 113 Figure 4.2.2: Meteorology on the 4 th of January- Typical cold day; from the morphed LA-2020 TMY3 file The simulation period was set to start at 6:00 AM on 3 rd January to provide a spin-up period prior to the day for which output was required. The diurnal temperatures are below the comfort level threshold. The heat/cold stress due to shade was quantified using the UTCI metric and compared for the base case and new site conditions. 4.2.3 Average/ Mild Day CFD simulation settings for the average day comprise meteorological settings for 20 th - 22 st August (Figure 4.2.3). 114 Figure 4.2.3: Mild day meteorological settings; from the LA-2020 morphed TMY3 file The temperatures are within the comfort level threshold. Outdoor thermal comfort and heat stress also depend on exposure to radiation which can contribute to heat stress and thermal discomfort despite comfortable air temperatures. It would be important to quantify UTCI improvement due to ambient air cooling and shade (lesser MRT) at the human height. Also, building heating and cooling loads were quantified for base case and shaded buildings on this day and the next. The improvement in shade in terms of MRT and ambient air cooling in terms of the air temperature in the canyon and at the building boundary layer were compared for the base scenario and the site with 18% tree canopy. The simulation period was started at 6:00 AM on 20 th August to provide a spin-up period prior to the day for which output was required. 115 4.3 CFD Output The important CFD outputs comprise microclimate variables- Tair, RH, wind speed and direction, and MRT. The improvement from shade and ambient air cooling are assessed at the site level for UTCI, and for BEM, values were collected at the building boundary layer/ façade at 3m height (mid-height). 4.3.1 Output variables The output values for T air, wind speed, direction, and RH were analysed and are presented in Chapter 5: Data and summary of results. These consist of microclimate heat maps of the entire site. BES was conducted for a building of interest. For this, micrometeorological values were collected at the building boundary layer/ façade from the buildings dynamic folder and the atmosphere folder (Table 4.3.1). Table 4.3.1: Data collection and analysis of CFD output Data domain Output folder Output variable names Site Atmosphere Biomet- MRT at 1m Air temperature at 1m height Biomet UTCI at 1 m human centroid height (all UTCI variables are processed by BIO-met at the specified reference level) Building boundary layer Building- Dynamic Wall: Wind Speed in front of facade Wall: Air Temperature in front of facade; both at k=3m section cut Atmosphere folder Wind direction Relative humidity; at 3m Tair represents the average value along the south and west facades only, as cooling is localized on those sides. BES was conducted for thermal zones in the West part of the 116 building which was lined with shade trees. The average hourly values from the CFD simulations for both the base case and shaded site replace their respective values in the EPW file. Thus, two new microclimate files were generated- one for the base scenario and another for the site with increased tree canopy cover, with hourly values replaced for each of the three analysis periods. 4.3.2 Outdoor heat stress analysis The UTCI option in the BIO-met application of ENVI-met provides hourly values of the UTCI distribution across the site. The atmosphere folder was selected for BIO-met to process the UTCI variables- T air, RH, wind speed, direction and MRT. Since shade is of interest, the decrease in MRT was quantified for areas under tree canopy in the building canyon where they are planted. The corresponding decrease in UTCI values represent the improvement in the thermal environment due to shade and potentially, ambient air cooling by evapotranspiration. Does cooling by evapotranspiration under the shade of trees contribute to a significant improvement in UTCI at the human height? This would depend on the reduction in ambient air temperatures under the tree canopy at a 1m height. The research objective was to investigate the relative contribution of the two different temperature reductions (MRT and T amb) associated with tree canopy cover. The data and maps for UTCI and MRT are collected and presented in Chapter 5: Data. 117 4.4 Building energy simulations Dynamic thermal energy simulations were conducted for a building of interest. Heating and cooling loads are a function of the building envelope and meteorological conditions and include internal heat gains/ losses from equipment, lighting and people. The changing variable is a building envelope shaded by trees and other changes at the building boundary layer such as changes in ambient air temperatures, wind speed, direction and RH due to trees. This is accounted for by modifying airport weather data with hourly weather output from CFD for the aforementioned variables. This section is about generating a building boundary layer weather data file by modifying airport data, BEM of the thermal zones and surrounding context, and obtaining heating and cooling loads for the baseline and the building in its improved context. 4.4.1 Weather file modification Two weather files that account for changes in the microclimate around the building boundary layer were generated – one replacing the EPW values with the output from the base case CFD simulation and another that replaced EPW values with output from the shaded site with increased shade tree canopy. Hourly average RH values were obtained at the south and west building façades. The EPW RH was replaced first. Then, holding RH and atmospheric pressure constant, the baseline ambient air temperatures (dry bulb temperature) at the building facade replaced their respective EPW values (DBT). This step automatically changes the dew point and wet bulb temperatures in the EPW file so that psychrometric relationships are preserved. 118 This conversion was verified using the psychrometric components in the Ladybug tools for Grasshopper. Finally, the EPW wind speed and wind direction values are replaced by average wind speed values at the west façade. For this, wind speed at the facade was first converted to 10m wind speeds to align with the wind speed calculation made by EnergyPlus. The Ladybug tools calculate the wind profile at the windows by considering the height of the box energy model above the origin (Rhino origin) and by applying the power law to the meteorological 10 m wind speeds obtained from the weather data file (Mackey et al. 19). The wind speed at a certain height according to the power law is given by the equation: u= ur(z/zr) a ; where ur is the wind speed at a reference height(10m in weather data) z is the height of the model above the origin zr is the reference height(10m) a(alpha), the coefficient that is approximately= 0.143 (Blanken and Barry, 2016) Therefore, wind speed at 10m; u10 = u/ (z/zr) a z-is 3m, half height of the building. zr is 10m u is the hourly wind speed value obtained at the facade at a certain height (wind speed was taken at 3m height) that needs to be converted to 10m reference winds to then be converted back for ventilation calculations. The same procedure was repeated for the site 119 with an increased tree canopy cover. Two site-specific sets of weather data were obtained for BES, each for the three-analysis period. These values replaced their respective hourly values in the original EPW file. Therefore, weather data was modified for the 3 analysis days for the baseline and for the shaded building. 4.4.2 BEM The buildings in public subsidized housing comprise envelopes with poor or no insulation (Pierce and Gabbe 2020). The buildings in Ramona Gardens are wood framed brick buildings constructed in 1940. The building of interest is a 2 story 42 m long and 9 m wide building. It comprises 8 apartment units. The model is resolved to represent units that are set up as conditioned space for which heating and cooling loads were quantified using the zone ideal air loads system (not actual energy consumption). The ideal air loads system is a theoretical HVAC system that is used to calculate the thermal energy required to meet the thermal energy demand of a zone. It is modelled with the assumption of meeting the heating and cooling loads exactly or “ideally” without accounting for any equipment efficiencies. Cooling load is the energy that needs to be removed from a space to keep temperatures between a certain thermostat range. The range has been set to 21- 27 o C considering a broader range of adaptive capacity of the residents (Pierce and Gabbe, 2020). 120 The other settings remain constant, such as people heat gains, infiltration, equipment and lighting loads in the space (Please see Chapter 5:Data under 5.4 Building energy modelling). 4.4.3 Heating and cooling loads Heating and cooling loads are obtained in a total of 6 simulations, 3 each for the base case and shaded building for the analysis periods using their respective microclimate weather data files (Table 4.4.3). Table 4.4.3: Summary of simulations Base case model peak loads Model in the site with added shade trees Model: Figure: Building in the base case site Model: Figure: Building shaded by trees in the site with increased shade tree canopy (showing south-west view) 1 Baseline-Peak cooling day weather data 2 Shaded building-Peak cooling day weather data 3 Baseline-Peak heating day weather data 4 Shaded building-Peak heating day simulation 121 5 Baseline-Average day weather data 6 Shaded building-Average day weather data 4.5 Summary A base case scenario with 6.6% tree canopy cover and the site with added shade trees- 18% canopy cover was modelled in Rhino and exported to ENVI-met Spaces. A morphed weather data file that is representative of current conditions in LA was used for simulations. The hottest, coldest and a mild day were chosen as analysis periods. Six CFD simulations were conducted, 3 each for the base scenario and the site with increased shade tree coverage. The microclimate variables were analysed at the site level to track improvement in the radiant environment in terms of MRT and the UTCI metric. The UTCI was compared for the base case and shaded sites on each of the three days. The variables- T air, wind speed, direction and RH were collected for weather file modification, at the building boundary layer at a 3m section cut (building mid-height) at the west façade (north west due to the 28 o tilt from north). The average values replaced the airport TMY3 data to generate 3 weather data files for the base case for each of the three analysis periods and 3 for the site with added tree canopy cover. A BEM model was generated for the base case scenario and the same model was used with the surrounding context to simulate the shading effect due to trees. The modified weather data file accounted for the changes in microclimate on the building heating and cooling loads. The heating and cooling loads for the base scenario and shaded building were compared. 122 5: DATA AND SUMMARY OF RESULTS For outdoor heat stress analysis, hourly heat maps were collected to visualize the spatial and temporal variation of UTCI with and without shade trees. A grid cell was chosen for analysis, in the building canyon. Hourly values of UTCI variables- humidity (BIO-met gives specific humidity instead of relative humidity), wind speed, MRT and air temperature were plotted for the baseline condition and the scenario with shade trees. The decrease in UTCI will be analysed in terms of the contribution of shade trees in reducing MRT in the building canyon and by how much ambient air temperature reduction can additionally reduce heat stress (UTCI) during the hottest part of the day, in the next chapter. For BEM, hourly values for the ambient air temperatures, wind speed, direction and RH are collected for the base model and shaded model. These values were used to replace their respective hourly values to obtain modified weather data for the base model and the site with additional shade trees. The new weather data files are used to simulate building heating and cooling loads. The heating and cooling loads are collected for each of the three analysis days for the base case model and the shaded model respectively. This chapter includes data collected for outdoor heat stress analysis under 5.1 CFD output and for building energy simulations, under 5.2 Building boundary conditions, 5.3 New weather data at the building boundary layer and 5.4 Building energy modelling. 123 5.1 CFD output CFD output includes the hourly heatmaps collected for MRT, Tair, wind speed and direction and RH over the entire site at a 1 m section cut. UTCI values are reported by BIO-met in the form of heatmaps and hourly values for selected grid cells. The improvement in the thermal environment due to shade is quantified at the sensor grid 01 over the walkway in the shaded building canyon (Figure 5.1). Figure 5.1: Analysis point 01 in the shaded Building canyon 124 5.1.1 Typical hot day results The baseline UTCI, wind speed, air temperature, MRT and specific humidity were plotted for the analysis point in the building canyon (Figure 5.1.1). Figure 5.1.1: Building canyon baseline-Hourly values on the 21st of September The UTCI, wind speed, air temperature, MRT and specific humidity were plotted for the building canyon in the scenario of increased tree canopy (Figure 5.1.12). 125 Figure 5.1.12: Building canyon in the site with shade trees-Hourly values on the 21st of September The site heat maps for MRT and UTCI show the range of temperatures across the day and the correlation between MRT and UTCI in radiation hours (Figure 5.1.13). 126 Figure 5.1.13: Baseline Hourly MRT and UTCI heatmap values on the 21st of September The site heat maps for MRT and UTCI show the range of temperatures across the day in the scenario with added shade trees and the correlation between MRT and UTCI in radiation hours (Figure 5.1.14). 127 Figure 5.1.14: Site with shade trees- Hourly MRT and UTCI heatmap values on the 21st of September 5.1.2 Typical cold day results The baseline UTCI, wind speed, air temperature, MRT and specific humidity were plotted for the building canyon (Figure 5.1.2). 128 Figure 5.1.2: Building Canyon-Hourly values on the 4th of Jan The UTCI and its dependent variables- wind speed, air temperature, MRT and specific humidity were plotted for the grid cell in the building canyon in the scenario of increased tree canopy (Figure 5.1.21). 129 Figure 5.1.21: Building canyon-Hourly values on the 4th of Jan The site heat maps for MRT and UTCI show the range of temperatures across the day and the correlation between MRT and UTCI in radiation hours in the base case scenario (Figure 5.1.22). 130 Figure 5.1.23: Baseline conditions The site heat maps for MRT and UTCI show the range of temperatures across the day in the scenario with added shade trees and the correlation between MRT and UTCI in radiation hours (Figure 5.1.24). 131 Figure 5.1.24: Heatmaps of the shaded site 5.1.3 Mild day results The baseline UTCI, wind speed, air temperature, MRT and specific humidity were plotted for the grid cell in the building canyon (Figure 5.1.3). 132 Figure 5.1.3: Building canyon baseline-Hourly values on the 21st of August The UTCI, wind speed, air temperature, MRT and specific humidity were plotted for the grid cell in the building canyon in the scenario of increased tree canopy (Figure 5.1.31). Figure 5.1.31: Building canyon in the site with shade trees-Hourly values on the 21st of August 133 The site heat maps for MRT and UTCI show the range of temperatures across the day and the correlation between MRT and UTCI in radiation hours (when there were maximum differences between the minimum and maximum mean radiant temperatures in the site) (Figure 5.1.32). Figure 5.1.32: Baseline Hourly MRT and UTCI heatmap values on the 21st of August The site heat maps for MRT and UTCI show the range of temperatures across the day in the scenario with added shade trees and the correlation between MRT and UTCI in radiation hours (when there were maximum differences between the minimum and maximum mean radiant temperatures in the site) (Figure 5.1.33). 134 Figure 5.1.33: Site with shade trees- Hourly MRT and UTCI heatmap values on the 21st of August 5.2 Building boundary conditions The changes in the microclimatic boundary layer conditions at the west façade were collected for each of the analysis days. The airport weather data was replaced by the respective hourly microclimate air temperature, wind speed, direction, and relative humidity data on each of the analysis periods for the baseline and shaded building. 5.2.1 Typical hot day results The air temperature, wind speed and direction, and relative humidity values were collected at the specific selected grid cells (at building midheight=3m). The greatest 135 difference in temperatures between the baseline and shaded conditions occurred at noon (Figure 5.2.1). Figure 5.2.1: Difference in air temperatures at the north west facade The average air temperature, relative humidity, wind speed and direction at the north west façade were collected for the base case scenario (Table 5.2.1). X (m) 0.0010.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 Y (m) 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 140.00 150.00 160.00 170.00 180.00 190.00 200.00 210.00 N ENVI-met <Right foot> Figure 1: Comparison Trees- Hot day 12.00.01 21.09.2021 with Trees-Hot day 12.00.01 21.09.2021 x/y Cut at k=1 (z=3.0000 m) absolute difference Potential Air Temperature < -0.52 K -0.46 K -0.39 K -0.32 K -0.26 K -0.19 K -0.12 K -0.06 K 0.01 K > 0.08 K Min: -0.52 K Max: 0.14 K 136 Table 5.2.1: Building boundary layer conditions in the baseline site Date Time Wind Speed (m/s) Wind Direction (deg) Air Temperature (°C) Relative Humidity (%) 21.09.2021 00.00.01 0.43021 29.608 16.389 98.92 21.09.2021 01.00.01 0.4326 29.58 16.168 99.935 21.09.2021 02.00.01 0.43452 29.553 15.956 100.9 21.09.2021 03.00.01 0.43604 29.528 15.822 101.52 21.09.2021 04.00.01 0.43714 29.504 16.267 99.237 21.09.2021 05.00.01 0.43786 29.481 16.821 95.987 21.09.2021 06.00.01 0.52499 30.412 17.231 93.709 21.09.2021 07.00.01 1.031 30.446 20.948 83.988 21.09.2021 08.00.01 0.31809 32.487 24.797 70.027 21.09.2021 09.00.01 0.67619 207.94 28.578 59.906 21.09.2021 10.00.01 1.7373 214.38 29.893 54.699 21.09.2021 11.00.01 2.2466 210.47 31.308 49.607 21.09.2021 12.00.01 2.7761 209.06 32.416 44.302 21.09.2021 13.00.01 1.9522 212.73 31.324 47.944 21.09.2021 14.00.01 2.4016 211.44 30.173 50.412 21.09.2021 15.00.01 3.2857 209.36 29.04 51.509 21.09.2021 16.00.01 2.0316 211.75 26.846 58.35 21.09.2021 17.00.01 2.2631 211.87 24.614 65.465 137 21.09.2021 18.00.01 1.5724 209.4 22.48 69.764 21.09.2021 19.00.01 1.668 208.65 21.28 73.247 21.09.2021 20.00.01 1.2673 207.02 20.209 78.126 21.09.2021 21.00.01 0.40528 29.027 19.182 80.403 21.09.2021 22.00.01 0.40893 28.887 18.848 85.331 21.09.2021 23.00.01 0.41 28.826 18.516 86.377 22.09.2021 00.00.01 0.41072 28.769 18.224 88.011 22.09.2021 01.00.01 0.41115 28.716 18.203 87.332 22.09.2021 02.00.01 0.4341 30.469 18.253 87.213 22.09.2021 03.00.01 0.74354 32.039 18.227 86.986 22.09.2021 04.00.01 0.72904 26.625 18.74 84.17 22.09.2021 05.00.01 0.42785 27.724 19.211 80.468 22.09.2021 06.00.01 0.13643 1.5028 19.606 76.843 22.09.2021 07.00.01 0.4361 31.862 22.703 71.683 22.09.2021 08.00.01 0.064107 275.5 25.868 63.401 22.09.2021 09.00.01 1.5587 213.69 29.411 56.232 22.09.2021 10.00.01 2.3213 209.83 30.605 50.339 22.09.2021 11.00.01 1.2647 210.56 31.698 44.846 22.09.2021 12.00.01 2.1994 210.67 32.649 44.013 22.09.2021 13.00.01 3.0744 210.85 31.516 47.375 22.09.2021 14.00.01 3.2591 209.7 30.217 48.049 22.09.2021 15.00.01 2.7574 211.8 28.774 51.27 138 22.09.2021 16.00.01 3.5183 210.16 26.723 56.923 22.09.2021 17.00.01 2.7612 209.85 24.673 61.823 22.09.2021 18.00.01 1.927 211.23 22.751 66.89 22.09.2021 19.00.01 1.5945 209.59 21.802 70.459 22.09.2021 20.00.01 1.5582 208.66 21.018 72.903 22.09.2021 21.00.01 1.6999 210.81 20.269 79.373 22.09.2021 22.00.01 1.6932 209.6 19.438 82.153 22.09.2021 23.00.01 1.1605 208.22 18.754 83.718 23.09.2021 00.00.01 1.1115 213.09 18.057 89.599 The average air temperature, relative humidity, wind speed and direction at the north west façade were collected for the site with added shaded trees (Table 5.2.11). Table 5.2.11: Building boundary layer conditions in the shaded site Date Time Wind Speed (m/s) Wind Direction (deg) Air Temperature (°C) Relative Humidity (%) 21.09.2021 00.00.01 0.38373 30.116 16.326 98.95 21.09.2021 01.00.01 0.38077 30.025 16.098 99.835 21.09.2021 02.00.01 0.37773 29.944 15.881 100.65 21.09.2021 03.00.01 0.3746 29.872 15.742 101.18 139 21.09.2021 04.00.01 0.37126 29.807 16.155 99.433 21.09.2021 05.00.01 0.36766 29.746 16.676 96.904 21.09.2021 06.00.01 0.43043 30.414 17.069 95.119 21.09.2021 07.00.01 0.72891 30.888 20.778 85.421 21.09.2021 08.00.01 0.076646 76.575 24.39 72.853 21.09.2021 09.00.01 0.74944 207.42 27.872 63.789 21.09.2021 10.00.01 1.2112 215.32 29.346 58.047 21.09.2021 11.00.01 1.3993 210.4 30.785 53.261 21.09.2021 12.00.01 1.7132 208.26 31.971 46.667 21.09.2021 13.00.01 1.1236 214.42 30.827 51.094 21.09.2021 14.00.01 1.5637 211.95 29.705 53.17 21.09.2021 15.00.01 2.0251 208.47 28.708 53.532 21.09.2021 16.00.01 1.1389 212.52 26.479 60.771 21.09.2021 17.00.01 1.3746 212.64 24.398 67.081 21.09.2021 18.00.01 0.7261 206.72 22.347 71.28 21.09.2021 19.00.01 1.1589 206.61 21.194 73.253 21.09.2021 20.00.01 0.8044 202.91 20.143 78.098 21.09.2021 21.00.01 0.33684 29.687 19.117 80.735 21.09.2021 22.00.01 0.33375 29.475 18.723 86.064 21.09.2021 23.00.01 0.33184 29.368 18.395 87.097 22.09.2021 00.00.01 0.32992 29.268 18.126 88.625 22.09.2021 01.00.01 0.32799 29.176 18.107 87.956 140 22.09.2021 02.00.01 0.33402 31.25 18.153 87.882 22.09.2021 03.00.01 0.58298 32.861 18.216 86.96 22.09.2021 04.00.01 0.46248 26.989 18.7 85.017 22.09.2021 05.00.01 0.25625 30.611 19.154 81.383 22.09.2021 06.00.01 0.11496 319.48 19.464 76.79 22.09.2021 07.00.01 0.30613 32.268 22.49 72.872 22.09.2021 08.00.01 0.1547 225.2 25.017 62.861 22.09.2021 09.00.01 1.3164 213.61 28.946 58.563 22.09.2021 10.00.01 1.3955 209.7 30.148 54.144 22.09.2021 11.00.01 0.67022 209.97 30.978 49.025 22.09.2021 12.00.01 1.509 210.49 32.112 47.122 22.09.2021 13.00.01 2.2423 210.79 31.054 49.767 22.09.2021 14.00.01 1.8989 208.99 29.873 50.306 22.09.2021 15.00.01 1.6613 212.59 28.395 53.879 22.09.2021 16.00.01 2.1756 209.59 26.474 59.017 22.09.2021 17.00.01 1.4672 208.67 24.538 63.374 22.09.2021 18.00.01 1.1513 211.05 22.659 68.118 22.09.2021 19.00.01 0.876 207.76 21.733 71.506 22.09.2021 20.00.01 1.1156 206.54 20.974 72.735 22.09.2021 21.00.01 1.1346 210.65 20.234 80.102 22.09.2021 22.00.01 1.1494 208.86 19.43 82.599 22.09.2021 23.00.01 0.84105 206.69 18.744 83.237 141 23.09.2021 00.00.01 0.77789 213.26 18.087 89.658 5.2.2 Typical cold day results The average air temperature, relative humidity, wind speed and direction at the north west façade (Figure 5.2.2) were collected for the base case scenario (Table 5.2.2). The greatest difference in temperatures between the baseline and shaded conditions occurred at 9 AM. Figure 5.2.2: Typical Cold day heat map at 2 pm at 3m section cut X (m) 0.0010.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 Y (m) 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 140.00 150.00 160.00 170.00 180.00 190.00 200.00 210.00 N ENVI-met <Right foot> Figure 1: Comparison NoTrees- Cold day 14.00.01 04.01.2022 with Trees-Cold day 14.00.01 04.01.2022 x/y Cut at k=1 (z=3.0000 m) absolute difference Potential Air Temperature < -0.03 K -0.00 K 0.02 K 0.05 K 0.08 K 0.11 K 0.13 K 0.16 K 0.19 K > 0.21 K Min: -0.03 K Max: 0.24 K 142 Table 5.2.2: Building boundary layer conditions in the baseline site Date Time Wind Speed (m/s) Wind Direction (deg) Air Temperature (°C) Relative Humidity (%) 04.01.2022 00.00.01 2.1967 27.339 12.46 73.38 04.01.2022 01.00.01 0.481 32.435 11.35 61.6 04.01.2022 02.00.01 0.074371 35.539 10.113 68.69 04.01.2022 03.00.01 0.23618 51.273 7.8455 84.728 04.01.2022 04.00.01 0.77439 20.034 7.9102 87.126 04.01.2022 05.00.01 2.6535 27.005 8.3377 84.602 04.01.2022 06.00.01 1.2514 26.846 7.7815 82.308 04.01.2022 07.00.01 1.0831 29.55 8.2322 78.091 04.01.2022 08.00.01 1.077 29.321 9.4262 78.244 04.01.2022 09.00.01 2.2803 28.422 11.353 76.787 04.01.2022 10.00.01 1.5652 25.267 13.797 75.131 04.01.2022 11.00.01 1.9574 27.856 15.118 77.832 04.01.2022 12.00.01 0.13007 205.7 16.65 69.518 04.01.2022 13.00.01 1.3024 206.8 17.325 66.018 04.01.2022 14.00.01 1.6867 215.1 17.41 68.288 04.01.2022 15.00.01 3.4215 209.68 16.736 71.389 04.01.2022 16.00.01 2.0943 210.23 16.548 68.087 143 04.01.2022 17.00.01 1.9626 211.01 15.248 74.53 04.01.2022 18.00.01 1.576 209.73 14.031 75.191 04.01.2022 19.00.01 1.7977 208.24 12.855 78.685 04.01.2022 20.00.01 0.78191 212.46 13.247 72.325 04.01.2022 21.00.01 1.0024 30.527 13.281 71.369 04.01.2022 22.00.01 2.303 28.096 12.804 82.114 04.01.2022 23.00.01 0.90984 26.126 11.638 83.824 05.01.2022 00.00.01 0.4516 25.638 11.006 85.541 05.01.2022 01.00.01 0.6127 233.22 10.912 85.312 05.01.2022 02.00.01 2.2076 30.638 9.7608 89.147 05.01.2022 03.00.01 1.0035 28.51 9.2365 90.448 05.01.2022 04.00.01 1.4336 26.738 9.2938 91.113 05.01.2022 05.00.01 0.90243 29.457 8.6959 87.627 05.01.2022 06.00.01 1.4619 26.762 8.7052 90.675 05.01.2022 07.00.01 1.1421 25.851 10.428 86.867 05.01.2022 08.00.01 0.48749 29.182 9.399 91.247 05.01.2022 09.00.01 0.49983 28.571 10.546 89.177 05.01.2022 10.00.01 0.89034 32.269 11.132 89.016 05.01.2022 11.00.01 1.4151 24.355 13.266 86.223 05.01.2022 12.00.01 0.34647 26.046 15.219 80.426 05.01.2022 13.00.01 3.0476 210.99 15.191 74.519 05.01.2022 14.00.01 2.7132 209.4 15.15 69.948 144 05.01.2022 15.00.01 4.1277 209.06 14.906 76.578 05.01.2022 16.00.01 1.4453 23.17 12.579 88.853 05.01.2022 17.00.01 1.224 206.14 12.125 88.485 05.01.2022 18.00.01 1.0329 30.248 12.014 91.271 05.01.2022 19.00.01 2.821 27.288 11.866 94.693 05.01.2022 20.00.01 1.8484 25.877 11.208 96.862 05.01.2022 21.00.01 2.6799 28.276 10.455 97.282 05.01.2022 22.00.01 2.9633 28.401 9.2793 97.192 05.01.2022 23.00.01 2.6008 27.811 9.26 97.302 06.01.2022 00.00.01 0.79581 27.38 9.1767 95.207 The average air temperature, relative humidity, wind speed and direction at the north west façade were collected for the site with the added shade trees (Table 5.2.21). Table 5.2.21: Building boundary layer conditions in the baseline site Date Time Wind Speed (m/s) Wind Direction (deg) Potential Air Temperature (°C) Relative Humidity (%) 04.01.2022 00.00.01 1.5137 26.942 12.451 74.033 145 04.01.2022 01.00.01 0.11952 194.09 11.318 59.644 04.01.2022 02.00.01 0.21961 203.64 10.077 67.215 04.01.2022 03.00.01 0.14753 90.254 7.8792 84.659 04.01.2022 04.00.01 0.61052 17.762 7.9754 86.819 04.01.2022 05.00.01 2.0466 26.806 8.3637 84.574 04.01.2022 06.00.01 0.70192 26.418 7.8064 82.251 04.01.2022 07.00.01 0.83551 30.723 8.244 78.641 04.01.2022 08.00.01 0.83298 30.462 9.4031 78.965 04.01.2022 09.00.01 1.8671 29.08 11.307 77.411 04.01.2022 10.00.01 1.1232 25.032 13.715 76.224 04.01.2022 11.00.01 1.5639 28.457 15.031 78.571 04.01.2022 12.00.01 0.37254 204.04 16.557 70.992 04.01.2022 13.00.01 1.4076 206.73 17.18 67.386 04.01.2022 14.00.01 1.2393 217.24 17.202 69.64 04.01.2022 15.00.01 2.5423 209.13 16.609 72.617 04.01.2022 16.00.01 1.3864 209.66 16.404 69.647 04.01.2022 17.00.01 1.4995 210.99 15.163 75.653 04.01.2022 18.00.01 1.1247 209 13.965 76.408 04.01.2022 19.00.01 1.5007 206.81 12.828 78.798 04.01.2022 20.00.01 0.60143 211.54 13.131 73.495 04.01.2022 21.00.01 1.014 30.014 13.265 72.003 04.01.2022 22.00.01 1.8023 28.774 12.801 82.562 146 04.01.2022 23.00.01 0.53464 25.689 11.622 84.447 05.01.2022 00.00.01 0.23421 25.702 10.977 86.173 05.01.2022 01.00.01 0.52119 245.9 10.901 85.819 05.01.2022 02.00.01 1.6565 30.33 9.7775 89.235 05.01.2022 03.00.01 0.59722 29.474 9.2474 90.812 05.01.2022 04.00.01 1.1376 26.548 9.3047 91.105 05.01.2022 05.00.01 0.62844 31.305 8.6932 88.038 05.01.2022 06.00.01 1.1612 27.153 8.7166 90.8 05.01.2022 07.00.01 0.84088 26.035 10.407 87.357 05.01.2022 08.00.01 0.29988 33.411 9.4168 91.406 05.01.2022 09.00.01 0.31131 32.326 10.557 89.391 05.01.2022 10.00.01 0.72087 35.055 11.13 89.242 05.01.2022 11.00.01 1.1785 23.691 13.177 86.761 05.01.2022 12.00.01 0.15172 23.277 15.114 81.583 05.01.2022 13.00.01 2.4968 210.88 15.069 75.682 05.01.2022 14.00.01 1.9139 208.58 15.061 70.932 05.01.2022 15.00.01 3.0045 208.39 14.861 77.102 05.01.2022 16.00.01 2.0162 23.138 12.577 88.838 05.01.2022 17.00.01 0.96116 204.73 12.094 88.208 05.01.2022 18.00.01 1.0975 30.636 12.028 91.28 05.01.2022 19.00.01 2.3406 27.77 11.874 94.785 05.01.2022 20.00.01 1.3615 25.13 11.222 96.82 147 05.01.2022 21.00.01 2.0498 28.992 10.473 97.216 05.01.2022 22.00.01 2.3252 29.084 9.3009 97.096 05.01.2022 23.00.01 1.9321 28.476 9.2805 97.223 06.01.2022 00.00.01 0.36791 30.85 9.2155 95.123 5.2.3 Mild day results The average air temperature, relative humidity, wind speed and direction at the north west façade, shown in dashed lines (Figure 5.2.3) were collected for the base case scenario (Table 5.2.3). The highest difference in air temperatures between the baseline and shaded building was at 9AM. 148 Figure 5.2.3: Mild day heat map at 9AM at 3m section cut Table 5.2.3: Building boundary layer conditions in the baseline site Date Time Wind Speed (m/s) Wind Direction (deg) Potential Air Temperature (°C) Relative Humidity (%) 21.08.2021 00.00.01 2.1672 209.17 20.674 91.039 21.08.2021 01.00.01 1.4762 210.05 20.664 91.183 21.08.2021 02.00.01 1.4697 210.06 20.224 93.253 21.08.2021 03.00.01 1.0301 212.2 20.212 95.681 21.08.2021 04.00.01 0.88115 208.57 20.303 94.866 X (m) 0.0010.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 Y (m) 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 140.00 150.00 160.00 170.00 180.00 190.00 200.00 210.00 N ENVI-met <Right foot> Figure 1: Comparison Final-No trees 09.00.01 21.08.2021 with Mild day- Trees plan2 09.00.01 21.08.2021 x/y Cut at k=1 (z=3.0000 m) absolute difference Potential Air Temperature below 0.05 K 0.05 to 0.09 K 0.09 to 0.13 K 0.13 to 0.17 K 0.17 to 0.21 K 0.21 to 0.25 K 0.25 to 0.29 K 0.29 to 0.33 K 0.33 to 0.37 K above 0.37 K Min: 0.00 K Max: 0.41 K 149 21.08.2021 05.00.01 0.47945 28.793 20.201 94.885 21.08.2021 06.00.01 0.48064 28.799 20.255 95.028 21.08.2021 07.00.01 0.48187 28.817 21.162 89.905 21.08.2021 08.00.01 0.48299 28.83 22.211 85.224 21.08.2021 09.00.01 0.42559 357.62 23.32 82.474 21.08.2021 10.00.01 3.5467 209.76 22.959 84.31 21.08.2021 11.00.01 2.1256 212.18 22.994 82.943 21.08.2021 12.00.01 2.4969 210.32 23.695 79.726 21.08.2021 13.00.01 2.4713 211.67 23.801 79.624 21.08.2021 14.00.01 2.4766 211.61 24.21 79.521 21.08.2021 15.00.01 3.0893 212.25 24.598 77.843 21.08.2021 16.00.01 3.3878 212.4 22.229 86.266 21.08.2021 17.00.01 2.4456 212.69 21.687 88.507 21.08.2021 18.00.01 2.4032 209.26 21.239 90.838 21.08.2021 19.00.01 2.4086 208.47 20.724 93.141 21.08.2021 20.00.01 2.7178 211.59 21.019 92.236 21.08.2021 21.00.01 1.391 211.91 20.494 94.459 21.08.2021 22.00.01 0.62558 24.087 20.939 92.784 21.08.2021 23.00.01 0.4736 31.08 20.926 91.219 22.08.2021 00.00.01 0.46986 30.925 20.862 89.123 22.08.2021 01.00.01 0.79916 210.98 20.833 93.189 22.08.2021 02.00.01 1.2891 208.84 20.89 92.446 150 22.08.2021 03.00.01 1.0122 31.004 20.509 92.602 22.08.2021 04.00.01 0.61273 206.86 20.926 92.817 22.08.2021 05.00.01 0.83116 26.646 20.879 95.659 22.08.2021 06.00.01 0.86482 27.046 21.299 95.446 22.08.2021 07.00.01 0.53463 31.054 22.226 90.207 22.08.2021 08.00.01 0.11128 0.57574 22.586 87.586 22.08.2021 09.00.01 0.11086 1.0405 24.921 80.111 22.08.2021 10.00.01 2.2977 211.24 25.838 77.152 22.08.2021 11.00.01 2.4621 211.99 24.86 78.997 22.08.2021 12.00.01 2.6314 209.24 25.083 77.434 22.08.2021 13.00.01 2.6278 209.3 25.568 75.704 22.08.2021 14.00.01 2.8327 211.15 25.519 76.623 22.08.2021 15.00.01 2.4366 208.74 25.136 76.66 22.08.2021 16.00.01 2.5468 212.41 24.821 76.694 22.08.2021 17.00.01 2.28 211.71 24.097 79.427 22.08.2021 18.00.01 2.8319 209.22 23.071 86.019 22.08.2021 19.00.01 2.107 210.55 22.381 89.555 22.08.2021 20.00.01 1.3636 209.14 21.798 88.99 22.08.2021 21.00.01 0.99822 208.28 22.142 88.359 22.08.2021 22.00.01 2.6586 211.92 21.736 90.954 22.08.2021 23.00.01 1.1914 209.68 21.71 94.782 23.08.2021 00.00.01 2.1821 211.57 21.67 91.344 151 The average air temperature, relative humidity, wind speed and direction at the north west façade were collected for the site with the added shade trees (Table 5.2.31). Table 5.2.31: Building boundary layer conditions in the shaded site Date Time Wind Speed (m/s) Wind Direction (deg) Potential Air Temperature (°C) Relative Humidity (%) 21.08.2021 00.00.01 0.77869 208.74 20.633 91.365 21.08.2021 01.00.01 0.32853 215.1 20.64 91.537 21.08.2021 02.00.01 0.29162 217.55 20.266 93.19 21.08.2021 03.00.01 0.37985 217.5 20.274 95.439 21.08.2021 04.00.01 0.38671 207.49 20.363 94.573 21.08.2021 05.00.01 0.39506 29.106 20.298 94.532 21.08.2021 06.00.01 0.38162 29.086 20.373 94.883 21.08.2021 07.00.01 0.36892 29.073 21.155 91.187 21.08.2021 08.00.01 0.28268 20.546 22.097 87.658 21.08.2021 09.00.01 0.67325 219.02 22.984 83.815 21.08.2021 10.00.01 1.3462 211.57 22.786 85.671 21.08.2021 11.00.01 0.74275 218.72 22.783 84.625 21.08.2021 12.00.01 1.0253 212.31 23.496 81.837 152 21.08.2021 13.00.01 1.0315 215.45 23.568 81.532 21.08.2021 14.00.01 1.1726 215.37 23.946 81.529 21.08.2021 15.00.01 1.5504 215.43 24.317 79.757 21.08.2021 16.00.01 1.4109 216.76 22.094 87.311 21.08.2021 17.00.01 1.0252 215.98 21.639 89.297 21.08.2021 18.00.01 0.9957 208.83 21.222 91.115 21.08.2021 19.00.01 0.91256 208.4 20.714 93.088 21.08.2021 20.00.01 1.0795 215.18 21.002 92.518 21.08.2021 21.00.01 0.48613 216.98 20.552 94.335 21.08.2021 22.00.01 0.64002 24.208 20.976 92.519 21.08.2021 23.00.01 0.23045 34.801 20.97 91.106 22.08.2021 00.00.01 0.032344 190.09 20.869 89.372 22.08.2021 01.00.01 0.64285 210.56 20.778 93.447 22.08.2021 02.00.01 0.51694 208.92 20.902 92.552 22.08.2021 03.00.01 0.68749 32.843 20.543 92.567 22.08.2021 04.00.01 0.54686 205.64 20.885 93.323 22.08.2021 05.00.01 0.57997 27.656 20.954 95.326 22.08.2021 06.00.01 0.31469 29.366 21.373 95.139 22.08.2021 07.00.01 0.19761 50.772 22.204 90.533 22.08.2021 08.00.01 0.11994 252.69 22.505 88.027 22.08.2021 09.00.01 0.24937 223.18 24.401 82.54 22.08.2021 10.00.01 1.1458 215.2 25.487 79.393 153 22.08.2021 11.00.01 0.9351 216.91 24.617 81.114 22.08.2021 12.00.01 0.99345 210.03 24.889 79.447 22.08.2021 13.00.01 1.067 211.87 25.325 77.712 22.08.2021 14.00.01 1.0896 214.96 25.251 78.559 22.08.2021 15.00.01 0.88759 208.77 24.848 77.954 22.08.2021 16.00.01 1.0525 217.12 24.527 78.724 22.08.2021 17.00.01 0.94467 213.06 23.865 81.385 22.08.2021 18.00.01 1.0587 210.08 22.946 86.998 22.08.2021 19.00.01 0.70553 214.51 22.306 90.292 22.08.2021 20.00.01 0.25865 211.3 21.763 89.162 22.08.2021 21.00.01 0.56107 211.43 22.078 89.038 22.08.2021 22.00.01 0.8885 214.16 21.697 91.314 22.08.2021 23.00.01 0.37403 212.87 21.67 94.945 23.08.2021 00.00.01 1.0843 213.98 21.633 91.715 5.3 Weather data modification The changes in boundary layer conditions were collected from the CFD results for the variables that are required for BEM. Hourly airport weather data was replaced by hourly values of the microclimate variables. 154 5.3.1 Typical hot day micrometeorology The new hourly values for each of the microclimate variables at building mid-height reflect the changes in building boundary layer conditions at the north west façade. The decrease in hourly wind speeds at the north west façade due to trees (the wind sheltering effect) was directly proportional to the magnitude of wind speed. Wind values were converted to 10m wind speeds using the wind power law equation, in Excel and were tabulated as new u10 for the baseline and the shaded façade. The baseline and shaded building boundary layer conditions are tabulated (Table 5.3.1). Table 5.3.1: Updated weather data for the baseline and shaded building Variable (3m section cut) Comparison Wind speed 155 Wind direction Air temperatur e 156 Relative humidity 5.3.2 Typical Cold day micrometeorology The new hourly values for each of the microclimate variables at building mid-height reflect the changes in building boundary layer conditions at the north west façade (Table 5.3.2). The notable decrease in wind speed at the west façade due to trees (wind sheltering effect) was directly proportional to the magnitude of wind speed. 157 Table 5.3.2: Updated weather data for the baseline and shaded building Variable (3m section cut) Comparison Wind speed Wind direction 158 Air temperature Relative humidity 5.3.3 Mild day micrometeorology The new hourly values for each of the microclimate variables at building mid-height reflect the changes in building boundary layer conditions at the north west façade (Table 5.3.3). 159 Table 5.3.3: Updated weather data for the baseline and shaded building Variable (3m section cut) Comparison Wind speed Wind direction 160 Air temperatur e Relative humidity The airport weather data was replaced by the respective hourly baseline and shaded building boundary layer conditions for each of the analysis days. 10m-wind values (new u10) were replaced first, followed by RH values (Figure 5.3.3). 161 Figure 5.3.3: Replacing airport data with baseline wind speed and direction in the base scenario EPW file; a capture of weather data modification for the mild day, using the Elements application Lastly, the dry bulb temperatures were replaced while holding atmospheric pressure and RH constant. This automatically changes the wet bulb and dew point temperatures based on new RH and air temperature values in the application. The conversion was verified using the relative humidity from air temperature and dew point temperatures component (Figure 5.3.31). 162 Figure 5.3.31: Verifying the psychrometric relationship between variables added in Elements; Ladybug tools (LBT) The values matched the converted values in Elements and the new weather data have correct psychrometric relationships (Figure 5.3.32). This process was repeated for each analysis period for the baseline and shaded building. 163 Figure 5.3.31:: Variables added in Elements have correct psychometric relationships (values in the blue panels in LBT match values highlighted in blue) RH doesn’t allow decimals in Elements 5.4 Building energy modelling Thermal energy modelling was conducted for an apartment unit to capture the exterior microclimatic effects of shading and ambient air cooling on building heating and cooling loads. The modified baseline weather data from ENVI-met was used to simulate heating and cooling loads for the building without trees and data from CFD for the neighbourhood after the tree planting intervention was used in a separate EPW file to simulate the effect of tree shading and cooling on the building heating and cooling loads (Figure 5.4). 164 Figure 5.4: Top- Baseline model; bottom-shaded building model The exterior wall construction set was custom-created and the interior wall, roof and window constructions were chosen from the existing library (Table 5.4). 165 Table 5.4: Zone boundary layer type and construction set Boundary layer Construction type Exterior wall Uninsulated Brick wall: (1IN Stucco, !- layer 1 Generic Brick, !- layer 2 Generic Wall Air Gap, !- layer 3 Generic Gypsum Board; !- layer 4) Interior adiabatic walls Generic Interior wall Ground Generic ground slab Window Generic double pane Roof Typical built-up roof The thermostat set point range was set to 21-27 O C for the analysis periods and internal loads are set up in the program by building type. A midrise apartment program was used to specify people, infiltration, ventilation, and other load attributes (Table 5.41). 166 Table 5.41: Loads by program Load type Loads People: ServiceHotWater: MidriseApartment Building_Service Hot Water [0.128864092763 L/h-m2] [schedule: MidriseApartment Building_People [0.025 people/m2] [schedule: MidriseApartment Building_People_Occ Lighting MidriseApartment Building [11.1 W/m2] [schedule: MidriseApartment Building_Lighting Schedule] Electric Equipment Electric Equipment [6.0 W/m2] [schedule: MidriseApartment Building_Electric] Infiltration MidriseApartment Building_Infiltration [0.000569 m3/s- m2] [schedule: MidriseApartment Building_Infiltration Schedule] Building Ventilation MidriseApartment Building_Ventilation [1.6e-05 m3/s- m2] [0.3 ACH] Setpoint: MidriseApartment Building_Setpoint [heating: 21.7C] [cooling: 24.4C] Override to 21-27 o C for hot and cold days 21-26 o C for the Mild day 5.4.1 Baseline Building loads Thermal energy is required to keep the space within the 27 o C set point on the hot days that were chosen as the analysis period. Mild and cold day conditions did not require any energy for cooling for the particular set point range (Table 5.4.1). Table 5.4.1: Hourly Baseline cooling loads Hours of the analysis period Typical Hot day-Cooling load(kWh) 21-22 nd September Typical Cold day- Cooling load(kWh) 4-5 th Jan Mild day-Cooling load(kWh) 21-22 nd August Cooling set point changed to 26 o C 0:00 0 0 0 1:00 0 0 0 167 2:00 0 0 0 3:00 0 0 0 4:00 0 0 0 5:00 0 0 0 6:00 0 0 0 7:00 0 0 0 8:00 0 0 0 9:00 0 0 0 10:00 0.403801 0 0 11:00 2.72917 0 0 12:00 5.032325 0 0 13:00 5.455776 0 0 14:00 4.216609 0 0 15:00 3.436619 0 0.163973 16:00 2.324332 0 0 17:00 0.232298 0 0 18:00 0 0 0 19:00 0 0 0 20:00 0 0 0 21:00 0 0 0 22:00 0 0 0 23:00 0 0 0 168 0:00 0 0 0 1:00 0 0 0 2:00 0 0 0 3:00 0 0 0 4:00 0 0 0 5:00 0 0 0 6:00 0 0 0 7:00 0 0 0 8:00 0 0 0 9:00 0.065647 0 0 10:00 1.59601 0 0.034323 11:00 3.443589 0 0.009335 12:00 5.144233 0 0 13:00 5.271256 0 0.521436 14:00 4.24832 0 1.395493 15:00 3.356551 0 1.657026 16:00 2.279633 0 1.67095 17:00 0.301408 0 0.594901 18:00 0 0 0 19:00 0 0 0 20:00 0 0 0 21:00 0 0 0 169 22:00 0 0 0 23:00 0 0 0 Thermal energy was required to keep the space above the 21C set point on the cool days that were chosen as the analysis period. Mild and cold day conditions did not require any energy for cooling. Table 5.4.2: Hourly Baseline heating loads Hours of the analysis period Typical Hot day-Heating load(kWh) 21-22 nd September Typical Cold day- Heating load(kWh) 4-5 th Jan Mild day-Heating load(kWh) 21-22 nd August 0:00 2.667524 11.681996 0 1:00 2.599262 9.484115 0 2:00 2.887666 11.126035 0 3:00 3.178372 14.079684 0 4:00 3.014683 15.631647 0 5:00 2.580419 15.491373 0 6:00 2.188211 15.740139 0 7:00 0.545516 15.496991 0 8:00 0 13.708966 0 9:00 0 11.158424 0 10:00 0 7.908794 0 170 11:00 0 5.556428 0 12:00 0 3.822395 0 13:00 0 2.60092 0 14:00 0 2.126837 0 15:00 0 2.263301 0 16:00 0 2.641073 0 17:00 0 3.699869 0 18:00 0 5.218156 0 19:00 0 6.657907 0 20:00 0 7.13081 0 21:00 0 6.973718 0 22:00 0 7.530397 0 23:00 0 8.828242 0 0:00 0.027038 9.985035 0 1:00 0.252779 10.462391 0 2:00 0.399791 11.609606 0 3:00 0.612252 13.026938 0 4:00 0.561814 13.320995 0 5:00 0.317724 13.886189 0 6:00 0.097666 14.294942 0 7:00 0 12.702791 0 8:00 0 12.145106 0 171 9:00 0 11.662216 0 10:00 0 10.37961 0 11:00 0 8.145484 0 12:00 0 5.520518 0 13:00 0 4.260824 0 14:00 0 4.119293 0 15:00 0 4.086299 0 16:00 0 5.688624 0 17:00 0 7.727478 0 18:00 0 8.299953 0 19:00 0 8.756106 0 20:00 0 9.555578 0 21:00 0 10.705524 0 22:00 0 12.510595 0 23:00 0 13.331241 0 5.4.2 Shaded building loads Thermal energy was required to keep the space within the 27 o C set point on the hot days that were chosen as the analysis period. Mild and cold day conditions did not require any cooling energy. The cooling set point was decreased to 26 o C for the mild day, and cooling loads were obtained for both the baseline and shaded building. 172 Table 5.4.2: Hourly Shaded building cooling loads Hours of the analysis period Typical Hot Day-Cooling load(kWh) 21-22 nd September Typical Cold Day- Cooling 4-5 th Jan Mild day-Cooling load(kWh) 21-22 nd August Cooling set point changed to 26 o C 0:00 0 0 0 1:00 0 0 0 2:00 0 0 0 3:00 0 0 0 4:00 0 0 0 5:00 0 0 0 6:00 0 0 0 7:00 0 0 0 8:00 0 0 0 9:00 0 0 0 10:00 0 0 0 11:00 1.495796 0 0 12:00 3.922473 0 0 13:00 4.320192 0 0 14:00 3.090877 0 0 15:00 2.226552 0 0 16:00 1.098409 0 0 173 17:00 0 0 0 18:00 0 0 0 19:00 0 0 0 20:00 0 0 0 21:00 0 0 0 22:00 0 0 0 23:00 0 0 0 0:00 0 0 0 1:00 0 0 0 2:00 0 0 0 3:00 0 0 0 4:00 0 0 0 5:00 0 0 0 6:00 0 0 0 7:00 0 0 0 8:00 0 0 0 9:00 0 0 0 10:00 0.786332 0 0 11:00 2.425358 0 0 12:00 4.098606 0 0 13:00 4.464945 0 0.063944 14:00 3.485783 0 0.559081 174 15:00 2.340018 0 0.424236 16:00 1.230942 0 0.05916 17:00 0.01936 0 0 18:00 0 0 0 19:00 0 0 0 20:00 0 0 0 21:00 0 0 0 22:00 0 0 0 23:00 0 0 0 Thermal energy was required to keep the space above the 21 o C set point on the cool days that were chosen as the analysis period. Mild and cold day conditions did not require any energy for cooling. 175 Table 5.4.21: Hourly Shaded building heating loads Hours of the analysis period Typical Hot day-Heating load(kWh) 21-22 nd September Typical Cold day- Heating load(kWh) 4-5 th Jan Mild day-Heating load(kWh) 21-22 nd August 3.022715 11.677958 0 2.946498 9.564309 0 3.197462 11.153875 0 3.460536 13.927283 0 3.270693 15.407768 0 2.815745 15.26216 0 2.419949 15.603942 0 0.707144 15.435862 0 0 13.670217 0 0 11.236144 0 0 8.063526 0 0 5.73069 0 0 4.0071 0 0 2.789185 0 0 2.367146 0 0 2.540745 0 0 2.857347 0 0 3.84577 0 176 0 5.302759 0 0 6.801129 0 0 7.294476 0 0 7.042738 0 0 7.532812 0 0.009123 8.819373 0 0.336632 9.957003 0 0.59977 10.416571 0 0.685224 11.563019 0 0.811023 12.964298 0 0.698574 13.258182 0 0.428892 13.814114 0 0.236985 14.217117 0 0 12.637366 0 0 12.105307 0 0 11.551068 0 0 10.323894 0 0 8.248587 0 0 5.680823 0 0 4.439574 0 0 4.306102 0 0 4.255694 0 177 0 5.782477 0 0 7.806658 0 0 8.366715 0 0 8.804566 0 0 9.584442 0 0 10.716078 0 0 12.50561 0 0 13.312445 0 5.5 Summary This chapter discussed the data required for outdoor heat stress analysis under 5.1 CFD output, and the data required for thermal energy simulations under 5.2 Building boundary conditions, 5.3 New weather data at the building boundary layer and 5.4 Building energy modelling. The changes in microclimate variables across the site were visualised as heat maps for the MRT and UTCI. Air temperature is an important metric for assessing heat mitigation but the reduction in ambient air temperatures was found to be orders of magnitude smaller than MRT reduction due to the blocking of direct shortwave reduction by the shade trees. The significance of ambient air cooling is discussed in the next chapter. The peak heat stress conditions are tabulated for the hottest time of the day. The range of maximum temperatures for all the analyses periods falls between no heat stress and strong heat stress zones for both scenarios. (Table 5.3). 178 Table 5.3: Summary of heat stress temperatures and categories (at Sensor grid 01) Analysis period Scenario Peak UTCI (at 12 noon) ( o C) Typical hot day- Sept 21st Baseline 36.1 Building canyon with shade trees 31 Typical cold day- Jan 4th Baseline 26.1 Building canyon with shade trees 18.7 Mild day – Aug 21st Baseline 32 Building canyon with shade trees 26.6 The UTCI heat stress categories are divided into 10 categories. The maximum UTCI values for the chosen point in space lie between the values for strong heat stress and no thermal stress (Figure 5.31). Maximum values represent the time when MRT is the highest due to maximum direct solar radiation and the potential of shade in reducing heat stress is the greatest. The focus is on the potential impact of shade trees in mitigating peak heat stress conditions, discussed in the next chapter under 6.1 The impact of shade trees on ambient air cooling and outdoor heat stress. 179 Figure 5.31– UTCI and the heat stress categories; legend for the colours in the previous table There were significant changes in the wind speed, direction, and RH in the case that the site had additional shade trees. At the building boundary layer, data was collected for the variables that are required in an EPW file for BES to obtain building heating and cooling loads. The heating and cooling loads were obtained for each scenario for a typical hot, cold and an average day, for the baseline building and scenario with shading (Table 5.31). 180 Table 5.31: Total building heating and cooling loads Scenario Analysis period Cooling loads(kWh) Heating loads(kWh) Baseline Hot days 49.5 21.9 Cold days 0 442.7 Mild days 6 0 Effect of trees: Reduced window transmission +Coupling Hot days 35 25.6 Cold days 0 444.6 Mild days 1.1 0 The significance of the changes in microclimate on the outdoor thermal environment and the building heating and cooling loads due to shade trees is discussed in the next chapter, along with the relative contribution of window transmitted solar radiation and coupling on the zone heating and cooling loads. 181 6. RESULTS AND DISCUSSION This chapter discusses the impact of shade trees on ambient air cooling and outdoor heat stress and accounting for the impact of trees/microclimate on building energy performance. 6.1 The impact of shade trees on ambient air cooling and outdoor heat stress This section is about assessing the potential mitigation of heat by shade trees in terms of the UTCI and ambient air temperatures. High MRT due to direct solar radiation has a profound negative effect on the thermal environment and can be used as a metric in itself to assess the benefit of shade trees, but vegetative cooling also includes changes in airflow, relative humidity, and ambient air temperatures by evapo transpirative cooling. The UTCI metric includes the above microclimate variables. 6.1.1 Outdoor heat stress A shaded grid cell was chosen for a point in space analysis to determine the decrease in UTCI by shade trees (Figure 6.1.1). 182 Figure 6.1.1: Grid cell location 01-shaded; 02-unshaded parking lot with maximum MRT A point in space analysis does not fully represent heat mitigation at the scale of the intervention. Therefore, a point in time analysis, during the time of peak radiation/heat stress, was conducted for the site and the variation in UTCI and MRT temperatures for all sensor grids on site were visualized as surface plots. Spatial autonomy results were calculated from the data distribution, which is essentially the number of grids that fall under the no heat stress threshold of 26 O C. Point in time heat mitigation analysis was not done for the baseline and shaded sites in the cold analysis period because the UTCI for all grids was under 26 o C (Please see the baseline and shaded heat maps for MRT and UTCI in Chapter 5 under 5.1.2 Typical cold day results). Heat stress on a typical hot day- point in time and point in space analyses Point in time during peak heat stress time 183 A spatial analysis (point in time during peak radiation, at noon) was done to compare the number of data points or grid cells that reflected an improvement in outdoor heat stress. The surface plot shows the correlation of MRT and UTCI, notably in areas where shade trees were added (Figure 6.1.11). 184 Figure 6.1.11: Peak UTCI at 12 noon on September 21 st The shaded building canyons had the least heat stress with grids falling in the 26-32 o C UTCI range of moderate heat stress. All sensor grids including those under shade tree 185 canopy were above the 26 o C no heat stress threshold, which explains why there are no data points in this range (Figure 6.1.12). Figure 6.1.12: No sensor grids fall in the no heat stress range in the site with added shade trees The area of the site within the strong heat stress zone decreased by 9.2% due to the addition of shade trees. The area of the site within the very strong heat stress category 186 decreased by 5.4% (Figure 6.1.13). The heat mitigation potential was limited for this range of radiation and environmental variables. Figure 6.1.13: Sensor grids in the strong heat stress in the existing conditions and scenario with trees Point in space (Grid cell 01) In the typical hot day analysis period, the UTCI in the building canyon improved by 6 o C. This is a significant change in the thermal environment from the strong heat stress category to the moderate heat stress category. The UTCI was 1 O C higher for the scenario with shade trees than the baseline at night due to higher MRT, when trees reduced the sky view factor and free radiative exchange with the sky was blocked by tree canopy (longwave radiation from surfaces was trapped in the canyon). This is a trade-off in terms of daytime cooling and slightly warmer temperatures at night but temperatures still lie within the no heat stress category at night (Figure 6.1.14). 187 Figure 6.1.14: UTCI Improvement on 21 st September Heat stress on a typical cold day in the canyon- Point in space analysis The UTCI in the building canyon with shade trees reduced the number of hours in the no heat stress zone. The reverse happened at night when the UTCI increased due to higher MRT. The higher MRT was favourable in bringing temperatures closer to the no heat stress zone at night There was no reduction in ambient air temperatures under shade at this sensor grid and the cooling effect was due to a reduction in MRT alone (Figure 6.1.15). 188 Figure 6.1.15: UTCI Improvement on 4 th January Heat stress on a typical mild day- point in time and point in space analyses Point in time A spatial analysis (point in time during peak radiation, at noon) was done to compare the number of data points or grid cells that reflect an improvement in outdoor heat stress. The surface plot shows the correlation of MRT and UTCI, notably in areas where shade trees were added (Figure 6.1.16). 189 Figure 6.1.16: Improvement in the areas that were added to the no heat stress zone (coloured green in the UTCI plots) 190 At noon, peak UTCI in the building canyon improved by 5.4 o C due to shade trees. This was a significant reduction because the UTCI dropped from the moderate heat stress category to the no heat stress zone at 1m pedestrian height (seen in green in the UTCI surface plots). Shade is effective and predictable. In unshaded areas, much higher MRT and UTCI prevails, for example, over the parking lot. There was a 15.6% increase in the number of grid cells in the no heat stress zone out of a total 5568. This reflects the importance of a tree placement plan to maximise shade. The plots reflect an improvement in spatial autonomy (the improvement in terms of the model grids that fall within a certain threshold; 9-26 O C in the case of the UTCI) resulting from a certain strategy that mitigates the driver of peak heat stress (Figure 6.1.17). Figure 6.1.17: Area of the site within the no heat stress zone improved by 15.6% Point in space (grid cell 01) All hours of the analysis period were brought within the no heat stress zone in the scenario with shade trees, on a mild day. There was no significant reduction in ambient air 191 temperatures under the tree canopy at this sensor grid and the cooling effect was due to a reduction in MRT alone (Figure 6.1.18). Figure 6.1.18: UTCI Improvement on 21 st August Urban heat stress caused by exposure to direct solar radiation due to a lack of shade can negatively affect the use of outdoor spaces. The UTCI was significantly improved in the areas where shade was added. However, there was no contribution of ambient air cooling at this point on site. Even at other locations on site, the decrease in ambient air temperatures is around 0.4 o C, which is insignificant when compared to the reduction of MRT in shade (Figure 6.1.19). A reduction in MRT by shading is a predictive indicator for heat mitigation because it relies on geometry that blocks radiation and the magnitude and 192 direction of direct solar radiation whereas cooling by evapotranspiration depends on many other variables discussed in the next section. Figure 6.1.19: left: Ambient air temperature reduction is between 0.1 and 0.4 o C; right- MRT reduced by as much as 40 o C in shade 6.1.2 Ambient air temperature reduction Ambient air temperature reduction was visualised as heat maps comparing the baseline scenario and the site with shade trees. The significance of airflow in transporting heat or the cool air from tree canopy was evident. Areas of the site that experienced high wind speeds showed a blown-off effect, where cool air was blown further in the direction of wind, but this also meant that the cool air dissipated more into the surroundings. This also meant that air under tree canopy was blown away faster before having a chance to get cooled down by evapotranspiration (Schiler, 2022). There is a smaller cooling effect at the southwest in the building canyons than at the north east corner of the map in the X (m) 0.0010.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 Y (m) 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 140.00 150.00 160.00 170.00 180.00 190.00 200.00 210.00 N ENVI-met <Right foot> Figure 1: Comparison Final-No trees 14.00.01 21.08.2021 with Mild day- Trees plan2 14.00.01 21.08.2021 x/y Cut at k=0 (z=1.0000 m) absolute difference Air temperature < 0.02 K 0.12 K 0.22 K 0.32 K 0.42 K 0.52 K 0.62 K 0.72 K 0.82 K > 0.92 K Min: -0.10 K Max: 0.56 K X (m) 0.0010.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 Y (m) 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 120.00 130.00 140.00 150.00 160.00 170.00 180.00 190.00 200.00 210.00 N ENVI-met <Right foot> Figure 1: Comparison Final-No trees 14.00.01 21.08.2021 with Mild day- Trees plan2 14.00.01 21.08.2021 x/y Cut at k=0 (z=1.0000 m) absolute difference Mean Radiant Temperature < -0.59 K 3.41 K 7.42 K 11.42 K 15.42 K 19.43 K 23.43 K 27.43 K 31.44 K > 35.44 K Min: -0.59 K Max: 39.45 K 193 building canyons(circled) because the magnitude of wind is greater at the southwest region (Figure 6.1.2). Figure 6.1.2: Left- Wind flow and direction on a typical hot day; right- Ambient air reduction is greater in areas of lower wind speed Air temperatures correlate with wind speeds which depend on urban morphology and forms. Hence the cooling potential of trees depends on airflow/ inflow conditions. Here, the weather data included relatively high wind speeds as evident from the blown-off effect. A decrease of 0.5 o C depicted in blue had the highest ambient air temperature reduction seen that day, at a 1m pedestrian height. The corresponding wind speeds in that area were low and transferred the cool air in the north east direction. Heat mitigation by trees is quantifiable in a range of air temperature reductions that vary with winds(dynamic), environmental factors and plant factors such as water stress or stomatal closures during high temperatures. The cooling effect is also localised to a certain distance of the tree 194 canopy. Air temperature reductions occurred during daylight hours (which are photosynthetically active hours for trees and when evapo transpiration occurs), and peaked at different times during the day for different analysis periods. The highest reduction in ambient air temperature in the cold days’ analysis period was a difference of 0.2 o C at 2PM (Figure 6.1.21). Figure 6.1.21: Jan 4th- Difference in air temperatures between the two scenarios at 2PM The greatest reduction in ambient air temperature in the mild days’ analysis period was a difference of 0.4 o C at 9 AM and at 1PM. The reduction at 9AM was well distributed and more significant in terms of the area covered. The maximum difference in ambient air temperatures is the same at 1PM but is limited at the top right corner of the site. The simulation results showed that air temperature is not a predictive indicator for heat 195 mitigation because the magnitude of decrease and area covered are limited and depend on so many variables and cannot be measured effectively (Figure 6.1.22). Figure 6.1.22: August 21st- Difference in air temperatures between the two scenarios at 9AM and 1PM respectively 6.2 Accounting for the impact of trees/microclimate on building energy performance Trees impact the outdoor microclimate variables that consequently impact indoor air temperatures and the building heating and cooling loads. The effect of reduced window solar radiation transmission on building annual heating and cooling loads was quantified with a number of assumptions. Firstly, the growth of trees(budding) over the year was not considered. Trees transmit different amounts of solar radiation depending on the solar angle in relation to gaps or leaf overlap and this varies hourly and seasonally. To estimate 196 the annual heating and cooling loads due to shading, tree transmission was fixed at 0.35, based on ENVI-met vertical façade irradiation on the west facade at 3PM; that is a fraction of irradiance transmitted through the tree canopy onto the facade (Chapter 3 under 3.5.2 Obtaining canopy transmission for shade trees and gap fraction for BEM). This was the solar radiation that was incident on the north west façade (at 3) after portions of total solar radiation were absorbed and reflected by foliage. A fraction of 0.3-0.35 were noted in literature studies as a reference (McPherson et al. 1994, 2007). There may be errors in the estimation of heating and cooling loads based on radiation due to the above assumptions and the geometric complexities and variations of tree forms in reality and their modelling. The annual window solar radiation transmission heat map is an indicator of solar radiation transmission trends based on solar orientation. Solar radiation transmission through windows on the west facade occurred between 1 and 7PM during the months with high solar altitude, from March to September (Figure 6.2). Figure 6.2: Zone transmitted solar radiation- No trees scenario 197 When the façade was shaded, radiant heat gain reduced along with the hours of transmission, now between 2 and 6 PM (Figure 6.21). Figure 6.21: Zone transmitted solar radiation- Shaded scenario The changes in annual heating and cooling loads for the baseline and shaded buildings are due to a decrease in radiant heat gain through windows (Table 6.2). Table 6.2: Annual radiant heat gain and building heating and cooling loads Scenario Type Energy (kWh) Baseline Windows transmitted total solar radiation 2363 Annual heating load 36845 Annual cooling load 1226 198 Shaded facade Windows transmitted total solar radiation 2093 Annual heating load 37960 Annual cooling load 1051 Reduced radiation transmission brought about a 14 % decrease in net annual cooling energy and a 3% increase in net heating energy annually (Figure 6.22). Figure 6.22: Net annual heating and cooling loads The zone is heating dominated due to solar orientation and the long cold season. There is a greater net reduction of cooling loads because radiation is blocked during the cooling period. To determine the contribution of evapotranspiration and dynamic changes in the site due to the tree planting intervention, the CFD-BEM methodology (coupling) was adopted. This chapter discusses the heating and cooling load estimates from the contribution of radiation alone and then the combined impact of radiation and coupling of 199 the microclimate. The analysis periods were restricted to a couple of days of typical hot, cold and mild weather conditions due to large computation times for CFD. The purpose of the tree planting intervention was to mitigate both outdoor and indoor heat with the assumption that apartments facing the south and west were hotter than the east facing zones. The 28-degree angle rotation of the site from north was probably overlooked by the designers, and trees were planted along facades facing the north west (stated as west in the report), and south west, where pervious ground cover was available. The impact of trees on building heating and cooling loads was quantified for the north west thermal zone for each of the analysis periods (Figure 6.24). Figure 6.24: Site map and the building of interest 200 6.2.1 Typical hot period heating and cooling loads There was a large net decrease in cooling loads and a net increase in heating loads due to shading and cooling by trees. The contribution of the microclimate to cooling load reduction is 17% more than shading alone. The contribution of the microclimate to the energy penalty was found to be 5% more than shading alone (Figure 6.2.1). It is to be noted that this analysis period has a large diurnal swing in temperatures, so the space had both heating and cooling loads. Figure 6.2.1: Net heating and cooling loads for September 21-22nd The cooling load reduction due to the combined effect of shading and cooling was 17% greater than just blocking radiation transmission through windows. The cooling load reduction due to shading alone is the lower estimate here. This shows the potential of shade trees in mitigating heat in the neighborhood and its significant impact on building 201 energy performance. There is a trade-off in terms of an increase in net heating loads by 17% (Figure 6.2.11). Figure 6.2.11: Heating and cooling load profile for September 21-22nd The energy penalty might not be an important consideration if the objective is to only reduce cooling loads in hot periods, especially in a warming climate, where cooling loads are increasing and heating loads are decreasing. This is an unrealistic view if one wants to consider overall energy usage. This study is focused on the cooling load reduction potential of shade trees. Deciduous trees like the sycamore shed leaves starting August 202 through the cold period and would not contribute to cooling load reduction when it is needed the most (in August and September, the hot months), because of high transmission and lack of ambient air cooling by evapotranspiration (due to defoliation). Heating energy reductions from planting deciduous trees could be significant but were not explored as the objective is to cool spaces during hot parts of the day and this can be achieved by trees that not only transmit little radiation but also provide volumetric cooling due to their vertical canopy profiles and high LAD. The shade trees suggested by the Urban Trees Initiative are thus an appropriate choice for the objectives mentioned by Margulies et al., as they helped reduce cooling loads significantly. A preliminary study was done to assess the potential of trees as a passive strategy in terms of maximising thermal autonomy, in anticipation of future work. The simulation was run in free running mode. The comfort hours and indoor temperatures were examined to determine the significance of shade trees on the indoor environment using the adaptive thermal comfort metrics. The adaptive thermal comfort component calculates comfort hours based on the relative humidity, met, clo and the operative temperature of the space which is a function of air temperature, radiant temperature and the wind speed. Wind speed was assumed to be constant at 0.1 m/s indoors. The other constants were met and clo. The neutral temperature at which comfort is achieved is based on the relationship between the outdoors and indoors. The variables that govern the difference in baseline and shaded comfort conditions are the indoor air and operative temperatures. The number of comfort hours (depicted by 0 in the profile) in the shaded scenario increased by 4 during daytime to a total of 20 hours across the 48-hour analysis period 203 (Figure 6.2.12). There was a maximum decrease of 2 o C in indoor air, radiant and operative temperatures during different times of day. Figure 6.2.12: Indoor temperature and comfort hours profile during the hot period 6.2.2 Typical cold period heating and cooling loads There were no cooling loads in the cold period. The energy penalty due to shading and the microclimate is 0.4%. The contribution of the microclimate to the energy penalty was found to be 0.3% more than with shading alone (Table 6.2.2). 204 Table 6.2.2: Heating and cooling loads on a typical cold day in LA Scenario Net Cooling loads (kWh) Net Heating loads (kWh) Baseline 0 442.7 Tree shading only 0 443.3 Tree shading and coupling 0 444.5 Net % change-Tree shade 0 0.1 Net % change-Shading+Coupling 0 0.4 The hourly outdoor and indoor temperatures are always under the heating setpoint of 21 O C. Therefore, heating load persists through the analysis period (Figure 6.2.2). There was little energy penalty in this analysis period, insignificant when compared to the loads. 205 Figure 6.2.2: Heating load profile In natural ventilation mode, all hours were too cold, for both baseline and shaded scenarios. The indoor air temperature and radiant temperatures were included as a preliminary study in anticipation of future work. The indoor temperature is greater than outdoor temperatures possibly due to internal heat gains and the moderating effect of the building envelope (Figure 6.2.21). 206 Figure 6.2.21: Indoor temperature and comfort hours profile during the cold period 6.2.3 Mild day heating and cooling loads The mild day analysis period had a significant cooling load reduction by 60% by shading alone and an additional 22% reduction due to the cooling effect of trees at the building boundary layer. There were no heating loads on this day (Figure 6.2.3). The cooling set point was 26 O C. 207 Figure 6.2.3: Net loads on the mild day The cooling load peak was shaved off significantly. The combined cooling from shading and evapotranspiration could help keep temperatures closer to the cooling setpoint without hours of mechanical ventilation that would have otherwise been required to bring indoor temperatures close to the required 26 o C (the cooling set point) (Figure 6.2.31). 208 Figure 6.2.31: Cooling load profile The number of hours that were too hot decreased by 6 and were added to the comfortable hours. This signifies the potential benefits of trees on maximising thermal autonomy. There are times when the indoor temperatures in the shaded building exceed baseline indoor temperatures. This needs to be investigated in a future study (Figure 6.2.32). 209 Figure 6.2.32: Comfort hours profile 6.3 Summary This chapter discusses the impact of shade trees on ambient air cooling and outdoor heat stress and accounted for the impact of trees/microclimate on building energy performance. The contribution of shade trees in improving the UTCI was significant in the mild conditions. Shade was not always accompanied by ambient air cooling and shade reduced peak heat stress by 6 o C by bringing peak MRT down by 40 o C. The decrease in UTCI was largely due to a reduction in MRT by blocking shortwave radiation, the driver of heat stress in the absence of shade. The cooling effect of trees is largely due to shade and not evapotranspiration (Figure 6.3). 210 Some key findings from this study that could help inform decision making include the spatial configuration of shade or tree placement, the time needed for the potential benefits and addressing the current lack of shade, and studying other mitigation strategies that could help reduce air temperatures. 1. The study highlights the importance of the spatial configuration of shading structures or the distribution of shade to avoid the disparity between the thermal conditions of shaded and unshaded areas, which can be as large as 15 o C on a hot day in LA and affect walkability and temperatures of parked vehicles in unshaded lots (Figure 6.3.1). It can also be a proxy for outdoor space usage. For better walkability in the neighborhood shade trees should also be planted along streets and this could be done by creating plant holes in impervious paved surfaces. Figure 6.3.1: UTCI difference without shade and under shade 211 2. It is also important to make decisions that address the current lack of shade while trees take at least 10-15 years to cast sizeable shade. These are time sensitive issues with relying only on shade trees for shade. Older residents might not be alive when shade trees have grown large enough for their potential cooling benefits (please see appendices for age sex pyramid of the study area in East LA). Their lives can currently be greatly improved by cooling the surroundings by the use of shading structures in the form of post tension or bamboo canopies/trellised areas. Immediate changes and investments in the form of other shading devices for example, trellis and deployable shades can address heat exposure of young children and the elderly to direct solar radiation in the outdoors, are required. 3. As the world gets warmer and extreme heat is exacerbated in urban areas, more heat mitigation strategies are required to achieve the greatest possible cooling potential. This includes mitigation strategies that can reduce air temperatures during periods of the time when heat stress is prevalent even under shade due to high ambient air temperatures. Simulation results showed the ununiform distribution of ambient air cooling and the relatively insignificant magnitude that did not reduce heat stress UTCI. The magnitude varied from 0.2 to 0.4 oC across the site, at different times of day, not always peaking during the time of highest heat stress. MRT, on the other hand was a predictive indicator that had the greatest reduction during the hottest time of day (Figure 6.3.2). 212 Figure 6.3.2: Insignificant ambient air reduction vs a large reduction in MRT- the determining variable for lowered UTCI The study demonstrated that cooling by evapotranspiration is unreliable, depending on many environmental and plant factors. Moreover, in light of future warming, shade trees that can withstand hotter conditions and drought are being considered to reduce plant mortality. These species tolerate hotter conditions and use water efficiently without transpiring large amounts of water. Their effect on reducing ambient air temperatures is further limited because of conservation measures. The trees modelled in ENVI-met were species that fit the above criteria but ENVI-met simulates ET as a function of soil water capacity and leaf surface area only, and does not account for species related ET rates. It is clear that other heat mitigation strategies that can predictably lower air temperatures is required. Other mitigation strategies that must be studied to inform decision making are discussed in the next chapter under 7.2.1 Short term considerations. Although ambient air temperature reductions were insignificant (a maximum decrease of 0.5 deg C) when compared to the drop in MRT (40 deg C) under tree canopy, it was observed that cool air was carried to the building boundary layer at a 3m section cut and 213 significantly affected heating and cooling loads of the thermal zone. There was a significant cooling load reduction in the hot and mild day analysis periods. The cooling load reduction due to reduced solar radiation transmission through windows was significant and was the lower estimate of cooling load reductions for both analysis periods. The decrease in air temperatures by a maximum of as little as 0.5 O C at the building boundary layer significantly reduced the cooling load on the hot and mild days. More studies are needed to determine if drought resistant high LAD trees like the modelled African sumac and Australian willow contribute to ambient air temperature reduction by ET in reality, with literature findings that drought resistant trees are adapted to conserving water by lowered ET rates. ENVI-met simulates ET rates based on LAD parameter alone and does not account for water conservation measures in different species that may affect ET rates and consequently ambient air cooling by ET, at the building boundary layer. It can therefore be considered that the difference between cooling loads for the two scenarios can be a minimum value governed by LAD and blocking radiation. However, the higher estimate in reductions would be dictated by environmental factors like airflow, and species-related ET cooling. The energy penalty on the cold analysis period was insignificant when compared to the magnitude of the heating loads on that day. On a cold day in January, the energy penalty was mainly contributed by cooler building boundary layer conditions (lower outdoor air temperatures at the west façade) whereas blocking radiation alone contributed a 0.1% energy penalty. The workflow was successful in accounting for the changes in microclimate due to shade and evapotranspiration, on the building heating and cooling loads. Furthermore, their relative contributions to net loads could also be determined 214 using the workflow. Preliminary studies were done in anticipation of future work. This included collecting the air and radiant temperatures of the zone in the baseline condition and the scenario with shading and coupling. 215 7. DISCUSSION AND FUTURE WORK This chapter includes 7.1 Discussion and 7.2 Future work. 7.1 Discussion The local climate and microclimatic elements were studied to understand the drivers of heat stress in Los Angeles. The air temperature and humidity lie within the comfort zone, and LA is comfortable for most parts of the year. However, in summer, heat stress is caused by direct solar radiation and is exacerbated due to the lack of adequate shade in urban areas. Heat stress is also caused by impervious land cover comprising heat retaining materials such as asphalt and concrete. A combination of these factors creates a hot microclimate in poorly landscaped areas. This is notable as huge differences between the temperatures in well vegetated affluent areas like Beverly Hills and the temperatures in neighborhoods (including traditionally redline neighbourhoods) that lack tree canopy cover. The performance aspects of trees were studied to understand the quantitative aspects of a recommendation of shade trees for impacted neighbourhoods, as outlined in the USC Urban Trees Initiative report. A redlined neighborhood, Ramona Gardens with a poor existing tree canopy cover, was chosen to determine the potential impact of trees in mitigating heat in the neighbourhood. The UTCI and building heating and cooling loads were chosen as metrics to compare the microclimate effect of increasing tree canopy cover by 12% with the baseline conditions (Figure 7.1). 216 Figure 7.1: A proposed increase in tree canopy cover in a Ramona Gardens Trees cool their surroundings not only by blocking solar radiation but also by evapo transpirative cooling. The reduction in ambient air temperatures occurs during radiation hours and is limited at night. The capabilities and limitations of different tools in modelling trees were studied to inform a workflow that required different tools with the required capabilities. A tool that can model trees as porous media and simulate dynamic changes for the site was required. ENVI-met was chosen for its dedicated tree modelling application and the functionality to simulate cooling by evapotranspiration. The application was studied in terms of its functionality and modelling UI. Verification and sensitivity studies by other researchers was studied. It was concluded that ENVI-met results were in good agreement with site measurements. Before conducting neighborhood scale simulations, ENVI-met was studied by conducting case studies to determine if results are within a known range of outcomes for the modelled 217 parameters. The results showed that ENVI-met modelled different materials appropriately. The Harris Courtyard simulation results were in close agreement with the surface temperatures measured using the FLIR E8 IR gun. This also showed that the sensitive parameters for surface temperatures- orientation, wall material properties and the FICUS tree were modelled appropriately (Figure 7.11). Figure 7.11: Harris Courtyard simulation results To obtain building heating and cooling loads, Ladybug tool for Grasshopper was chosen. A workflow that incorporates the changes in microclimate on the building heating and cooling loads was developed by coupling ENVI-met results with the Ladybug tools via a 218 modified weather file with CFD output data for the specific variables that are required for EnergyPlus simulations. The analysis period was limited to a typical hot day, a typical cold day and a mild day due to high computation time (Figure 7.12). Figure 7.12: The CFD-BEM coupling methodology The results signified the potential mitigation of heat stress by trees in terms of a decrease in outdoor heat stress UTCI values and the decrease in cooling loads. In the outdoor environment, shade trees decreased the heat stress by lowering the mean radiant temperature by blocking direct solar radiation, signifying the importance of shade in alleviating outdoor heat stress, a proxy for the usability of outdoor spaces (Figure 7.13). 219 Figure 7.13: Heat stress was significantly reduced under shade trees However, the hot day UTCI analysis showed the limited potential for heat mitigation by shade as air temperatures were also high, around 5-6.2 o C higher than on the mild day, and contributed to heat stress in both scenarios. The site area was in the moderate to strong heat stress zone even with an increase in shade tree canopy (Figure 7.14). 220 Figure 7.14: Strong and heat moderate stress on site The relative differences are due to shade only and are limited. With longer and hotter periods currently observed in LA, heat mitigation strategies should focus on the high air 221 temperatures as drivers of heat stress. Then mitigation strategies aimed at mitigating heat by decreasing air temperatures are required. Even without climate change or local urban heat, LA has high air temperatures for extended periods of time especially in August and September (Figure 7.14). Figure 7.15: High ambient air temperatures in August and September Ambient air temperature reduction at pedestrian height was localized and not uniformly distributed across the site. Ambient air temperature reduction depends on a number of variables and the research showed that it was limited when compared to reductions in MRT. Moreover, the maximum reduction is not always at the time of peak heat stress during the day (Figure 7.16). Ambient air cooling due to trees is not a predictive indicator for heat mitigation or cooler neighborhoods. This is worrisome due to the points mentioned above and get exacerbated with climate change and extreme heat days where the drivers are high ambient air temperatures and high humidity. Studies have shown the extremely low cooling effect due to reduced evapo transpiration under conditions of high air temperatures and vapour pressure (Bruse, 2016). More wind studies are required with 222 a realistic range of wind speeds for urban areas because the role of wind in carrying cool air across the site is apparent. Figure 7.16: Maximum ambient air temperature reductions on Jan 4 th at 2PM, and August 21 st at 9 AM and 1 PM respectively The mitigation of heat during peak times of heat stress (noon) was due to shade, not evapo transpirative cooling. Reduction in heat stress (UTCI) was due to MRT reduction by as much as 40 o C under tree canopy at noon whereas ambient air cooling was limited to 0.4 o C (Figure 7.17) and did not always occur during peak heat stress time when cooling is needed the most. 223 Figure 7.17: Reduction in ambient air temperatures is limited when compared to MRT under the canopy; on a typical hot day The trend of changes in the heating and cooling loads were the opposite of what is being observed and reported in a warming climate. The cooling loads decreased significantly. The cooling loads accounting for both blocked radiation and changes in outdoor air temperature due to trees was far greater than just simulating blocked radiation. There was a heating load penalty. The objective was to mitigate heat and cooling loads. Therefore, benefits of reducing cooling loads during the hottest times of the day outweighs the penalty (Figure 7.18). 224 Figure 7.18: Cooling load reductions on a typical hot day Preliminary studies also showed an increase in comfort hours or passive survivability for the scenario with shade trees, which is extremely significant for neighbourhoods dealing with energy poverty or lack of AC, and for times of power outages and in favouring natural ventilation for indoor air quality purposes (Figure 7.19). 225 Figure 7.19: An increase in comfort hours during the day The plot also shows an anomaly in terms of increased indoor temperatures in the shaded scenario for some parts of the day and needs to be explored in future studies. The cooling effect of trees has largely been described in subjective terms like “it feels so much cooler under tree canopy” and “my house hardly needed AC after the tree grew large and shaded it”. The former is usually attributed to cooler air under tree canopy and the latter to shade. The research showed that the cooling effect outdoors is due to lessened MRT in shade, associated with direct shortwave radiation intercepted before striking the human body. Wind and air temperature comparison maps showed how the cooling effect around tree canopy gets diffused with warmer ambient air/ site inflow winds. Outdoor evaluations combine dynamic and thermodynamic processes. Ambient air reduction in the outdoor 226 environment did not provide cooling due to the dominating effect of radiation when compared to the indoor environment where radiation is already controlled to a large extent and even a small reduction in outdoor air temperature significantly impacted indoor heat gains, and therefore the cooling load. The scope of the research was to conduct an objective evaluation of the impact of the microclimate by considering changes in the environmental parameters due to trees on heat stress levels and on building heating and cooling loads. This was accomplished through the simulation workflow and by comparing the results for the two site scenarios in terms of metrics that account for the changes in environmental parameters due to shade trees. The UTCI metric is a function of the environmental factors including air temperature, MRT, relative humidity and wind speed. Building heating and cooling loads are also determined by the above environmental parameters except MRT. Thermal comfort is subjective whereas heat stress is objective because it is a physiological response of the human body to the thermal environment. It is important to lower the heat load of the environment on the human body by improving the thermal conditions. Heat stress is the cause of fatalities in poorly designed thermal environments and get exacerbated when combined with personal parameters such as age, health conditions like obesity and diabetes etc., which are prevalent in subsidized housing communities (Gabbe and Pierce, 2021). The simulation studies showed the outcome of changes in shade tree canopy cover on the human body in terms of the UTCI heat stress levels. 227 Building heating and cooling loads are important to study because residential space cooling is a large part of the total electricity use in the US, and this is expected to grow in a warming climate. An increasing cooling load can exert enormous stress on power plants and can lead to increased GHG emissions. It is also a good idea to limit heat gain from outdoors in the first place that then helps limit anthropogenic heat expelled from the space plus heat generated in running the equipment. 7.2 Future work Future work is research that could not be completed due to high computation times and gaps in research that can be addressed in the long term. It also includes scenarios that can be studied using the established workflow. 7.2.1 Short term considerations The workflow that was adopted helped quantify and compare the scenarios using many appropriate metrics. There are a number of important research goals that were considered but not completed in the interest of time. They can be broadly classified into four areas including the different types of trees in different climate types, other heat mitigation strategies using the workflow, developing heat mitigation targets and guidelines for planning cool urban spaces, and studying water conservation and drought-resistant landscapes in LA using satellite data from NASA’s ECOTSRESS (https://ecostress.jpl.nasa.gov/). 228 1. The different types of trees that could be studied include studying appropriate tree types- whether deciduous or evergreen, depending on the building orientation, the climate zone and the objective. In cold climates, where radiant heat gain is beneficial in the cold season as a passive heating strategy, it would be appropriate to plant deciduous trees especially along the west façade (Figure 7.2.1). Figure 7.2.1: Studying the impact of deciduous trees on building heating and cooling loads for different wall orientations and climate types 229 Other landscape designs involving different land cover (simple plants in ENVI-met), and tree placement scenarios in terms of symmetry and an organic placement could also be studied. 2. The other heat mitigation strategies that could be studied using the workflow are natural systems like water elements and green facades. It would be useful to quantify heat mitigation by green walls and its impact on building heating and cooling loads (Figure 7.2.11). Figure 7.2.11: Studying façade greening that can naturally cool indoor spaces and enhance outdoor thermal comfort; ENVI-met Database Manager ENVI-met has a number of façade greening options that could be simulated and validated with a real green façade. The near wall air temperatures and transmission through foliage could be coupled with a BES tool, and wall construction layers could be 230 manipulated to account for a change in heat transfer through the wall on building heating and cooling loads. ENVI-met can also simulate stagnant water elements and could be studied in isolation and in combination with other natural systems such as trees or facade greening to determine potential ambient air temperature reductions from evaporative cooling. 3. Heat mitigation targets and guidelines for planning cool urban spaces can be developed. Currently, targets are vague in terms of the units or lack thereof in describing targets and are described in terms of the urban rural differential in temperatures (Figure 7.2.12). Figure 7.2.12: Ambiguity in determining heat mitigation targets (https://plan.lamayor.org/) There are two problems with the above target. First is the lack of clarity in what exactly a rural area is in LA. Secondly no units or type of temperature- whether air or MRT are provided. It is true that it is difficult to describe heat mitigation targets due to the hyperlocal nature of changes in the microclimate resulting from different interventions and urban morphologies but could get clear with more research. The built forms and morphology including the spacing of buildings and the density of urban forms is important for air 231 circulation in a site. The Stewart and Oke local climate zone scheme is helpful in describing or abstracting neighborhoods and urban developments in terms of different characteristics and could be studied to obtain statistical models of urban heat mitigation that vary across the different combination of urban characteristics such as land cover, vegetation, density of urban forms, etc (Figure 7.2.13). Figure 7.2.13: LCZ scheme (Stewart and Oke 2012) 232 4. The research considered planting trees that have a high water use efficiency which means that plants can withstand less frequent irrigation without undergoing water stress and considered some drought-resistant trees in the new tree inventory. This is important because plant mortality is high in areas that lack proper irrigation or because of the wrong type of tree chosen for that region. In light of the problem of water scarcity and the possibility of droughts in LA, satellite data such as ECOSTRESS data is finding application in mapping droughts, calculating evapotranspiration rates for land parcels and finding the urban cooling rate by different vegetation cover (turf vs trees vs arid landscapes etc). It would be extremely useful to identify and map real problems and changing landscapes using real and comprehensive datasets. Another research area that comes to mind is comparing ECOSTRESS data analysis results with ENVI-met simulations to validate ET cooling simulated in ENVI-met for different vegetation types. 7.2.2 Long term research There are five major categories of future work including developing workflows that include future climate change scenarios, exploring coupling potentials, determining metrics for outdoor thermal comfort autonomy, and doing real life studies. 1. Developing workflows that include future climate change scenarios is important because as the world gets warmer or with future heat related events, strategies that are currently effective in providing comfort may no longer be effective in the future as the drivers of heat stress may change. Consequently, the objectives 233 would also be different. An understanding of climate and how one might model future scenarios and weather variables is important. 2. Exploring coupling potentials: The workflow did not account for certain heat exchanges and processes between the built environment and the outdoors, which has potentially led to an underestimation of the impact of trees on buildings. Ventilative and infiltration rates can be more accurately simulated if wind pressure coefficients at windows could be obtained from CFD. Instead, the wind speed change at the facade was used to modify the weather file. BEM then calculates the pressure coefficient at windows based on the reduced wind speed. This is not accurate for complex obstructions such as trees. The current version of ENVI-met does not generate this output and the output cannot be generated from other CFD software due to the differences in modelling trees. 3. Another key consideration is the coupling process itself. Chain coupling included CFD results to modify the weather data file. The new weather data file was used to simulate building heating and cooling loads to account for changes outside the building envelope. BEM can only be forced with one weather data. However, the microclimate surrounding the buildings is dynamic. The value of any single variable may vary a lot in different locations/ along different facades of the buildings. Strong coupling or the simultaneous solving of air temperature, moisture, flow, and heat equations is required for the most accurate accounting of the exterior microclimate on indoor thermal comfort and heating and cooling loads for multiple zones in different orientations. There is currently no single tool that can link all these variables simultaneously and may be of interest for future research. As separate 234 tools, CFD tools like ENVI-met and BEM tools such as EnergyPlus are validated but the results of coupling need to be extensively studied and validated. 4. Determining measurable performance indicators for outdoor thermal comfort autonomy. Predictive performance indicators are required to compare the effectiveness of different heat mitigation strategies. Due to the dynamic and unpredictable nature of weather variables, more research is needed on obtaining a range of outcomes or indicators to compare different strategies. 5. Detailed research should be done that compares simulated results with real life studies of tree cover and buildings. An initial study could include a site with one simple building and a trees /no trees comparison could be done to compare real values with software simulation values. 7.3 Conclusion The heat mitigating effect of a 12 % increase in shade tree canopy in a neighbourhood that lacked adequate tree canopy was assessed using objective metrics that reflect the impact of the changes in microclimate on the human body and building energy performance. Current weather conditions were studied, including obtaining weather data with current outdoor air temperatures that is required for reasonable comfort and energy outcomes from simulations. A workflow that coupled CFD microclimate results and BEM was developed. The cooling loads that were obtained from simulations were significantly lower for the mild and hot analysis periods for the shaded building than for the building without trees along its northwest facade. There was a significant heating load penalty on the hot days and the heating loads in the cold day analysis period was insignificant. The 235 study showed that the proposed shade trees significantly mitigated heat in the shaded parts of the site during mild conditions by lowering the UTCI to under the no heat stress zone for 15.6 % of the grid cells representing the area of the site that was in the no heat stress zone that day. In the hot days analysis period, the heat mitigating effect by shade trees was not significant because all the grid cells were above the no stress category even under shade. MRT was a more predictive indicator of heat mitigation than ambient air cooling. A broader range of a combination of variables are required to assess heat mitigation if solar radiation is not the only driver of heat stress. Further research should be done on the impact of vegetation including studies that include “future” weather files from predicted climate change models. 236 REFERENCES Palme, Massimo & Salvati, Agnese. (2021). Urban Microclimate Modelling for Comfort and Energy Studies. 10.1007/978-3-030-65421-4. Salvati, Agnese & Kolokotroni, Maria. (2020). Impact of urban albedo on microclimate and thermal comfort over a heat wave event in London. Barry, Roger & Blanken, Peter. (2016). Microclimate and local climate. 10.1017/CBO9781316535981. Gabbe, C. & Pierce, Gregory. (2020). Extreme Heat Vulnerability of Subsidized Housing Residents in California. Housing Policy Debate. 30. 1- 18.10.1080/10511482.2020.1768574. Middel, A., Chhetri, N., & Quay, R. (2015). Urban forestry and cool roofs: Assessment of heat mitigation strategies in Phoenix residential neighborhoods. Urban Forestry and Urban Greening, 14(1), 178–186 Santamouris, M. (2020). Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact Yang, Xiaoshan & Zhao, Lihua & Bruse, Michael & Meng, Qinglin. (2012). An integrated simulation method for building energy performance assessment in urban environments. Energy and Buildings. 54. 243–251. 10.1016/j.enbuild.2012.07.042. Mitchell, Bruce & Chakraborty, Jayajit. (2015). Landscapes of thermal inequity: Disproportionate exposure to urban heat in the three largest US cities. Environmental Research Letters. 10. 115005. 10.1088/1748-9326/10/11/115005. Synergies with the global climate change. Energy and Buildings, 207, https://doi.org/10. 1016/j.enbuild.2019.109482 Schiler, Marc & Greenberg, Donald. (1979). Computer Simulation of Foliage Shading in Building Energy Loads. 142- 148. 10.1109/DAC.1979.1600101. Simon, Helge & Sinsel, Tim & Bruse, Michael. (2020). Introduction of Fractal-Based Tree Digitalization and Accurate In-Canopy Radiation Transfer Modelling to the Microclimate Model ENVI-met. Forests. 11. 10.3390/f11080869. Santamouris, M. (2014). On the energy impact of urban heat island and global warming on buildings. Energy and Buildings, 82, 100–113. Rosheidat, Akram & Hoffman, Dan & Bryan, Harvey. (2022). VISUALIZING PEDESTRIAN COMFORT USING ENVI-MET. Shashua-Bar, L., & Hoffman, M.E. (2004). The combined mitigating effect of colonnaded ground areas and vegetation on the UCL air temperature. 237 Joshua Brook-Lawson, Sophie Holz . (2020). CFD Comparison Project for Wind Simulation in Landscape Architecture Taká cs, A., Kiss, M.A., & Gulyás, Á. (2015). Microclimate regulation potential of different tree species: transmissivity measurements in Szeged, Hungary. Sailor, D, and Akbari, H. 1992. "High-albedo materials for reducing building cooling energy use". United States. https://doi.org/10.2172/7000986. https://www.osti.gov/servlets/purl/7000986. Kolokotroni, M., Gowreesunker, B. L., & Giridharan, R. (2013). Cool roof technology in London: An experimental and modelling study. Energy and Buildings, 67, 658–667. Margulies et. al. (2021). Maximizing the benefits of increased urban canopy on the eastside of Los Angeles. Urban-Trees-Initiative-Report-.pdf (usc.edu). Last accessed 20 th June 2021 Baniassadi, Amir & Sailor, David & Ban-Weiss, George. (2019). Potential energy and climate benefits of super-cool materials as a rooftop strategy. Urban Climate. 29. 100495. 10.1016/j.uclim.2019.100495. Lobaccaro, G., & Acero, J. A. (2015). Comparative analysis of green actions to improve outdoor thermal comfort inside typical urban street canyons. Urban Climate, 14(Part 2), 251– 267 Venter, Z. S., Krog, N. H., & Barton, D. N. (2020). Linking green infrastructure to urban heat and human health risk mitigation in Oslo, Norway. Science of the Total Environment, 709 Carleton, Andrew M. “Microclimate and Local Climate: Roger G. Barry and Peter D. Blanken. New York: Cambridge University Press, 2016.Arctic, Antarctic, and Alpine Research 49, no. 1 (2017): 187–188. (Blanken and Barry, 2016) Hoffman, Jeremy & Shandas, Vivek & Pendleton, Nicholas. (2020). The Effects of Historical Housing Policies on Resident Exposure to Intra-Urban Heat: A Study of 108 US Urban Areas. Climate. 8. 12. 10.3390/cli8010012. Kastner, Patrick & Dogan, Timur. (2020). Predicting space usage by multi-objective assessment of outdoor thermal comfort around a university campus. Gromke, Christof & Blocken, Bert & Janssen, Wendy & Merema, Bart & Hooff, Twan & Timmermans, Harry. (2014). CFD analysis of transpirational cooling by vegetation: Case study for specific meteorological conditions during a heat wave in Arnhem, Netherlands. Building and Environment. 83. 10.1016/j.buildenv.2014.04.022. Hsieh, Chun-Ming & Li, Juan-Juan & Liman, Zhang & Schwegler, Ben. (2018). Effects of tree shading and transpiration on building cooling energy use. Energy and Buildings. 159. 10.1016/j.enbuild.2017.10.045. 238 Mackey, Christopher; Galanos, Theodore; Norford, Leslie; Sadeghipour Roudsari, Mostapha. “Wind, Sun, Surface Temperature, and Heat Island: The Critical Variables for High ‐Resolution Outdoor Thermal Comfort.” In Proceedings of the 15th International conference of Building Performance Simulation Association. San Francisco, USA, Aug 7- 9 2017. Simpson, J. R., & McPherson, E. G. (1998). Simulation of tree shade impacts on residential energy use for space conditioning in Sacramento. Atmospheric Environment, 32, 69–74. https://doi.org/10.1016/S1352-2310(97)00181 Maiullari, Daniela & Mosteiro Romero, Martín & Esch, M.M.E.. (2018). Urban Microclimate and Energy Performance: An Integrated Simulation Method. Sunarya, Wendy (2020): The importance of site on house heating energy modelling in Wellington - Integrating EnergyPlus with ENVI-met for site modelling. Open Access Te Herenga Waka-Victoria University of Wellington. Thesis. https://doi.org/10.26686/wgtn.17142758.v1 Meili, Naika & Manoli, Gabriele & Burlando, Paolo & Carmeliet, Jan & Chow, Winston & Coutts, Andrew & Roth, Matthias & Velasco, Erik & Vivoni, Enrique & Fatichi, Simone. (2021). Tree effects on urban microclimate: Diurnal, seasonal, and climatic temperature differences explained by separating radiation, evapotranspiration, and roughness effects. Urban Forestry & Urban Greening. 58. 126970. 10.1016/j.ufug.2020.126970. Baghaeipoor, Golnar & Nasrollahi, Nazanin. (2019). The Effect of Sky View Factor on Air temperature in High-rise Urban Residential Environments. 6. 42-51. 10.15627/jd.2019.6. Ayyad, Y & Sharples, S. (2019). Envi-MET validation and sensitivity analysis using field measurements in a hot arid climate. IOP Conference Series: Earth and Environmental Science. 329. 012040. 10.1088/1755-1315/329/1/012040. Christina Chatzipoulka, Koen Steemers, Marialena Nikolopoulou. (2020) Density and coverage values as indicators of thermal diversity in open spaces: Comparative analysis of London and Paris based on sun and wind shadow maps Margulies, Esther, Personal communication, December 23, 2021 Schiler, 2021, Personal communication, September 23, 2021 Schiler, 2022, Personal communication, March 23, 2022 239 APPENDICES African sumac modelled from observation /F[+fB]/ff[-B] f[&(45)B][//+(50)fB][//^(45)fB]f[+(40)B]f[\\-(25)B]f[/////-(25)B] ff[/+(85)B]f[//-(85)B]ff[^(85)B]ff[-(85)B]f[\\+(85)B] ff[/+(85)fB]f[//-(85)fB]ff[^(85)fB][-(85)fB]f[\\+(85)fB]f[/+(85)fB] Australian Willow tree V fLfL[/+(60)FLCL][///-(55)FLCL]fLfL[\- (60)FLCL][/&(55)FLCL]fLfL[/^(55)FLCL][////+(60)FLCL] A fLfL[/+(60)fLCL][///-(55)fLCL]fLfL[\-(60)fLCL][/&(55)fLCL]fLfL[/^(55)fLCL][////+(60)fLCL] B [/+(50)CL]fL[//-(40)CL]fL[///^(40)CL][///&(50)CL]fL[\\\+CL][//-CL][/^CL][///&CL] C FL[/+ffCL]fL[//-ffCL] Age sex pyramid for the study area in East LA

Abstract (if available)

Abstract The type of building and paving materials, the amount of shade or tree canopy cover, and the local climate zone typology contribute to the formation of a microclimate that is distinct from the general climate of an area. Urban heat mitigation measures largely involve manipulating one or a combination of the above characteristics to improve thermal comfort. For example, an effective heat mitigation strategy for a redlined neighborhood characterized by a lack of shade trees and extensive paved surfaces could be to plant more shade trees. Trees provide cooling by shading and evapotranspiration, and sometimes by wind sheltering effects. They contribute to changes in the microclimate boundary conditions around buildings in terms of the surface temperatures, ambient air temperatures, wind speed, direction and relative humidity. Modelling the performance aspects of trees and trees as porous media, and simulating the cooling by evapotranspiration due to trees have presented major challenges in research, and a workflow that incorporates the cooling effect of trees on building heating and cooling loads has largely been non-existent.
The heat mitigation due to a tree planting initiative was quantified for the outdoor environment in terms of the universal thermal climate index (UTCI) and for building heating and cooling loads, for the base case scenario with a 6.6% tree canopy cover and the site with added shade trees. To find the impact of a 12% increase in tree canopy cover on outdoor thermal stress, and on the micro-climate variables required for building energy simulations (BES), computational fluid dynamics (CFD) simulations were carried out using the software ENVI-met. For outdoor heat stress analysis, ENVI-met was used to model and simulate trees as porous media, which is required to account for ray transfer through canopy and represent shade and MRT reduction appropriately. A morphed TMY3 weather file for Los Angeles was used to represent current air temperatures to obtain appropriate comfort and energy outcomes. High MRT due to shortwave radiation in the absence of shade was the peak driver of heat stress in the mild and hot periods and the peak time of heat stress was at noon at peak radiation. In shade, heat stress was eliminated in the mild period, which means that areas under shade were brought to within the no heat stress zone, but was only mitigated in the hot period (strong heat stress to moderate heat stress). In the hot period, high ambient air temperatures caused thermal stress and due to limited ambient air cooling by trees, UTCI improvements were largely due to a significant drop in MRT in shade.
The impact of the changes in microclimate variables on building heating and cooling loads were simulated using a modified EPW data file that included CFD results of hourly air temperature, wind speed, direction and relative humidity values at the building boundary layer. The results showed a decrease in ambient air temperatures by a maximum of 0.4 C in the typical hot period, increase in RH during daytime and decrease in wind speeds proportional to the magnitude in the base case scenario, at the building boundary layer. There was a significant decrease in cooling loads on the mild and hot days. There was a significant energy penalty at night on the typical hot days in September. The energy penalty on a typical cold day in January was not significant when compared to the total loads that day. The reduction in MRT due to shade was the most significant contributor to decreasing outdoor heat stress in the site. The study showed that ambient air cooling by evapotranspiration is not a predictable outcome and is limited when compared to the contribution of shade to improving the outdoor thermal environment.

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Asset Metadata

Creator Pai, Kalsank Krupa (author)

Core Title Microclimate and building energy performance

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Degree Conferral Date 2022-08

Publication Date 05/23/2022

Defense Date 04/20/2022

Publisher University of Southern California. Libraries (digital)

Tag CFD,coupled energy modeling,ENVI-met,evapotranspiration,Ladybug-Honeybee,microclimate,OAI-PMH Harvest,universal thermal climate index,USC Urban Trees Initiative,weather data modification and analysis

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kensek, Karen M. (committee chair), Konis, Kyle S. (committee member), Schiler, Marc Eugene (committee member)

Creator Email kkrupap@gmail.com,krupapai1993@gmail.com

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