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Figure 4.36 The daily temperature distribution of the three scenarios during summer on the 21“ ¢ August and winter on the 21* of January.

Figure 4 36 The daily temperature distribution of the three scenarios during summer on the 21“ ¢ August and winter on the 21* of January.

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Figure 2.1. The chronological steps followed for the articles extracted for literature review improved comfort and more sustainable energy solutions (Smith and More, 2002).
Figure 2.1. The chronological steps followed for the articles extracted for literature review improved comfort and more sustainable energy solutions (Smith and More, 2002).
Figure 2.2. Schemes of simulated street canyons. Source: Toudert and Mayer, 2006
Figure 2.2. Schemes of simulated street canyons. Source: Toudert and Mayer, 2006
Figure 2.3. Fisheye images to calculate the SVF using Rayman software
Figure 2.3. Fisheye images to calculate the SVF using Rayman software
Figure 2.4. Various orientations tested through simulation by standardizing the H/W ratio.
Figure 2.4. Various orientations tested through simulation by standardizing the H/W ratio.
Figure 2.5. Daily variation of the mean solar radiation intensity on the ground in Jerusalem streets on the 21 September.
Figure 2.5. Daily variation of the mean solar radiation intensity on the ground in Jerusalem streets on the 21 September.
Figure 2.6. Schematic representations of radiative exchanges of a tree.
Figure 2.6. Schematic representations of radiative exchanges of a tree.
Figure 2.6. Wind speed in an open space in Fleuriot Square where case (a) shows an empty situation and case (b) is with the application of vegetation and water pond. air within the layout as shown in Figure 2.7 (Robitu et al., 2005).
Figure 2.6. Wind speed in an open space in Fleuriot Square where case (a) shows an empty situation and case (b) is with the application of vegetation and water pond. air within the layout as shown in Figure 2.7 (Robitu et al., 2005).
Figure 2.8. The effect of calculated cooling efficiency of different assumed air change rates in the courtyards. cooling efficiency levels according to the landscape techniques used (Bar et el., 2009).
Figure 2.8. The effect of calculated cooling efficiency of different assumed air change rates in the courtyards. cooling efficiency levels according to the landscape techniques used (Bar et el., 2009).
Figure 2.10. Three case scenarios whereas cases 2-1 and 2-3 has proved to have more pleasant outdoor environment.
Figure 2.10. Three case scenarios whereas cases 2-1 and 2-3 has proved to have more pleasant outdoor environment.
Source: www.envi-met.com
Source: www.envi-met.com
Figure 3.1. UAE location on the world map left and Dubai location on the UAE map right
Figure 3.1. UAE location on the world map left and Dubai location on the UAE map right
Figure 3.2. Temperature and precipitation all over the year in Dubai
Figure 3.2. Temperature and precipitation all over the year in Dubai
Figure 3.12. Solar position of Dubai at 12.00 on the 21st of August.
Figure 3.12. Solar position of Dubai at 12.00 on the 21st of August.
Figure 3.5. Annual and monthly daylight hours representing the solar intensity.
Figure 3.5. Annual and monthly daylight hours representing the solar intensity.
Figure 3.6. Monthly dry bulb, humidity and comfort ranges. Source: Eco-tect software
Figure 3.6. Monthly dry bulb, humidity and comfort ranges. Source: Eco-tect software
Figure 3.7. Dubai wind rose representing the wind speed intensity and direction emphasizing the prevailing wind.
Figure 3.7. Dubai wind rose representing the wind speed intensity and direction emphasizing the prevailing wind.
Figure 3.8. Psychometric chart of Dubai indicating the comfort zone.
Figure 3.8. Psychometric chart of Dubai indicating the comfort zone.
Figure 3.9. Psychometric chart of Dubai indicating the comfort zone. Source: Eco-tect software
Figure 3.9. Psychometric chart of Dubai indicating the comfort zone. Source: Eco-tect software
Figure 3.10. DKV location on Dubai’s sea coast. the users’ comfort.
Figure 3.10. DKV location on Dubai’s sea coast. the users’ comfort.
Figure 3.11. Selected area of study within DKV.
Figure 3.11. Selected area of study within DKV.
Figure 3.12. Image of the Building within the area of study. Source: Online Google maps
Figure 3.12. Image of the Building within the area of study. Source: Online Google maps
Figure 3.13. Image of the Building within the area of study.
Figure 3.13. Image of the Building within the area of study.
Table 3.2. Test matrix used for the simulation analysis. Red cells represent the fixed variables during simulation.
Table 3.2. Test matrix used for the simulation analysis. Red cells represent the fixed variables during simulation.
Figure 3.14 Selected area of study within DKV used for the simulations.
Figure 3.14 Selected area of study within DKV used for the simulations.
Figure 3.15. The ‘existing’ case scenario representing simulations one and two (August 21* and January 21") configuration was used for testing the existing site conditions for extreme winter on the
Figure 3.15. The ‘existing’ case scenario representing simulations one and two (August 21* and January 21") configuration was used for testing the existing site conditions for extreme winter on the
Figure 3.16. The ‘NS’ testing the orientation variable representing simulation three (August a1") and a tree planted every 12m.
Figure 3.16. The ‘NS’ testing the orientation variable representing simulation three (August a1") and a tree planted every 12m.
Figure 3.17. The ‘EW’ testing the orientation variable representing simulation four (August 21“
Figure 3.17. The ‘EW’ testing the orientation variable representing simulation four (August 21“
Figure 3.18 The ‘SE-NW’ testing the orientation variable representing simulation five (August 21%)
Figure 3.18 The ‘SE-NW’ testing the orientation variable representing simulation five (August 21%)
Figure 3.19. The ‘SW-NE’ testing the orientation variable representing simulation six (August 21%)
Figure 3.19. The ‘SW-NE’ testing the orientation variable representing simulation six (August 21%)
Figure 3.20. The ‘H:W 0.5 ratio testing the geometry variable representing simulation seven (August 21
Figure 3.20. The ‘H:W 0.5 ratio testing the geometry variable representing simulation seven (August 21
Figure 3.21. The H:W 4 ratio testing the geometry variable representing simulation nine (August 21
Figure 3.21. The H:W 4 ratio testing the geometry variable representing simulation nine (August 21
Figure 3.22. The ‘H:W ratio 2 testing the geometry variable representing simulation eight (August 21‘
Figure 3.22. The ‘H:W ratio 2 testing the geometry variable representing simulation eight (August 21‘
Figure 3.23. The ‘continuous grass’ testing the vegetation variable representing simulation ter (August 21")
Figure 3.23. The ‘continuous grass’ testing the vegetation variable representing simulation ter (August 21")
Figure 3.24. The ‘grass pieces’ testing the vegetation variable representing simulation elever (August 21")
Figure 3.24. The ‘grass pieces’ testing the vegetation variable representing simulation elever (August 21")
Figure 3.25 The ‘continuous grass & tree groups’ testing the vegetation variable representing simulation twelve (August 21“)
Figure 3.25 The ‘continuous grass & tree groups’ testing the vegetation variable representing simulation twelve (August 21“)
Figure 3.26. The ‘tree groups’ testing the vegetation variable representing simulation thirteen (August 21")
Figure 3.26. The ‘tree groups’ testing the vegetation variable representing simulation thirteen (August 21")
Figure 3.27. The ‘continuous trees’ testing the vegetation variable representing simulation fourteer (August 21")
Figure 3.27. The ‘continuous trees’ testing the vegetation variable representing simulation fourteer (August 21")
Figure 3.28. The ‘no tree’ testing the vegetation variable representing simulation fifteen (August 21“
Figure 3.28. The ‘no tree’ testing the vegetation variable representing simulation fifteen (August 21“
Figure 3.29. The ‘enhanced’ scenario testing the all the bioclimatic principles representing simulation sixteen and seventeen two (August 21“ and January 21").
Figure 3.29. The ‘enhanced’ scenario testing the all the bioclimatic principles representing simulation sixteen and seventeen two (August 21“ and January 21").
Figure 3.30. The‘worst’ scenario testing the non existence of any of the bioclimatic principle: representing simulation eighteen and nineteen two (August 21" and January 21")
Figure 3.30. The‘worst’ scenario testing the non existence of any of the bioclimatic principle: representing simulation eighteen and nineteen two (August 21" and January 21")
Table 3.4 The simulations were based upon the current data where some are fixed data in the software and some are input data
Table 3.4 The simulations were based upon the current data where some are fixed data in the software and some are input data
Table 4.1 The average, maximum and minimum temperature and wind values obtained from the simulations during summer on 21“ August and winter on 21“ January. Figure 4.1. The daily temperature patterns during summer on 21“ August and winter on 21“ January demonstrating the peak thermal stress zone.
Table 4.1 The average, maximum and minimum temperature and wind values obtained from the simulations during summer on 21“ August and winter on 21“ January. Figure 4.1. The daily temperature patterns during summer on 21“ August and winter on 21“ January demonstrating the peak thermal stress zone.
Figure 4.2 The daily wind patterns during summer on 21“ August and winter on 21“ January demonstrating the peak thermal stress zone.
Figure 4.2 The daily wind patterns during summer on 21“ August and winter on 21“ January demonstrating the peak thermal stress zone.
Figure 4.3 The temperature values behavior during summer on 21“ August and during winter on 21* January.
Figure 4.3 The temperature values behavior during summer on 21“ August and during winter on 21* January.
Figure 4.4 The wind speed values behavior during summer on 21“ August and during winter on 21° January.
Figure 4.4 The wind speed values behavior during summer on 21“ August and during winter on 21° January.
Figure 4.5 The four orientations demonstrating the average, maximum and minimum temperature values highlighting the SW-NE orientation as the selected parameter incorporated in the enhanced model.
Figure 4.5 The four orientations demonstrating the average, maximum and minimum temperature values highlighting the SW-NE orientation as the selected parameter incorporated in the enhanced model.
Figure 4.6 The average and maximum temperatures of the four orientations compared to the existing scenario with the lowest average temperature of the SW-NE orientation.
Figure 4.6 The average and maximum temperatures of the four orientations compared to the existing scenario with the lowest average temperature of the SW-NE orientation.
Figure 4.7 The average and maximum wind speed of the four orientations compared to the existing scenario with the lowest average and maximum wind speed of the SW-NE orientation.
Figure 4.7 The average and maximum wind speed of the four orientations compared to the existing scenario with the lowest average and maximum wind speed of the SW-NE orientation.
Figure 4.8 The four orientations demonstrating the average, maximum and minimum wind speed values highlighting the SW-NE orientation as the selected parameter incorporated in the enhanced model.
Figure 4.8 The four orientations demonstrating the average, maximum and minimum wind speed values highlighting the SW-NE orientation as the selected parameter incorporated in the enhanced model.
Figure 4.9 The daily temperature behavior of the four orientations and the existing scenario with the SW-NE orientation of the lowest value and the SE-NW of the highest value with very slight differences.
Figure 4.9 The daily temperature behavior of the four orientations and the existing scenario with the SW-NE orientation of the lowest value and the SE-NW of the highest value with very slight differences.
Figure 4.10 The daily wind speed behavior of the four orientations and the existing scenario with the SW-NE orientation of the lowest value and the SE-NW of the highest value stabilized all during the day.
Figure 4.10 The daily wind speed behavior of the four orientations and the existing scenario with the SW-NE orientation of the lowest value and the SE-NW of the highest value stabilized all during the day.
Figure 4.11 The solar path represents the shading principle provided within the space based upon each orientation. EW space top left, SW-NE space top right, NW-SE space bottom right and NS space bottom left.
Figure 4.11 The solar path represents the shading principle provided within the space based upon each orientation. EW space top left, SW-NE space top right, NW-SE space bottom right and NS space bottom left.
Figure 4.12 The average and maximum temperatures of the three models where model one and two tests the same ratio versus the third model of a smaller ratio.
Figure 4.12 The average and maximum temperatures of the three models where model one and two tests the same ratio versus the third model of a smaller ratio.
Figure 4.13 The daily temperatures of the three models where model one and two with the highest and lowest values tests the same ratio versus the third model of a smaller ratio.
Figure 4.13 The daily temperatures of the three models where model one and two with the highest and lowest values tests the same ratio versus the third model of a smaller ratio.
Figure 4.14 Average, maximum and minimum temperature and wind values of the three different H:W ratios where the nominated ratio records the lowest temperature values and the highest wind speed values.
Figure 4.14 Average, maximum and minimum temperature and wind values of the three different H:W ratios where the nominated ratio records the lowest temperature values and the highest wind speed values.
Figure 4.15 The average wind speed comparison between the three tested ratios and the existing scenario presents a logical sequence with the lowest values for the ratio of 4.
Figure 4.15 The average wind speed comparison between the three tested ratios and the existing scenario presents a logical sequence with the lowest values for the ratio of 4.
Figure 4.16 The daily temperature values of the three ratios and the existing scenario with a very slight difference yet in a logical sequence where the highest values belongs to the lowest ratio.
Figure 4.16 The daily temperature values of the three ratios and the existing scenario with a very slight difference yet in a logical sequence where the highest values belongs to the lowest ratio.
Figure 4.17 The average temperature comparison between the three tested ratios and the existing scenario presents a logical sequence with the lowest values for the ratio of 4.
Figure 4.17 The average temperature comparison between the three tested ratios and the existing scenario presents a logical sequence with the lowest values for the ratio of 4.
Figure 4.18 The daily temperature values of the three ratios and the existing scenario with a very sligh difference where the highest values belongs to the highest ratio.
Figure 4.18 The daily temperature values of the three ratios and the existing scenario with a very sligh difference where the highest values belongs to the highest ratio.
Figure 4.19 The six landscape strategies average and maximum temperatures and the existing scenario values reveals that the trees & grass proposal has the least values.
Figure 4.19 The six landscape strategies average and maximum temperatures and the existing scenario values reveals that the trees & grass proposal has the least values.
Figure 4.20 The six landscape strategies average and maximum wind speed values and the existing scenario values.
Figure 4.20 The six landscape strategies average and maximum wind speed values and the existing scenario values.
Figure 4.21 The average, maximum and minimum temperatures of the six vegetation strategies.
Figure 4.21 The average, maximum and minimum temperatures of the six vegetation strategies.
Figure 4.22 The daily temperature values of the different strategies revealing very slight differences yet the trees & grass as the most effective with lowest values.
Figure 4.22 The daily temperature values of the different strategies revealing very slight differences yet the trees & grass as the most effective with lowest values.
Figure 4.23 The daily wind speed values of the different strategies revealing more considered differences than the temperature values yet the trees & grass with lowest values.
Figure 4.23 The daily wind speed values of the different strategies revealing more considered differences than the temperature values yet the trees & grass with lowest values.
Figure 4.24 The average and maximum temperature values for the most effective independent variable that recorded the lowest temperature values.
Figure 4.24 The average and maximum temperature values for the most effective independent variable that recorded the lowest temperature values.
Figure 4.25 The average and maximum wind speed values for the most effective independent variable that recorded the lowest temperature values.
Figure 4.25 The average and maximum wind speed values for the most effective independent variable that recorded the lowest temperature values.
Table 4.2 The temperature and wind speed values of the summer on the 21“ August and winter on the 21* January.
Table 4.2 The temperature and wind speed values of the summer on the 21“ August and winter on the 21* January.
Figure 4.26 The daily average values of temperature indicates the peak thermal stress zone with the maximum temperature during summer between 12.00 and 15.00 shifted slightly during winter
Figure 4.26 The daily average values of temperature indicates the peak thermal stress zone with the maximum temperature during summer between 12.00 and 15.00 shifted slightly during winter
Figure 4.27 The daily wind speed values with the maximum wind speed period during the peak thermal stress zone during summer and much more stable during winter.
Figure 4.27 The daily wind speed values with the maximum wind speed period during the peak thermal stress zone during summer and much more stable during winter.
Figure 4.28 The average, maximum and minimum temperature values of the enhanced scenario during summer on the 21 of August and winter on the 21 January.
Figure 4.28 The average, maximum and minimum temperature values of the enhanced scenario during summer on the 21 of August and winter on the 21 January.
Figure 4.29 The average, maximum and minimum wind speed values of the enhanced scenario during summer on the 21“ of August and winter on the 21“ January.
Figure 4.29 The average, maximum and minimum wind speed values of the enhanced scenario during summer on the 21“ of August and winter on the 21“ January.
Table 4.3 Temperature and wind values during summer and winter for the worst case scenario
Table 4.3 Temperature and wind values during summer and winter for the worst case scenario
Figure 4.30 The daily temperature values during both summer on the 21“ of August and winter on the 21° of January showing the peak heat stress period behavior.
Figure 4.30 The daily temperature values during both summer on the 21“ of August and winter on the 21° of January showing the peak heat stress period behavior.
Figure 4.31 The daily wind speed values during both summer on the 21“ of August and winter on the 21" of January showing the peak heat stress period behavior.
Figure 4.31 The daily wind speed values during both summer on the 21“ of August and winter on the 21" of January showing the peak heat stress period behavior.
Figure 4.32 The average, maximum and minimum wind speed values of the worst case scenario during summer on the 21“ of August and winter on the 21“ January.
Figure 4.32 The average, maximum and minimum wind speed values of the worst case scenario during summer on the 21“ of August and winter on the 21“ January.
Figure 4.33 The average, maximum and minimum temperature values of the worst cas scenario during summer on the 21“ of August and winter on the 21“ January.
Figure 4.33 The average, maximum and minimum temperature values of the worst cas scenario during summer on the 21“ of August and winter on the 21“ January.
Table 4.4 Summary of results of the temperature values of the three scenarios during summer on the 21“ of August and winter on the 21“ of January highlighting the highest in winter and lowest in summer.
Table 4.4 Summary of results of the temperature values of the three scenarios during summer on the 21“ of August and winter on the 21“ of January highlighting the highest in winter and lowest in summer.
Figure 4.34 The average and maximum temperatures during summer on the 21“ of August and winter on the 21“ of January.
Figure 4.34 The average and maximum temperatures during summer on the 21“ of August and winter on the 21“ of January.
Figure 4.35 The average and maximum wind speed during summer on the 21“ of August and winter on the 21‘ of January with the worst case as the highest value in all cases.
Figure 4.35 The average and maximum wind speed during summer on the 21“ of August and winter on the 21‘ of January with the worst case as the highest value in all cases.
Figure 4.37 The daily wind speed distribution of the three scenarios during summer on the 21* of August and winter on the 21* of January.
Figure 4.37 The daily wind speed distribution of the three scenarios during summer on the 21* of August and winter on the 21* of January.
considered to be the crucial aspect raising the temperature levels.
considered to be the crucial aspect raising the temperature levels.
Figure 5.2. The thermal distribution of the EW orientation at 14.00 on the 21* of August indicating the NW wind. that of the EW and SE-NW orientation in Figure 5.2 & 5.4 consecutively.
Figure 5.2. The thermal distribution of the EW orientation at 14.00 on the 21* of August indicating the NW wind. that of the EW and SE-NW orientation in Figure 5.2 & 5.4 consecutively.
Figure 5.3. The thermal distribution of the NS orientation at 14.00 on the 21% of August indicating the NW wind.
Figure 5.3. The thermal distribution of the NS orientation at 14.00 on the 21% of August indicating the NW wind.
Figure 5.4. The thermal distribution of the SE-NW orientation at 14.00 on the 21* of August indicating the NW wind.
Figure 5.4. The thermal distribution of the SE-NW orientation at 14.00 on the 21* of August indicating the NW wind.
Figure 5.5. The thermal distribution of the SW-NE orientation at 14.00 on the 21* of August indicating the NW wind.
Figure 5.5. The thermal distribution of the SW-NE orientation at 14.00 on the 21* of August indicating the NW wind.
Figure 5.6. The process of diurnal heat gain and nocturnal heat loss based upon two of the tested H:W ratios.
Figure 5.6. The process of diurnal heat gain and nocturnal heat loss based upon two of the tested H:W ratios.
mihi ade mei Figure 5.7. The thermal distribution of the 4 H:W ratio at 14.00 on the 21" of August.
mihi ade mei Figure 5.7. The thermal distribution of the 4 H:W ratio at 14.00 on the 21" of August.
Figure 5.8. The thermal distribution of the 2 H:W ratio at 14.00 on the 21“ of August.
Figure 5.8. The thermal distribution of the 2 H:W ratio at 14.00 on the 21“ of August.
Figure 5.9. The thermal distribution of the 0.5 H:W ratio at 14.00 on the 21“ of August.
Figure 5.9. The thermal distribution of the 0.5 H:W ratio at 14.00 on the 21“ of August.
Figure 5.10. The thermal distribution of the vegetation strategy containing continuous grass at 14.00 on the 21" of August.
Figure 5.10. The thermal distribution of the vegetation strategy containing continuous grass at 14.00 on the 21" of August.
“Figure 5.11. The thermal distribution of the vegetation strategy containing pieces of grass a 14.00 on the 21" of August.
“Figure 5.11. The thermal distribution of the vegetation strategy containing pieces of grass a 14.00 on the 21" of August.
shen roo san Figure 5.12. The thermal distribution of the vegetation strategy containing continuous trees at 14.00 on the 21" of August.
shen roo san Figure 5.12. The thermal distribution of the vegetation strategy containing continuous trees at 14.00 on the 21" of August.
“Figure 5.13. The thermal distribution of the vegetation strategy containing trees groups at 14.00 on the 21* of August.
“Figure 5.13. The thermal distribution of the vegetation strategy containing trees groups at 14.00 on the 21* of August.
Sr soe Figure 5.14. The thermal distribution of the vegetation strategy containing no trees at 14.00 on the 21 of August.
Sr soe Figure 5.14. The thermal distribution of the vegetation strategy containing no trees at 14.00 on the 21 of August.
Figure 5.15. The thermal distribution of the vegetation strategy containing continuous grass and groups of trees at 14.00 on the 21“ of August.
Figure 5.15. The thermal distribution of the vegetation strategy containing continuous grass and groups of trees at 14.00 on the 21“ of August.
Figure 5.16. The standard deviation between the average and maximum temperatures for the tested H:W ratios.
Figure 5.16. The standard deviation between the average and maximum temperatures for the tested H:W ratios.
Figure 5.17. The standard deviation between the average and maximum temperatures for the tested orientations.
Figure 5.17. The standard deviation between the average and maximum temperatures for the tested orientations.
Figure 5.18. The standard deviation between the average and maximum temperatures for the tested vegetation strategies.
Figure 5.18. The standard deviation between the average and maximum temperatures for the tested vegetation strategies.
Figure 5.19. The inversely relationship between the vegetation scale and the temperature indicating a
Figure 5.19. The inversely relationship between the vegetation scale and the temperature indicating a
Figure 5.20. The thermal distribution of the enhanced scenario at 14.00 on the 21st of August.
Figure 5.20. The thermal distribution of the enhanced scenario at 14.00 on the 21st of August.
Figure 5.21. The thermal distribution of the enhanced scenario at 21.00 on the 21st of August.
Figure 5.21. The thermal distribution of the enhanced scenario at 21.00 on the 21st of August.
Figure 5.22. The thermal distribution of the existing scenario at 14.00 on the 21st of August.
Figure 5.22. The thermal distribution of the existing scenario at 14.00 on the 21st of August.
Figure 5.23. The thermal distribution of the existing scenario at 21.00 on the 21st of August.
Figure 5.23. The thermal distribution of the existing scenario at 21.00 on the 21st of August.
Figure 5.24. The thermal distribution of the worst case scenario at 14.00 on the 21st of August.
Figure 5.24. The thermal distribution of the worst case scenario at 14.00 on the 21st of August.
Figure 5.25. The thermal distribution of the worst case scenario at 21.00 on the 21st of August.
Figure 5.25. The thermal distribution of the worst case scenario at 21.00 on the 21st of August.
Figure A.1 Daily temperature pattern for EW orientation based on results every 30 minutes
Figure A.1 Daily temperature pattern for EW orientation based on results every 30 minutes
Figure A.2 Daily wind speed pattern for EW orientation based on results every 30 minutes
Figure A.2 Daily wind speed pattern for EW orientation based on results every 30 minutes
Figure A.3 Daily temperature pattern for NS orientation based on results every 30 minutes
Figure A.3 Daily temperature pattern for NS orientation based on results every 30 minutes
Figure A.4 Daily wind speed pattern for NS orientation based on results every 30 minutes
Figure A.4 Daily wind speed pattern for NS orientation based on results every 30 minutes
Figure A. 5 Daily temperature pattern for SE-NW orientation based on results every 30 minutes
Figure A. 5 Daily temperature pattern for SE-NW orientation based on results every 30 minutes
Figure A.6 Daily wind speed pattern for SE-NW orientation based on results every 30 minute:
Figure A.6 Daily wind speed pattern for SE-NW orientation based on results every 30 minute:
Figure A.7 Daily wind speed pattern for SW-NE orientation based on results every 30 minutes
Figure A.7 Daily wind speed pattern for SW-NE orientation based on results every 30 minutes
Figure A.8 Daily wind speed pattern for SW-NE orientation based on results every 30 minutes
Figure A.8 Daily wind speed pattern for SW-NE orientation based on results every 30 minutes
Figure A.9 Daily temperature pattern for 0.5 ratio based on results every 30 minutes
Figure A.9 Daily temperature pattern for 0.5 ratio based on results every 30 minutes
Figure A.10 Daily wind speed pattern for 0.5 ratio based on results every 30 minutes
Figure A.10 Daily wind speed pattern for 0.5 ratio based on results every 30 minutes
Figure A.11 Daily temperature pattern for 2 ratio based on results every 30 minutes
Figure A.11 Daily temperature pattern for 2 ratio based on results every 30 minutes
Figure A.12 Daily wind speed pattern for 2 ratio based on results every 30 minutes
Figure A.12 Daily wind speed pattern for 2 ratio based on results every 30 minutes
Figure A.13 Daily temperature pattern for 2 ratio based on results every 30 minutes
Figure A.13 Daily temperature pattern for 2 ratio based on results every 30 minutes
Figure A.14 Daily wind speed pattern for 2 ratio based on results every 30 minutes
Figure A.14 Daily wind speed pattern for 2 ratio based on results every 30 minutes
Figure A.15 Daily temperature pattern for grass pieces vegetation strategy based on results every 30 minutes
Figure A.15 Daily temperature pattern for grass pieces vegetation strategy based on results every 30 minutes
Figure A.16 Daily wind speed pattern for grass pieces vegetation strategy based on results every 30 minutes
Figure A.16 Daily wind speed pattern for grass pieces vegetation strategy based on results every 30 minutes
Figure A.17 Daily temperature pattern for no trees vegetation strategy based on results every 30 minutes
Figure A.17 Daily temperature pattern for no trees vegetation strategy based on results every 30 minutes
Figure A.18 Daily wind speed pattern for no trees vegetation strategy based on results every 30 minutes
Figure A.18 Daily wind speed pattern for no trees vegetation strategy based on results every 30 minutes
Figure A.19 Daily temperature pattern for continuous trees vegetation strategy based o1 results every 30 minutes
Figure A.19 Daily temperature pattern for continuous trees vegetation strategy based o1 results every 30 minutes
Figure A.20 Daily wind speed pattern for continuous trees vegetation strategy based on results every 30 minutes
Figure A.20 Daily wind speed pattern for continuous trees vegetation strategy based on results every 30 minutes
Figure A.21 Daily temperature pattern for tree groups vegetation strategy based on results every 30 minutes
Figure A.21 Daily temperature pattern for tree groups vegetation strategy based on results every 30 minutes
Figure A.22 Daily wind speed pattern for tree groups vegetation strategy based on results every 30 minutes
Figure A.22 Daily wind speed pattern for tree groups vegetation strategy based on results every 30 minutes
Figure A.23 Daily temperature pattern for continuous grass vegetation strategy based or results every 30 minutes
Figure A.23 Daily temperature pattern for continuous grass vegetation strategy based or results every 30 minutes
Figure A.24 Daily wind speed pattern for continuous grass vegetation strategy based on results every 30 minutes
Figure A.24 Daily wind speed pattern for continuous grass vegetation strategy based on results every 30 minutes
Figure A.25 Daily temperature pattern for continuous grass and tree groups vegetation strategy based on results every 30 minutes
Figure A.25 Daily temperature pattern for continuous grass and tree groups vegetation strategy based on results every 30 minutes
Figure A.26 Daily wind speed pattern for continuous grass and tree groups vegetation strategy based on results every 30 minutes
Figure A.26 Daily wind speed pattern for continuous grass and tree groups vegetation strategy based on results every 30 minutes
Figure C.1 Daily temperature gradient every 30 minutes during summer for the existing scenario
Figure C.1 Daily temperature gradient every 30 minutes during summer for the existing scenario
Related topics:
Bioclimatic Urban DesignThermal comfortUrban Canyonoutdoor microclimate
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