Impact of Neighborhood Morphology on Air Pollution Dispersion Patterns Due to Unplanned Building Demolition: a Parametric Study

SHRESTHA, SAMATA M.S. MAY 2019 ARCHITECTURE AND ENVIRONMENTAL DESIGN

IMPACT OF NEIGHBORHOOD MORPHOLOGY ON AIR POLLUTION DISPERSION PATTERNS DUE TO UNPLANNED BUILDING DEMOLITION: A PARAMETRIC STUDY

Thesis Advisor: Dr. Adil Sharag-Eldin

An unplanned demolition of a building is either natural such as earthquake or human caused disaster like terrorist attack and wars. Unplanned building demolition generates a considerable amount of dust cloud and demolition waste comprising Particulate Matter (PM) of various sizes. A significant body of evidence relates chronic and acute adverse health effects to increased exposure of PM to the public. However, the literature review reveals a limited number of studies addressing the impact of unplanned demolition on local air quality. This is primarily due to the post-disaster situation is chaotic. The study focuses on investigating neighborhood environmental morphologies that reduce pollution dispersion at pedestrian levels. The primary objective of this research is to investigate the pattern and characteristics of pollution dispersion due to unplanned building demolition in a neighborhood.

CFD-based air quality model “ENVI-met” was used to simulate the pollution dispersion in selected ten types of neighborhood morphologies. The research compares simulated pollution blooms resulting from a building collapse amid of ten different neighborhood morphologies. For each neighborhood configuration, the simulation produced thirty-six horizontal and forty-eight vertical dispersion measurements. The analysis confirmed that the dust plume generated during unplanned building demolition dispersed 200m beyond the source at almost of the neighborhoods. However, the concentration levels were different depending upon the type of

neighborhood morphologies. The research identifies the sensitivity of an area to human health in the neighborhood facing unplanned demolition. The thesis concludes with proposing few design recommendations for street canyon, wind flow, building design, vegetation placement, and overall safety to help urban designers minimize the impact of unplanned building demolition and air pollution dispersion. The findings of this research are significant to urban designers to improve the quality of air through planning; to residents, rescue workers and victims to identify the exposure to PM during disaster and seek timely medical attention; and policy makers to acknowledge the need of air quality standards for short term high pollution levels.

Keywords: unplanned building demolition, air pollution, neighborhood morphology, pollution dispersion pattern, parametric study, public health, urban design.

IMPACT OF NEIGHBORHOOD MORPHOLOGY ON AIR POLLUTION DISPERSION PATTERNS DUE TO UNPLANNED BUILDING DEMOLITION: A PARAMETRIC STUDY

A thesis submitted to Kent State University in partial fulfillment of the requirements for the degree of Master of Science in Architecture and Environmental Design

Samata Shrestha

May 2019

Thesis written by

Samata Shrestha

B. Arch., Tribhuvan University, 2015

M.S. in Architecture and Environmental Design, Kent State University, 2019

Approved by:

Adil Sharag-Eldin, Ph.D., LEED, A.P. ____ , Advisor

Reid Coffman, Ph.D ______, Coordinator, Master of Science in Architecture and Environmental Design

Mark Mistur, AIA ____, Dean, College of Architecture and Environmental Design

TABLE OF CONTENTS

List of Figures ...... vii List of Tables ...... xx Acknowledgements ...... xxi Chapter 1: Introduction ...... 1 1.1. Background ...... 1 1.2. Justification for Research ...... 3 1.3. Research Approach ...... 4 1.4. Research Objectives ...... 7 1.5. Scope of the Research ...... 8 Chapter 2: Literature Review ...... 11 2.1. Introduction to Particulate Matter ...... 11 2.2. Standard Regulations for Particulate Matter ...... 12 2.3. Health Impact resulting from short term exposure to high-level PM ...... 16 2.3.1 Case study of Unplanned Building Demolition: World Trade Center: ...... 20 2.3.1.1. Particulate matter generated and exposure ...... 21 2.3.1.2. Radius of pollution dispersion ...... 22 2.3.1.3. Health impacts ...... 23 2.4. Causes of Unplanned Demolition and Pollution Emission ...... 25 2.6. Particulate Matter Emission from Planned Demolition ...... 26 2.7. Monitoring implosion pollution ...... 29 2.7.1. Building implosion at Baltimore, MD ...... 29 2.7.2. Types of Pollution generated ...... 30 2.7.3. Radius of Pollutants Dispersion ...... 30 2.8. Dispersion of air pollution in Urban Morphology ...... 32 2.8.1. Street Canyon ...... 33 2.8.2. Effect of Wind Speed ...... 35 2.9. Selection of Neighborhood Morphologies for Air Pollution Modelling ...... 35 2.10. Modelling air pollution dispersion ...... 37 2.11. General Structure of Air Quality Models ...... 38 2.12. Main Parameters of an Air Quality Model ...... 39 2.12.1. Emissions ...... 39 2.12.2. Meteorology ...... 40

2.13. Validation of Model...... 40 2.14. Advantage of using Air quality Simulation Method ...... 41 2.15. Disadvantage of using Air quality Simulation Method ...... 42 2.16. The micro-climate model ENVI-MET/ Test Software ...... 43 Chapter 3: Research Method ...... 45 3.1. Data Source ...... 45 3.2. Emission Estimation ...... 46 3.2.1 Model area/ Study site ...... 46 3.2.2 Input parameters ...... 48 3.2.3 Pollution Dispersion Model Simulation ...... 52 3.2.4 Test Results and Comparisons ...... 54 3.3. Model validation by Simulation of Implosion of Calgary General Hospital, Canada ...... 57 3.3.1. Data Inputs ...... 58 3.3.2. Results ...... 59 3.3.3. Discussion ...... 61 3.4. The Parametric Study: Simulation of ten identified Urban Morphologies using ENVI-met ...... 63 3.4.1. Input parameters ...... 63 3.4.2. Data Sampling and Model set up ...... 63 3.4.3. Result Visualization ...... 65 3.4.4. Data Collection and Analysis ...... 74 3.4.4.1. Compact High Rise ...... 75 3.4.4.2. Compact Mid Rise ...... 79 3.4.4.3. Compact Low-Rise ...... 82 3.4.4.4. Compact Mid Rise+ Open High Rise ...... 85 3.4.4.5. Open High Rise ...... 88 3.4.4.6. Open Mid Rise ...... 91 3.4.4.7. Open Low Rise ...... 94 3.4.4.8. Large Low Rise ...... 97 3.4.4.9. Sparsely Built ...... 100 3.4.4.10. Low Rise with Dense Trees ...... 103 Chapter 4: Discussion ...... 107 4.1. Neighborhood density and street width ...... 107 4.2. Distance from source ...... 110 4.3. Wind direction and Speed ...... 111

4.4. Vegetation ...... 113 4.5. Pollution dispersion pattern/radius in selected neighborhood morphologies...... 115 4.6. Sensitivity of an area to human health ...... 117 Chapter 5. Design Recommendations and Conclusion ...... 121 5.1. Design Recommendations ...... 121 5.2. Conclusion ...... 124 5.3. Delimitations of the study ...... 126 5.4. Limitation of the study ...... 126 5.5. Recommendations for future research...... 127 References ...... 129 APPENDICES ...... 141 Appendix A: ENVI_met results for different morphologies ...... 143 Appendix B: Types of Air Quality Model ...... 203 Appendix C: General design of ENVI-met ...... 207

LIST OF FIGURES

Figure 2.1- Particulate Matter relative size comparison...... 12 Figure 2.2- Effects of different size PM in human body...... 17 Figure 2.3- Exposure zone recognized by World Trade Center Health Program...... 22 Figure 2.4- Different types of respiratory illness and cancer reported in WTCHP members...... 24 Figure 2.5- Implosion site showing the location of seven outdoor sampling sites...... 30 Figure 2.6- Wind flow regime in urban area for different street width...... 33 Figure 2.7- Wind Pattern in a Street Canyon...... 34 Figure 2.8- Concept of an air quality models...... 39 Figure 3.1- Google earth image of the site location, surrounding and the sampling locations. ... 46 Figure 3.2- Test Model plotted in ENVI-met space area...... 46 Figure 3.3- Creating a model domain in ENVI-met...... 47 Figure 3.4- Diurnal Temperature and Relative Humidity ...... 48 Figure 3.5- Main Configuration Wizard ...... 50 Figure 3.6- Setting up time and date for the simulation ...... 50 Figure 3.7- Basic meteorological data input ...... 51 Figure 3.8- Hourly Temperature and Relative humidity data input...... 51 Figure 3.9- Extended Meteorology setting in ENVI_met ...... 51

Figure 3.10- Estimated 24-hour PM10 emission rate ...... 52 Figure 3.11- Assigning the multi pollutant mode in the configuration wizard ...... 53

Figure 3.12- Data set input for PM10 emission in ENVI_met database library ...... 53 Figure 3.13- At sample location L1 ...... 55 Figure 3.14- At sample location L2 ...... 55 Figure 3.15- At sample location L3 ...... 56

Figure 3.16- Comparison between PM10 Concentration at different sample locations ...... 56 Figure 3.17- Google Earth image showing the location of implosion and surrounding monitoring locations...... 57 Figure 3.18- Meteorological background values for temperature and humidity in the 2m level within a 24hr cycle ...... 58 Figure 3.19- Implosion site and surrounding modelled in ENVI-met ...... 60 Figure 3.20- Pollution dispersion simulation result from Calgary Hospital Implosion ...... 60

vii

Figure 3.21- Gradient height depends on the roughness of the terrain...... 61 Figure 3.22- Pollution dispersion simulation result from Calgary Hospital Implosion ...... 62 Figure 3.23- Neighborhood morphology plotted in ENVI_met ...... 64 Figure 3.24- Schematic map of the model set-up showing three monitoring stations around the source, wind directions and street width ...... 65 Figure 3.25- PMC within compact midrise settlement at 10:05am and 0-degree wind direction. (Horizontal) ...... 66 Figure 3.26- PMC within compact midrise settlement at 10:05am and 0-degree wind direction. (Vertical) ...... 66 Figure 3.27- PMC within compact midrise settlement at 10:25am and 0-degree wind direction. (Horizontal) ...... 67 Figure 3.28- PMC within compact midrise settlement at 10:25am and 0-degree wind direction. (Vertical) ...... 67 Figure 3.29- PMC within compact midrise settlement at 10:45am and 0-degree wind direction. (Horizontal) ...... 68 Figure 3.30- PMC within compact midrise settlement at 10:45am and 0-degree wind direction. (Vertical) ...... 68 Figure 3.31- Wind speed within compact midrise settlement at 0-degree wind direction (Horizontal) ...... 69 Figure 3.32- Wind speed within compact midrise settlement at 0-degree wind direction. (Vertical) ...... 69 Figure 3.33- PMC within compact midrise settlement at 10:05am and 315-degree wind direction. (Horizontal) ...... 70 Figure 3.34- PMC within compact midrise settlement at 10:05am and 315-degree wind direction. (Vertical) ...... 70 Figure 3.35- PMC within compact midrise settlement at 10:25am and 315-degree wind direction. (Horizontal) ...... 71 Figure 3.36- PMC within compact midrise settlement at 10:25am and 315-degree wind direction. (Vertical) ...... 71 Figure 3.37- PMC within compact midrise settlement at 10:45am and 315-degree wind direction. (Horizontal) ...... 72

viii

Figure 3.38- PMC within compact midrise settlement at 10:45am and 315-degree wind direction. (Vertical) ...... 72 Figure 3.39- Wind speed within compact midrise settlement at 315-degree wind direction (Horizontal) ...... 73 Figure 3.40- Wind speed within compact midrise settlement at 315-degree wind direction (Vertical) ...... 73 Figure 3.41- Compact Highrise settlement, Sendai, Japan...... 76 Figure 3.42- Pollution Dispersion in High Rise Compact 0 degree (Horizontal) ...... 76 Figure 3.43- Pollution Dispersion in High Rise Compact 315 degree (Vertical) ...... 77 Figure 3.44- Pollution Dispersion in High Rise Compact 315 degree (Horizontal) ...... 77 Figure 3.45- Pollution Dispersion in High Rise Compact 0 degree (Vertical) ...... 77 Figure 3.46- Pollution Dispersion in High Rise Compact 0 degree (Plan) ...... 78 Figure 3.47- Pollution Dispersion in High Rise Compact 0 degree (Section) ...... 78 Figure 3.48- Compact Mid-Rise Settlement, Rome...... 79 Figure 3.49- Pollution Dispersion in Compact Mid Rise 315 degree (Horizontal) ...... 79 Figure 3.50- Pollution Dispersion in Compact Mid Rise 0 degree (Horizontal) ...... 80 Figure 3.51- Pollution Dispersion in Compact Mid Rise 0 degree (Vertical) ...... 80 Figure 3.52- Pollution Dispersion in Compact Mid Rise 315 degree (Vertical) ...... 80 Figure 3.53- Pollution Dispersion in Compact Mid Rise (Plan) ...... 81 Figure 3.54- Pollution Dispersion in Compact Mid Rise (Section) ...... 81 Figure 3.55- Compact Low-Rise settlement, Barcelona, Spain...... 82 Figure 3.56- Pollution Dispersion in Compact Low Rise 0 degree (Horizontal) ...... 83 Figure 3.57- Pollution Dispersion in Compact Low Rise 315 degree (Horizontal) ...... 83 Figure 3.58- Pollution Dispersion in Compact Low Rise 0 degree (Vertical) ...... 83 Figure 3.59- Pollution Dispersion in Compact Low Rise 315 degree (Vertical) ...... 84 Figure 3.60- Pollution Dispersion in Compact Low Rise ...... 84 Figure 3.61- Pollution Dispersion in Compact Low Rise (Section) ...... 84 Figure 3.62- Compact midrise + Open High-Rise Settlement, Kowloon, Hongkong...... 85 Figure 3.63- Pollution Dispersion in Compact Mid+ Open High Rise 315 degree (Horizontal) . 86 Figure 3.64- Pollution Dispersion in Compact Mid+ Open High Rise 0 degree (Horizontal) ..... 86 Figure 3.65- Pollution Dispersion in Compact Mid+ Open High Rise 0 degree (Vertical) ...... 86

Figure 3.66- Pollution Dispersion in in Compact Mid+ Open High Rise 315 degree (Vertical) . 87 Figure 3.67- Pollution Dispersion in in Compact Mid+ Open High Rise ...... 87 Figure 3.68- Pollution Dispersion in in Compact Mid+ Open High Rise 0 degree (Section) ...... 87 Figure 3.69- Open High-Rise Settlement, Manhattan, New York, ...... 88 Figure 3.70- Pollution Dispersion in Open High Rise 315 degree (Horizontal) ...... 89 Figure 3.71- Pollution Dispersion in Open High Rise 0 degree (Horizontal) ...... 89 Figure 3.72- Pollution Dispersion in Open High Rise 0 degree (Vertical) ...... 89 Figure 3.73- Pollution Dispersion in Open High Rise 315 degree (Vertical) ...... 90 Figure 3.74- Pollution Dispersion in Open High-Rise Settlement (Section) ...... 90 Figure 3.75- Pollution Dispersion in Open High-Rise Settlement ...... 90 Figure 3.75- Open Mid-Rise settlement, Frankfurt, Germany...... 91 Figure 3.76- Pollution Dispersion in Open Mid Rise 0 degree (Horizontal) ...... 92 Figure 3.77- Pollution Dispersion in Open Mid Rise 315 degree (Horizontal) ...... 92 Figure 3.78- Pollution Dispersion in Open Mid Rise 0 degree (Vertical) ...... 92 Figure 3.79- Pollution Dispersion in Open Mid Rise 315 degree (Vertical) ...... 93 Figure 3.80- Pollution Dispersion in Open Mid-Rise Settlement ...... 93 Figure 3.81- Pollution Dispersion in Open Mid-Rise Settlement (Section) ...... 93 Figure 3.82- Open Low Rise, New Zealand...... 94 Figure 3.83- Pollution Dispersion in Open Low Rise 0 degree (Horizontal) ...... 95 Figure 3.84- Pollution Dispersion in Open Low Rise 0 degree (Vertical) ...... 95 Figure 3.85- Pollution Dispersion in Open Low Rise 315 degree (Horizontal) ...... 95 Figure 3.86- Pollution Dispersion in Open Low Rise 315 degree (Vertical) ...... 96 Figure 3.87- Pollution Dispersion in Open Low-Rise Settlement (Section) ...... 96 Figure 3.88- Pollution Dispersion in Open Low-Rise Settlement ...... 96 Figure 3.89- Large Low-Rise Settlement, Christchurch, New Zealand...... 97 Figure 3.90- Pollution Dispersion in Large Low Rise 315 degree (Horizontal) ...... 98 Figure 3.91- Pollution Dispersion in Large Low Rise 0 degree (Vertical) ...... 98 Figure 3.92- Pollution Dispersion in Large Low Rise 0 degree (Horizontal) ...... 98 Figure 3.93- Pollution Dispersion in Large Low Rise 315 degree (Vertical) ...... 99 Figure 3.94- Pollution Dispersion in Large Low-Rise Settlement ...... 99 Figure 3.95- Pollution Dispersion in Large Low-Rise Settlement (Section) ...... 99

Figure 3.96- Sparsely Built Settlement, France. Source: ©2018 Google ...... 100 Figure 3.97- Pollution Dispersion in Sparsely Built 0 degree (Horizontal) ...... 101 Figure 3.99- Pollution Dispersion in Sparsely Built 0 degree (Vertical) ...... 101 Figure 3.98- Pollution Dispersion in Sparsely Built 315 degree (Horizontal) ...... 101 Figure 3.100- Pollution Dispersion in Sparsely Built 315 degree (Vertical) ...... 102 Figure 3.101- Pollution Dispersion in Sparsely Built Settlement (Section) ...... 102 Figure 3.102- Pollution Dispersion in Sparsely Built Settlement...... 102 Figure 3.103- Low Rise with Dense Trees, London...... 103 Figure 3.104- Pollution Dispersion in Low Rise with Dense Trees 0 degree (Horizontal) ...... 104 Figure 3.105- Pollution Dispersion in Low Rise with Dense Trees 0 degree (Vertical) ...... 104 Figure 3.106- Pollution Dispersion in Low Rise with Dense Trees 315 degree (Horizontal) .... 104 Figure 3.107- Pollution Dispersion in Low Rise with Dense Trees 315 degree (Vertical) ...... 105 Figure 3.108- Pollution Dispersion in Low Rise with Dense Settlement (Section) ...... 105 Figure 3.109- Pollution Dispersion in Low Rise with Dense Settlement ...... 105 Figure 4.1- Formation of Skimming regime in a Compact Midrise Settlement ...... 108 Figure 4.2- Formation of wake interface regime in Open High-Rise settlement ...... 109 Figure 4.3- Formation of wake interface regime in Open Low-Rise settlement ...... 109 Figure 4.3- Formation of isolated roughness regime in open low-rise settlement...... 109 Figure 4.4- Pollution concentration at vertical 1m, 15m, 30m and 45m form the ground surface at wind direction 0 and 315 degree ...... 110 Figure 4.5- Pollution concentration at horizontal 20m, 50m and 100m form the source at wind direction 0 and 315 degree ...... 111 Figure 4.6- Pollution concentration at vertical 1m, 15m, 30m and 45m form the ground surface at wind direction 0 and 315 degree ...... 112 Figure 4.7- Effects of trees in open Highrise settlement ...... 114 Figure 4.8- Effects of trees in open Highrise settlement ...... 114 Figure 4.9- Effects of trees in open Highrise settlement ...... 114 Figure A1- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 143 Figure A2- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 143

Figure A3- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:45am (Plan) ...... 144 Figure A4- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:25am (Plan) ...... 144 Figure A5- Wind Speed in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 145 Figure A6- Wind Speed in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 145 Figure A7- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 146 Figure A8- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 146 Figure A9- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:25am (Plan) ...... 147 Figure A10- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:45am (Plan) ...... 147 Figure A11- Wind Speed in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 148 Figure A12- Wind Speed in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 148 Figure A13- Pollution Dispersion in Compact Midrise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 149 Figure A14- Pollution Dispersion in Compact Midrise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 149 Figure A15- Pollution Dispersion in Compact Midrise Settlement at 0 degree Wind direction at 10:45am (Plan) ...... 150 Figure A15- Pollution Dispersion in Compact Midrise Settlement at 0 degree Wind direction at 10:25am (Plan) ...... 150 Figure A17- Wind Speed in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 151

xii

Figure A18- Wind Speed in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 151 Figure A19- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 152 Figure A20- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 152 Figure A21- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:45am (Plan) ...... 153 Figure A22- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:25am (Plan) ...... 153 Figure A23- Wind Speed in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 154 Figure A24- Wind Speed in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 154 Figure A25- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 155 Figure A31- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 158 Figure A32- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 158 Figure A33- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan) ...... 159 Figure A34- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan) ...... 159 Figure A35- Wind Speed in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 160 Figure A36- Wind Speed in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 160 Figure A37- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan) ...... 161

xiii

Figure A38- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Section) ...... 161 Figure A39- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan) ...... 162 Figure A40- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:45am (Plan) ...... 162 Figure A41- Wind Speed in Compact midrise + Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 163 Figure A42- Wind Speed in Compact midrise + Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 163 Figure A43- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 164 Figure A44- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 164 Figure A45- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:25am (Plan) ...... 165 Figure A46- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:45am (Plan) ...... 165 Figure A47- Wind Speed in Compact midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 166 Figure A48- Wind Speed in Compact midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 166 Figure A49- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 167 Figure A50- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 167 Figure A51- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan) ...... 168 Figure A52- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:45am (Plan) ...... 168

xiv

Figure A53- Wind Speed in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 169 Figure A54- Wind Speed in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 169 Figure A55- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 170 Figure A56- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 170 Figure A57- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:25am (Plan) ...... 171 Figure A58- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:45am (Plan) ...... 171 Figure A59- Wind Speed in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 172 Figure A60- Wind Speed in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 172 Figure A61- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 173 Figure A62- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 173 Figure A63 Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:25am (Plan) ...... 174 Figure A64- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:45am (Plan) ...... 174 Figure A65- Wind Speed in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 175 Figure A66- Wind Speed in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 175 Figure A67- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 176

Figure A68- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 176 Figure A69- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:25am (Plan) ...... 177 Figure A70- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:45am (Plan) ...... 177 Figure A71- Wind Speed in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 178 Figure A72- Wind Speed in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 178 Figure A73- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 179 Figure A74- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 179 Figure A75- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan) ...... 180 Figure A76- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan) ...... 180 Figure A77- Wind Speed in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 181 Figure A78- Wind Speed in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 181 Figure A79- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 182 Figure A80- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 182 Figure A82- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan) ...... 183 Figure A81- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan) ...... 183

xvi

Figure A83- Wind Speed in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 184 Figure A84- Wind Speed in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 184 Figure A85- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 185 Figure A86- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 185 Figure A87- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan) ...... 186 Figure A88- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan) ...... 186 Figure A89- Wind Speed in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 187 Figure A90- Wind Speed in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Section) ...... 187 Figure A91- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 188 Figure A92- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 188 Figure A93- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan) ...... 189 Figure A94- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan) ...... 189 Figure A95- Wind Speed in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 190 Figure A96- Wind Speed in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Section) ...... 190 Figure A97- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 191

xvii

Figure A98- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Section) ...... 191 Figure A99- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:25am (Plan) ...... 192 Figure A100- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:45am (Plan) ...... 192 Figure A101- Wind Speed in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Plan) ...... 193 Figure A102- Wind Speed in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Section) ...... 193 Figure A103- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 194 Figure A104- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Section) ...... 194 Figure A105- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:25am (Plan) ...... 195 Figure A106- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:45am (Plan) ...... 195 Figure A107- Wind Speed in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Plan) ...... 196 Figure A108- Wind Speed in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Section) ...... 196 Figure A109- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Plan) ...... 197 Figure A110- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Section) ...... 197 Figure A111- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:25am (Plan) ...... 198 Figure A112- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:45am (Plan) ...... 198

xviii

Figure A113- Wind Speed in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Plan) ...... 199 Figure A114- Wind Speed in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Section) ...... 199 Figure A115- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Plan) ...... 200 Figure A116- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Section) ...... 200 Figure A117- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:25am (Plan) ...... 201 Figure A118- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:45am (Plan) ...... 201 Figure A119- Wind Speed in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Plan) ...... 202 Figure A120- Wind Speed in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Section) ...... 202 Figure B1- Types of air quality models ...... 204 Figure C1- Schematic of basic model layout...... 207

xix

LIST OF TABLES

Table 2.1- History of NAAQS Standard for PM (1971-2006) ...... 13 Table 2.2- Current PM regulations by EPA ...... 14 Table 2.3- PM Standards around world ...... 15 Table 2.4- Summary of past studies showing PM2.5 and PM2.5 concentration after different building demolition activities ...... 27 Table 2.5- Outdoor PM10 Concentration at different sampling locations pre and post implosion...... 31 Table 2.6- Various LCZ classifications ...... 36 Table 3.1- Input parameters for the test simulation for ENVI-met ver 4.3.1 ...... 49 Table 3.3- PMC and WS measurements at 3 different Vertical locations at different times and wind directions in Compact Highrise Settlement ...... 74 Table 3.4- PMC and WS measurements at 3 different horizontal locations at different times and wind directions in Compact Highrise Settlement ...... 74 Table 4.1- Pollution dispersion pattern in different neighborhood morphologies ...... 115 Table 4.2- Sensitivity of an area to human health in horizontal distance from source...... 117 Table 4.3- Sensitivity of an area to human health at progressive vertical distances from source...... 118

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. Adil Sharag-Eldin, for his invaluable patience, guidance and support. I am grateful to him for sharing his extensive knowledge on the field of atmospheric sciences and phenomenon resulting in the advancement of my research approach. I would like to extend my gratitude towards my committee members Dr. Christopher J. Woolverton,

Dr. Elwin C. Robison, and Dr. Rui Liu for their helpful comments and suggestions.

I want to thank my family, friends, and peers for encouraging me during this time. I know I could not have accomplished this without their support.

Thank you everyone!

xxi

CHAPTER 1: INTRODUCTION

1.1. Background Demolition of a building generates considerable amount of dust cloud and demolition waste comprising of Particulate Matter (PM), including PM10 (≤10 μm), PM2.5 (≤2.5 μm) and

PM1 (≤1 μm), and airborne ultrafine particles (≤0.1 μm) (Rui et al., 2008; Kumar et al., 2012;

Cheng 2014; Singh et al., 2014). Such activities have the capability of substantial temporary increase in the local concentration levels of coarse and fine particulate matter. These high levels of PM can adversely affect public health depending upon the size and composition of the particulate matter (Beck et al. 2003; Stefani et al. 2005; IAQM, 2014; Azarmi et al., 2015). A demolition of a building may either be planned or unplanned. Examples of planned demolition primarily include implosion, wrecking balls and high reach arm. Meanwhile, unplanned demolition is a phenomenon where structures collapse creating a dense dust cloud without prior preventive measures to reduce the pollution levels in the surrounding area. Such unplanned demolitions are either the results of natural event (earthquake and dust storms) or human made as in case of structure failure, terrorist attack and wars.

The dust plume generated during the collapse of 605,254 buildings during the Nepal earthquake (Benfield, 2015) and the collapse of World Trade Center (WTC), New York caused by the 9/11/2001 terrorist attack are the examples of unplanned demolition. The impact of unplanned demolition is listed by several studies carried with the 9/11 victims after the collapse of World Trade Center. About 60,000 to 70,000 responders and volunteers were exposed to the dense cloud of highly polluted dust engulfing Manhattan and parts of Brooklyn, New York.

Many of them are reported to have been affected by serious and chronic respiratory ailments, including a variety of cancers, asthma, interstitial lung disease, asbestosis, chronic sinusitis, chronic rhinitis and sleep apnea (Banauch et al., 2003; Mendelson et al., 2007; Maslow et. al.,

2010). Studies dating back to second world war have pointed out the relationship between dust from an unplanned event and respiratory illnesses. The study by (Uchimaya, 2013) shows that soon after World War II, many of the U.S. military personnel and their families stationed in

Yokohama City experienced high occurrence of asthma. These asthmas were reported to be caused by air pollution due to dust and smoke during the war. Meanwhile, 30% of the 594 victims developed upper respiratory tract infection after the earthquake in El Salvador in 2001

(Woersching and Snyder, 2004) and 14% experienced respiratory illnesses after the Bam earthquake in 2003.

An unplanned demolition causes long-range dispersion of various-sized hazardous

Particulate Matter (PM), depending upon the intensity of the disaster. There is substantial epidemiological and toxicological evidence showing the effects of various sizes of PM on human health and environment (Vineis et al., 2004; Turner et al., 2011; Lim et al., 2012). However, far less attention has been paid to associated emissions and dispersion of particulate matter resulting from an unplanned building demolition. In fact, today we remain largely unaware of the characteristics of this massive dust dispersion process on its surrounding. Previous research has pointed out that pollution dispersion patterns are highly dependent on urban morphologies

(Priyadarsini et al., 2005; Pan et al., 2009; Maignant et al., 2011; Tiwary et al., 2011; Edussuriya et al, 2014). However, the outcomes were tested for comparatively lower street level traffic pollution. The dispersion pattern of high emission and sudden high concentrated pollution levels caused due to unplanned demolition on different urban morphologies are still unknown.

1.2. Justification for Research PM generated during an unplanned demolition might be short-lived but is a thousand-fold higher than the PM emissions from other sources, such as transportation. There are limited studies that measure the ambient fine and ultrafine PM emissions of the physical demolition and construction processes (Hansen et. al 2008; Kumar et. al 2012; Azarmi et. al 2014) as compared to those vehicle and machinery related pollution (Frank and Engelke 2005; Levy et al. 2010;

Lindgren 2010). Meanwhile, the intense, short-term bursts of pollutants and public exposure to these highly concentrated pollution levels during an unplanned demolition are widely overlooked because unplanned demolition is an unexpected event, and upon its arrival, the primary attention goes to the victims of building collapse and saving those who are trapped. The multitudes who are exposed to high concentration of air pollutants receive little or no medical attention and are almost forgotten. The deficiency of the study in this area indicate that there might be a presence of “unintended release” of ultrafine particles from building demolition activities (Kumar et. al

2012).

During the studies of planned building demolitions, such as implosions, the majority of the fine particles were PM10 and PM2.5 size range and the bulk of the particle mass concentration were occupied by particles of size greater than 100µm (Beck et al., 2003, Stefani et al., 2005).

These implosions were planned ahead, where the implosion site was fenced, hazardous materials such as asbestos and lead were removed beforehand to reduce the pollution emission, public access was restricted for 30 minutes following the implosion. Also, the adjacent buildings were draped with heavy gauze plastic or woven vinyl. However, during unplanned demolition, such preventive measures are not available. Since the dust plume is unrestricted, these unplanned demolitions have the ability to produce higher levels of pollution compared to planned ones.

Also, they can disperse to longer distances affecting the air quality and increasing the health risk

3 to the entire neighborhood where the demolition takes place. The respiratory health conditions of survivors and responders of 9/11, years after the collapse of WTC, makes it clear that effects of unplanned demolition do not end with the disaster itself (Banauch et al., 2003; Mendelson et al.,

2007; Maslow et. al., 2010). For some of the exposed population, inhaled PM stays in the victim’s body causing several health issues over a period of days or years. Despite the potential of exposure and public health threat, little has been done to assess the airborne particle hazard associated with unplanned building demolition.

Assessing air pollution dispersion behavior in a settlement due to unplanned demolition is important as there is a clear need to fully understand the relationship between the intense, short- term bursts of the pollution, and public exposure to such highly concentrated pollution. Even though there are numerous methods to study the impact of traffic on air pollution, similar studies on construction and demolition-related behavior is yet to be established (Azarmi et al. 2014).

Therefore, the focus of this thesis remains mainly on assessing the pollution dispersion patterns generated from a high emission source in different neighborhood morphologies. It also provides a detailed assessment of pollution dispersion pattern in different types of neighborhood morphologies, with respect to the required parameters which assist in identifying the vulnerable neighborhood if an unplanned demolition takes place.

1.3. Research Approach: Existing literature shows several methods for monitoring air pollutant concentration and pollution assessment methods. Traditionally, fixed monitoring stations built by government authorities or environmental agencies were used to measure the pollution levels and long-term pollution estimations (Xie, Semanjski, Gautama, & Tsiligianni, 2017). However, the measurements from fixed stations lack the spatial resolution of the dispersal. With the

4 advancement in technologies, low-cost portable air pollution monitoring devices are becoming highly popular. Numerous studies show the use of such mobile sensors to measure the pollution concentrations in ambient air as being more effective in mapping a pollutant dispersion pattern

(Kumar et al., 2015; Farrell et al., 2015). Such portable monitors can be mounted in vehicles, bags, bicycles, traffic police officers and trams around the selected sampling locations, to obtain pollution data with timestamps and GPS coordinates (Wallace et al., 2009; Wang et al., 2009;

Zwack et al., 2011; Kingham et al., 2013; MacNaughton et al., 2014; Shi et al., 2016; Shirai et al., 2016). The acquired measurements from the mobile sensors though concentrated on a particular “hotspot”, provide high spatial and temporal variation compared to the fixed monitoring system.

Long term pollutant concentration estimation and human exposure can be assessed with various techniques developed over time such as geostatistical techniques, linear regression,

Gaussian models, and artificial intelligence to compressed sensing. Spatial interpolation, land-use regression models and dispersion model are three methods that are generally used in existing studies. The air pollution dispersion models are adopted for this study to estimate pollution levels, dispersion patterns, and public exposure. With the advancement of computational capabilities of personal computers, dispersion models can be conducted on different spatial and temporal scales (Sivacumar et al., 2001; Vardoulakis et al., 2003).

Dispersion models are cost-efficient, allow shorter sampling periods, and represent a lower investment. Such dispersion models are widely used by researchers and policy makers to test future projects, plans, regulations, and their feasibility. Gaussian-based dispersion models such as CALINE3 and CALINE4 are commonly used in the study of traffic pollution distribution

(Benson et al., 1979, 1984). AERMOD is a regulatory near-field steady state Gaussian plume

5 model used by the U.S. Environmental Protection Agency (EPA) to model particle distribution

(Cimorelli, 2005). Other than the Gaussian, the steady-state box models such as- VITO proposed by The Flemish institute for technological research (Mensink et al., 2003), Lagrangian-Eulerian

Model, EPISODE developed by The Norwegian Institute for Air Research (Walker et al., 1999;

Oftedal et al., 2008) and Eulerian Model, ENVI_met designed at University of Mainz, Germany

(Bruse, 2007; Simon et al., 2012; Wang et al., 2016) are some examples of pollution distribution models used globally to assess the air pollutant dispersion and human exposure. ENVI-met is used in this study because of its capability to provide high-resolution simulations of urban micro- climates, wind patterns, encounters with vegetation and particulate matter dispersion. Appendix

B contains detailed descriptions of these types of modelling programs and techniques.

Sources of emissions and emission rates are equally important for an air pollution dispersion model to simulate the distribution of atmospheric pollutants (Soulhac et al., 2001;

Britter and Hanna, 2003). The author could not locate the existing study providing the real-time pollution emission from an unplanned demolition. It is difficult to monitor the real-time emission data due to unplanned demolition since the event is unexpected. Even the presence of regular fixed and mobile air quality monitoring stations near unplanned demolition might not be able to register accurate pollution data of such high-level concentrations, as they are configured to measure the daily pollution levels at comparatively lower levels. So, this thesis refers to planned demolition i.e. implosion studies to estimate the rate of emission as they are the closest phenomena available to have been studied. Implosion of a building is a strategically planned demolition, where explosives are used to collapse the building on itself, seeking minimum physical damage to its surrounding. Despite prior planning, studies show that the dust plume generated during building implosions causes short term high, air pollution levels on communities

6 where it is imploded (Beck et al. 2003; Stefani et al. 2005). During planned demolition studies, air pollution monitoring instruments are calibrated to measure the expected high-level pollution.

The sampling locations for concentrations are planned and placed at suitable distances, as per the meteorological conditions at the implosion site to get the best accuracy. The literature search has revealed very few published studies on pollution-related data collection from building implosion dust clouds which could support this thesis, due to the similarity of the impact.

This thesis presents the experimental design and results of a parametric study while discussing emission estimation results, pollution dispersion patterns in neighborhood morphologies, and recommendations for urban design to minimize public exposure. Parametric modelling is used in this study to model different types of neighborhood morphologies identified by Stewart and Oke. The automatic change in the feature of interlinked attributes is the standout amongst the most vital highlights of parametric modelling (Murena et al., 2009). Parametrics have particularly helped researchers to define most of the neighborhood arrangements, and not just specific instances within ENVI_met. Parametric models are validated with several empirical parameters often derived from experimental data. The validity of results obtained is limited to street geometries and dispersion conditions, like those for which the validation was carried out.

1.4. Research Objectives: The primary objectives of this thesis are:

1. To estimate pollution generation of unplanned demolition using existing data on planned

demolition.

2. To study the patterns and characteristics of air pollution dispersion due to an unplanned

building demolition in selected neighborhood morphologies caused by various physical

and meteorological parameters.

3. To identify the sensitivity of an area to the human health within the neighborhood by

estimating the radius of pollution dispersion.

4. To propose design recommendations for urban designers to help them identify suitable

planning approaches to minimize the impact of unplanned building demolition and air

pollution dispersion in vulnerable neighborhoods.

1.5. Scope of the Research: This thesis consists of five chapters.

Chapter 1 discusses the importance and motivation behind this research, introduces the objectives, and sets out the approaches taken to achieve those objectives.

Chapter 2 presents the background concepts of this thesis and presents a review of the existing knowledge of airborne PM and ultrafine particles relating to the sources of the particles and their impacts on the environment and on human health. This chapter describes the causes of unplanned building demolitions, considers case studies, the dispersion of pollutants in the neighborhood morphologies and the structure of air quality models.

Chapter 3 describes the research method used in this study. It also describes the process of emission estimation through model calibration and validation of the model by simulation. In addition, the data collected from different urban morphologies are visualized, explained and presented in this section.

Chapter 4 presents the discussion based on meteorological and morphological parameters obtained from the observation of results. Pollution dispersion pattern and radius in selected neighborhood morphologies are presented. Sensitivity of an area to human health is also identified in this chapter.

Chapter 5 proposes design recommendations suitable for urban planning approaches in vulnerable neighborhoods. It also reviews the stated objectives of research and an overall conclusion derived from the research. Limitations, delimitations of the research, and future work recommendations are listed.

CHAPTER 2: LITERATURE REVIEW

2.1. Introduction to Particulate Matter According to the EPA- “Particulate Matter, is a complex mixture of extremely small, including both organic and inorganic, particles that get into the air.” This particulate matter (PM) is categorized based on aerodynamic diameter. The aerodynamic diameter is “the diameter of a sphere, not necessarily sphere shape of density 1000 kg/m3 with the same settling velocity as the particle of interest”- (NOISH, 2011). The effect of PM on human health depends on the size of particles, their composition and the amount respired or inhaled. The smaller the size of particle is the more likely it is to travel deep into human respiratory system (Oberdorster, 2000; Donaldson et al., 2005). PM of 2.5-10 µm in diameter is more hazardous because of its light weight, which lets it to avoid gravity and stay in air for a prolonged period (Brown et al., 2013; Schulze et al.,

2017).

Figure 2.1 shows the comparison of PM10 with grains of beach sand and human hair. This figure provides a clear concept about PM size. The coarse PM is both inhalable and respirable

(US EPA, 2008; Brown et al., 2013). Total inhalable dust approximates to the fraction of airborne material which enters the nose and mouth during breathing and is therefore available for deposition in the respiratory tract. Respirable dust approximates that fraction which penetrates to the gas exchange region of the lung. All demolition sites generate high levels of dust (typically from concrete, cement, wood, stone, silica), defined as particulate matter, up to 75 µm diameter

(WHO, 1999). The dust concerning human health from construction and demolition works, is classified as PM10, PM2.5 and ultrafine PM according to recent studies.

Grain of fine Typical width of beach sand human hair 90µm 60µm PM10- 10 µm PM2.5- 2.5 µm Inhalable Respirable Soot, dust, Combustion pollen, mold particles, metals

Figure 2.1- Particulate Matter relative size comparison. Adapted from US EPA.

A review of the literature presents enough evidence to show building demolition activities are important sources of Particle Emission responsible for degradation of air quality in the microscale environments (Beck et al., 2003; Muleski et al., 2005; Dorevitch et al., 2006;

Hansen et al., 2008; Joseph et al., 2009; Font et al., 2014; Azarmi et al., 2015a). However, the particle emission inventory for different particle sizes, due to demolition of a building is missing in the literature and standard regulations. The focus of this thesis is limited to coarse PM (2.5-10

µm in diameter) arising from building demolition activities, both due to study length constraints and the lack of published data on concentration levels of smaller PM resulting from an implosion.

2.2. Standard Regulations for Particulate Matter: National Ambient Air Quality Standards (NAAQS), are the standards for toxic pollutants.

It was founded by United States Environmental Protection Agency (EPA) under the Clean Air Act to evaluate and regulate the threshold levels of various pollutants from different sources. In 1971,

12 the ambient air quality standard for the Suspended Particulate Matter (SPM) with a diameter

≤10µm was established (table 2.2). SPM is categorised as two types according to their size. First is the fine particle with a diameter close to 1µm and another is coarse particulate matter, that are particles larger than fine particles. Research shows a correlation between fine particulates PM2.5 and the rise in lung cancer or cardiopulmonary disease related fatalities which is then supported by the similar studies conducted in different countries (Dockery et al.,1993). As a result, a new air quality standard for PM2.5 was established in 1997.

Table 2.1- History of NAAQS Standard for PM (1971-2006); Source- US EPA, 2008

In 2012, the revised standard for the Particulate Matter proposed two categories based on the effect on human health and environment (US EPA, 2012). The focus of primary standards is to protect human health, responding to suitable and safe air quality thresholds for the sensitive populations such as children, the elderly, and individuals suffering from respiratory diseases.

Secondary Standards aims to protect the environment from being exposed to unsafe levels of pollutants and maintain public welfare. Table 2.2 shows the current standards for pollutant levels

3 in the US. The 24-hour primary and secondary standards for fine particles (PM2.5) is 35µg/m based on the 3-year average of the 98th percentile of 24-hour PM2.5 concentrations at each population-oriented monitor within an area. Meanwhile the annual standards for primary PM2.5 is set at 12 µg/m3 and secondary is set at 15 µg/m3 based on the 3-year average of the annual arithmetic mean PM2.5 concentrations, spatially averaged across an area. For both primary and

3 secondary (PM10) the 24 hours standard is set at 150 µg/m with 1-expected-exceedance averaged over 3 years at each monitor within an area (US EPA, 2016).

Table 2.2- Current PM regulations. Source: US EPA, 2016

Internationally, countries around the world have also developed their own air quality standards

(table 2.3). Difference in air quality standards between the countries is due to potentially lack of long-term evidence to support a safe level of exposure for human health and environment (Kim

14 et al., 2015). Also, the criteria for air quality standards are defined based on the geographical location, difference in weather conditions, population density, and economic progress of the countries, which makes the air quality standard vary in different countries.

Table 2.3- PM Standards around world. Source: Kim et al., 2015

During unplanned demolition the pollution increases dramatically by thousands, but it only lasts for few minutes to an hour until it settles. Therefore, the dust plume generated barely violates the EPAs air quality standard for daily and annual average. For example, the PM10 concentration levels were increased by more than 3000 folds during a 22-storey building implosion in Baltimore, MD. The concentration levels at one of the sampling locations peaked at

54000 µg/m3 for 40 minutes (Beck et al., 2003). However, PM10 levels on the day of the implosion did not exceed the National Ambient Air Quality Standard (NAAQS) for PM10 of

150µg/m3. Similarly, the collapse of the New York WTC twin tower resulted in the highest particulate count that Manhattan has ever recorded in the history (Francesca Lyman, 2003). The tragic event violated no pollution standards (EPA, 2001), this is because the air quality regulations are set up to measure particulate matter loadings over 24-hour periods rather than intense, short-term bursts. Meanwhile the survivors of the 9/11 incident facing respiratory and cardiovascular illnesses are only increasing (WTCHP, 2017). Even though it is difficult to make standards governing emissions in unplanned demolitions, the thesis attempts to provide the best estimates of the effect of short-term exposure to high pollution concentration on health hazards to the global policy makers.

2.3. Health Impact resulting from short term exposure to high-level PM The demolition process generates fugitive dust particles which are diverse in size and composition, leading to both minor and chronic health issues (US EPA, 2001; Greater London

Authority, 2014). The minor health problems can be discomfort to eyes, irritation in nose, mouth, respiratory tract and skin while the chronic health problems can be as serious as cancer,

16 premature death, and heart diseases (Brook et al. 2010; IAQM, 2014). Particulate matter of sizes less than 10 microns are invisible to human eyes and may not appear as an obvious hazard.

However, due to their lightweight they tend to travel long distances affecting the health of both residents close to the source and to individuals living in nearby neighborhood (Xie et al., 2017).

Figure 2.1 explains how different size of particulate matter responds upon inhaled by the human.

They penetrate much further into the airways, down to the alveoli in the deep lung areas. Health effects from particles and fibres interacting with certain materials are immediate while those from other types of materials may take many years to develop. It is therefore essential that exposure to all forms and sizes of particle pollution is kept to a minimum for all populations and especially for those who already suffer from cardio-vascular and respiratory difficulties.

Particles 10 microns and larger Nasal Passage

Dust Particles inhaled by the victims in a dust cloud during implosion Trachea Particles 5-10 microns

Bronchi

Smallest cavities of Particles less than 5 lung (the Alveoli) microns

Figure 2.2- Effects of different size PM in human body. Adapted from: CCOHS

It is visually evident that the huge dust cloud is generated from the building collapse during an unplanned demolition. The extent of the hazard will depend on several factors, including composition of this dust cloud, i.e., the building material used to construct the building, fraction carried by the air, and the fraction of ambient particles inhaled. Meanwhile,

(Kumar et al., 2012) has identified three categories for the exposure to construction related air pollutants: (1) people on site, (2) people passing by a construction site and (3) people occupying nearby buildings. Impact on human health depends on the exposure intensity which is determined by the proximity to the source of emission, wind flow and exposure duration.

The relationship between short term exposure to high ambient particle pollution concentrations and adverse respiratory effects are clearly demonstrated in numerous studies conducted after the WTC collapse in 2001 (Lindsted et al., 1996; Checkoway and Eisen,

1998; Fireman et al. 2004; Jordan et al., 2011; Wisnivesky et al., 2011; Zeig-Owens et al., 2011;

Solan et al., 2013; Stein et al., 2016). Coarse particulate matter (2.5-10 µm in diameter) has been associated with several acute and chronic respiratory illnesses and premature mortality (Levy et. al 2010; Laden et al. 2006). The research carried out by the American Heart Association has

showed that exposure to PM2.5 over a few hours to weeks can trigger. cardiovascular disease-

related mortality. and nonfatal events (Brook et al., 2010). In addition, the same research by

Brook shows the decrease in cardiovascular mortality with the reductions in PM levels.

Research which studied 27,449 people who inhaled harmful dust while engaged in rescue and recovery operations during and after the 9/11 terrorist attacks in the U.S reported a high rate of abnormal respiratory function, with 3/4 having low forced vital capacity (FVC) (Wisnivesky et al., 2011). FVC is a crucial measurement of pulmonary function used to assess various lung illnesses. Examination of WTC dust has uncovered the presence of particles small enough to

18 infiltrate deep into the lungs of survivors, recovery workers, volunteers and residents reaching distal airways and alveoli (Lioy et al., 2002; Gavett, 2003; McGee et al., 2003; Lioy and

Georgopoulos, 2006).The matter began to come to attention of scientists and researchers when one of the survivors from the WTC incident died barely five weeks later from sarcoidosis, an immune disorder caused by highly concentrated toxic exposure to WTC dust. In addition to that,

9/11 cancer victims’ rate has increased by 3800% within just five years from the number being

249 known cases in 2013 to 10,000 known cases as of 2018 (WTCHP, 2018).

Studies dating back to World War II have shown a relationship between the dust generated by unplanned building demolition and the increasing respiratory illnesses in the exposed people. U.S. military personnel and their families stationed in Yokohama City during

World War II, were observed to develop high occurrence of asthma (Uchimaya, 2013). These asthmas were reported to be caused by air pollution due to dust and smoke during war

(Uchimaya, 2013). Meanwhile, 30% of 594 victims developed upper respiratory tract infection after the earth quake in El Salvador in 2001 and 14% experienced respiratory illnesses after the

Bam earthquake in 2003 (Woersching and Snyder, 2004). While, it is understood through the animal and controlled human exposure studies that the particles deposited in the respiratory tract in sufficient amounts in a short time can cause respiratory related illnesses (Sunil et al., 2017), t the fraction of ambient particles that are mainly responsible for the observed health effects is still a matter of controversy. The extent of pulmonary inflammation can be different in each individual and it depends on particle dose, composition, and type (U.S. EPA, 2009). The study conducted by Kloog (Kloog et al., 2013) showed that for short-term exposure, the PM related

3 mortality was increased by 2.8% with every 10 μg/m increase in PM2.5 exposure, which means exposure to a highly concentrated dust plume during a building collapse increases the risk of

19 cancer by a thousand-fold. Other studies suggested that inhalation of dust and other pollutants can give rise to the respiratory and other long-term effects (Guidotti et al., 2011; Wisnivesky et al., 2011; Berger et al., 2013). which once again proves the importance of preventing transient high concentrated dust inhalation.

2.3.1 Case study of Unplanned Building Demolition: World Trade Center: The objective of this case study is to document the short and long-term impact on health of residents in surrounding neighborhoods and exposed population during the collapse of World

Trade Center on 09/11/2001, one of the widely researched examples of an unplanned building demolition. This study is a summary of systematic search and in-depth study of peer reviewed findings on exposure, and cancer resulting from the particulate matter (dust) generated by the collapse of the WTC, that have been published in a scientific and medical literature.

The collapse of 110 story twin towers produced massive dust plume comprising a complex and unique mixture of chemical agents (including particulates), to which large number of populations were exposed. According to the initial list of agents, the population around the disaster site of WTC were exposed to: Chrysotile asbestos, metals, polychlorinated polycyclic compounds, polycyclic aromatic hydrocarbons, volatile organic compounds, crystalline silica, fibrous glass, particulate matter (dust) and titrated water (COPC, 2003). However, the case study focuses only on the particulate matter (dust) and its related impacts.

2.3.1.1. Particulate matter generated and exposure: Cement and gypsum (Calcium Sulphate) wallboard were identified as the primary source for the majority of settled dust from the 424,000 tons of concrete used to build the twin towers.

(Herdt-Losavio, 2008). The settled dust comprised of various size of the particulate matter, however, 95% of the mass was in particles > 53µm in diameter (Lioy et al. 2002). High alkaline

(pH 9.0- 11.0) properties were observed in the coarse particulate matter of size 10-53 µm

(Landrigan, 2004). Such particulate matter when inhaled are settled in the nose and upper airways of human body. The deposited PM2.5 was measured to be approximately 11,000 tons

(Landrigan et al. 2004. During October 2001 and April 2002, NIOSH carried out the study on 54 truck drivers collecting the debris at WTC site, to measure the personal exposure to the particulate matter. The result showed maximum exposure during the month of October was recorded at 1700 µm/m3. For the month of April, 195 µm/m3 was the highest exposure while the median exposure was 144 µm/m3 (Geyh et al. 2005).

Individuals caught in the initial dust and smoke cloud that encompassed Lower

Manhattan during and after the destruction of the twin tower on 9/11 were exposed from 4–8

3 hours to high levels of airborne PM2.5, that is, thousands of μg/m (EPA, 2002). Amid the initial few days after the catastrophe, PM2.5 levels at the WTC perimeter exceeded the EPA daily

3 National Ambient Air Quality Standard for PM2.5 of 65 μg/m for 24-hr. High PM2.5 concentration levels near WTC Ground Zero area even after a month from twin tower collapse is assumed to have increased the risks of chronic health effects for the most highly exposed individuals. While, PM2.5 concentration over Lower Manhattan had come back to a great extent to regular levels of NYC and different US urban territories by mid to late October, with just a couple of WTC or close-by locales incidentally drawing closer or surpassing the air quality level of concern.

2.3.1.2. Radius of pollution dispersion: When the World Trade Center was destroyed on 9/11/01, massive clouds of toxic dust spread throughout New York City, engulfing office buildings, parks, and residential communities

(Lioy et al., 2002). The dust cloud trailing from Lower Manhattan across Brooklyn and into

Long Island Sound demonstrated by the satellite image by the USGS provides clear insight about the extensive dispersion of asbestos fibres, crushed cement, lead, copper, fiberglass and other dangerous substances. Exposure zones (Figure 2.3) were established by the federal government based on the data from scientific studies that analysed the pervasiveness of 9/11 toxic dust throughout New York City. The demolished dust and construction material were transported, sorted, or stored in Fresh Kills Landfill in Staten Island and are also categorized to be part of the

Exposure Zones. The World Trade Center Health Program (WTCHP) utilizes a more extensive definition that envelops the region of Manhattan south of Houston Street and any square in

Brooklyn that is completely or halfway contained inside a 1.5-mile range of the previous World

Trade Center site (Howard, 2014).

BELOW HOUSTON STREET (SURVIVORS)

BELOW CANAL STREET

(RESPONDERS AND SURVIVORS)

(SURVIVORS)

1 MILE RADIUS FROM WTC INTO BROOKLYN

Figure 2.3- Exposure zone recognized by World Trade Center Health Program. (Adapted from WTCHP)

2.3.1.3. Health impacts: There is overwhelming epidemiologic evidence suggesting the development of chronic respiratory conditions, lung diseases, and cancers in the individuals working/living in Lower

Manhattan on and after 9/11 incident were caused by the dust generated during collapse of the

WTC. A report by the New York Fire Department, showed that out of 7000 FDNY Firefighters and EMTs receiving the treatment for 9/11 injuries or illnesses, 5,400 members were diagnosed with lower respiratory diseases such as asthma, chronic bronchitis, and less commonly emphysema, COPD, sarcoidosis or pulmonary fibrosis, 5,200 members were diagnosed with upper respiratory diseases such as chronic rhinosinusitis and/or vocal cord diseases, 5,400 members were diagnosed with gastroesophageal reflux disorders, and1,100 have developed a cancer caused by 9/11 toxins (FDNY, 2015). Of those, 44 have died despite access to treatment, where individual members may have been affected by more than one category of illness. The

9/11 cancer victims’ rate has increased by 3800% within just five years from the number being

249 known cases in 2013 to 10,000 known cases as of 2018 (Howard, 2014). Meanwhile,

NIOSH in 2015 predicted 35,000 new 9/11 related cancer diagnoses by 2016 to 2025.

The incidence rate of cancer among those exposed in the dust of WTC is higher by 21% than in the general U.S. population. According to The New York City Fire Department estimation the number of firefighters is at higher risk for cancer is as many as 9000. Figure 2.4 shows the number of victims certified and enrolled in WTCHP for various respiratory illnesses and cancer. The World Trade Center Health Registry has already identified a slight increase in prostate and thyroid cancer diagnoses, as well as certain blood cancers among rescue and recovery workers and volunteers who were intensely exposed to WTC Dust out-of-doors during the first few days after 9/11. Also, in residents and local workers, many of who were heavily exposed to WTC Dust from the initial dust clouds while evacuating, and subsequently from more

23 chronic exposures from resuspended dusts and fumes in the outside streets and from indoor exposures over extended periods of time.

25000 22206 19753 20000

15000 11324

10000 # of of # Members 7382 6792

5000 2992 2625 2879 2707 1665 845 288 0 Chronic Rhinosinusitis GERD Asthma Cancers Chronic Respiratory WTC- Exacerbated disorder Chronic Obstructive Pulmonary Disease (COPD) Responders Survivors

Figure 2.4- Different types of respiratory illness and cancer reported in WTCHP members. Source: WTCHP, 2017

Though cancer can take years or even decades to develop, there are several studies that find exposure to the dust at the site of WTC on 9/11/2001 and its following days is associated with the cancer. This incident provides with an idea of magnitude of the impact of an unplanned building demolition on human health. The short-term exposure to highly concentrated particles during an unplanned demolition are difficult to regulate. However, the timely identification of the exposed population to the specific level of concentration is crucial to minimize the severe health hazards. Providing the exposure map for different types of neighborhood is helpful as it generates an awareness in the residents, rescue workers and responders about the air quality they are being exposed to and the possible health problems.

2.4. Causes of Unplanned Demolition and Pollution Emission: This research identifies unplanned demolition as the sudden collapse of a built structure without necessary standard protective measures required to minimize the dust plume dispersion in the neighborhood air. An unplanned demolition can occur either as a result of natural or human-made causes. Causes of natural unplanned demolition includes the collapse of structures during natural disaster such as earthquake or construction failure due to internal and external reasons. Meanwhile, the human-made unplanned demolitions are caused by activities such as: terrorist attacks and building explosion in war zones. Earthquake is one of the most frequent natural disasters, especially in countries lying in the divergence, transform and convergence zone of the earth’s tectonic plate (Duarte et al., 2016). Occurrence of an earthquake brings down multiple buildings at the same time depending upon its intensity, resulting in huge dust plumes which might take from hours to days to settle. Earthquakes aren’t predictable in terms of when they strike, but some areas are more likely to be hit. Countries like Japan, Nepal, India, Ecuador,

Philippines, Pakistan, El Salvador, Mexico, Turkey and Indonesia are ranked on top ten as the countries most prone to Earthquake.

Earthquakes disproportionately have impacted developing countries more than the developed countries. The 1995 earthquake in Kobe, Japan, resulted in $160 billion economic loss

(Toshihisa Toyoda, 2008) while 2010 Haiti earthquake resulted in only $7 billion economic loss.

While at the same time fatalities after the Haiti quake were 20 times the Japanese fatalities from

Kobe. This difference in fatalities can be attributed to the poor building construction technique, high building and population density, and unmanaged urban planning of these economically developing countries. For example: the Nepal Earthquake in 2015, completely damaged 1,38,182 houses and partially damaged 1,22,694 other homes across Nepal. Meanwhile, the Sichuan earthquake China in 2008, which was higher in magnitude than the Nepal earthquake, destroyed

25 only over 7000 buildings. Meanwhile, literature review shows different images of dust plume generated from the built structure collapse and thick settled dust within its surrounding during both earthquakes.

Likewise, building collapse due to terrorist attacks and bombing also generates massive dust clouds. One of the most researched examples of humanmade unplanned demolition is the collapse of the WTC due to terrorist attack on 9/11/2001. The initial data collection and analyses by the United States Geological Survey (USGS) reported, “Six million ft2 of masonry, 5 million ft2 of painted surfaces, 7 million ft2 of flooring, 600,000 ft2 of window glass, 200 elevators, and everything inside cascaded as dust when the towers collapsed.” The dust plume generated from this unplanned demolition contained asbestos, metals, polychlorinated polycyclic compounds, polycyclic aromatic hydrocarbons, volatile organic compounds, crystalline silica, fibrous glass, particulate matter (dust), titrated water and many other fugitive chemicals/dusts in huge amount.

So, there is sufficient evidences suggesting unplanned building demolition activities are important sources of PM and degrade the surrounding air in the neighborhood where the incident takes place. However, due to the relative unpredictability of natural disasters and sparsity of data collection during and immediate after most of the disaster there is very little evidence of large collection of spatial and temporal PM data from the pollution plume dispersed during an unplanned building collapse.

2.6. Particulate Matter Emission from Planned Demolition: The thesis required the information about the PM distribution in the pollution plume as well as concentrations measured at different locations to understand the patterns and characteristics of air pollution dispersion. So, this thesis took the reference of existing planned demolition i.e. implosion studies to quantify the rate of emission. Although, there is limited

26 literature on implosions and related dust clouds itself. This section of study presents a summary of systematic search and in-depth study of peer reviewed findings on particulate matter concentration at different sampling locations from the building implosion site. Table 2.1 lists the relevant studies and the subsequent text briefly discusses some of these key articles. The discussion is followed by a case study of an implosion of a building at Baltimore, MD discussing the PM concentration at different downwind and upwind direction. This information was used in interpreting the data obtained from simulation of the parametric study.

Table 2.4- Summary of past studies showing PM2.5 and PM2.5 concentration after different building demolition activities PM Type Activity Where Source

Airborne Indoor and Outdoor, concentration of Seven-story building within Streifel et al. (1983) Building Implosion thermotolerant hospital complex fungi (Minneapolis, USA)

Indoor and Outdoor, Twenty two-story PM Building Implosion Beck et. al. (2003) 10 residential apartment complex (Baltimore, USA)

Outdoor, seven buildings PM and PM Building Implosion Stefani et al. (2005) 2.5 10 over 3 storey a hospital complex (Calgary, Canada)

Building implosions are an increasingly common method of planned building demolition around the world. Part of it can be attributed the requirement to meet new urban design guidelines and respond to demand from the adoption of new building technologies. With the increase in the rate of this practice the concern related to hazardous implosion dust are also increasing within air quality management committee and researchers. For example, Beck et al.

(2003) observed the PM10 levels at different downwind and upwind distances from the imploded building. PM10 concentrations were recorded for a series of time after the implosion of the

22-storey residential apartment complex. The result showed a dramatic increase in the background PM10 level by 3000-fold at one of the sampling locations. Meanwhile, at all other downwind sampling locations, PM10 levels were increased up to 8 times depending upon the distance from the source. The study also reports that the large implosion dust clouds dissipated in

20–30 min at around 1 km distance. The background pollution levels were at higher levels for 40 minutes. Meanwhile, in a study carried out on Calgary General Hospital implosion, PM10 and

PM2.5 the implosion-created dust cloud, traveled much further out to 20 km. The result also showed that the amount of coarse particulate matter (2.5-10 µm in diameter) generated from the implosion was five times larger than the fine particulate matter (2.5 microns in diameter). The

3 3 highest PM10 and PM2.5 value was recorded at 68,942 µg/m and 7363 µg/m , 8 minutes from the implosion. Likewise, a hospital was imploded in Minneapolis, USA, and researchers investigated the indoor and outdoor airborne fungal concentrations pre and post implosion. The results showed the increment on the outdoor concentrations up to 3 times at 60 m from the implosion site.

These three studies show noticeable increment of the concentration levels within the first few minutes of the implosion. The Baltimore building implosion study showed the dispersion to be up to 1 km while the pollutants in case of Calgary were dispersed up to 20 km away from the source. The difference in the dispersion radius can be attributed to the type of settlement as

Calgary Hospital was imploded in open settlement while the apartment building was imploded in compact settlement. Irrespective of the case, huge amount of coarse particulate matter was generated and thick dust coatings on nearby vehicles and buildings were reported. Since these

28 were all planned events, all friable and some nonfriable asbestos, glass windows were removed from the structure to reduce the amount of particles generation. Fence and curtains were provided in the nearby buildings to minimize the dust intrusion and dissipation at the longer distances. Therefore, it can be understood the amount of coarse PM from an unplanned building demolition of the same size buildings can be much more than the results in the planned studies discussed above. Also, the particulate matter can be dispersed at farther distances with no fence or curtains, which makes the population at wide range vulnerable. Because of the limited literature on unplanned building demolitions, these findings can be a useful assessing tool for understanding the impact of unplanned building demolition in its neighborhood.

2.7. Monitoring implosion pollution

2.7.1. Building implosion at Baltimore, MD: This research, even though being a planned building implosion, is chosen as a case study because it provides first hand data for PM10 concentration during the period while the demolition took place. The study was carried out by a researcher in Baltimore to assess the impact of the building implosion on its surrounding community. This 22-storey residential apartment building was imploded on 10:00 a.m. on Saturday, August 19, 2000. The study became even more crucial since the implosion site was one block away from the John Hopkins Hospital, Baltimore,

Maryland. A portable nephelometer was used to measure the PM10 concentration levels at the outdoor and indoor sampling locations. Figure 2.4 shows the location of the implosion site and seven outdoor sampling sites. The study measured the pollution levels at different distances in the upwind and downwind direction based on the meteorological condition of the implosion day.

H3 H1 H2 Source 150m

300m

600m C4

800m C5

C6 1200m

Figure 2.5- Implosion site showing the location of seven outdoor sampling sites. Adapted from - Beck et al. (2003)

2.7.2. Types of Pollution generated The results from this research was published in the Journal of the Air & Waste

Management Association and presents only the data about the particle concentration from PM10

(Beck et al., 2003).

2.7.3. Radius of Pollutants Dispersion Four indoor and seven outdoor sampling locations were selected on the basis of the meteorological condition of the implosion day to measure the PM10 concentration levels. The results showed that the pollution was dispersed as far as 1130m southeast in the downwind direction and 780m northeast in the upwind direction. While the research concludes that there was no measurable effect observed upwind of the implosion, the peak PM10 concentration levels in downwind direction were higher in the sample location close to the implosion site and

30 dropped significantly as the distance was increased. Table 2.2 presents the results for PM10 concentration at 7 outdoor sampling location where the PM10 levels varied with distance from

54,000–589 µg/m3 exceeding pre-implosion levels for sites 100 and 1130 m 3000 and 20 times, respectively.

Table 2.5- Outdoor PM10 Concentration at different sampling locations pre and post implosion. Source: Beck et al. (2003)

Air Sampling Location ID H1 H2 H3 C4 C5 C6 C7

Distance from implosion (m) 100 160 300 475 825 1130 780

Direction relative to implosion North Northeast Northeast Southeast Southeast Southeast Northeast

Background concentration (µg/m3) 17 (12) 20 (16) 21 (17) 18 (11) 13 (12) 29 (5) 27 (6) and standard deviation

Peak concentration (µg/m3) 54,000 605 36 5686 1578 589 42

Time to peak concentration (min) 1.3 5.3 No effect 2.75 5.2 7 No effect

Average concentration over peak 1524 185 No effect 420 528 98 No effect

Duration of peak (min) 40 14 No effect 38 7 12 No effect

Estimated 24-hr TWA 72 17 No effect 29 18 15 No effect

This case study shows planned building demolition creates almost the same magnitude of dust plume size, apart from the planned aspect where the dispersion distance is restricted.

Because of the unavailability of data related to PM concentration caused due to unplanned demolition, the PM concentration data collected during building implosion in this case study is used to quantify the amount of pollution emission. The data extraction process is further explained in chapter 3. The aim of the thesis is to model an air pollution dispersion pattern using the CFD algorithm. The CFD model requires hourly pollution emission rate data. So, this implosion study was helpful to calibrate air quality model in order to estimate the pollution emission for an unplanned building demolition.

2.8. Dispersion of air pollution in Urban Morphology: Air pollution dispersion is dependent on the processes in the atmosphere which are differentiated on spatial and temporal scales. These scales extend from macroscale (typical lengths >1,000km) to microscale (typical lengths <1km). In macroscale dispersion the air flow is largely related to synoptic phenomena whereas in microscale dispersion the atmospheric flow is very complex and depends strongly on the detailed surface features. This thesis focuses on simulating the dispersion of air pollution on the microscale. Local scale dispersion incidents are examples of microscale phenomena and appropriate simulation modelling tools can incorporate the dispersion scale for successful operations (Moussiopoulos et al., 1999). The key attributes of pollution dispersion with in urban canyons can be widely understood through the research of

Nicholson, 1967; Priyadarsini et al., 2005; Pan et al., 2009; Maignant et al., 2011; Tiwary et al.,

2011; and Edussuriya et al, 2014.

Characteristics of wind flow and pollutant dispersion pattern are presented in several studies

(Nicholson, 1967; Chan et al., 2002; Assimakopoulos et al., 2003; Tsai et al., 2004; Kang et al.,

2008; Kim and Baik, 2010; Tewari et al., 2010; Yoshie et al., 2011; Yazid et al., 2014, Shen et al., 2015). Based on the existing studies, the air flow patterns and related dispersion of pollutants in such models are dependent on meteorological and morphological factors: (i) aspect ratio and geometry of the buildings (Jeong and Andrews, 2002; Sagrado et al., 2002; Vardoulakis et al.,

2003) and street canyon ratio (Chang and Meroney, 2003; Liu et al., 2004), (ii) building position with respect to the wind direction (Shein et al., 2015), (iii) the dynamic attributes of the wind speeds, directions (Chan et al., 2002; Kim and Baik, 2004; Park et al., 2004 ) and (iv) turbulence intensity that consequently produces air vortices because of the surface roughness as shown in figure 2.5 (Tominaga and Stathopoulos, 2010; Yoshie et al., 2011; Kikumoto and Ooka, 2012).

(a) Isolated roughness flow (H/W= 0.1667)

W (b) Wake Interface flow (H/W= 0.25)

Figure 2.6- Wind flow regime in urban area for different street width. Adapted from- (Oke, 1988)

2.8.1. Street Canyon: Depending upon building aspect ratio (height-to-width), microscale dispersion phenomenon occurs at the urban street canyon levels. The study by Oke, 1988 shows the formation of three different flow regimes with the increase in the aspect ratio inside a street canyon (see figure 2.5). Many studies have suggested that pollution concentration in the street canyon increases with the higher (H/W) aspect ratio (Li et al., 2008, 2009). The results from the studies on neighborhood morphology with aspect ratios (H/W>2) showed a high number of pollutants gathered at the ground level of the wind ward wall and high concentration at upper part of the leeward wall (Liu et al., 2004, Kim and Baik,1999). Due to the dominance of molecular diffusion over advection and turbulent diffusion, extremely weak vortices are formed at the bottom of street which in turn, at the ground level, causes heavy accumulation. More complex wind flows occur near the ends of the canyon at intersections with other streets. There,

33 the low-pressure corners and the wind circulation create horizontal air vortices which bring fresh air into the canyon (Karousos, 2006).

Both Murena (Murena et al., 2008) and Li (Li et al., 2008, 2009) show that the vortices induced in deep street canyons (H/W=5.7) do not affect the transportation of pollutants such as occurred at shallower street canyons at the pedestrian level. Meanwhile, three times more pollution concentration was observed in a deep street canyon (H/W=5.7) compared to shallower street canyons (H/W=1.0) monitoring station in Greece city (Murena and Favale, 2003, 2007).

This study clearly identifies the requirement of various monitoring stations at microscale instead of at a city scale. Most of the studies present the results from the vehicular pollution concentration which are comparatively lower than those anticipated from an unplanned building demolition. However, these studies provide knowledge about the general characteristics of the particle dispersion around building and street canyons. This shows the significance to understand the nature of high particulate emission in different settlement.

Mean wind

Leeward side

Primary vortex Building Building

Street canyon Figure 2.7- Wind Pattern in a Street Canyon. Adapted from- (Dabberdt et al., 1973)

2.8.2. Effect of Wind Speed Another important factor identified in the dispersion of particulate matter by the literature is wind speed. Several research studies show that the high wind speed increases the concentration of windblown dust and soil particles while low wind speed tends to decrease the concentration (Hinds, 1999; Seinfeld and Pandis, 2006; Huang et al., 2000; Tsai and Chen, 2004;

Baik and Kim, 2002). The numerical study by Huang (Huang et al.,2000) shows the decrease in the PM concentration inside the street canyon when the wind speed is increased and acts perpendicular to the canyon axis. This numerical study was supported by the field measurements carried out by Tsai and Chen (Tsai and Chen, 2004) in their research. They found that the PM concentration at the leeward side was 9-16% higher than the windward side when the wind speed was <1m/s while, 64-107% higher concentration was noticed when the wind speed was between

2-4 m/s. Baik and Kim (Baik and Kim, 2002) explained the trend in relation to the effect of wind speed on turbulent intensity and vortex strength resulting in the difference in concentration magnitude.

2.9. Selection of Neighborhood Morphologies for Air Pollution Modelling: Although various building configurations exist in urban areas, idealized building configurations suggested by Stewart and Oke in 2012 have been considered to represent the basic configuration directly affecting the mechanism of dispersion processes in urban neighborhoods.

The primary purpose of Local Climate Zones (LCZs) is the observation of Urban Heat Island studies by standardizing the classification of urban and rural field sites. LCZs are fundamentally composed of several elements of any settlement such as built structures, roads, vegetation, soil types and water bodies, all in varying amounts and arranged uniformly into 17 schemes. Out of

17 built types, only 10 represent configurations with features relevant to pollution dispersion studies.

The 10-neighborhood configurations used for this thesis are:

Table 2.6- Various LCZ classifications. Adapted from (Stewart and Oke, 2012)

Configuration Classification Aspect Ratio Description

Compact high-rise >2 LCZ 1

Compact midrise 0.75- 2 LCZ 2

Compact low-rise 0.75- 1.5 LCZ 3

Open high-rise 0.75- 1.25 LCZ 4

Open midrise 0.3- 0.75 LCZ 5

Open low-rise 0.3- 0.75 LCZ 6

Configuration Classification Aspect Ratio Description

Large low-rise 0.1-0.3 LCZ 8

Sparsely built 0.1-0.25 LCZ 9

Compact midrise+ Open high-rise 0.75- 2 Combination of LCZ 2 and LCZ 2 LCZ 34

Large low-rise with scattered >1 Combination of LCZ 8 and trees LCZ B

LCZ8B

2.10. Modelling air pollution dispersion:

The impact of air pollution on. urban environments has become. an important research matter (Georgii, 1969; Oke, 1988; Bitan, 1992), which has led to the several studies using modelling software to investigate the influence of built structures and vegetation on pollutant accumulation/dissipation patterns. With the advancement of computational capabilities of personal computers, dispersion models can be conducted on different spatial and temporal scales

(Sivacumar et al., 2001; Vardoulakis et al., 2003). Gaussian-based dispersion models such as

CALINE3 and CALINE4 are commonly used in the study of traffic pollution distribution

(Benson et al., 1979, 1984). AERMOD is a regulatory near field steady state Gaussian plume model used by U.S. Environmental Protection Agency (EPA) to model particle distribution

(Cimorelli, 2005). Other than Gaussian model, steady state box model, VITO proposed by The

Flemish institute for technological research (Mensink et al., 2003), Lagrangian-Eulerian Model,

EPISODE developed by The Norwegian Institute for Air Research (Walker et al., 1999; Oftedal et al., 2008) and Eulerian Model, ENVI_met designed at University of Mainz, Germany (Bruse,

2007; Simon et al., 2012; Wang et al., 2016) are some of the examples of pollution distribution model used globally to assess the air pollutant dispersion and human exposure.

At present, air quality models are broadly utilized for assessing the traffic-induced pollution at street level. Such models provide the predictions of present and future air pollution levels as well as temporal and spatial variations (Sharma and Khare, 2001). These models are extremely helpful for understanding dispersion behavior and; physical and chemical processes of the particulate matter in the street canyons, if the model is utilized proficiently. Different types of air quality models are explained in Appendix B.

2.11. General Structure of Air Quality Models: Air quality models work in three modules, pre- processor module, working space and post processor module. Figure 2.7 shows the concept of air quality models. In the pre-processor model all the input parameters required for the model to start working are provided. The dispersion models vary depending on the mathematics used to develop the model, but all require the input of data that may include Meteorological conditions, source term, emissions, terrain, location. The physical model is plotted in working space. Then the software solves the mathematical equation to provide the resulting pollution data. The post processor module visualizes the results obtained after the simulation which can be read by everyone. On the basis of these results, the policies and guidelines for standard air quality are formatted.

Figure 2.8- Concept of an air quality models.

2.12. Main Parameters of an Air Quality Model: These modelling tools require input data to simulate the atmospheric phenomena. Two main types of input data are basically required and are explained (Moussiopoulos et al., 1996):

2.12.1. Emissions: The pollutant dispersion model requires the input data for emission rates in 24 values for each hour of the day. Such modelling tools allows the user to choose the suitable source type

(point, line and area) and its location, appropriate height of pollutant release and emission rates.

Emissions can vary significantly depending on intensity of construction and demolition activity, the specific operation in action and meteorological conditions including wind direction (Chang et al. 1999). The diurnal emission rate is estimated in terms of particle number and mass emissions

39 per unit time (s–1), mass (kg–1) and a combination of both (s–1 kg–1). In the new type of models, data for the emission rates are dynamically adjusted during the simulation according to the hour of simulation (linear interpolation). The quantity of a pollutant released into the air with a source activity are estimated with the help of Emissions Factor (EF). However, for construction activities, the estimates using EF are not yet possible due to the lack of research in that field.

2.12.2. Meteorology: The screening type models use default meteorology data as input, describing a critical meteorological situation. Other models use a set of meteorological cases as input, described in wind and stability classes. The new types of models make use of pre-processed meteorological data based on similarity theory for the atmospheric surface layer. The measurements that are required include variables, such as wind and temperature profiles, cloud cover or solar radiation, surface roughness etc.

2.13. Validation of Model: Computational Fluid Dynamic (CFD) models are used globally to investigate the air pollution problems therefore, it is crucial to assure the reliability of a pollutant dispersion model.

The accuracy of such models relies highly upon the CFD equations used to interpret the dispersion pattern and the credibility of the input data. There are several ways to validate and verify the reliability of the model depending upon the research approach. However, the most reliable and popular method is by comparing the simulated results with the real-time field measurement for pollution concentration or physical model simulations under specific meteorological and emission parameters. Such validation process ensures the configurations can

40 be modelled with confidence and shows if any situations are less realistic to simulate (Tominaga,

2016). Obha (Obha, 2016) provides the review of guidelines for CFD modelling including verification and validation used by different countries for different sectors such as atmospheric dispersion, aerodynamic fluid mechanics, nuclear power studies and HVAC systems.

Meanwhile, study by Kingett-Mitchell (FST, 2005), provides comprehensive guidelines to evaluate the credibility of the model used.

Apart from those, inter-model comparisons can also verify the model performance and uncertainties (Carmichael et al., 2001; 2002; ADORC, 2006). Also, sensitivity analysis and a parametric study are performed to assess the inherent uncertainty due to the insufficient understanding of physical processes. While the findings can be reported to document model limitations and parameter settings instead of examining the reliability of the model for the selected application. The goal of verification and validation is to ensure that the CFD code predicts the reliable results adequate for practical purposes. Under the best circumstances, a dispersion model simulates concentrations within about a factor of two of actual observations.

However, research shows that the models often overestimate or underestimate concentrations.

This can be attributed to the erroneous input data. It is, therefore, very important to make a cautious interpretation concerning any predictive models.

2.14. Advantage of using Air quality Simulation Method: 1. CFD model can simulate wind flows and pollution dispersion that are difficult, expensive

or impossible to study using traditional (experimental) techniques.

2. High resolution spatial and temporal prediction of the of pollution dispersion is possible

while, the basic physics of the phenomena are not limited by scale constraints and the

atmospheric motions are simulated correctly.

3. Examination of the interactions of different nonlinear processes determines their

interdependence. For example: Wind flow alteration caused by construction or

demolition in the physical environment, air quality deterioration affiliated with

contaminant transport or pollutant dispersion and heat distribution changes related to

modifications in land usage.

4. Simulations are faster and can process in parallel, depending upon the computer

processor.

2.15. Disadvantage of using Air quality Simulation Method: 1. It is not really possible to conclude the CFD results as 100% accurate and reliable but the

results can only point to the verification and validation bench marks even when

everything is carefully considered, there are still issues concerning Error Propagation.

2. Small errors in input data for setting up initial and boundary condition, emission data and

mathematical model selection can lead to completely different solutions.

3. Difficulty in considering atmospheric stability parameters that are dominant in convective

cases and providing proper boundary conditions for wind and turbulence parameters.

4. The accuracy of the results is limited by the available computing power with grid

resolution, choice of time increments, turbulence model selection, or specification of

boundary conditions.

5. Extensive knowledge and experience of the operation model is necessary to generate fine

results.

2.16. The micro-climate model ENVI-MET/ Test Software: ENVI-met is a non-hydrostatic three-dimensional microclimate model developed by Prof.

Michael Bruse, University of Mainz, Germany. Literature study shows the use of ENVI-met model by several researchers in the field of atmospheric sciences to investigate different facets of urban microclimate (Bruse, 1999; Ozkeresteci et al., 2003; Rosheidat et al., 2008; Huttner et al.,

2011; Simon et al., 2012; Wang et al., 2016; Morakinyo et al., 2016; Tsoka et al., 2018). This prognostic model can simulate diverse planetary boundary layer processes such as wind flow, turbulence, micro-climate and particle from of 1s to 10s resolution for an urban boundary layer climate (Singh et al., 2015). It uses the fundamental laws of fluid dynamics and thermo dynamics to evaluate the atmospheric phenomenon of microclimate during a diurnal cycle from 24 to 48 hours. (Described in Appendix C)

ENVI-met distinguishes itself from other CFD-models due to the implementation of a detailed vegetation model which not only describes the effect on the wind field and turbulent kinetic energy but also describes the thermodynamic effects of the vegetation on the ambient air, as well as the effects on the diffusion and deposition of particulate matter. The model is particularly useful for small-scale air quality modelling in complex areas like an irregular street canyon with vegetation objects, and multiple emission sources (Janssen et al., 2008). Hence, the model is particularly suitable for this research since the focus is on the short-term high pollution levels at neighborhood scale and impact due to various parameters, one of them being vegetation.

Apart from that, ENVI-met with LEONARDO is used in this study because of its capability to provide high-resolution simulations output of urban micro-climates, wind patterns, encounters with vegetation, and particulate matter dispersion. LEONARDO module is used to visualise and interpret the large volume of output data provided by ENVI_met in binary files.

CHAPTER 3: RESEARCH METHOD

Air pollution dispersion studies mostly depend on two approaches: monitoring and modelling. Monitoring is carried out to quantify the emission of particles released, which is then followed by modelling through which the dispersion pattern and particle concentration at different location is measured. Emissions factors (EF) are used to estimate the particle emission from various pollution sources, but the EF for construction activities are uncertain while EF for demolition activities are almost non-existent (Rohman, 2014). The thesis focuses on the particle emission from an unplanned demolition which is relatively unpredictable making it difficult to engage in field measurement of the emission factors in the available timeframe and budget.

Meanwhile, the selected modelling software (ENVI-met) requires the user to provide the source of the pollution at a specific height on a specific three-dimension cell. This source uses hourly emission rate for a 24-hour period.

3.1. Data Source: This thesis used the research findings of an implosion of a 22-story building in east

Baltimore, MD. The research paper was published in Journal of the Air & Waste Management

Association under the title of “The Impact of a Building Implosion on Airborne Particulate

Matter in an Urban Community” by (Beck et al., 2003) This study provides some first-hand data regarding time- and space resolved concentrations of PM (nominally 0.5–10 µm) during the planned implosion of a residential building. The actual data from this study such as location, longitude, latitude, meteorology, and background PM concentration were used to setup the initial

45 model in ENVI-met. Meanwhile the resulted PM concentration data during the implosion was used to estimate the emission rate from the building implosion.

3.2. Emission Estimation

3.2.1 Model area/ Study site The surrounding site was plotted according to the Google Earth image from 2002 (figure

3.1) to ensure the historical accuracy and avoid the post demolition site changes. Area source of pollution was placed in the location of imploded building at 1m height from the ground level.

The intent of this study is to match the particle and evaluate the particle dispersion and deposition model of ENVI-met based on particle sedimentation and deposition processes for building demolition. Figure 3.1 shows the location in Baltimore, Maryland of the implosion site with the position of the imploded building, the surrounding residential communities, as well as all monitoring locations used to assess the implosion’s air quality effect. The site and the sampling location were plotted in ENVI-met model area (Figure 3.2).

Source

Figure 3.1- Google earth image of the site location, Figure 3.2- Test Model plotted in ENVI-met space area. surrounding and the sampling locations.

To plot the objects in the model, an accurate model domain (Figure 3.3) comprising the information related to the model size, grid size, soil type and geographical location must be created in ENVI-met spaces. This test model area covers 60 X 60 X 30 or grid represents was digitized in a resolution of 2,100 X 1,920X 150m. The longitude and latitude for the site was

39.2904° N and 76.6122° W respectively. According to Beck et al., the monitoring sensors were placed at distances ranging from 475 to 1130 m from the source or imploded building. Figure 3.1 shows the location of seven outdoor sampling stations in both upwind and downwind directions.

However, during the emission modelling process, I compared three out of seven of the concentration locations measured at the downwind sampling locations because only one wind direction can be assigned while modelling in ENVI-met for one-hour period. Receptors were placed at the exact same downwind distances in the ENVI-met model as in the building implosion study. The particulate concentration was measured at 1m above the ground level as the implosion study also sampled the concentration 1m from the ground.

Figure 3.3- Creating a model domain in ENVI-met.

3.2.2 Input parameters: Meteorological and background pollution data are essential parameters for the simulation.

The building was imploded on 10:00 a.m. on Saturday, August 19, 2000. All the meteorological parameter measurements were initiated two hours before the implosion and continued for two hours after the implosion.

The ambient temperature of 21.6 °C was recorded for the morning of the implosion and meteorologically was characterized by a clear sky with light winds. Meanwhile, a relative humidity of 65% was noted (Baek et. al). The 24-hour temperature and relative humidity data were retrieved from an .EDW file created by the DOC and parsed using climate consultant software to use as an input for ENVI_met (Figure 3.4). The full forcing mode was enabled to allow the program to use the parameters of diurnal variations as boundary conditions for the meteorological parameters: air temperature and humidity, wind speed and direction and radiation

(shortwave direct, diffuse and longwave), to match closely to existing conditions (Bruse, 2009).

Figure 3.4- Diurnal Temperature and Relative Humidity

A Davis Meteorological station (Davis Instruments Corp.) was placed on the roof of a two-story row home northeast of the implosion site to measure the wind speed and direction.

The wind speed was noted at 1.8 m/sec with a peak velocity of 4.9 m/sec occurring within 30 min post implosion and the direction was from the northwest (Baek et al., 2003).

Outdoor PM10 concentrations before, after and during the implosion were measured using a portable direct-reading nephelometer with data logging capability (MIE pDR1000,

ThermoAndersen) placed at the selected locations. The mean background levels at all outdoor sites for 2 hours before the implosion ranged from 13 to 29 µg/m3 (Baek et al., 2003)

Climate consultant was used to compare and verify the climate data for 10:00 a.m. on Saturday,

August 19, 2000 at Baltimore, Maryland with the data presented on the case study by the researcher. The simulation file (.SIM) with the information presented below in table 3.1 prepared in notepad is used to input the diurnal variations of temperature and relative humidity and other atmospheric parameters to the ENVI-met pre-processor module to model the initial boundary condition.

Table 3.1- Input parameters for the test simulation for ENVI-met ver 4.3.1

Parameter Value

Start Simulation at Day (DD/MM/YYYY) 19/08/2000

Start Simulation at Time (HH:MM:SS) 08:00:00

Total Simulation Time in Hours 6

Wind Speed in 10 m ab. Ground [m/s] 1.75

Wind Direction (0:N..90:E..180:S..270:W..) 315.1

Roughness Length z0 at Reference Point [m] 0.01

Initial Temperature Atmosphere [K] 297.68

Specific Humidity in 2500 m [g Water/kg air] 4

Relative Humidity in 2m [%] 66

The series of images presented below provides an overview of the pre-processor module of the ENVI_met 4.3.1. All the values of meteorological parameters are pulled to this module from the uploaded .SIM file and with the completion of this process the initial boundary condition for the 3D model is prepared.

Load .INX Input file Parameters

Preview of Area Input file (.INX)

Figure 3.5- Main Configuration Wizard

Figure 3.6- Setting up time and date for the simulation

Figure 3.7- Basic meteorological data input

Figure 3.8- Hourly Temperature and Relative humidity data input

Figure 3.9- Extended Meteorology setting in ENVI_met

3.2.3 Pollution Dispersion Model Simulation The thesis uses the atmospheric dispersion modelling (the conversion of emissions to

concentrations) to provide an assessment of effects of Particulate Matter generated from the

building demolition activities. The pre-processor module of this numerical model offers the

option of inputting a single pollutant or multiple pollutants with their active chemistry. For this

research, the single pollutant mode is selected for coarse pollutants and the atmospheric

chemistry of pollutants is disabled (Figure 3.11), since the study only focuses on the dispersion

patterns of particles rather than composition of a particle.

The series of PM10 emission profile for 24 hours were estimated to get reasonable result

for PM10 concentration at three different locations (L1 at 475m, L2 at 825m and L3 at 1130m)

downwind from the imploded building(source) as measured in Beck et. al. Finally, the 24-hour

emission profile for PM10 as shown in the Figure 3.10 below was able to generate the closet

accuracy of the PM10 concentration value. This generated series of diurnal PM10 emission profile

was now used to create the pollution database in ENVI-met database library. The emission area

sources were placed 1m above the ground level. Finally, the completed model is then simulated

for hours to obtain the PM10 concentration in the form of the dispersion model at the desired

timeframe.

25000 20000 20000

15000

10000

5000 18 18 18 18 18 18 18 18 18 18 72 18 18 18 18 18 18 18 18 18 18 18

PM10 emission PM10 emission (µg/m3) 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 time (h)

Figure 3.10- Estimated 24-hour PM10 emission rate

Figure 3.11- Assigning the multi pollutant mode in the configuration wizard

Figure 3.12- Data set input for PM10 emission in ENVI_met database library

3.2.4 Test Results and Comparisons

The result from the estimated PM10 emission simulation was successful in predicting reported measured PM10 concentrations. Based on meteorological conditions on the morning of the implosion, distance, and direction from the implosion site, three outdoor community air sampling locations were selected. In the ENVI-met model the receptors were placed exactly at the same distance and place only for the sample location 1m above the ground level (C4, C5 and

C6) as the actual measurement was taken in Baltimore building implosion. Based on this reliable result, the further parametric study for the was carried out by the author.

The real time PM10 concentration measured at the three sites, L1 at 475m, L2 at 825m and L3 at 1130m downwind (southeast) of the implosion site were compared with the simulation results. The ENVI-met simulation result was able to produce 5458 µg/m3 at location L1 whereas

3 in the real time measurement it produced was 5686 µg/m . Meanwhile at L2 and L3 the PM10 concentration from the simulation results were 1457 µg/m3 and 603 µg/m3 while the real time measurement was recorded at 1578 µg/m3 and 589 µg/m3 respectively. All the tested locations were located in the downwind direction. 20000 mgs−1m−2 particulate matter of less than 10µ diameter were released at 10am in the model to obtain the closest reliable results.

Table 3.2- Simulation Results for PM10 concentrations at 3 sampling locations

Air Sampling Location ID L1 L2 L3

Distance from the Implosion (m) 475 825 1130

Direction relative to Implosion Southeast Southeast Southeast

Background concentration (µg/m3) 18 13 29

Peak concentration (µg/m3) 5686 1578 589

ENVI-met Simulation results (µg/m3) 5458 1457 603

Difference (%) -2.29% -1.21% 0.14%

PM10 Concentration:

5458 µg/m 3

Figure 3.13- At sample location L1

PM10 Concentration

3 1457 µg/m

Figure 3.14- At sample location L2

PM10 Concentration: 3 603 µg/m

Figure 3.15- At sample location L3

6000

5000

µg/m3) 4000

3000

2000

1000 PM10 concentration( 0 Sample L1 Sample L2 Sample L3

Peak Concentration on site ENVI_met results

Figure 3.16- Comparison between PM10 Concentration at different sample locations

3.3. Model validation by Simulation of Implosion of Calgary General Hospital, Canada For the validation of modelling assumptions made in simulating the 10 different morphologies, a real time implosion of Calgary General Hospital is simulated using ENVI-met.

Chapter 3.2.1 discusses the pollution emission and meteorological data inputs. Results from the simulations are presented in chapter 3.2.2 and these outcomes are compared and discussed further in chapter 3.2.3. The results are discussed by making the comparison between the real time results by Stefani et. al. (2005) with the simulated results. The paper is published in the

Journal of Air and Waste Management under the title “The Implosion of the Calgary General

Hospital: Ambient Air Quality Issues”.

This hospital was located in the older residential community of Calgary, Canada. Figure

3.15 shows the exact location of the implosion site and the points were the pollution levels were measured pre, post and during the implosion. In this thesis, the pollution concentration measured at sample location 5 in both real time and the simulation results are compared and discussed.

2 1 4

7 6

Figure 3.17- Google Earth image showing the location of implosion and surrounding monitoring locations.

3.3.1. Data Inputs The implosion of Calgary General Hospital took place on October 4, 1998 at 8am in the morning. In total seven buildings over three stories were imploded. The total surface area for the implosion was 84,000m2.

• Emission:

The 24-hour pollution emission profile obtained from the model calibration is used as the pollution emission data for this simulation study. 20000 mgs−1m−2 emission rates are used over the source surface area of 84, 0000 sq. m. The surface area is increased by 4 times than in the

Baltimore implosion simulation to accommodate the scale of this hospital complex.

• Meteorology:

The meteorology on the morning of implosion day is characterized as a clear sky with northwest winds at 1.94 m/s speed at the ground level. The 24-hour temperature and the relative humidity was taken from weather underground’s website for the day.

Figure 3.18- Meteorological background values for temperature and humidity in the 2m level within a 24hr cycle

3.3.2. Results

The results published in Stefani et. al. (2005), provides the TSP, PM10 and PM2.5 at 500m,

2.5 km, 3 km, 6 km, 8 km, 13 km, 17 km, 20 km and 25 km post implosion from the implosion site. All these data were taken at 8:12- 8:19am, 8:21am, 8:23-8:24am, 8:33-8:38am, 8:42-8:43,

8:51-8:55am, 9:03-9:04am, 9:17-9:19am, 9:28am respectively. It also provides pre-implosion background pollution levels 500m from the site at 5:00-8:00am.

Modelling a large area with ENVI-met takes a very long time and requires high graphic quality of the computer. Therefore, the author of this thesis compared the PM10 concentration only at 500m from site between simulated and the real time data. The compared PM10 concentrations were measured 10 minutes after the implosion. The main model area of 60 X 60

X 30 with the grid of size 25 X 20 X 5 was plotted in ENVI-met to simulate the pollution dispersion. The simulation was carried out for 3 hours starting at 15 minutes before the source emission. The simulation started at 7:45am to 10:45am and results were noted for every 5 minutes. Altogether 36 sets of resulting data were collected from the simulation ran for every 5 minutes interval.

At 500m from the implosion site, ten minutes after the implosion, the result published in

3 Stefani et. al (2005) shows that the PM10 concentration were recorded at 68,942 µg/m .

Meanwhile, the simulation result obtained from ENVI-met for PM concentration for the same implosion at the same distance and time is observed almost 50% less than the real time measurement levels. The amount of PM concentration in the simulated result is 36,002 µg/m3.

Figure 3.19 shows the sample location at 500m away from the site. The pollution dispersion pattern and the concentration of coarse Particulate Matter at various distances in the windward direction can be observed in Figure 3.20.

Source Sample Location

Figure 3.19- Implosion site and surrounding modelled in ENVI-met

4.2.3. Discussions

PM Concentration: 36000 µg/m3

Figure 3.20- Pollution dispersion simulation result from Calgary Hospital Implosion

3.3.3. Discussion The results obtained from the simulation carried out for the validation of the hypothesis was observed to be significantly lower than expected. As per the assumption, the emission rate of

20,000 mgs−1m−2 over the area of 84,000 m2 was estimated to produce 60,663 µg/m3 PM concentration at 500m away from the source in windward direction. But, the result from the actual readings was double of the model’s estimate. The model assumption underestimated the particulate matter concentration.

The difference in the results is attributed to the type of urban morphology. The Baltimore implosion was carried out in the compact neighborhood while the Calgary regional hospital was imploded in a sparsely built neighborhood. Figure 3.21shows the difference in the results is attributed to terrain as it affects the degree of exposure of the site under investigation (Cochran and Derickson, 2005 and Stathopoulos et al., 2007). Comparing the wind profile of Baltimore to

Calgary the author found that at the same height at ground level the wind speed at Baltimore is

55% of Calgary. Since decomposition of PM is inversely proportional to wind speed, the PM concentration was expected to be half as much as the predicted concentration levels. So, the pollution concentration has to be multiplied with a factor to account the difference in the urban terrain.

Figure 3.21- Gradient height depends on the roughness of the terrain. Adapted from: Adil Sharag-Eldin, 1998

Therefore, the model was simulated again with roughness length at reference point 0.001m for the open settlement and the wind speed was doubled. The result showed that the modelled

PM concentration were only 7% lower than the estimated PM Concentration at 56000 µg/m3 at

500m from the source. The other factor for the slight discrepancy in the PM concentrations might be attributed to the presence of waterbody within the close proximity of dispersion distance which could contribute to the drop of the pollution from the expected levels. Hence, the result obtained from this model validation was robust in estimating emission of PM from the planned demolition.

PM Concentration: 56000 µg/m3

Figure 3.22- Pollution dispersion simulation result from Calgary Hospital Implosion

3.4. The Parametric Study: Simulation of ten identified Urban Morphologies using ENVI- met The first step for the parametric study was to model all the 10 identified neighborhood morphologies in ENVI_met. The modelling process includes assigning proper street configurations, building heights, land covers and vegetations. Meanwhile the input data for all the 10 morphologies included:

3.4.1. Input parameters:

• Emissions The 24-hour pollution emission profile obtained from the model calibration was used as the pollution emission data for the neighborhood morphology simulation study. 20000 mgs−1m−2 emission rates are used over the source surface area of 1280 sq. m. The surface area is reduced by 20 times compared to the Baltimore implosion simulation to accommodate the neighborhood scale. The source is placed at 1 meter high from the ground level.

• Meteorology The meteorological parameters such as wind speed, relative humidity, and temperature, are used as same in simulation in chapter 3.1. While the simulation is carried out using same wind direction, additionally, morphologies are also simulated using 0-degree wind direction.

3.4.2. Data Sampling and Model set up: PMC and wind speed were recorded for every 5 minutes at 20m, 50m and 100m horizontally in the windward direction of the adjacent street from the source. Pollution concentration and wind speed data were also recorded at 1m, 15m, 30m and 45m vertically from the ground 20m away from the source, 5 minutes after the source emission. The pollution dispersion was simulated for 2 hours. The simulation is started 15 minutes before the source emission. All the other 10 morphologies were modelled with the same approach.

Figure 3.23- Neighborhood morphology plotted in ENVI_met

In figure 3.21 the layout of the neighborhood canyon and building geometry modelled in

ENVI_met are illustrated. ENVI_met allows the varied selection of soil and surfaces, receptor placements, source types, building configurations and vegetations. The example is of the LCZ 2 category, compact midrise built type. This deep street morphology is plotted in ENVI_met where street canyon aspect ratio is (0.75-2). The height of the buildings is up to 20m and the street width is 8m. The land cover is mostly asphalt-paved with absence of vegetation. Figure 3.22 shows the sampling location around the site in the windward direction at 20m, 50m and 100m from the source. Two arrows represent the wind direction from 0 and 315 degrees. An area source of 40 X 32m was placed within the selected Compact Mid-Rise neighborhood.

Wind Direction N

Source

8m 20m

50m

100m

Figure 3.24- Schematic map of the model set-up showing three monitoring stations around the source, wind directions and street width

3.4.3. Result Visualization: As shown in figure 3.19, the configuration and the area input files are set up as per the

ENVI-met input requirements. As per the software’s nature, a large volume of outputs are obtained from the simulations. The LEONARDO module is then used to further visualize the output. This module enables the visualization of the ENVI-met binary files in a Windows compatible module. The simulation results can be analysed from a multitude of perspectives with the software tool LEONARDO, which comes with multiple illustration facilities.

The series of figures below shows an assembly of visualized outputs for PMC and wind speed in the compact midrise neighborhood. All the other visualized outputs for rest of the 9 neighborhood morphologies are included in appendix A.

Figure 3.25- PMC within compact midrise settlement at 10:05am and 0-degree wind direction. (Horizontal)

Figure 3.26- PMC within compact midrise settlement at 10:05am and 0-degree wind direction. (Vertical)

Figure 3.27- PMC within compact midrise settlement at 10:25am and 0-degree wind direction. (Horizontal)

Figure 3.28- PMC within compact midrise settlement at 10:25am and 0-degree wind direction. (Vertical)

Figure 3.29- PMC within compact midrise settlement at 10:45am and 0-degree wind direction. (Horizontal)

Figure 3.30- PMC within compact midrise settlement at 10:45am and 0-degree wind direction. (Vertical)

Figure 3.31- Wind speed within compact midrise settlement at 0-degree wind direction (Horizontal)

Figure 3.32- Wind speed within compact midrise settlement at 0-degree wind direction. (Vertical)

Figure 3.33- PMC within compact midrise settlement at 10:05am and 315-degree wind direction. (Horizontal)

Figure 3.34- PMC within compact midrise settlement at 10:05am and 315-degree wind direction. (Vertical)

Figure 3.35- PMC within compact midrise settlement at 10:25am and 315-degree wind direction. (Horizontal)

Figure 3.36- PMC within compact midrise settlement at 10:25am and 315-degree wind direction. (Vertical)

Figure 3.37- PMC within compact midrise settlement at 10:45am and 315-degree wind direction. (Horizontal)

Figure 3.38- PMC within compact midrise settlement at 10:45am and 315-degree wind direction. (Vertical)

Figure 3.39- Wind speed within compact midrise settlement at 315-degree wind direction (Horizontal)

Figure 3.40- Wind speed within compact midrise settlement at 315-degree wind direction (Vertical)

3.4.4. Data Collection and Analysis Thirty-six different samples (table 3.3) of PMC and wind speed were recorded at various

windward horizontal distances at progressing times for two different wind directions (0 and 315

degree). Meanwhile, forty-eight samples (table 3.4) were collected for PMC and wind speed at

various vertical distances at progressive time for two different wind directions (0 and 315

degree) for each simulated morphology. The primary aim of this methodology was to acquire

results explanatory for the specific conditions of the study and in consistent, comparable formats

with each other which was beneficial in interpreting the data for the parametric study. Microsoft

Excel was used extensively to plot the spreadsheet for all the monitored data (table 3.3, 3.4).

Table 3.3- PMC and WS measurements at 3 different Vertical locations at different times and wind directions in Compact Highrise Settlement WS at WS at PM conc. PM conc. PM conc. PM conc. WS at WS at Wind Distance 30m 45m at 1m at 15m at 30m at 45m 1m from 15m Direction (m) from from from GL from GL from GL from GL GL from GL GL GL 20 33384 17449 9035 3350 1.7 1.8 1.80 1.95 0 degree 50 28835 16969 9806 3800 1.5 1.7 1.83 1.96 (vertical) 100 21068 14035 9349 4084 1.6 1.9 1.96 2.03 315 20 35544 20585 12199 6386 1.4 1.4 1.36 1.40 degree 50 29488 19109 12533 6963 1.2 1.28 1.29 1.33 (vertical) 100 12287 9116 6743 3922 1.1 1.25 1.29 1.34

Table 3.4- PMC and WS measurements at 3 different horizontal locations at different times and wind directions in Compact Highrise Settlement

Wind Distance PM conc. at PM conc. at PM conc. at Wind speed Wind speed Wind speed Direction (m) 10:05:00 10:25:00 10:45:00 at 10:05:00 at 10:25:00 at 10:45:00 20 33383 27669 11208 1.66 1.66 1.66 0 degree 50 28834 23916 9687 1.54 1.54 1.53 (horizontal) 100 21067 17509 7085 1.57 1.57 1.57 20 35543 23574 11968 1.36 1.36 1.35 315 degree 50 29487 19575 9938 1.20 1.20 1.19 (horizontal) 100 12287 8182 4155 1.14 1.14 1.14

The processing of the model outputs involves descriptive and regression statistical analysis of the predicted data, comparison between monitored and predicted concentrations and model validation techniques. The conducted statistical analysis of the simulated data is presented below including the outputs from Microsoft Excel software, which was broadly utilized in order to process and graphically visualize the results.

The PM concentration and wind speed are plotted against the horizontal distance from the source at different time as well as different vertical distance from the ground for the 10 different settlements. These comparisons are made at an alternating angle of 0 and 315 degree.

3.4.4.1. Compact High Rise: Figure 3.40 and 3.41 shows pollution drops with increment in the time and distances in both wind direction in compact high-rise settlement. In this type of settlement, the pollution levels are still higher at 100 meters within the neighborhood almost 1 hour from the implosion in both wind directions. Meanwhile, 315-degree wind transported almost 50% less PM at 100m. This shows the high-rise buildings creates a tunnel like pathway between them in both horizontal and vertical direction leading pollutants to travel farther away in the windward direction. While the pollutants travelling in the vertical direction settle down due to gravity, they are reintroduced into the atmosphere due to the wind, increasing the pollution concentration at that level. However, when the wind direction is 315 degree the adjacent buildings act as a barrier restricting the pollution transport farther than in 0 degree. The buildings height up to 50m restricts the pollution plume to blow over them therefore the pollution at the lower levels are comparatively higher at 1m and

15m above the ground level.

0 20 40 60 80 100 120 40000 1.68

35000 1.66

30000 1.64 PM conc. at 10:05:00 AM 25000 1.62 PM conc. at 10:25:00 AM 20000 1.6 PM conc. at 10:45:00 AM 15000 1.58 Wind speed at 10:05:00 AM

PM PM Concentration 10000 1.56 Wind speed at 10:25:00 AM Wind speed at 10:45:00 AM 5000 1.54

0 1.52 20 50 100 Horizontal distance from the source (m)

Figure 3.42- Pollution Dispersion in High Rise Compact 0 degree (Horizontal)

0 20 40 60 80 100 120 40000 1.6 PM conc. at 1m from ground 35000 1.4 30000 1.2 PM conc. at 15m from ground 25000 1 PM conc. at 30m from ground 20000 0.8 PM conc. at 45m from ground 15000 0.6 Wind speed at 1m from ground 10000 0.4 PM PM Concentration Wind speed at 15m from ground 5000 0.2 Wind speed at 30m from ground 0 0 20 50 100 Wind speed at 45m from ground Horizontal Distance from the source (m)

Figure 3.43- Pollution Dispersion in High Rise Compact 315 degree (Vertical)

0 20 40 60 80 l) 100 120 40000 1.4 35000 1.35 30000 PM conc. at 10:05:00 AM 1.3 25000 PM conc. at 10:25:00 AM 20000 1.25 PM conc. at 10:45:00 AM 15000 1.2 Wind speed at 10:05:00 AM 10000 PMConcentration Wind speed at 10:45:00 AM 1.15 5000 Wind speed at 10:45:00 AM 0 1.1 20 50 100 Horizontal Distance from the source (m)

Figure 3.44- Pollution Dispersion in High Rise Compact 315 degree (Horizontal)

0 20 40 60 80 100 120 40000 2.5 35000 PM conc. at 1m from ground 2 30000 PM conc. at 15m from ground

25000 1.5 PM conc. at 30m from ground 20000 PM conc. at 45m from ground 1 15000 Wind speed at 1m from ground

PM PM Concentration 10000 0.5 Wind speed at 15m from ground 5000 Wind speed at 30m from ground 0 0 Wind speed at 45m from ground 20 50 100 Horizontal Distance from the source(m)

Figure 3.45- Pollution Dispersion in High Rise Compact 0 degree (Vertical)

l) 77

Figure 3.46- Pollution Dispersion in High Rise Compact 0 degree (Plan)

Figure 3.47- Pollution Dispersion in High Rise Compact 0 degree (Section)

3.4.4.2. Compact Mid Rise: PM movement pattern in Compact Midrise is similar to the Compact High Rise.

However, the PMC is higher in such type of settlement. This phenomenon is caused due to comparatively lower height of buildings which allows PM to blow above the buildings therefore settle at the longer distances. Figure 3.47 shows concentration levels at 20m is comparatively higher in Compact Midrise. The PM concentration are higher at different distances and at different times in the NNW wind direction as compared to northernly wind. PM concentration is increased with the increment in horizontal distance in the higher elevations. On the other hand, wind speed increases with vertical distance and the slope increases with the horizontal distance.

0 20 40 60 80 100 120 60000 1.6 1.4 PM conc. at 10:05:00 AM 50000 1.2 PM conc. at 10:25:00 AM 40000 1 PM conc. at 10:45:00 AM 30000 0.8 Wind speed at 10:05:00 AM 0.6 20000 0.4 Wind speed at 10:25:00 AM

PM PM Concentration 10000 0.2 Wind speed at 10:45:00 AM 0 0 20 50 100 Horizontal distance from source (m) Figure 3.49- Pollution Dispersion in Compact Mid Rise 315 degree (Horizontal)

0 20 40 60 80 100 120 45000 1.52 40000 1.51 35000 1.5 PM conc. at 10:05:00 AM

30000 1.49 PM conc. at 10:25:00 AM 25000 1.48 20000 1.47 PM conc. at 10:45:00 AM 15000 1.46 Wind speed at 10:05:00 AM 10000 1.45 PM Concentration Wind speed at 10:25:00 AM 5000 1.44 Wind speed at 10:45:00 AM 0 1.43 20 50 100 Horizontal distance from source (m) Figure 3.50- Pollution Dispersion in Compact Mid Rise 0 degree (Horizontal)

0 20 40 60 80 100 120 45000 2.5 40000 PM conc. at 1m from ground 35000 2 PM conc. at 15m from ground 30000 1.5 PM conc. at 30m from ground 25000 20000 PM conc. at 45m from ground 1 15000 Wind speed at 1m from ground 10000 PMConcentration 0.5 Wind speed at 15m from ground 5000 Wind speed at 30m from ground 0 0 20 50 100 Wind speed at 45m from ground Horizontal distances from source (m)

Figure 3.51- Pollution Dispersion in Compact Mid Rise 0 degree (Vertical)

0 20 40 60 80 100 120 60000 2.5 PM conc. at 1m from ground 50000 2 PM conc. at 15m from ground 40000 1.5 PM conc. at 30m from ground 30000 PM conc. at 45m from ground 1 20000 Wind speed at 1m from ground

PM PM Concentration Wind speed at 15m from ground 10000 0.5 Wind speed at 30m from ground 0 0 Wind speed at 45m from ground 20 50 100 Horizontal distance from source (m)

Figure 3.52- Pollution Dispersion in Compact Mid Rise 315 degree (Vertical)

Figure 3.53- Pollution Dispersion in Compact Mid Rise (Plan)

Figure 3.54- Pollution Dispersion in Compact Mid Rise (Section)

3.4.4.3. Compact Low-Rise: This type of settlement consists of low height buildings up to 9m height with narrow streets.

The low height of the buildings does not let the pollution to travel high enough vertically as compared to compact high rise and midrise settlements. The pollution plume passes over the buildings which then is dispersed and settled around the neighborhood in the wind ward direction. Figure 3.54 shows that the PM is settled mostly at 20m and 50m from the source hence less PM is transported at 100m from the source.

Figure 3.56 shows due to the shallow street canyon the pollution at 15, 30 and 45 m from the ground are comparatively lower than in compact high rise and midrise settlement. Meanwhile at

45m the ground is unaffected by the pollution plume being almost at the background concentration levels. In Figure 3.55, when the wind direction is 315 degree the buildings still acts as a barrier restricting the pollution to travel at longer distances. Therefore, making the PM concentration drop sharply at 50 and 100m from the source. The horizontal tunnel like pathway pushes the PM farther at the lower levels but are free to movement at the higher levels and were noticed to deposited at the terrace of the buildings.

0 20 40 60 80 100 120 40000 1.47 35000 1.46 30000 1.45 PM conc. at 10:05:00 AM 1.44 25000 PM conc. at 10:25:00 AM 1.43 20000 PM conc. at 10:45:00 AM 1.42 15000 1.41 Wind speed at 10:05:00 AM 10000 PM PM Concentration 1.4 Wind speed at 10:25:00 AM 5000 1.39 Wind speed at 10:45:00 AM 0 1.38 20 50 100 Horizontal distance from the source (m)

Figure 3.56- Pollution Dispersion in Compact Low Rise 0 degree (Horizontal)

0 20 40 60 80 100 120 60000 1.4

50000 1.2 1 PM conc. at 10:05:00 AM 40000 PM conc. at 10:25:00 AM 0.8 30000 PM conc. at 10:45:00 AM 0.6 Wind speed at 10:05:00 AM 20000 0.4 PM PM Concentration Wind speed at 10:25:00 AM 10000 0.2 Wind speed at 10:45:00 AM

0 0 20 50 100 Horizontal distance from source (m) Figure 3.57- Pollution Dispersion in Compact Low Rise 315 degree (Horizontal)

0 20 40 60 80 100 120 40000 2.5 35000 PM conc. at 1m from ground 2 30000 PM conc. at 15m from ground

25000 1.5 PM conc. at 30m from ground 20000 PM conc. at 45m from ground 1 15000 Wind speed at 1m from ground 10000 PM PM Concentration 0.5 Wind speed at 15m from ground 5000 Wind speed at 30m from ground 0 0 20 50 100 Wind speed at 45m from ground Horizontal distance from the source (m) Figure 3.58- Pollution Dispersion in Compact Low Rise 0 degree (Vertical)

0 20 40 60 80 100 120 60000 1.6 PM conc. at 1m from ground 1.4 50000 1.2 PM conc. at 15m from ground 40000 1 PM conc. at 30m from ground 30000 0.8 PM conc. at 45m from ground 0.6 20000 Wind speed at 1m from ground 0.4 PM PM Concentration 10000 Wind speed at 15m from ground 0.2 Wind speed at 30m from ground 0 0 20 50 100 Wind speed at 45m from ground Horizontal distance from source (m)

Figure 3.59- Pollution Dispersion in Compact Low Rise 315 degree (Vertical)

Figure 3.60- Pollution Dispersion in Compact Low Rise

Figure 3.61- Pollution Dispersion in Compact Low Rise (Section)

3.4.4.4. Compact Mid Rise+ Open High Rise:

Pollution concentration is comparatively lower in these types of settlement compared to types 1,2 and 3. The adjacent tall building directs the pollution plume in the vertical direction at the higher levels dispersing it to wider distances at 20m above from source. Pollutants are trapped and settled in proximity from the implosion due to building height, therefore the residents in the nearby neighborhood buildings are the most affected. Building residents adjacent to the horizontal tunnel pathway are also affected as the pollutants are swept speedily through the passage settling wherever the wind speed is low.

Pollution levels at 30 and 45m from the ground are almost equal to back ground levels, this is because the plume disperses at the 20m, height of the midrise buildings. This indicates that the residents in the upper stories of high-rise buildings are unaffected. Pollution levels at 50 and

100m at all times are comparatively lower when the wind direction is 315 degree because the compact settlement and adjacent tall building restricts the pollution movement. Figure 3.64 clearly shows that the maximum amount of plume settles in front of the building opposite to the source.

0 20 40 60 80 100 120 35000 1.8

30000 1.6 1.4 25000 PM conc. at 10:05:00 AM 1.2 PM conc. at 10:25:00 AM 20000 1 PM conc. at 10:45:00 AM 15000 0.8 0.6 Wind speed at 10:05:00 AM 10000 PM PM Concentration 0.4 Wind speed at 10:25:00 AM 5000 0.2 Wind speed at 10:45:00 AM 0 0 20 50 100

Horizontal distance from source (m)

Figure 3.60- Pollution Dispersion in Compact Mid+ Open High Rise 315 degree (Horizontal)

0 20 40 60 80 100 120 30000 1.4

25000 1.2 PM conc. at 10:05:00 AM 1 20000 PM conc. at 10:25:00 AM 0.8 15000 PM conc. at 10:45:00 AM 0.6 10000 Wind speed at 10:05:00 AM 0.4

PM Concentration Wind speed at 10:25:00 AM 5000 0.2 Wind speed at 10:45:00 AM 0 0 20 50 100 Horizontal distance from source (m) Figure 3.63- Pollution Dispersion in Compact Mid+ Open High Rise 0 degree (Horizontal)

0 20 40 60 80 100 120 35000 2.5 PM conc. at 1m from ground 30000 2 PM conc. at 15m from ground 25000 PM conc. at 30m from ground 20000 1.5 PM conc. at 45m from ground 15000 1 10000 Wind speed at 1m from ground 0.5 PM PM Concentration 5000 Wind speed at 15m from ground 0 0 Wind speed at 30m from ground 20 50 100 Wind speed at 45m from ground Horizontal distance from source (m)

Figure 3.64- Pollution Dispersion in Compact Mid+ Open High Rise 0 degree (Vertical)

0 20 40 60 80 100 120 30000 2.5 PM conc. at 1m from ground 25000 2 PM conc. at 15m from ground 20000 1.5 PM conc. at 30m from ground 15000 PM conc. at 45m from ground 1 10000 Wind speed at 1m from ground 0.5 PM Concentration PM 5000 Wind speed at 15m from ground 0 0 Wind speed at 30m from ground 20 50 100 Wind speed at 45m from ground Horizontal distance from source (m)

Figure 3.65- Pollution Dispersion in in Compact Mid+ Open High Rise 315 degree (Vertical)

Figure 3.66- Pollution Dispersion in in Compact Mid+ Open High Rise

Figure 3.67- Pollution Dispersion in in Compact Mid+ Open High Rise 0 degree (Section)

3.4.4.5. Open High Rise: This type of settlement contains wide streets and open spaces. The openness in urban design allows the pollutants to move freely, dispersing into wider area, thereby, decreasing the pollutant concentration levels near the collapsed building. The pollution levels are least in such type of settlement compared to other 10 types. The lateral radius of pollution is increased in such settlements compared to the compact ones. Vegetation present around the neighborhood does not make any difference as the building height itself is 50m. These tall buildings restrict the pollution movement when the plume passes through them.

Pollution levels are comparatively lower when the wind direction is 315 degree. This is because the plume can disperse in the wider area with in the vicinity in the presence of wider streets and open areas, reducing the radius of pollution transport in the windward direction.

Figure 3.69 shows that the pollution at 15, 30 and 45m above the ground are higher than in close proximity because the plume is pushed upwards when it hits the tall buildings, resulting the higher PM concentrations at upper levels.

0 20 40 60 80 100 120 14000 2 1.8 12000 1.6 10000 1.4 PM conc. at 10:05:00 AM PM conc. at 10:25:00 AM 8000 1.2 1 PM conc. at 10:45:00 AM 6000 0.8 Wind speed at 10:05:00 AM 4000 0.6 PM PM Concentration Wind speed at 10:25:00 AM 0.4 2000 0.2 Wind speed at 10:45:00 AM 0 0 20 50 100 Horizontal distance from source (m)

Figure 3.69- Pollution Dispersion in Open High Rise 0 degree (Horizontal)

0 20 40 60 80 100 120 18000 1.4 16000 1.39 14000 1.38 PM conc. at 10:05:00 AM 12000 1.37 PM conc. at 10:25:00 AM 10000 1.36 PM conc. at 10:45:00 AM 8000 1.35 6000 Wind speed at 10:05:00 AM 1.34 PM PM Concentration 4000 Wind speed at 10:25:00 AM 1.33 2000 Wind speed at 10:45:00 AM 0 1.32 20 50 100 Horizontal distance from source(m) Figure 3.70- Pollution Dispersion in Open High Rise 315 degree (Horizontal)

0 20 40 60 80 100 120 14000 2.5

12000 PM conc. at 1m from ground 2 10000 PM conc. at 15m from ground PM conc. at 30m from ground 8000 1.5 PM conc. at 45m from ground 6000 1 Wind speed at 1m from ground 4000 PM PM Concentration Wind speed at 15m from ground 0.5 2000 Wind speed at 30m from ground 0 0 Wind speed at 45m from ground 20 50 100 Horizontal distance from source (m) Figure 3.71- Pollution Dispersion in Open High Rise 0 degree (Vertical)

0 20 40 60 80 100 120 18000 2.5 16000 PM conc. at 1m from ground 14000 2 PM conc. at 15m from ground 12000 1.5 PM conc. at 30m from ground 10000 PM conc. at 45m from ground 8000 1 6000 Wind speed at 1m from ground

PM PM Concentration 4000 0.5 Wind speed at 15m from ground 2000 Wind speed at 30m from ground 0 0 Wind speed at 45m from ground 20 50 100 Horizontal distance from source (m) Figure 3.72- Pollution Dispersion in Open High Rise 315 degree (Vertical)

Figure 3.73- Pollution Dispersion in Open High-Rise Settlement

Figure 3.74- Pollution Dispersion in Open High-Rise Settlement (Section)

3.4.4.6. Open Mid Rise: The pollution levels are least compared to other settlements in open midrise settlements.

The pollution dispersion pattern is similar to the compact high-rise settlement. However, the PM concentrations at the measured distances are higher in this type of settlement. In figure 3.74, PM concentration varies with distance and time. The highest concentration is measured at 50m and the lowest is measured at 100m from the source at 1m height. While in Figure 3.75 the significant decrease in pollution concentration can be noted with increase in time and distance. In both cases, wind speed remains independent of time, but some fluctuations can be observed over the increase in horizontal distance.

Figure 3.76 shows, at 1m high from the ground, the wind speed increases at 50m horizontal distance and decreased below the initial concentration at 100m. Notable decrease in

PM concentration is observed with the increase in the vertical height, while it increased with the increase in horizontal distance. Meanwhile in figure 3.76 pollution levels dropped significantly at all horizontal distances 1m above the ground level. Other elevations also show the same trend with comparatively low pollution levels. Wind speed in both cases are increased with the vertical distance.

0 20 40 60 80 100 120 16000 1.8 14000 1.6 12000 1.4 PM conc. at 10:05:00 AM 1.2 10000 PM conc. at 10:25:00 AM 1 8000 0.8 PM conc. at 10:45:00 AM 6000 0.6 Wind speed at 10:05:00 AM 4000 0.4 PM PM Concentration Wind speed at 10:25:00 AM 2000 0.2 0 0 Wind speed at 10:45:00 AM 20 50 100 Horizontal distance from source (m)

Figure 3.76- Pollution Dispersion in Open Mid Rise 0 degree (Horizontal)

0 20 40 60 80 100 120 25000 1.6 1.4 20000 1.2 PM conc. at 10:05:00 AM

15000 1 PM conc. at 10:25:00 AM 0.8 PM conc. at 10:45:00 AM 10000 0.6 Wind speed at 10:05:00 AM 0.4 PM PM Concentration 5000 Wind speed at 10:25:00 AM 0.2 Wind speed at 10:45:00 AM 0 0 20 50 100 Horizontal distance from source (m) Figure 3.77- Pollution Dispersion in Open Mid Rise 315 degree (Horizontal)

0 20 40 60 80 100 120 16000 2.5 14000 PM conc. at 1m from ground 2 12000 PM conc. at 15m from ground 10000 1.5 PM conc. at 30m from ground 8000 PM conc. at 45m from ground 1 6000 Wind speed at 1m from ground 4000 PM PM Concentration 0.5 Wind speed at 15m from ground 2000 Wind speed at 30m from ground 0 0 20 50 100 Wind speed at 45m from ground Horizontal distance from source (m) Figure 3.78- Pollution Dispersion in Open Mid Rise 0 degree (Vertical)

0 20 40 60 80 100 120 25000 2.5 PM conc. at 1m from ground 20000 2 PM conc. at 15m from ground

15000 1.5 PM conc. at 30m from ground PM conc. at 45m from ground 10000 1 Wind speed at 1m from ground

PM PM Concentration 5000 0.5 Wind speed at 15m from ground Wind speed at 30m from ground 0 0 20 50 100 Wind speed at 45m from ground Horizontal distance from source (m)

Figure 3.79- Pollution Dispersion in Open Mid Rise 315 degree (Vertical)

Figure 3.80- Pollution Dispersion in Open Mid-Rise Settlement

Figure 3.81- Pollution Dispersion in Open Mid-Rise Settlement (Section)

3.4.4.7. Open Low Rise: These types of settlements consist of low height buildings (≥ 9m), wide streets and vegetation. In open low-rise settlements, the pollution dispersion pattern as seen in Figure 3.85 sweeps speedily in the windward direction. The pollution dispersion is rather narrow with less lateral dispersion in the adjacent streets. This is due to the low height of the buildings which does not restrict the pollution dispersion at higher levels and allows pollutants to blow over them.

The obstructions are created by the tall trees present in the site. Figure 3.86 clearly shows the obstruction created by the trees. These trees slow down the wind speed making PM persist for longer periods in the air increasing the PM concentration at that area. As expected, Pollution

Concentration levels at higher levels are minimal because the low height of buildings are unable to push the plume higher than urban canopy boundary layer. Meanwhile, the higher pollution levels at 50 and 100m from the source can also be attributed to the low height of the building being unable to restrict the plume in proximity of source.

0 20 40 60 80 100 120 40000 1.22 35000 1.2 1.18 30000 PM conc. at 10:05:00 AM 1.16 25000 PM conc. at 10:25:00 AM 1.14 20000 PM conc. at 10:45:00 AM 1.12 15000 1.1 Wind speed at 10:05:00 AM

PM PM Concentration 10000 1.08 Wind speed at 10:25:00 AM 5000 1.06 Wind speed at 10:45:00 AM 0 1.04 20 50 100 Horizontal distance from source (m)

Figure 3.83- Pollution Dispersion in Open Low Rise 0 degree (Horizontal)

0 20 40 60 80 100 120 60000 1.4

50000 1.2 PM conc. at 10:05:00 AM 1 40000 PM conc. at 10:25:00 AM 0.8 30000 PM conc. at 10:45:00 AM 0.6 20000 Wind speed at 10:05:00 AM 0.4

PM Concentration PM Wind speed at 10:25:00 AM 10000 0.2 Wind speed at 10:45:00 AM 0 0 20 50 100 Horizontal distance from source (m)

Figure 3.84- Pollution Dispersion in Open Low Rise 315 degree (Horizontal)

0 b 20 40 60 80 100 120 40000 2.5 35000 PM conc. at 1m from ground 2 30000 PM conc. at 15m from ground

Figure 3.85- Pollution Dispersion in Open Low Rise 0 degree (Vertical)

0 20 40 60 80 100 120 60000 1.6 1.4 PM conc. at 1m from ground 50000 1.2 PM conc. at 15m from ground 40000 1 PM conc. at 30m from ground 30000 0.8 PM conc. at 45m from ground 0.6 20000 Wind speed at 1m from ground 0.4 PM PM Concentration Wind speed at 15m from ground 10000 0.2 Wind speed at 30m from ground 0 0 Wind speed at 45m from ground 20 50 100 Horizontal distance from source (m) Figure 3.86- Pollution Dispersion in Open Low Rise 315 degree (Vertical)

Figure 3.87- Pollution Dispersion in Open Low-Rise Settlement

Figure 3.88- Pollution Dispersion in Open Low-Rise Settlement (Section) 96

3.4.4.8. Large Low Rise: This type of settlement consists of few buildings with large surface areas (such as industrial areas) and open spaces with no vegetation. Figure 3.91 shows the plume blows speedily in the windward direction with less minimum dispersion due to absence of obstruction.

Due to abundant open area and no obstruction the pollution distribution is almost uniform at 20,

50 and 100m from the source when the wind direction is 0 degree. Meanwhile, when the wind direction is 315 degree the pollution plume encounters obstacles (Figure 3.92), where the wind speed sharply drops (Figure 3.90) increasing the PM concentration. However, the size of building being large restricts the plume from to passing further at 100m when the wind direction is 315 degree.

Similarly, Figure 3.90 shows the wind speed and pollution distribution is uniform in the vertical direction at 0-degree wind direction while in 315-degree pollution level decreases sharply with the distance. Also, it returns to background levels 15, 30 and 45m above the ground in almost all cases. This settlement shows if the obstruction is large the pollution plume distribution can be minimized at the preceding area.

0 20 40 60 80 100 120 20000 1.39 18000 1.38 16000 1.37 14000 1.36 PM conc. at 10:05:00 AM 12000 1.35 PM conc. at 10:25:00 AM 10000 1.34 PM conc. at 10:45:00 AM 8000 1.33 Wind speed at 10:05:00 AM 6000 1.32 PM PM Concentration Wind speed at 10:25:00 AM 4000 1.31 2000 1.3 Wind speed at 10:45:00 AM 0 1.29 20 50 100 Horizontal distance from source (m)

Figure 3.90- Pollution Dispersion in Large Low Rise 0 degree (Horizontal) 0 20 40 60 80 100 120 50000 1.325 45000 40000 1.32 PM conc. at 10:05:00 AM 35000 1.315 30000 PM conc. at 10:25:00 AM 25000 1.31 PM conc. at 10:45:00 AM 20000 Wind speed at 10:05:00 AM 15000 1.305

PM PM Concentration Wind speed at 10:25:00 AM 10000 1.3 5000 Wind speed at 10:45:00 AM 0 1.295 20 50 100 Horizontal distance from source (m) Figure 3.91- Pollution Dispersion in Large Low Rise 315 degree (Horizontal)

0 20 40 60 80 100 120 20000 2.5 18000 PM conc. at 1m from ground 16000 2 PM conc. at 15m from ground 14000 12000 1.5 PM conc. at 30m from ground 10000 PM conc. at 45m from ground 8000 1 Wind speed at 1m from ground 6000

PM PM Concentration 4000 0.5 Wind speed at 15m from ground 2000 Wind speed at 30m from ground 0 0 20 50 100 Wind speed at 45m from ground Horizontal distance from source (m)

Figure 3.92- Pollution Dispersion in Large Low Rise 0 degree (Vertical)

0 20 40 60 80 100 120 50000 2.5 45000 PM conc. at 1m from ground 40000 2 PM conc. at 15m from ground 35000 30000 1.5 PM conc. at 30m from ground 25000 PM conc. at 45m from ground 20000 1 Wind speed at 1m from ground 15000

PM PM Concentration 10000 0.5 Wind speed at 15m from ground 5000 Wind speed at 30m from ground 0 0 20 50 100 Wind speed at 45m from ground Horizontal distance from source (m) Figure 3.93- Pollution Dispersion in Large Low Rise 315 degree (Vertical)

Figure 3.94- Pollution Dispersion in Large Low-Rise Settlement

Figure 3.95- Pollution Dispersion in Large Low-Rise Settlement (Section)

3.4.4.9. Sparsely Built: Sparsely built settlements have few buildings with a proportionately lower surface area.

The PM concentration at 50m from the source is higher in 0-degree wind direction than the 20m. this is because of the accumulation of particulate matter in front of the obstructing building. The pollution plume sweeps speedily until it hits the obstruction. Figure 3.95, shows the pollution concentration is increased which drops at 100m is still slightly higher than at 20m.

Meanwhile, when the wind direction is 315 degree the pollution plume is obstructed by the surrounding buildings and the PM concentration is significantly lower in both vertical and horizontal measured distances. In figure 4.35, pollution levels are decreased with an increase in vertical distance and remains almost constant at 30m and 45m above ground levels at all measured horizontal distances. Increase in wind speed can be seen with increase in vertical and horizontal distance. In Figure 3.98, the PM concentration levels are almost constant at all the other levels except at 1m from the ground, which decreases sharply with increase in horizontal distance. Meanwhile, increase in wind speed is noticed with increase in vertical distance.

100

0 20 40 60 80 100 120 35000 1.2

30000 1 PM conc. at 10:05:00 AM 25000 0.8 PM conc. at 10:25:00 AM 20000 0.6 PM conc. at 10:45:00 AM 15000 0.4 Wind speed at 10:05:00 AM 10000

PM PM Concentration Wind speed at 10:25:00 AM 5000 0.2 Wind speed at 10:45:00 AM 0 0 20 50 100 Horizontal distance from source (m) Figure 3.97- Pollution Dispersion in Sparsely Built 0 degree (Horizontal)

0 20 40 60 80 100 120 70000 1.4 60000 1.2 PM conc. at 10:05:00 AM 50000 1 PM conc. at 10:25:00 AM 40000 0.8 PM conc. at 10:45:00 AM 30000 0.6 Wind speed at 10:05:00 AM 20000 0.4

PM PM Concentration Wind speed at 10:25:00 AM 10000 0.2 Wind speed at 10:45:00 AM 0 0 20 50 100 Horizontal distance from source (m)

Figure 3.98- Pollution Dispersion in Sparsely Built 315 degree (Horizontal)

0 20 40 60 80 100 120 35000 2.5 30000 PM conc. at 1m from ground 2 PM conc. at 15m from ground 25000 PM conc. at 30m from ground 20000 1.5 PM conc. at 45m from ground 15000 1 Wind speed at 1m from ground 10000

PM PM Concentration 0.5 Wind speed at 15m from ground 5000 Wind speed at 30m from ground 0 0 Wind speed at 45m from ground 20 50 100 Horizontal distance from source (m)

Figure 3.99- Pollution Dispersion in Sparsely Built 0 degree (Vertical)

101

0 20 40 60 80 100 120 70000 2.5 PM conc. at 1m from ground 60000 2 PM conc. at 15m from ground 50000 PM conc. at 30m from ground 40000 1.5 PM conc. at 45m from ground 30000 1 Wind speed at 1m from ground 20000

PM PM Concentration 0.5 10000 Wind speed at 15m from ground 0 0 Wind speed at 30m from ground 20 50 100 Wind speed at 45m from ground Horizontal distance from source (m)

Figure 3.100- Pollution Dispersion in Sparsely Built 315 degree (Vertical)

Figure 3.101- Pollution Dispersion in Sparsely Built Settlement.

Figure 3.102- Pollution Dispersion in Sparsely Built Settlement (Section)

102

3.4.4.10. Low Rise with Dense Trees: These settlements consist of open spaces, few low height buildings, and with abundant of trees. The pollution concentration levels at all points are lower except for 20m from the source which is extremely high. Pollution concentration when 315 degree is higher as the trees and surrounding buildings create obstructions and slow down the wind speed resulting in PM accumulation. When the wind speed is 0 degree the pollution takes a dip at 50m and is maximum at 100m which accumulates the PM and due to higher wind speed at the corner of the building

PM re blow back into air increasing concentration.

Pollution concentration is lowest at 50m from the source at all times compared. In figure

3.104, pollution levels are decreased with increase in vertical distance and remains almost constant at 30m and 45m above ground levels at all measured horizontal distances. Increase in wind speed can be seen with an increase in vertical distance. In figure 3.105, the PM concentration levels are almost constant at all the other levels except at 1m from the ground, which decreases sharply with increase in horizontal distance. Meanwhile, an increase in wind speed is noticed with increase in vertical distance.

103

0 20 40 60 80 100 120 18000 1.2 16000 1 14000 PM conc. at 10:05:00 AM 12000 0.8 PM conc. at 10:25:00 AM 10000 0.6 PM conc. at 10:45:00 AM 8000 6000 0.4 Wind speed at 10:05:00 AM

PM PM Concentration 4000 Wind speed at 10:25:00 AM 0.2 2000 Wind speed at 10:45:00 AM 0 0 20 50 100 Horizontal distance from source (m)

Figure 3.104- Pollution Dispersion in Low Rise with Dense Trees 0 degree (Horizontal)

0 20 40 60 80 100 120 60000 1.4

50000 1.2 PM conc. at 10:05:00 AM 1 40000 PM conc. at 10:25:00 AM 0.8 30000 PM conc. at 10:45:00 AM 0.6 20000 Wind speed at 10:05:00 AM 0.4

PM PM Concentration Wind speed at 10:25:00 AM 10000 0.2 Wind speed at 10:45:00 AM 0 0 20 50 100 Horizontal distance from source (m)

Figure 3.105- Pollution Dispersion in Low Rise with Dense Trees 315 degree (Horizontal)

0 20 40 60 80 100 120 18000 2.5 16000 PM conc. at 1m from ground 2 14000 PM conc. at 15m from ground 12000 1.5 PM conc. at 30m from ground 10000 PM conc. at 45m from ground 8000 1 6000 Wind speed at 15m from ground

PM PM Concentration 4000 0.5 Wind speed at 1m from ground 2000 Wind speed at 30m from ground 0 0 Wind speed at 45m from ground 20 50 100 Horizontal distance from source (m)

Figure 3.106- Pollution Dispersion in Low Rise with Dense Trees 0 degree (Vertical)

104

PM PM Concentration 10000 0.5 Wind speed at 15m from ground

0 0 Wind speed at 30m from ground 20 50 100 Wind speed at 45m from ground Horizontal distance from source (m)

Figure 3.107- Pollution Dispersion in Low Rise with Dense Trees 315 degree (Vertical)

Figure 3.108- Pollution Dispersion in Low Rise with Dense Settlement

Figure 3.109- Pollution Dispersion in Low Rise with Dense Settlement (Section)

105

106

CHAPTER 4: DISCUSSION

In this chapter, the results obtained from the simulations are discussed on the basis of criteria such as neighborhood density, street width, wind speed, wind direction, distance from source

(vertical and horizontal) and land cover and vegetation.

4.1. Neighborhood density and street width: In compact settlements with narrow street widths and an absence of open area the PM concentration is comparatively higher at all the measured street and vertical levels at all the measured distances from the source due to the formation of a steady circulatory vortex in the street canyon bringing a skimming regime with in the compact settlement (Figure 4.1). This slows airflow in deep street canyons in comparison with uniform or shallow ones resulting the pollutants to say longer in the air. Meanwhile, between the three types of compact settlements studied, compact high-rise settlements show lower pollution levels because tall buildings promote more vertical flow up from the street canyon to the urban boundary layer pushing away from ground level as compared to midrise and low rise settlements. Similarly, the Compact

Midrise+ Open Highrise configuration behaved the same as the compact high-rise. The similarity could be explained by the wind behavior around high-rise buildings which dominate the flow regime regardless of the in-between structures.

The lowest pollution concentration is observed in open high rise and open mid-rise settlement due to the wake interface regime formation (Figure 4.2). The external wake of each individual building, i.e. the horse shoe vortex, interacts with that of neighboring buildings, spreading the pollutants inside the wake covering the larger area while reducing the

107 concentration levels. At the same time, the internal wake is not affected by neighboring buildings. However, in open low rise settlements the individual building is viewed as isolated forming isolated roughness regime where there is no interaction between windward and leeward flows (Figure 4.3). This leads to the double-peak distribution in the lateral direction increasing the pollution level at the adjacent street level, rather than spreading around the area like in open mid and highrise. In large low-rise configurations the source is not exposed to obstruction, so the dispersion rate remained almost constant at all the distance in northern wind direction.

Meanwhile, the pollution plume in sparsely built configurations only faces obstacle after

20m, therefore the PM concentration at 20m is like config. 8. Around 50 m due to obstruction caused by the building, isolated roughness regime is formed increasing the pollution at 50m by almost 45%. Finally, in Low rise with Dense trees configuration obstructions are created by trees which does not necessarily affect this high amount pollution dispersion at the initial distance but is reduced at 50m point, which is located adjacent to trees. However, it rises again when it on goes isolated roughness regime around 100m.

Figure 4.1- Formation of Skimming regime in a Compact Midrise Settlement

108

Figure 4.2- Formation of wake interface regime in Open High-Rise settlement

Figure 4.3- Formation of wake interface regime in Open Low-Rise settlement

109

4.2. Distance from source: In almost all the 10 built type configuration a direct relationship is noticed between the

Pollution Concentration and horizontal distance from the source, except for configuration 8, 9 and 10 at 0-degree wind direction. The discrepancy in config. 8, 9 and 10 is because of the obstruction created by the buildings or vegetation at or around 50m distance. Figure 4.4 shows the decrease in pollution concentration with increase in time for config. 1-7 in both the wind direction. Meanwhile, in config. 8,9 and 10 PM concentration drops sharply with distance by more than 50% similar to 1-7 configuration when the wind direction is 315 degree but shows different trend when the wind direction is 0 degree.

Similarly, the increment in the vertical distance from the source shows the similar trend which can be observed in figure 4.5. Data are recorded at the various vertical distances 20m away from the source. The dispersion pattern in the compact settlement at vertical distance are slower than in open settlement since the pollution reaches urban boundary layer faster as open settlement possess shallow street canyon.

70000

60000

50000

40000

30000

20000

10000

Pollution Concentration(µg/m3) 0 1. Compact 2. Compact 3. Compact 4. Compact 5. Open High 6. Open Mid 7. Open Low 8. Large Low 9. Sparsely 10. Low Rise High Rise Mid Rise Low Rise Mid Rise + Rise Rise Rise Rise Built with Open High Scattered Rise Trees Built Types

0 degree 315 degree (at 20m) 0 degree 315 degree (at 50m) 0 degree 315 degree (at 100m)

Figure 4.4- Pollution concentration at vertical 1m, 15m, 30m and 45m form the ground surface at wind direction 0 and 315 degree

110

60000

50000

40000

30000

20000 PM Concentration PM 10000

0 1. Compact 2. Compact 3. Compact 4. Compact 5. Open High 6. Open Mid 7. Open Low 8. Large Low 9. Sparsely 10. Low Rise High Rise Mid Rise Low Rise Mid Rise + Rise Rise Rise Rise Built with Scattered Open High Trees Rise Built types

1m from ground lvl 15m from ground lvl 30m from ground lvl 45m from ground lvl (at 0 degree) 1m from ground lvl 15m from ground lvl 30m from ground lvl 45m from ground lvl (at 315 degree)

Figure 4.5- Pollution concentration at horizontal 20m, 50m and 100m form the source at wind direction 0 and 315 degree

4.3. Wind direction and Speed:

The understanding of wind speed and direction are the utmost important aspects in the air pollution dispersion modelling. As the thesis concerns with sudden high-level emissions from a single source, the dispersion pattern of these pollutants with respect to wind speed and direction does not ensure the breathable air quality levels in the surrounding neighborhood for certain time depending on the intensity of a disaster. However, significant difference in pollution levels within simulated morphologies can be observed with the change in wind speed and direction.

Due to barriers such as buildings and trees, airflow in the urban canopy layer is more blocked in comparison with airflow in the urban boundary layer.

Figure 4.6 shows the pollution concentration and wind speed data from two different wind directions, namely 0 and 315 at 2m away from the source. In 0-degree, higher pollution levels are observed in comparatively lower wind speed and vice versa. The wind speed in open midrise, open highrise and compact highrise morphologies are observed comparably higher. But the compact high settlement produces higher pollution levels and other two configuration

111 produces the least pollution between the compared configurations. The difference is caused because of the high-rise buildings that accelerated and pushed the majority of airflow above the urban canopy layer and away from the ground and making the pollutants stay longer in the air.

Meanwhile in open settlement found ample distance between building forming wake interface regime, which in turn reduced the pollution levels. The wind speed in configuration 4 which is the combination of open high rise and compact midrise shows the combined effect in pollution concentration results. The similarity can be explained by the wind behavior around high-rise buildings which dominate the flow regime regardless of the in-between structures.

At the same time, the figure also shows that the wind coming from an angle produced higher level pollution concentration compared to northern wind except in the case of config. 4 where the tall building right adjacent to source is responsible for restriction of pollution dispersion. In addition, the wind speed follows the similar trend as 0 degree where pollution is higher at lower wind speed and lower at comparatively higher wind speed.

70000 1.8 1.6 60000 1.4 50000 1.2 40000 1

30000 0.8 0.6

20000 Wind Speed(m/s) 0.4

PM C oncentration (µg/m3) 10000 0.2 0 0 1. Compact 2. Compact 3. Compact 4. Compact 5. Open 6. Open Mid 7. Open 8. Large 9. Sparsely 10. Low High Rise Mid Rise Low Rise Mid Rise + High Rise Rise Low Rise Low Rise Built Rise with Open High Scattered Rise Built types Trees

PM concentration at 0 deg PM concentration at 315 deg Wind Speed at 0 deg Wind Speed at 315 deg

Figure 4.6- Pollution concentration at vertical 1m, 15m, 30m and 45m form the ground surface at wind direction 0 and 315 degree

112

4.4. Vegetation: An increasing number of studies show that vegetation has dust collecting capacities

“phytotechnologies” which can decrease the concentrations of ambient air pollutants. However, the thesis deals with much higher-level emission in an urban area, placing vegetation was not enough to ensure the quality of ambient air of the affected neighborhood. However, decrease in pollution concentration dispersion is noticed with the increase in the amount of vegetation and open space in figure 4.45. Meanwhile the pollution concentration is increased around the tree area as the particulate matter sticks to the tree and settle around it.

In built type 5, 6,7, 9 and 10 vegetation are introduced. While 5,6,7 and 9 possess few numbers of trees, config.10 is provided with abundant vegetation. The vegetation between the building blocks represented a much larger obstruction, the wind coming from the WNW, was blocked by the series of trees and reduced its speed. The only odd result is the Sparsely built at

315 degree. angle of incidence because the measurement is taken at only 20 m from the middle of the block which in this case very close to the demolished block.

In the Figure 4.7, Figure 4.8 and Figure 4.9 trees are placed in high rise, midrise and low-rise settlement. In high rise settlement, trees have least effect on the pollution movement in the vertical direction as the building height itself is responsible to push the pollution plume above from ground. However, the tall buildings and trees reduces the radius of pollution as most of the

PM settle near the obstructions. In midrise settlement the trees are as same height as buildings, so it restricts the transportation of PM at longer distances. Finally, in low-rise settlement trees are responsible for slowing the wind speed letting the PM settle at longer distance. The radius of pollution is increased as the building height and trees are not able to restrict the pollution dispersion. However, the vertical radius is lower in such type of settlement.

113

Figure 4.7- Effects of trees in open Highrise settlement

Figure 4.8- Effects of trees in open Highrise settlement

Figure 4.9- Effects of trees in open Highrise settlement

114

4.5. Pollution dispersion pattern/radius in selected neighborhood morphologies: Table 4.1 shows the Pollution dispersion pattern in ten different LCZ configuration when in two different wind direction. This table provides a clear insight of dispersion behavior of the high number of particulate matter released during the unplanned building demolition allowing the reader to identify and compare the radius of pollutant dispersion by an unplanned building demolition. This tabular compilation can be extremely useful to the residents, first responders, rescue workers and the medical team to identify the preliminary health risk to the exposed population based on proximity as well as wind speed and direction and duration of exposure.

Table 4.1- Pollution dispersion pattern in different neighborhood morphologies

ENVI-met PM (0 deg) PM (315 deg) PM Conc. Built Configuration Model Distribution Distribution (µg/m3)

115

ENVI-met PM (0 deg) PM (315 deg) PM Conc. Built Configuration Model Distribution Distribution (µg/m3)

116

4.6. Sensitivity of an area to human health: The literature only provides the standards for 24- hour average pollution concentration.

Meanwhile, such short-term bursts are responsible to produce high amount of PM for few minutes which are equally hazardous to human health. The thesis makes an effort to identify the sensitivity of an area with in a neighborhood as per time and distance from a source. These estimates can provide the residents, rescue workers and medical helpers with a brief idea with the disaster related PM induced victims. Also, for the policy makers and the urban designers these estimates can provide a basis to mandate stringent guidelines to regulate the pollution dispersion patterns as well as addressing the effect of short-term high concentration bursts.

The sensitivity is classified as:

>10000: High

10000-6000: Medi

<6000: Low

Table 4.2- Sensitivity of an area to human health in horizontal distance from source.

Distance from PM conc. at PM conc. at PM conc. at LCZ Type source (m) 10:05:00 AM 10:25:00 AM 10:45:00 AM 20 33383 27669 11208 Compact 50 28834 23916 9687 Highrise 100 21067 17509 7085 20 41877 27944 14184 Compact 50 34454 22991 11668 Midrise 100 23678 15837 8053 20 36295 23902 12054 Compact Low 50 27116 17790 8972 rise 100 1190 11704 5904

117

Distance from PM conc. at PM conc. at PM conc. at LCZ Type source (m) 10:05:00 AM 10:25:00 AM 10:45:00 AM 20 28859 18813 9373 Compact Mid+ 50 19969 12867 6386 Open Highrise 100 11905 7567 3739 20 12481 8137 4091 Open Highrise 50 10016 6465 3245 100 5263 3387 1697 20 14445 9333 4625 Open Midrise 50 15144 9571 4698 100 10408 6506 3176 20 35239 23182 11642 Open Low rise 50 32605 21223 10641 100 14857 9468 4732 20 16924 11138 5551 Large Low rise 50 17583 11422 5656 100 14643 9341 4588 20 18115 12023 6000 Sparsely Built 50 30795 20148 10102 100 20457 13126 6557 20 14535 9731 4913 Low rise with 50 10384 6923 3492 dense trees 100 15641 10139 5090

Table 4.3- Sensitivity of an area to human health at progressive vertical distances from source.

Distance from PM conc. at various vertical distances from the source LCZ Type source (m) 1m 15m 30m 45m 20 33383 17449 9035 3350 Compact 50 28834 16969 9806 3800 Highrise 100 21067 14034 9349 4084

118

20 41877 14527 2613 106 Compact 50 34454 14814 3284 272 Midrise 100 23678 13127 3590 579 20 36295 5860 288 27 Compact Low 50 27116 6842 790 106 rise 100 1190 5783 1217 276 20 28859 8484 1575 138 Compact Mid+ 50 19969 8220 2429 319 Open Highrise 100 11905 6336 2822 650 20 12481 4357 1683 224 Open Highrise 50 10016 5758 3254 895 100 5263 3694 2634 1086 20 14445 1802 390 31 Open Midrise 50 15144 5069 1679 158 100 10408 5683 2501 436 20 35239 2016 153 9 Open Low rise 50 32605 4830 581 46 100 14857 5355 1127 125 20 16924 611 77 11 Large Low rise 50 17583 1519 413 46 100 14643 2594 714 173 20 18115 579 36 3 Sparsely Built 50 30795 3454 413 46 100 20457 5695 1602 285 20 58974 1276 58 4 Low rise with 50 11982 1146 101 9 dense trees 100 4606 580 60 11

119

As per the sensitivity of the area the required precaution should be undertaken to minimize the respiratory illnesses in the residents. Some of the points are noted below:

High Sensitivity Area:

• Evacuate immediately to the safer zones.

• A safety kit comprising of “N-95” filtering facepiece respirator, a type of particulate

respirator should be mandatory at all homes in such disaster-prone zones. Residents and

rescue workers should always wear this mask near high sensitivity area.

• First Responders exposed to pollution plume without any precaution must be provided with

immediate medical observation to identify the persuasiveness of the PM in victim’s body.

Medium Sensitivity Area:

• Evacuation to safer area is recommended.

• N-95 air purifying mask should be worn at all times.

• Residents should seek for immediate medical attention.

Low sensitivity Area:

• Residents can remain indoors with their windows and other openings closed.

• Primary cleanup of PM with water sprays can be carried out to reduce the PM concentration

in air. N-95 air purifying mask should be worn.

• Sensitive population like old people, babies and respiratory patients should seek

immediate medical help. While, other residents should get timely checkups.

120

CHAPTER 5. DESIGN RECOMMENDATIONS AND CONCLUSION

5.1. Design Recommendations: Urban planners have control of several design parameters such a zoning, master plan, site orientation, site coverage, building proportion and so on. Ensuring the proper planning of urban ventilated corridors is crucial, so that the ambient mechanical wind does not get blocked by the built or vegetative obstructions. Explicit guidelines for urban planning cannot be proposed just with the investigation of wind flow and pollution dispersion, as various contemplations are required. Moreover, the level of PM concentrations and the radius of dispersion are extremely high during unplanned building demolition. However, to focus on the impact of neighborhood morphology on the pollution dispersion following design ideas are recommended in order to potentially increase the extent of areas of lower health risks and reduce the impact of the areas that have higher pollution depositions:

1. Street canyon aspect ratio:

• While planning in vulnerable areas such as disaster prone and frequent war zones, street

widths should be carefully considered as to allow access to individuals and groups who

could be rescued with other trapped individuals. Street canyon aspect ratio of 0.7-1.25 is

effective for proper risk minimization strategy.

• At Aspect Ratio (h/w) = 0.7, wake interface flow changes to skimming flow resulting in

effective dispersion of pollutants. For instance, the open Highrise neighborhood had the

lowest pollution concentration compared to the other neighborhood configuration.

121

• Building placements at relatively narrow yet uniform distance restricts the pollution

dispersion within the street canyon. But in the case of unplanned demolition the dust

plume travels more than 100m depending upon the intensity of building collapse.

• The difference in building height within the neighborhood promotes beneficial urban

ventilation by allowing effective dispersion of pollutants. The height of the leeward

building is important. The higher the leeward building it accelerates the canyon air above

the roof where pollutants are retained and stagnated at the upstream. Low height of

leeward building ensures minimum retention of pollutants while increasing at the

upstream the concentration at pedestrian level which can be reduced by re-entrainment of

fresh air into the canyon cavity from the downstream of leeward building.

2. Wind flow

• Planning urban ventilated corridors are necessary for effective pollution dispersion. Wide

corridor in the downwind direction increases the Wind Speed.

• The tall buildings and vegetation promote high wind speed and low wind speed is noticed

with low sized vegetation and buildings. Low wind speed slowdowns the dispersion

process allowing the chemical alteration of pollutants in atmosphere. However, retaining

pollutants at one area and cleaning off them promptly can be beneficial in case of

unplanned building demolition where large amount of dust plume and demolition waste

are accumulated.

• Wide street width at the downwind path of the perpendicular canyon allows effective

pollution dispersion with minimum retention. Meanwhile, the narrow street width retains

the pollutants and forces them to escape through roof level.

122

3. Vegetation:

• Depending on the type of street canyon, tall trees generally possess negative impact on

air quality as it slows the wind speed allowing maximum retention of pollutants in the

canyon. So, proper position of suitable vegetation should be identified for effective

dispersion.

• Low level dense vegetation (i.e. hedges) placed at the center of the streets instead of

sidewalk promotes better air quality for the pedestrians.

• Pollution absorbing trees should not be placed in front of building openings and

balconies.

• Appropriate selection of vegetation combined with physical pollution control measures

can effectively improve air quality and personal exposure conditions.

4. Building Design:

• Building openings facing the street should be avoided if not should be tightly sealed.

Particle filter nets should be placed at all the street facing openings.

• Balconies facing the street should be avoided.

• Pollution absorbing façade such as green wall can be used but the timely clean-up of the

absorbed particulate matter must be assured.

• Overall, the building should be planned in such a way that the intake of the air for the

ventilation system of the building should be located at the less polluted side, which can

be calculated by CFD models.

123

5. Safety regulations:

• Use of water spray techniques to reduce concentrations near windows and public

buildings such as schools, hospitals, and senior living facilities, etc

• Proper zoning during the planning phase should be ensured for the schools, hospitals and

senior homes.

• Air Ventilation Assessment via wind tunnel testing should be mandated by policy makers

for any new development works to ensure that they do not cause stagnation of pollutants.

CFD study can replace wind tunnel testing in case of the small projects.

5.2. Conclusion Collapse of buildings during earthquakes, terrorist attacks and wars are frequent events in many parts of the world. Upon arrival of such disasters, physical damage and human loss are inevitable depending on the intensity of disaster. Physical damage includes the partial or full collapse of several built structures creating a massive dust cloud and demolition waste. The main concern during such unplanned event becomes the rescue of trapped victims and provide medical attention for physical injuries. The rescue workers and residents jump into the dust plume to save their families and other victims without realising the quality of contaminated air they are breathing. The quality of air might not be the priority at that instance, but the effects of inhaling the fine particulate matter generated from hazardous building materials cannot be overlooked while there are overwhelming literature studies on the 9/11 victims suffering from chronic respiratory illnesses even after years.

So, this thesis was designed to address the impact of the dust plume generated during unplanned building demolition. The lack of existing study investigating human exposure to

124 highly concentrated short-term pollution during accidental building collapse justified the necessity of this research. The research acknowledges the relation between the poor air quality in the neighborhood during unplanned building demolition to the related health issues which was widely overlooked due to other primary concerns during disaster chaos. Since the post disaster situation is chaotic, the study focused on investigating if planning neighborhood beforehand had any impact on the pollution dispersion pattern. Ten different neighborhood morphologies were simulated to fully understand the characteristic of particulate matter movement around the settlement with varying meteorological and physical parameters.

The findings showed that the PM concentration level during the unplanned demolition within its neighborhood is more than thousand times higher than the regular high pollution levels depending upon the intensity of the disaster. However, these PM concentrations were short lived lasting only for few hours while dispersing more than 200 meters away from the source depending upon the type of morphologies. The results are discussed explicitly while identifying the pollutant dispersion pattern and wind flow in each neighborhood configurations. The discussion is followed by the identification of the sensitivity of an area to human health which helps the residents, rescue workers, victims and the medical helpers to identify the level of risk they were exposed during the collapse and seek the recommended help immediately. The thesis concludes with the overarching design recommendations to increase the extent of areas of lower health risks and reduce the impact of the areas that have higher pollution depositions. The recommendations were proposed with the aim to help the urban planners and policy makers to regulate the pollution dispersion patterns as well as addressing the effect of short-term high concentration bursts.

125

5.3. Delimitations of the study 1. The results in this research are based on the principal of Computational Fluid Dynamics,

which are known for having difficulty in considering all the atmospheric stability

parameters. Thereby, leading to the difference in the results than the actual atmospheric

phenomenon.

2. The thesis investigates the effect of collapse of a single building of same size and

emission rate in all neighborhoods. This was done to identify the impact in the similar

condition at different configuration.

3. The study attempts to address the solution to minimize the pollution levels, however

depending on the intensity of the natural and man-made disasters the findings from this

research might not correspond.

4. The study does not take into account the kinetic energy released during the collapse of

the building which in many cases is responsible for pushing the particulate matter further

away from the source. In this research, all the sources are placed at 1m height from the

ground surface.

5.4. Limitation of the study: 1. The real scenario contains several types of neigborhood morphologies. However, due the

time constraint the study only simulated 10 neighborhood morphologies.

2. Only 2 wind direction were tested i.e. perpendicular and oblique.

3. Due to the limitation of the air quality model used and the large number of simulations

carried out only 400X400m size neighborhood were simulated to complete the study in

allocated timeframe.

126

4. The study was designed to understand the effects of vegetation on the pollution

dispersion pattern. Only one type vegetation is tested that was 20m high deciduous trees.

5. Due to the availability of only coarse PM concentration data from the implosion study,

this thesis investigated only the coarse particulate matter (2.5-10 µm in diameter).

6. The study focused only on the exterior conditions only and doesn’t look into the pollution

infiltration into the building’s interior.

5.5. Recommendations for future research The simulation results presented in this report fulfils the set thesis objectives within the time dedicated for this thesis study. However, simulation models can always be improved to deliver more accurate results and expanded to study the complete processing. Based on the shortcomings and opportunities available to improve the simulation model of the air pollution dispersion process, some recommendations for further research in this field are proposed as follows.

1. Even though, it is difficult to record such high-level pollution by the monitoring stations

which are configured to measure the daily pollution levels at comparatively lower levels,

the real time emission or concentration data from an unplanned demolition can be truly

beneficial for the research to study further.

2. PM of less than 2.5µm and ultrafine particles can be studied.

3. Different morphological configurations with courtyard, waterbodies, topography and

many more can be simulated to add to the study.

4. Various types of trees (evergreen, deciduous and coniferous) can be studied to understand

their pollution retaining characteristics within the settlement.

127

5. The deeper study of an aspect ratio of street canyon can predict the effect of such short-

term high-level pollution in the outdoor and indoor environments.

6. Run very large sites for design exploration exercises, these options are possible if parallel

processing power of the CPU beyond 16 cores is available.

128

REFERENCES

1. ADORC. (2006). MICS-Asia Phase II – A model inter comparison study in Asia. Acid Deposition and Oxidant Research Center, Niigata, Japan. http://www.adorc.gr.jp/adorc/mics.html 2. Ali-Toudert, F., & Mayer, H. (2007). Effects of asymmetry, galleries, overhanging façades and vegetation on thermal comfort in urban street canyons. Solar Energy,81(6), 742-754. 3. Agency for Toxic Substances and Disease Registry. (2003). Toxicological Profile for Asbestos; U.S. Department of Health and Human Services: Atlanta, GA. 4. Ahmad, K., Khare, M., & Chaudhry, K.K. (2005). Wind tunnel simulation studies on dispersion at urban street canyons and intersections - a review, Journal of Wind Engineering and Industrial Aerodynamics 93: 697-717. 5. Assimakopoulos, V., Apsimon, H., & Moussiopoulos, N. (2003). A numerical study of atmospheric pollutant dispersion in different two-dimensional street canyon configurations. Atmospheric Environment,37(29), 4037-4049. 6. Azarmi, F., Kumar, P., & Mulheron, M. (2014). The exposure to coarse, fine and ultrafine particle emissions from concrete mixing, drilling and cutting activities. Journal of Hazardous Materials 279, 268-279. 7. Azarmi, F., Kumar, P., Marsh, D., & Fuller, G.W. (2015a). Assessment of long-term impacts of PM10 and PM2.5 particles from construction works on surrounding areas. Environmental Science: Processes & Impacts 18, 208-221. 8. Azarmi, F., Kumar, P., Mulheron, M., Colaux, J., Jeynes, C., Adhami, S., & Watts, J. (2015b). Physicochemical characteristics and occupational exposure to coarse, fine and ultrafine particles during building refurbishment activities. Journal of Nanoparticle Research 17, 343. 9. Chan, A.T., So, E. S. P., & Samad, S. C. (2001). Strategic guidelines for street canyon geometry to achieve sustainable street air quality. Atmospheric Environment, vol.35, 5681- 5691. 10. Baik, J.J., & Kim, J.J. (1999). A numerical study of flow and pollutant dispersion characteristics in urban street canyons, Journal of Applied Meteorology 38: 1576-1589. 11. Baik, J.J., & Kim, J.J. (2002). On the escape of pollutants from urban street canyons, Atmospheric Environment 36: 527-536. 12. Banerjee, T., & Christian, R.A. (2011). Three-Dimensional Analysis of Pollution Dispersion in Street Canyon, World Academy of Science, Engineering and Technology (81): 863-869. 13. Banauch, GI., Alleyne. D., Sanchez, R., Olender, K., Cohen, HW., & Weiden, M. (2003). Persistent hyperreactivity and reactive airway dysfunction in firefighters at the World Trade Center. Am J Respir Crit Care Med 168:54–62. 14. Bansal, A., & Mishra, G. (2017). Recycling and Reuse of Construction and Demolition waste : sustainable approach Recycling and Reuse of Construction and Demolition waste : sustainable.

129

15. Benson, P.E. (1979). CALINE3-A Versatile Dispersion Model for Predicting Air Pollutant Levels Near Highways and Arterial Streets. Interim Report; Technical Report; California State Department of Transportation: Sacramento, CA, USA. 16. Benson, P.E. (1984). Caline4-a Dispersion Model for Predicting Air Pollutant Concentrations Near Roadways. Final Report; Technical Report; California State Department of Transportation: Sacramento, CA, USA, 17. Benfield, A. (2015). 2015 Nepal Earthquake Event Recap Report, (September). Retrieved from http://wiki.openstreetmap.org/wiki/2015_Nepal_earthquake 18. Beck, C. M., Geyh, A., Srinivasan, A., Breysse, P. N., Eggleston, P. A., & Buckley, T. J. (2003). The Impact of a Building Implosion on Airborne Particulate Matter in an Urban Community. Journal of the Air and Waste Management Association, 53(10), 1256–1264. https://doi.org/10.1080/10473289.2003.10466275 19. Becker, S., Lienhart, H. & Durst, F. (2002). Flow around three-dimensional obstacles in boundary layers, Journal of Wind Engineering and Industrial Aerodynamics 90 (4-5): 265- 279. 20. Bitan, A. (1992). The high climatic quality city of the future. Atmospheric Environment 26B, 313–329. 21. Britter, R. E., & Hanna, S. R. (2003). Flow and dispersion in urban areas. Annual Review of Fluid Mechanics, 35(1), 469–496. https://doi.org/10.1146/annurev.fluid.35.101101.161147 22. Brook, J., Robert, D., Rajagopalan, S., Pope, C., Jeffrey R Brook, Aruni, B., Ana, D., Holguin, F., Hong, Y., Luepker, R., & Mittleman, A. (2010). "Particulate matter air pollution and cardiovascular disease an update to the scientific statement from the American Heart Association." Circulation 121 (21):2331-2378. 23. Brown, J. S., Gordon, T., Price, O., & Asgharian, B. (2013). Thoracic and respirable particle definitions for human health risk assessment. Particle and Fibre Toxicology,10(1), 12. 24. Bruse, M. (2004). ENVI-met 4.0 updated model overview: http://www.envi-met.com. 25. Bruse, M. (2007). ENVI-met implementation of the gas/particle dispersion and deposition model PDDM: http://www.envi-met.com. 26. Bruse, M. (2007) Particle filtering capacity of urban vegetation: A microscale numerical approach. Berliner Geographische Arbeiten, 109, 61-70. 27. Carmichael, G.R., Hayami, H., Calori, G., Uno, I., Cho, S.Y., Engardt, M., Kim, S.B., Ichikawa, Y., Ikeda, Y., Ueda, H., & Amann, M. (2001). Model Inter comparison Study of Long-Range Transport and Sulfur Deposition in East Asia (MICS-Asia), Water, Air, Soil Pollution 130:51-62. 28. Carmichael, G.R., Calori, G., Hayami, H., Uno, I., Cho, S.Y., Engardt, M., Kim, S.B., Ichikawa, Y., Ikeda, Y., Woo, J.H., Ueda, H., & Amann, M. (2002). The MICS-Asia Study: model inter comparison of long-range transport and sulfur deposition in East Asia, Atmospheric Environment 36:175-199. 29. Chan, L.Y., & Kwok, W.S. (2000). Vertical dispersion of suspended particulates in urban area of Hong Kong, Atmospheric Environment 34: 4403-4412.

130

30. Chang, C.H., & Meroney, R.N. (2003). Concentration and flow distributions in urban street canyons: wind tunnel and computational data, Journal of Wind Engineering and Industrial Aerodynamics 91: 1141-1154. 31. Checkoway, H., & Eisen E.A. (1998) Developments in occupational cohort studies Epidemiol. Rev., 20 (1), 100-111. 32. Chen, L.C., Cohen, M.D., Chee, G.R., Prophete, C.M., Haykal-Coates, N., Wasson, S.J., Conner, T.L., Costa, D.L., Gavett, S.H. (2003). Chemical Analysis of World Trade Center Fine Particulate Matter for Use in Toxicological Assessment; Environ. Health Perspect. 111 (7), 972-980. 33. Cheng, S.M., & Streifel, A.J. (2001). Infection Control Considerations during Construction Activities: Land Excavation and Demolition; Am. J. Infect. Control, 321-328. 34. Cheng, H. (2014). “Correlation and measures associated with construction dust and haze.” Journal of Construction Safety, 29(4), 50-52. 35. Cimorelli, A.J., Perry, S.G., Venkatram, A., Weil, J.C., Paine, R.J., Wilson, R.B., Lee, R.F., Peters, W.D., & Brode, R.W. (2005). AERMOD: A dispersion model for industrial source applications. Part I: General model formulation and boundary layer characterization. J. Appl. Meteorol. 682–693. 36. Cochran, L. and Derickson, R. (2005). Low-rise Buildings and Architectural Aerodynamics. Architectural Science Review, vol. 48, no. 3, pp. 265-276. 37. Dabberdt, W.F., Ludwig, F.L., & Johnson, W.B. (1973). Validation and applications of an urban diffusion model for vehicular pollutants. Atmospheric Environment 7, 603–618. 38. Dockery, DW., Pope, CA., Xu, X., Spengler JD., Ware, JH., & Fay, ME. (1993). An association between air pollution and mortality in six U.S. cities. N Engl J Med 329:1753– 1759. 39. Donaldson, K., Tran, L., Jimenez, L.A., Duffin, R., Newby.E., Mills, N., MacNee, W., & Stone,V. (2005). Combustion-derived nano particles: a review of their toxicology following inhalation exposure. Part. Fibre Toxicol. 2 (1), 10. 40. Dorevitch, S., Demirtas, H., Perksy, V. W., Erdal, S., Conroy, L., Schoonover, T., & Scheff, P. A. (2006). Demolition of High-Rise Public Housing Increases Particulate Matter Air Pollution in Communities of High-Risk Asthmatics. Journal of the Air & Waste Management Association,56(7), 1022-1032. 41. Duarte, J. C., & Schellart, W. P. (2016). Introduction to Plate Boundaries and Natural Hazards. Plate Boundaries and Natural Hazards Geophysical Monograph Series,1-10. 42. Edussuriya, P.S., Ye, A.M., & Chan, A.T.Y. (2005). Urban form and air quality: understanding pollutant air movement in residential environment in Hong Kong; in Fabricating Sustainability: Proceedings of the 39th Architectural Science Association Conference, New-Zealand. 43. Edussuriya, P., Chan, A., & Ye, A. (2009). Urban Morphology and Air Quality in residential environment in Hong Kong, Part I District Level Analysis, Atmospheric Environment, 45, 4789-4803. 44. Edussuriya, P., Chan, A., & Aldo, M. (2014). Urban Morphology & Air Quality in dense residential environment: correlation between morphological parameters & air pollution at street level, Journal of Engineering & Technology, 9, 64-80.

131

45. Farrell, W.J., Weichenthal, S., Goldberg, M., Hatzopoulou, M. (2015). Evaluating air pollution exposures across cycling infrastructure types: Implications for facility design. J. Transp. Land Use, 8, 131–149. 46. Font, A., Baker, T., Mudway, I.S., Purdie, E., Dunster, C., Fuller, G.W. (2014). Degradation in urban air quality from construction activity and increased traffic arising from a road widening scheme. Sci Total Environ 497-8: 123-132. 47. FR. (2005). Part III. Environmental Protection Agency 40 CFR Part 51. Revision to the Guideline on Air Quality Models: Adoption of a preferred general purpose (flat and complex terrain) dispersion model and other revisions; final rule. United States Federal Register. http://www.epa.gov/scram001/guidance/guide/appw_05.pdf . 48. FST. (2005). Calpuff model validation in New Zealand – Methodology and Issues. Prepared by Kingett-Mitchell Limited for the Foundation for Science and Technology, Takapuna, Auckland, New Zealand. http://www.niwascience.co.nz/ncces/air_quality/co1x0405_p13.pdf 49. Gavett, SH., Haykal-Coates, N., Highfill JW., Ledbetter, AD., Chen, LC., Cohen, MD. (2003). World Trade Center fine particulate matter causes respiratory tract hyperresponsiveness in mice. Environ Health Perspect 111:981–991. 50. Georgii, H.W. (1969). The effects of air pollution on urban climates. Bulletin of the World Health Organization 40, 624–635. 51. Geyh, A., Tao, XG., Gross, R., Massa, J., Ashwell, L., Zybert, P., Herman, D., Langlieb, A., & Breysse, P. (2004). Health effects among workers involved in the cleanup and recovery effort at the World Trade Center (WTC) disaster site: The WTC clean up and recovery workers health. Epidemiology 15(4). 123–S124. 52. Greater London Authority. (2014). The Control of Dust and Emissions During Construction and Demolition – Supplementary Planning Guidance. London: Greater London Authority. 53. Grimmond and Oke. (1998). Aerodynamic properties of urban areas derived from analysis of surface form, Journal of applied meteorology, 38, 1262-1292. 54. Godleski, J.J., Verrier, R.L., Koutrakis, P., & Catalano, P. (2000). Mechanism of Morbidity and Mortality from Exposure to Ambient Air Particles; Research Report; Health Effects Institute: Cambridge, MA. 55. Guidelines for Controlling Dust and Protecting Health at Implosions; City of Philadelphia Department of Public Health Air Management Services: Philadelphia, PA, March 2001. 56. Hansen, D., Blahout, B., Benner, D., Popp, W., 2008. Environmental sampling of particulate matter and fungal spores during demolition of a building on a hospital area. Journal of Hospital Infection 70, 259-264. 57. Herdt-Losavio, ML., Mauer, MP., & Carlson, GA. (2010c) Asthma and lower respiratory symptoms in New York State employees who responded to the World Trade Center disaster. Int Arch Occup Environ Health. 21–27. 58. Hinds, W.C. (1999). Aerosol Technology, Properties, Behavior, and Measurement of Airborne Particles. John Wiley & Sons Inc., New York. 59. Hong, H. (1999). A Two-Dimensional Air Quality Model in an Urban Street Canyon: evaluation and sensitivity analysis, Atmospheric Environment. Vol 34, 689-698.

132

60. Howard, J. (2014). WTC health program, health conditions medically associated with World Trade Center-related health conditions. NIOSH; Washington. 61. Huang, S., Li, Q.S. & Xu, S. (2007). Numerical evaluation of wind effects on a tall steel building by CFD, Journal of Constructional Steel Research 63: 612-627. 62. Huang, H., Akutsu, Y., Arai, M., & Tamura, M. (2000). A two-dimensional air quality model in an urban street canyon: evaluation and sensitivity analysis, Atmospheric Environment 34 (5): 689-698. 63. Hunter, L.J., Johnson, G.T., & Watson, I.D. (1992). An investigation of three-dimensional characteristics of flow regimes within the urban canyon, Atmospheric Environment: 425- 432. 64. Huttner, S. (2012) Further development and application of the 3D microclimate simulation ENVI-met. Dissertation, Johannes Gutenberg-University Mainz. 65. Institute of Air Quality Management. (2014). Guidance on the Assessment of Dust from Demolition and Construction. London: Institute of Air Quality Management (IAQM). Retrieved from 24 September 2015 from www.iaqm.co.uk/text/guidance/construction-dust- 2014.pdf 66. Jeong, S.J. & Andrews, M.J. (2002). Application of the kappa– epsilon turbulence model to the high Reynolds number skimming flow field of an urban street canyon. Atmos Environ. 1137–1145. 67. Joseph, J., Patil, R.S., & Gupta, S.K. (2009). Estimation of air pollutant emission loads from construction and operational activities of a port and harbor in Mumbai, India. Environmental Monitoring and Assessment 159, 85-98. 68. Ahmad, K., Khare, M., & Chaudhry, K. K. (2005). Wind tunnel simulation studies on dispersion at urban street canyons and intersections—a review. Journal of Wind Engineering and Industrial Aerodynamics, vol. 93, pp. 697–717. 69. Kang, Y.S., Baik, J.J., Kim, J.J. (2008). Further studies of flow and reactive pollutant dispersion in a street canyon with bottom heating. Atmospheric Environment 42, 4964e4975. 70. Karppinen, A., Kukkonen, J., Elolähde, T., Konttinen, M., & Koskentalo, T. (2000). A modelling system for predicting urban air pollution: Atmospheric Environment, 34(22), 3735–3743. https://doi.org/10.1016/S1352-2310(00)00073-X 71. Kikumoto, H., & Ooka, R. (2012). A study on air pollutant dispersion with bimolecular reactions in urban street canyons using large-eddy simulations. Journal of Wind Engineering and Industrial Aerodynamics,104-106, 516-522. doi:10.1016/j.jweia.2012.03.001 72. Kim, J.J., & Baik, J.J. (2004). A numerical study of the effects of ambient wind direction on flow and dispersion in urban street canyons using the RNG k-ε turbulence model, Atmospheric Environment 38 (19): 3039-3048. 73. Kim, K., Kabir, E., & Kabir, S. (2015). A review on the human health impact of airborne particulate matter. Environment International,74, 136-143. doi:10.1016/j.envint.2014.10.005

133

74. Kim, K.Y., Kim, Y.S., Roh, Y.M., Lee, C.M., Kim, C.N. (2008). Spatial distribution of particulate matter (PM10 and PM2.5) in Seoul metropolitan subway stations. Journal of Hazardous Materials 154, 440-443. 75. Kingham, S., Longley, I., Salmond, J., Pattinson, W., & Shrestha, K. (2013). Variations in exposure to traffic pollution while travelling by different modes in a low density, less congested city. Environ. Pollut. 181, 211–218. 37. 76. Kloog, I., Coull, B., Zanobetti, A., Koutrakis, P., & Schwartz, J. (2012). Acute and Chronic Effects of Particles on Hospital Admissions in New-England. Department of Environmental Health-Exposure, Epidemiology, and Risk Program, Harvard School of Public Health. 77. Karousos, I. A. (2006). Modelling of air quality in street canyons. Leeds: Institute for Transport Studies. 78. Kumar, P. (2009). Measurements and modelling of the dispersion of nanoparticles in the urban environment (Doctoral dissertation, University of Cambridge), pp. 226. 79. Kumar, P., Mulheron, M., Fisher, B., & Harrison, R.M. (2012b). New Directions: Airborne ultrafine particle dust from building activities – A source in need of quantification. Atmospheric Environment 56, 262-264. 80. Kumar, P., Ketzel, M., Vardoulakis, S., Pirjola, L., & Britter, R. (2011b). Dynamics and dispersion modelling of nanoparticles from road traffic in the urban atmospheric environment – a review. Journal of Aerosol Science 42, 580-603. 81. Kumar, P., Fennell, P. & Britter, R. (2008). Effect of wind direction and speed on the dispersion of nucleation and accumulation mode particles in an urban street canyon, Science of The Total Environment 402: 82–94. 82. Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino, S., Bell, M., Norford, L., & Britter, R. (2015). The rise of low-cost sensing for managing air pollution in cities. Environ. Int., 75, 199–205. 83. Laden, F., Joel, S., Frank, E. S., & Dockery, D.W. (2006). "Reduction in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities study." American Journal of Respiratory and Critical Care Medicine 173 (6):667. 84. Launder, B.E., & Spalding, D. B. (1974). The numerical computation of turbulent flows, Comp. Methods Appl. Mech. Eng., 3, 269–289 85. Levy, Jonathan, I., Jonathan, J., Buonocore, & Katherine V.S. 2010. "Evaluation of the public health impacts of traffic congestion: a health risk assessment." Environmental Health 9 (65):1-12. 86. Landrigan, P.J., Lioy, P.J., Thurston, G., Berkowitz, G., Chen, L.C., & Chillrud, S.N. (2004). The NIEHS World Trade Center Working Group. Health and environmental consequences of the World Trade Center disaster. Environ Health Perspect. 112:731–9. 87. Li, X.X., Liu, C.H., Leung, D.Y.C. & Lam, K.M. (2006). Recent progress in CFD modelling of wind field and pollutant transport in street canyons, Atmospheric Environment 40: 5640-5658. 88. Lioy, P.J., Weisel, C.P., Millette, J.R., Eisenreich, S., Vallero, D., Offenberg, J., Buckley, B., Turpin, B., Zhong, M., Cohen, M.D., Prophete, C., Yang, I., Stiles, R., Chee, G., Johnson, W., Porcja, R., Alimokhtari, S., Hale, R.C., Weschler, C., & Chen, L.C. (2002).

134

Characterization of the Dust/ Smoke Aerosol That Settled East of the World Trade Center (WTC) in Lower Manhattan after the Collapse of the WTC 11 September 2001; Environ. Health Perspect. 110 (7), 703-714. 89. Liu, Leung, C.H., D.Y.C. & Barth, M.C. (2005). On the prediction of air and pollutant exchange rates in street canyons of different aspect ratios using large-eddy simulation, Atmospheric Environment 39: 1567-1574. 90. Liu, L.J., Slaughter, J.C., & Larson, T.V. (2002). Comparison of Light Scattering Devices and Impactors for Particulate Measurements in Indoor, Outdoor, and Personal Environments; Environ. Sci. Technol. 36 (13), 2977-2986. 91. Liu J. et al. (1996). E-ε modelling of turbulent air flow downwind of a model forest edge, Boundary Layer Met., 77, 21–44 92. Maslow, C.B., Friedman, S.M., Pillai, P.S., Reibman, J., Berger, K.I., Goldring, R.M., Stellman, S.D., & Farfel, M. (2010). Chronic and acute exposures to the effects of the WTC disaster and lower respiratory symptoms: area residents and area workers. Presented at the Ninth International Conference on Urban Health, OS45.1. 93. MacNaughton, P., Melly, S., Vallarino, J., Adamkiewicz, G., & Spengler, J.D. (2014). Impact of bicycle route type on exposure to traffic-related air pollution. Sci. Total Environ. 490, 37–43. 33. 94. Morakinyo, T. E., Lam, Y. F., & Hao, S. (2016). Evaluating the role of green infrastructures on near-road pollutant dispersion and removal: Modelling and measurement. Journal of Environmental Management, 182(July), 595–605. https://doi.org/10.1016/j.jenvman.2016.07.077 95. McGee, J.K., Chen, L.C., Cohen, M.D., Chee, G.R., Prophete, C.M., Haykal-Coates, N., Wasson, S.J., Conner, T.L., Costa, D.L., & Gavett, S.H. (2003). Chemical analysis of World Trade Center fine particulate matter for use in toxicologic assessment. Environmental Health Perspectives 111, 972-980. Marvroidis I. and Griffiths R.F. (2001) Local characteristics of atmospheric dispersion within building arrays, Atmospheric Environment, Vol 35, pp. 2941 –2954. 96. McKinsey & Company. (2004). FDNY Fire Operations response on September 11, 27–42. Retrieved from http://www.nyc.gov/html/fdny/pdf/mck_report/fire_operations_response.pdf 97. Mellor, G. L. & Yamada, T. (1975). A simulation of the Wangara atmospheric boundary layer data. J. Atmos. Sci,. 32, 2309–2329. 98. Mendelson, D.S., Roggeveen, M., Levin, S. & Herbert, R. (2007). Air trapping detected on end-expiratory high-resolution computed tomography in symptomatic World Trade Center rescue and recovery workers. J Occup Environ Med;49:840–845. 99. Mensink, C., Colles, A., Janssen, L., & Cornelis, J. (2003). Integrated air quality modelling for the assessment of air quality in streets against the council directives. Atmos. Environ. 37, 5177–5184. 100. Moussiopoulos, N., Berge, E., Bohler, Gronskei, K.-E., Mylona, S., & Tombrou, M. (1996). Ambient air quality, pollutant dispersion and transport models. European Environment Agency, Topic Report 19, Air Quality. Copenhagen, 94 pp.

135

101. Maignant, G., & Dutozia, J. (2011). Air pollution and urban morphology: A complex relation or how to optimize the pedestrian movement in town. 102. Muleski, & Gregory, E. (2005). Particulate Emissions from Construction Activities. Journal of the Air & Waste Management Association, vol. 55, no. 6, 2005, pp. 772–783., doi:10.1080/10473289.2005.10464669. 103. Murena, F., Favele, G., Vardoulakis, S., & Solazzo, E. (2009). Modelling dispersion of traffic pollution in a deep street canyon: Application of CFD and operational models, Atmospheric Environment 43 (14): 2303–2311. 104. Murena, F., Garofalo, N., & Favale, G. (2008). Monitoring CO concentration at leeward and windward sides in a deep street canyon. Atmospheric Environment,42(35), 8204-8210. doi:10.1016/j.atmosenv.2008.07.048 105. National Institute for Occupational Safety and Health (NIOSH), (2011). Scientific and smedical evidence related to cancer for the World Trade Center Health Program. http://www.cdc.gov/niosh/docs/2011-197/pdfs/2011-197.pdf〉 106. Naveed, B., Comfort. A.L., Ferrier, N., Segal, L.N., Kasturiarachchi, K.J., & Kwon, S. (2011b). WTC Dust induces GM-CSF in serum of FDNY rescue workers with accelerated decline of lung function and in cultured alveolar macrophages. Am J Respir Crit Care Medi. 2011b;183:A4770. 107. Nicholson, N. L. (1967). The Geographers Role In Contemporary Canadian Society: A Symposium. The Canadian Geographer/Le Géographe Canadien,11(3), 157-160. doi:10.1111/j.1541-0064.1967.tb00615.x 108. Oberdorster, G., Gelein, R., Ferin, J., & Weiss, B. (1995). Association of particulate air pollution and acute mortality: Involvement of ultrafine particles? Inhalation Toxicol 7:111 124. 109. Oberdorster, G. (2000). Pulmonary effects of inhaled ultrafine particles. Int. Arch. Occup. Environ. Health 74 (1), 1-8. 110. Oberdorster, G., Sharp, Z., Atudorei, V., Elder, A., Gelein, R., Kreyling, W., & Cox, C. (2004). Translocation of inhaled ultrafine particles to the brain. Inhalation Toxicol 16(6/7):437 445. 111. Oftedal, B., Brunekreef, B., Nystad, W., Madsen, C., Walker, S.E., & Nafstad, P. (2008). Residential outdoor air pollution and lung function in schoolchildren. Epidemiology. 19, 129–137. 112. Oke, T.R. (1988). Street design and urban canopy layer climate, Energy and Buildings 11 (1-3): 103-113.2 113. Ozkeresteci, I., Crewe, K., Brazel, A.J., & Bruse, M. (2003): Use and evaluation of the envi–met model for environmental design and planning: An experiment on linear parks, 21st International Cartographic Conference ICC Cartographic Renaissance, Durban, South Africa. 114. Park, M., Hagashima, A., & Tanimoto, J. (2012). Effect of urban vegetation on outdoor thermal environment: field measurement at a scale model site. Build Environ 56: 38–46.

136

115. Prezant, D.J., Weiden, M., Banauch, G.I., McGuinness, G., Rom, W.N., Aldrich, T.K., & Kelly, K.J. (2002). Cough and Bronchial Responsiveness in Firefighters at the World Trade Center Site; New Engl. J. Med. 347(11), 806-815. 116. Priyadarsini, R. & Wong, N. (2005). Parametric studies on urban geometry, airflow and temperature. International journal on architectural science, vol. 6, no. 3, pp. 114-132. 117. Public, P., Francisco, S., & Area, B. (2012): Understanding Particulate Matter: Protecting Public Health in the San Francisco Bay Area. 118. Rosheidat, A., Bryan, H., & Hoffman, D. (2008). Visualizing Pedestrian Comfort in a Hot Arid Urban Environment Using ENVI-met, SIMBUILD2008, International Building Performance Simulation Association (IBPSA). University of California, Berkeley. 119. Rui, D.M., Chen, J.J. & Feng, Y.C. (2008). “Study on source apportionment for ambient air PM10 in Nanjing.” Journal of Environmental Science and Management, 33(4), 56-58. 120. Sagrado, A.P., van Beeck, J., Rambaud, P., & Olivari, D. (2002). Numerical and experimental modelling of pollutant dispersion in a street canyon. Journal of Wind Engineering and Industrial Aerodynamics 90, 321–339. 121. Schulze, F., Gao, X., Virzonis, D., Damiati, S., Schneider, M., & Kodzius, R. (2017). Air Quality Effects on Human Health and Approaches for Its Assessment through Microfluidic Chips. Genes,8(10), 244. doi:10.3390/genes8100244 122. Seinfeld, J.H., & Pandis, S.N. (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. 2nd Edition, John Wiley & Sons, New York. 123. Simon, H. (2012). Anwendung eines mehrdimensionalen Interpolationsverfahrens mit einem mikroskaligen Klimamodell - ENVI-met. Diplomarbeit, Geographisches Institut der University at Mainz. 124. Sivacoumar, R., & Thanasekaran, K. (2001). Comparison and performance evaluation of models used for vehicular pollution prediction. J. Environ. Eng. 127, 524–530. 125. Sharma, P., & Khare, M. (2001). Modelling of vehicular exhausts—a review. Transportation Research-D 6, 179–198. 126. Shirai, Y., Kishino, Y., Naya, F., & Yanagisawa, Y. (2016). Toward On-Demand Urban Air Quality Monitoring using Public Vehicles. In Proceedings of the 2nd International Workshop on Smart, Trento, Italy, 12–16. 127. Soulhac, L., Perkins, R.J., & Salizzoni, P. (2008). Flow in a street canyon for any external wind direction, Boundary-Layer Meteorology 126: 365-388. 128. Soulhac, L., and Salizzoni, P. (2010). Dispersion in a street canyon for a wind direction parallel to the street axis, Journal of Wind Engineering and Industrial Aerodynamics 98: 903-910. 129. Soulhac, L., Mejean, P., & Perkins, R.J. (2002). Modeling transport and dispersion in a streetcanyon. Int. J. Environ. Pollut. In press 130. Shen, L., Mickley, L. J., & Murray, L. T. (2017). Influence of 2000–2050 climate change on particulate matter in the United States: Results from a new statistical model. Atmospheric Chemistry and Physics,17(6), 4355-4367. doi:10.5194/acp-17-4355. 131. Shi, Y., Lau, K.K.L., & Ng, E. (2016). Developing street-level PM2.5 and PM10 land use regression models in high-density Hong Kong with urban morphological factors. Environ. Sci. Technol. 50, 8178–8187. 34.

137

132. Sivertsen, B. (2002) Meteorological Data for Air Pollution Assessment, National Training Course on Air Quality Monitoring and Management, Nigeria. http://www.nilu.no/index.cfm?ac=publications&folder_id=4309&publication_id=2320&vi ew=rep 133. Stefani, D., Wardman, D., & Lambert T. (2005). The Implosion of the Calgary General Hospital: Ambient Air Quality Issues, Journal of the Air & Waste Management Association, 55:1, 52-59, DOI: 10.1080/10473289.2005.10464605. https://doi.org/10.1080/10473289.2005.10464605 134. Snyder, W.H. (1981). Guidelines for fluid modeling of atmospheric diffusion. Rep. EPA- 600/8-81-0009, EPA, Res., Triangle Park, NC 135. Stein, C.R., Wallenstein, S., & Shapiro, M. (2016). Mortality among World Trade Center rescue and recovery workers. Am. J. Ind. Med, 87-95 136. Solan, S., Wallenstein, S., & Shapiro, M. (2013). Cancer incidence in World Trade Center rescue and recovery workers, 2001–2008 Environ. Health Perspect., 121 (6), 699- 704. 137. Stewart, I. D., & Oke, T. R. (2012). Local Climate Zones for Urban Temperature Studies. Bulletin of the American Meteorological Society,93(12), 1879-1900. doi:10.1175/bams-d-11-00019.1 138. Stathopoulos, T., Stathopoulos, T., Baniotopoulos, C. C., Pfeiffer, F., Rammerstorfer, F. G., Salençon, J., Schrefler, B. & Serafini, P. (2007), "Introduction to Wind Engineering, Wind Structure, Wind-Building Interaction", in Wind Effects on Buildings and Design of Wind-Sensitive Structures CISM Courses and Lectures, Springer, Vienna, pp. 1-30. 139. Sunil, V. R., Vayas, K. N., Fang, M., Zarbl, H., Massa, C., Gow, A. J., & Laskin, D. L. (2017). World Trade Center (WTC) dust exposure in mice is associated with inflammation, oxidative stress and epigenetic changes in the lung. Experimental and Molecular Pathology,102(1), 50-58. doi: 10.1016/j.yexmp.2016.12.005 140. Tiwary, A. (2011). Air flow and concentration fields at urban road intersections for improved understanding of personal exposure. Environment International, 1137(5), pp.1005–1018 141. Tominaga, Y., & Stathopoulos, T. (2011). CFD modeling of pollution dispersion in a street canyon: Comparison between LES and RANS, Journal of Wind Engineering and Industrial Aerodynamics 99: 340–348. 142. Tominaga, Y., Ohba, R., Leitl, B., Kondo, H., Grawe, D., & Meroney, R.(2016). Review of CFD Guidelines for Dispersion Modeling. Fluids,1(2), 14. doi:10.3390/fluids1020014. 143. Tong, N. Y. O., & Leung, D. Y. C. (2012). Effects of building aspect ratio, diurnal heating scenario, and wind speed on reactive pollutant dispersion in urban street canyons. Journal of Environmental Sciences (China), 2091–2103. https://doi.org/10.1016/S1001- 0742(11)60971-6\ 144. Toyoda, T. (2008). Economic impacts of Kobe Earthquake: A quantitative evaluation after 13 years. 5th International Conference on Information Systems for Crisis Response and Management (Proceedings of ISCRAM 2008), (May 2008), 606–617. 145. Tsai, M.Y., & Chen, K.S. (2004). Measurements and three-dimensional modeling of air pollutant dispersion in an Urban Street Canyon, Atmospheric Environment 38: 5911–5924.

138

146. Tsai, S., Chang, C., Liou, S., & Yang, C. (2014). The Effects of Fine Particulate Air Pollution on Daily Mortality: A Case-Crossover Study in a Subtropical City, Taipei, Taiwan. International Journal of Environmental Research and Public Health,11(5), 5081- 5093. doi:10.3390/ijerph110505081 147. Turner, M.C., Krewski, D., Pope, C.A., Chen, Y., Gapstur, S.M., & Thun, M.J. (2011). Long-term ambient fine particulate matter air pollution and lung cancer in a large cohort of never-smokers. American Journal of Respiratory and Critical Care Medicine 184, 1374- 1381 148. U.S. EPA. (2001). Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment of Scientific and Technical Information, Preliminary Draft OAQPS Staff Paper. Research Triangle Park, NC 27711: Office of Air Quality Planning and Standards. 149. U.S. EPA. (2008). Integrated Review Plan for the National Ambient Air Quality Standards for Particulate Matter. National Center for Environmental Assessment and Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. Report No. EPA 452/R-08-004. Available: https://www3.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html. 150. U.S. EPA. (2011). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA 452/R-11-003. Available: https://www3.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html. 151. U.S. EPA. (2012). Responses to Significant Comments on the 2012 Proposed Rule on the National Ambient Air Quality Standards for Particulate Matter. Available: http://www3.epa.gov/ttn/naaqs/standards/pm/data/20121214rtc.pdf 152. U.S. Environmental Protection Agency. (2013). "Particulate Matter (PM)." http://www.epa.gov/airscience/air-particulatematter.htm. 153. Vardoulakis, S., Fisher, B.E.A., Pericleousa, K. & Gonzalez-Flesca, N. (2003). Modelling air quality in street canyons: a review, Atmospheric Environment 37: 155-182. 154. Vos, P.E.J., Maiheu, B., Vankerkom, J., & Janssen, S. (2013). Improving local air quality in cities: to tree or not to tree? Environ. Pollut. 183, 113e122. http://dx.doi.org/10.1016/j.envpol.2012.10.021. 155. Walker, E., Slørdal, L.H., Guerreiro, C., Gram, F., & Grønskei, K.E. (1999). Air pollution exposure monitoring and estimation. Part II. Model evaluation and population exposure. J. Environ. Monit. 321–326. 156. Wallace, J., Corr, D., Deluca, P., Kanaroglou, P., & McCarry, B. (2009). Mobile monitoring of air pollution in cities: The case of Hamilton, Ontario, Canada. J. Environ. Monit. 11, 998–1003. 31. 157. Wang, M., Zhu, T., Zheng, J.; Zhang, R.;,Zhang, S.; Xie, X.; Han, Y.; & Li, Y. (2009). Use of a mobile laboratory to evaluate changes in on-road air pollutants during the Beijing 2008 Summer Olympics. Atmos. Chem. Phys. 2009, 9, 8247–8263. 32. 158. Wang Y., Berardi U., & Akbari H., (2015). The Urban Heat Island effect in the city of Toronto. Procedia Engineering, 118, 137-144.

139

159. Wang Y., Berardi U., & Akbari H., (2016). Comparing the effects of urban heat island mitigation strategies for Toronto, Canada. Energy and Buildings, 114, 2-19. 160. World Health Organization (WHO). (1999). Hazard Prevention and Control in the Work Environment: Air-borne Dust WHO/SDE/OEH/99.14. 161. Wilson, J. D. (1988). A second order closure model for flow through vegetation, Boundary Layer Met. 42, 371–392 162. Woersching, J. C., & Snyder, A. E. (2003). Earthquakes in El Salvador: A descriptive study of health concerns in a rural community and the clinical implications, part i. Disaster Management & Response,1(4), 105-109. doi:10.1016/s1540-2487(03)00049-x 163. Wisnivesky, J.P., Teitelbaum, S.L., Todd, A.C., Boffetta, P., Crane, M., Crowley, L., Dellenbaugh, C., Harrison, D., Herbert, R., Kim, H., Jeon, Y., Kaplan, J., Katz, C., Levin, S., Luft, B., Markowitz, S., Moline, J.M., Ozbay, F., Pietrzak, R.H., Shapiro, M., Sharma, V., Skloot, G., Southwick, S., Stevenson, L.A., Udasin, I., Wallenstein, S., & Landrigan, P.J. (2011). Persistence of multiple illnesses in World Trade Center rescue and recovery workers: a cohort study. Lancet. 164. Wania, A., Bruse, M., Blond, N., & Weber, C. (2012). Analysing the influence of different street vegetation on traffic-induced particle dispersion using microscale simulations. J. Environ. Manage 94, 91e101. http://dx.doi.org/10.1016/j.jenvman.2011.06.036. 165. Xie, X., Semanjski, I., Gautama, S., & Tsiligianni, E. (2017). A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods, 1–21. https://doi.org/10.3390/ijgi6120389 166. Yamada, T. (1982). A numerical model study of turbulent airflow in and above a forest canopy. J. Met. Soc. Japan, 439-454. 167. Yazid, A. W., Sidik, N. A., Salim, S. M., & Saqr, K. M. (2014). A review on the flow structure and pollutant dispersion in urban street canyons for urban planning strategies. Simulation,90(8), 892-916. doi:10.1177/0037549714528046 168. Yoshie, R., Mochida, A., Tominaga, Y., Kataoka, H., Harimoto, K., Nozu, T., & Shirasawa, T. (2007). Cooperative project for CFD prediction of pedestrian n wind environment in the Architectural Institute of Japan. Journal of Wind Engineering and Industrial Aerodynamics, 95 (9-11), 1551-1578. 169. Zeig-Owens, R., Webber, M.P., Hall C.B. (2011). Early assessment of cancer outcomes in New York City firefighters after the 9/11 attacks: an observational cohort study Lancet, 378 (9794), 898-905 170. Zwack, L.M., Paciorek, C.J., Spengler, J.D., & Levy, J.I. (2011). Modeling spatial patterns of traffic-related air pollutants in complex urban terrain. Environ. Health Perspect. 119, 852. 36.

140

APPENDICES

141

142

Appendix A: ENVI_met results for different morphologies: 1. Compact Highrise:

Figure A1- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A2- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Section)

143

Figure A3- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A4- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:45am (Plan)

144 Figure A3- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)Figure A4- Pollution Dispersion in Compact Highrise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A5- Wind Speed in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A6- Wind Speed in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Section)

145

Figure A7- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A8- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Section)

146

Figure A9- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A10- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:45am (Plan)

147

Figure A11- Wind Speed in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A12- Wind Speed in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Section)

148

2. Compact Midrise:

Figure A13- Pollution Dispersion in Compact Midrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A14- Pollution Dispersion in Compact Midrise Settlement at 0 degree Wind direction at 10:05am (Section)

149

Figure A15- Pollution Dispersion in Compact Midrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A16- Pollution Dispersion in Compact Midrise Settlement at 0 degree Wind direction at 10:45am (Plan)

150 Figure A17- Wind Speed in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)Figure A16- Pollution Dispersion in Compact Midrise Settlement at 0 degree Wind direction at 10:45am (Plan) Figure A17- Wind Speed in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A18- Wind Speed in Compact Highrise Settlement at 0 degree Wind direction at 10:05am (Section)

151

Figure A19- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A20- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Section)

152

Figure A21- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A22- Pollution Dispersion in Compact Highrise Settlement at 315 degree Wind direction at 10:45am (Plan)

153

Figure A23- Wind Speed in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A24- Wind Speed in Compact Highrise Settlement at 315 degree Wind direction at 10:05am (Section)

154

3. Compact Low-rise:

Figure A25- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A26- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)Figure A25- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A26- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)

Figure A27- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan)Figure A26- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:05am (Section) 155

Figure A27- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A28- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan)Figure A27- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A28- Pollution Dispersion in Compact Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A29- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)Figure A28- Pollution Dispersion in Compact156 Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan) Figure A29- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A30- Wind Speed in Compact Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)Figure A29- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A30- Wind Speed in Compact Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)

157

Figure A31- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A32- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)Figure A31- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A32- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)

Figure A33- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan)Figure A32- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)

158

Figure A33- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A34- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan)Figure A33- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A34- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A35- Wind Speed in Compact Low-rise159 Settlement at 315 degree Wind direction at 10:05am (Plan)Figure A34- Pollution Dispersion in Compact Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A35- Wind Speed in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A36- Wind Speed in Compact Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)

160

4. Compact Midrise + Open Highrise:

Figure A37- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A38- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Section)Figure A37- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A38- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Section)

Figure A39- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)Figure A38- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Section) 161

Figure A39- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A40- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:45am (Plan)Figure A39- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A40- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A41- Wind Speed in Compact midrise + Open Highrise162 Settlement at 0 degree Wind direction at 10:05am (Plan)Figure A40- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A41- Wind Speed in Compact midrise + Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A42- Wind Speed in Compact midrise + Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section)

163

Figure A43- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A44- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section)Figure A43- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A44- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section)

Figure A45- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:25am (Plan)Figure A44- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section) 164

Figure A45- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A46- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:45am (Plan)Figure A45- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A46- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A47- Wind Speed in Compact midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)Figure A46- Pollution Dispersion in Compact Midrise + Open Highrise Settlement at 315 degree Wind direction at 10:45am (Plan) 165

Figure A47- Wind Speed in Compact midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A48- Wind Speed in Compact midrise + Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section)

166

5. Open Highrise:

Figure A49- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A50- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section)Figure A49- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A50- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section)

Figure A51- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)Figure A50- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section) 167

Figure A51- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A52- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:45am (Plan)Figure A51- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A52- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A53- Wind Speed in Open Highrise Settlement168 at 0 degree Wind direction at 10:05am (Plan)Figure A52- Pollution Dispersion in Open Highrise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A53- Wind Speed in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A54- Wind Speed in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section)

169

Figure A55- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A56- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section)Figure A55- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A56- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section)

Figure A57- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:25am (Plan)Figure A56- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section)

170

Figure A57- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A58- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:45am (Plan)Figure A57- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A58- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A59- Wind Speed in Open Highrise Settle6ment171 at 315 degree Wind direction at 10:05am (Plan)Figure A58- Pollution Dispersion in Open Highrise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A59- Wind Speed in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A60- Wind Speed in Open Highrise Settlement at 315 degree Wind direction at 10:05am (Section)

172

6. Open Midrise:

Figure A61- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A62- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:05am (Section)Figure A61- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A62- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:05am (Section)

Figure A63 Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:25am (Plan)Figure A62- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:05am (Section)

173

Figure A63 Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A64- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:45am (Plan)Figure A63 Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A64- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:45am (Plan)

174 Figure A65- Wind Speed in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)Figure A64- Pollution Dispersion in Open Midrise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A65- Wind Speed in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A66- Wind Speed in Open Highrise Settlement at 0 degree Wind direction at 10:05am (Section)

175

Figure A67- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A68- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Section)Figure A67- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A68- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Section)

Figure A69- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:25am (Plan)Figure A68- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Section) 176

Figure A69- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A70- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:45am (Plan)Figure A69- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A70- Pollution Dispersion in Open Midrise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A71- Wind Speed in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Plan)Figure A70- Pollution Dispersion in Open 177Midrise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A71- Wind Speed in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A72- Wind Speed in Open Midrise Settlement at 315 degree Wind direction at 10:05am (Section)

178

7. Open Low-rise:

Figure A73- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A74- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)Figure A73- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A74- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)

Figure A75- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan)Figure A74- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Section) 179

Figure A75- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A76- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan)Figure A75- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A76- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A77- Wind Speed in Open Low-rise Settlement180 at 0 degree Wind direction at 10:05am (Plan)Figure A76- Pollution Dispersion in Open Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A77- Wind Speed in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A78- Wind Speed in Open Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)

181

Figure A79- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A80- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)Figure A79- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A80- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)

Figure A82- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan)Figure A80- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)

182

Figure A81- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A83- Wind Speed in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)Figure A81- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A82- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A81- Pollution Dispersion in Open Low-rise183 Settlement at 315 degree Wind direction at 10:25am (Plan)Figure A82- Pollution Dispersion in Open Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A83- Wind Speed in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A84- Wind Speed in Open Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)

184

8. Large Low-rise:

Figure A85- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A86- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)Figure A85- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A86- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)

Figure A87- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan)Figure A86- Pollution Dispersion in Large 185Low -rise Settlement at 0 degree Wind direction at 10:05am (Section)

Figure A87- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A88- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan)Figure A87- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A88- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A89- Wind Speed in Large Low-rise Settlement186 at 0 degree Wind direction at 10:05am (Plan)Figure A88- Pollution Dispersion in Large Low-rise Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A89- Wind Speed in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A90- Wind Speed in Large Low-rise Settlement at 0 degree Wind direction at 10:05am (Section)

187

Figure A91- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A92- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)Figure A91- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A92- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)

Figure A93- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan)Figure A92- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)

188

Figure A93- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A94- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan)Figure A93- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A94- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A95- Wind Speed in Large Low-rise Settlement189 at 315 degree Wind direction at 10:05am (Plan)Figure A94- Pollution Dispersion in Large Low-rise Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A95- Wind Speed in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A96- Wind Speed in Large Low-rise Settlement at 315 degree Wind direction at 10:05am (Section)

190

9. Sparsely Built:

Figure A97- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A98- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Section)Figure A97- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A98- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Section)

Figure A99- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:25am (Plan)Figure A98- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Section) 191

Figure A99- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A100- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:45am (Plan)Figure A99- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A100- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A101- Wind Speed in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Plan)Figure A100- Pollution Dispersion in Sparsely Built Settlement at 0 degree Wind direction at 10:45am (Plan) 192

Figure A101- Wind Speed in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A102- Wind Speed in Sparsely Built Settlement at 0 degree Wind direction at 10:05am (Section)

193

Figure A103- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A104- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Section)Figure A103- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A104- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Section)

Figure A105- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:25am (Plan)Figure A104- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Section)

194

Figure A105- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A106- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:45am (Plan)Figure A105- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A106- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A107- Wind Speed in Sparsely Built Settlement195 at 315 degree Wind direction at 10:05am (Plan)Figure A106- Pollution Dispersion in Sparsely Built Settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A107- Wind Speed in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A108- Wind Speed in Sparsely Built Settlement at 315 degree Wind direction at 10:05am (Section)

196

10. Low Rise with Dense Trees:

Figure A109- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A110- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Section)Figure A109- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A110- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Section)

Figure A111- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:25am (Plan)Figure A110- Pollution Dispersion in Low rise 197with dense trees settlement at 0 degree Wind direction at 10:05am (Section) Figure A111- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A112- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:45am (Plan)Figure A111- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:25am (Plan)

Figure A112- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A113- Wind Speed in Low rise with dense trees198 settlement at 0 degree Wind direction at 10:05am (Plan)Figure A112- Pollution Dispersion in Low rise with dense trees settlement at 0 degree Wind direction at 10:45am (Plan)

Figure A113- Wind Speed in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Plan)

Figure A114- Wind Speed in Low rise with dense trees settlement at 0 degree Wind direction at 10:05am (Section)

199

Figure A115- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A116- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Section)Figure A115- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A116- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Section)

Figure A117- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:25am (Plan)Figure A116- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Section)

200

Figure A117- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A118- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:45am (Plan)Figure A117- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:25am (Plan)

Figure A118- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:45am (Plan)

201 Figure A119- Wind Speed in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Plan)Figure A118- Pollution Dispersion in Low rise with dense trees settlement at 315 degree Wind direction at 10:45am (Plan)

Figure A119- Wind Speed in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Plan)

Figure A120- Wind Speed in Low rise with dense trees settlement at 315 degree Wind direction at 10:05am (Section)

202

Appendix B: Types of Air Quality Model The dispersion of air pollution can be modeled in various ways. The dispersion can be shown in spatial and temporal scale. The spatial scale describes be further classified as global, regional-to-continental, local to regional or local models. Further the temporal scale signifies episodic or statistical, long-term models. Also, Eulerian and Lagrangian models can be used to describe the dispersion through the treatment of the transport equations. Figure 2.6 summarizes the main existing model types and is followed by their brief descriptions.

Dispersion models based on boundary layer parameterization have come into practice in the last two decades. This model has come into practice due to the scientific advancement in the understanding of the structure of the boundary as well as the dispersion science. New methodologies are applied to produce the meteorological input data for these models. This new method involves the study of the vertical profiles of speed, temperature and turbulence which are in turn dependent on the height of the boundary layer and a Monin-obukhov length scale. Further it is to be notified that the Monin-Obukhov length scale is determined by the temperature, the friction velocity and the heat flux. Since the use of the practically operational models are increasing for the regulatory and planning purposes, more accuracy and reliability is sought for in the results. Further, given that it involves the human health, the air quality guidelines are made stricter and more detailed in many countries. Therefore, the evaluated models are then required to suffice the requirement of the modern air quality management. This requirement is further enforced more strictly in the urban area due to the higher population density (Moussiopoulos et al., 1996).

203

Figure B1- Types of air quality models

B1. Deterministic model:Figure C1- Schematic of basic model layout. Adapted from: (Bruse, 2007)Figure B1- In deterministic models,Types the of output air quality regarding models the model is determined through the parameter values and the initial conditions. It is a mathematical model between which outcomes are exactly decided through regarded relationships amongst states or events, without any wagon for around variation. In such models, a partial input intention constantly produces the equal output, certain as among a recognized chemical reaction. Deterministic models are divided into two groups: steady state and time dependent models. There are three models widely used under this category:

i) Gaussian Plume Model:

Gaussian plume dispersion model is one of the oldest and most widely used model. It is one of the types of steady state model. The turbulent diffusion equation is a partial differential equation that can keep solved including various numerical methods. Assuming a homogenous, steady- state flow and a steady-state point source, equation be able additionally stand analytically integrated yet consequences the generic Gaussian plume.

204

ii) Lagrangian model:

The basis of lagrangian model lies on the primary idea of pollutant particles in the atmosphere move along trajectories determined by the wind field, the buoyancy and the turbulence effects. This model follows the pollution plume parcels. The model the motion of the parcels randomly as they move in the atmosphere. Statistics of trajectories are computed to analyses the large number of air pollution parcels and eventually generate the air pollution dispersion model. Lagrangian model can compute both Puff and trajectory models.

iii) Eulerian model:

Eulerian model is similar to the Lagrangian model since it also computes the large number of air parcels movement from its original position. Eulerian model uses the fixed three-dimensional

Cartesian grid to reference the movement of the plume rather than moving the grid with the movement. The main idea of any Eulerian models is to solve numerically the atmospheric transport equation. Refined sub models are incorporated in Advanced Eulerian models for the depiction of turbulence (e.g. second-order closure models and large-eddy simulation models).

B2. Statistical model:

Statistical modelling of air pollution is the method were the basement of the data collected from the various monitoring station are gathered and analysed. Statistical models of air pollution have been developed primarily to provide a simpler and less data-demanding approach to estimating atmospheric concentrations, either for the purpose of air quality management (e.g. as screening models) or for exposure assessment in epidemiological studies. Several strategies have been developed over a course of time to interpret the data obtained and form the statistical outcome. Out of them, two approaches are of particular utility in exposure assessment:

205

i) Semi Empirical Model:

Various models are designed for this category and such models are mostly used for practical applications. Such models are attributed as very simple with a high degree of empirical parameterizations. Meanwhile, the concept of these models with in the category is vastly different. Box model is one of the examples of this model category and other kind of examples of this parametric model are:

• Simplified dispersion models, in which the dynamic transfer equations have been reduced

to a series of formulae.

• GIS-based models, where associations between source and receptor are represented by

empirically defined equations, derived using regression analysis or similar techniques.

B3. Physical model: In the physical model laboratory simulations are conducted in order to figure out the air pollution model. Data are generated from the simulation and often the video of the process itself are presented. It is an experimental model which takes in account of several factors such as differences in ventilation of the street canyon, changes in the wind speed, pollution concentration, etc.

206

Appendix C: General design of ENVI-met The ENVI-met model is comprised of 4 sub models which are linked to each other.

a. 1 D boundary model:

b. The 3D core/atmosphere model

c. The soil model

d. The vegetation model

Figure C1- Schematic of basic model layout. Adapted from: (Bruse, 2007)

Figure C1- Schematic of basic model layout. Adapted from: (Bruse, 2007) C1. 1 D boundary model:

ENVI-met requires the set boundary condition for vertical and the horizontal borders to simulate the part of the atmosphere. 1D boundary model is responsible for creating the one- dimensional profiles for meteorological parameters such as air temperature, specific humidity, wind vectors (horizontal), kinetic energy and turbulent exchange. To create the vertical boundary of the 1D boundary model, the users must input the latitude, longitude, the date and duration of the simulation, the horizontal wind speed at 10 meters height and the wind direction, the

207 roughness length, air temperature in 2 meters height and the vertical humidity proﬁle calculated through both speciﬁc humidity (at 2500 m) and relative humidity (in 2 m height) (Bruse, 1999).

C2. The 3D core/ atmospheric model:

The atmospheric model is where the main prognostic variables of urban climate such as wind field, air temperature and humidity distribution, turbulence, particle dispersion, radiation, exchange processes on ground and building surfaces are simulated.

C2.1. Wind Field:

ENVI-met uses eulerian approach for calculation of mass, momentum, and an energy budget (Wania et al., 2012). The nonhydrostatic three-dimensional Navier-Stokes equation is employed to evaluate the temporal and spatial development of the wind speed and direction. The density of air is assumed constant and is eliminated from the Navier-Stokes equations using the

Boussinesq approximation, since air based on the Boussinesq approximation is treated as an incompressible fluid, the conservation of mass needs to be ensured (equation 2.2)

휕푢 휕푢 휕푝′ 휕2푢 + 푢 = − + 퐾 ( ) + 푓(푣 − 푣 ) − 푆 (2.1) 휕푡 푖 휕푥 휕푥 푚 휕푥2 푔 푢 푖 푖 휕휈 휕휈 휕푝′ 휕2휈 (2.1) + 푢푖 = − + 퐾푚 ( 2) − 푓(푢 − 푢푔) − 푆푣 휕푡 휕푥푖 휕푦 휕푥푖

휕푤 휕푤 휕푝′ 휕2푤 휃(푧) + 푢푖 = − + 퐾푚 ( 2 ) + 푔 − 푆푤 휕푡 휕푥푖 휕푧 휕푥푖 휃푟푒푓(푧)

휕푢 휕푣 휕푤 + + = 0 (2.2) 휕푥 휕푦 휕푧

Where, (2.2)

p’ = local pressure perturbation

Km = local exchange coefficient

208

Θ(z) = potential temperature at height level z

f = Coriolis parameter with the geostrophic wind components ug and vg (Su, Sv and Sw =

local source / sink terms accounting for wind speed reduction due to vegetation

ui, xi = three-dimensional advection and diffusion terms written in Einstein summation

(ui = u; v;w; xi = x; y; z)

C2.2. Turbulence:

Turbulence is a key process in dispersion simulations. Turbulence in the urban environment is generally abundant due to the presence of built and vegetative obstructions. They create adverse roughness which affects the flow of wind and pollution dispersion patterns

(Blackledge et al., 2012). Atmospheric dispersion can be regarded as a sum of two main effects: the mechanical turbulence caused by wind shear (3D wind field, surface roughness and complex terrain), and the thermal turbulence caused by buoyancy (3D temperature field, sun elevation, cloud cover, albedo, sensible heat and latent heat). Turbulence is extremely difficult to model in a fully deterministic sense, based on the principles of fluid dynamics. To complete the simulation task in short period of time with coarser resolution, ENVI-met uses a 1.5 order turbulence closure model which parametrizes the turbulent flows presenting mean flow characteristics.

(Mellor and Yamada, 1974, 1982) and equations for turbulence (E) and its dissipation (є) are added:

휕퐸 휕퐸 휕2퐸 + 푢푖 = 퐾퐸 ( 2 ) + 푃푟 − 푇ℎ + 푄퐸 − 휖 휕푡 휕푥푖 휕푥푖 (2.3) 휕 휕휖 ⅆ2휖 휖 휖 휖2 휖 + 푢 = 퐾 ( ) + 푐 푃 − 푐 푇ℎ − 푐 + 푄휖 휕푡 푖 휕푥 휖 휕푥2 1 퐸 푟 3 퐸 2 퐸 푖 푖 (2.3)

209

The default values for the empirical constants c1, c2 and c3 used in ENVI-met are taken from

Launder and Spalding (Launder and Spalding, 1974):

c1 = 1:44 c2 = 1:92 c3 = 1:44

Meanwhile, the equation (2.4) and (2.5) is implemented in case of change in values for special flow situations:

휕푢푖 휕푢푗 휕푢푖 (2.4) 푃푟 = 푘푚 ( ) 휕푥푗 휕푥푖 휕푥푗 푔 휕휃 (2.4) 푇ℎ = 푘 (2.5) 휃 (푧) ℎ 휕푧 푟푒푓 (2.5) Where,

Pr = production of turbulent energy caused by wind shearing

Th = dissipation of turbulent energy caused by thermal stratification (buoyancy production)

(can be neglected under stable condition)

Θref (z) = potential temperature at the inflow boundary in height z

The turbulence caused by the vegetation QE is expressed as:

3 (2.6) 푄퐸 = 푐푑,푓퐿퐴퐷(푧) ⋅ 푊 − 4푐푑,푓퐿퐴퐷(푧) ⋅ |푊| ⋅ 퐸

The accelerated cascade of turbulence energy from large scales to smaller ones near plant foliage(2.6) Qє is expressed as (Liu, 1996; Wilson, 1988)

3 푄휖 = 1.5푐푑,푓퐿퐴퐷(푧) ⋅ 푊 − 6푐푑,푓퐿퐴퐷(푧) ⋅ |푊| ⋅ 휖 (2.7)

(2.7)

210

C2.3. Particle dispersion model:

The ENVI-met particle model can simulate the particulate matter in its model area. For the calculation of atmospheric dispersion of a particulate substance, standard advection-diffusion equation is used and written in Eulerian notation as:

휕푋 휕푋 휕푋 휕푋 + 푢 + 휈 + 푤 = (2.8) 휕푡 휕푥 휕푦 ⅆ푧

휕 휕 휕 휕 휕 휕 (퐾 푋) + (퐾 푋 ) + (퐾 푋) + 푄 (푥, 푦, 푧) + 푆 (푥, 푦, 푧) (2.8) 휕푥 푋 휕푥 휕푦 푋 휕푦 휕푧 푋 휕푧 푥 푥

Where,

x = particulate atmospheric component under investigation in [mgkg-1].

Qx and Sx = source and sink terms (sedimentation or chemical transformation).

This numerical model allows its users to place four different source geometry throughout the model with respect to their spatial structure (and consequently in the unit of their definition)

−1 i. Point Sources: qp, Unit [mgs ]

−1 −1 ii. Line Sources: ql, Unit [mgs m ]

−1 −2 iii. Area Sources: qf, Unit [mgs m ]

−1 −3 iv. Volume Sources: qv, Unit [mgs m ] (not available in science version)

The source geometry within ENVI-met is distinguished between point, line or area sources.

The type of source to be selected depends on the nature of the object generating pollution. For instance, point source can be used for the pollution from the chimneys whereas line sources are used to represent the pollution emission alongside the street lanes. Finally, Area sources are more seldom but can be imagined e.g. as type of landcover that emits pollutants. This makes the model

211 particularly suitable for this research, because it provides an option for Area source geometry, as the research focuses on the study of pollution dispersion pattern resulting from the unplanned building demolition.

One of the crucial things to understand for implementation of the particle model is the difference between these four, source geometries while defining it in the model area. With the grid cell dimensions Δx, Δy and Δz all the particle sources can be transformed into q*(mgs-1):

푞∗ = 푞 푝 (2.9)

= 푞푙 ⋅ 훥푥, 푦

= 푞푎 ⋅ 훥푥훥푦 (2.9)

= 푞푣 ⋅ 훥푥훥푦훥푧

The source term Qx can then be written as:

푞∗ 푄 = (2.10) 푥 훥푥훥푦훥푧 ⋅ 휌

It is to be noted that use of air volume in (mg m−3) as output of local concentration is more (2.10) common than the use of air mass in (mg kg−1). Thus, for the output the local concentration is converted by

χ∗ = χ · ρ

The sources with time dependent emission rates are allowed to be simulated in ENVI- met. Thus, for the implementation, 24 values representing the emission rates q(h) for each hour h [0-23] of the day is used to represent each source. The actual emission rate is then obtained through linearly interpolated as:

60 − 푚 푚 푞(ℎ, 푚) = ⋅ 푞(ℎ) + ⋅ 푞(ℎ + 1) 60 60

212

The calculation restarts with h in a loop, as it goes from h=23 to h=0 so as to restart the application. The emission rate frequency is to be updated by the user and is normally around 1-

10 minutes. The changes in the emission rate instigate no trend correction at the atmospheric grid points. Further to avoid unrealistic oscillations in case of rapid change in emission. A smaller update interval is induced.

C2.4. Particle sedimentation and deposition implementation:

Usable numerical parameterizations are not yet available for the deposition of particles at different surfaces. The model approach based on classical approaches is thus used to analyse the particle dynamics in the atmosphere and at surfaces. The components of the local sink term Sχ are:

• concentration change due to gravitational settling including deposition at surfaces (composed of downward flux χ↓ and flux received from grid boxes above χ ↓)

−1 −1 • deposition at leaf surfaces (χplant) (All fluxes in [mg kg s ]).

The total amount of deposed particle mass on a surface [mg m2 s −1], is then obtained to be as

(2.11) with ρ being the air density (=1.29 kgm−3).

Further, the deposition velocity of a particle can be defined as the inverse of the sum of two different transfer resistances ra and rb , given by the equation below:

1 (푃) (2.12) 휈푑,푝푎푟푡 = (푃) + 휈푠,0 푟푎 + 푟푏 + 푟푎푟푏휈푠,0 (2.12)

213

Where,

ra : aerodynamic resistance of the ground or leaf surface

rb : sub-layer resistance of the surface

vs,0 : settlement speed close to the surface, set equal to vs

훥(푤) Further, 푟푎 = (푤) 퐾ℎ,0

where ∆(w) is the distance between the point where K (w) h,0 is defined and the ground or wall surface (∆z in the case of roofs and the ground surface).

푃 At plant leafs, the aerodynamic resistance 푟푎 is calculated following the expression given by

Braden (1982):

(2.13) 퐷∗ 푟푃 = 퐴√ 푎 푚푎푥(푢, 0.005) (2.13)

Where,

u: wind speed at the leaf surface

A and D∗ : plant specific parameters which are set to A=87 s0.5m−1

D∗ represents the typical leaf diameter

The quasi-laminar layer creates an additional resistance 푟푏 to be estimated as

1 푟 = (2.14) 푏 푢 ∗ (푆푐−2/3 + 10−3/푆푡)

(2.14)

214

Further, Dχ is the relevant diffusion velocity which is in case of particles equal to the Brownian diffusivity is estimated to be as

푘 ⋅ 푇 ⋅ 퐶 퐷 = 퐶 푋 3휋 ⋅ 휇 ⋅ 퐷

where k is the Boltzmann constant (=1.38·10−23 JK−1), and Cc is the slip correction factor.

C2.5. Ground and buildings:

To calculate the ground surface temperature T0 the energy balance of the ground surface must be iteratively solved by the equation (2.15). (2.15) 0 = 푄푠푤,푛푒푡 + 푄푙푤,푛푒푡 − 퐺0 − 퐻0 − 퐿퐸0

Where, (2.15)

Qsw,net = the net incoming shortwave radiation,

Qlw,net = the net longwave radiation,

G0 = the soil heat flux,

H0 = the sensible heat flux and

LE0 = the latent heat flux

The net incoming shortwave radiation is calculated by,

∗ (2.16) 푄푠푤,푛푒푡 = (푐표푠 훽 ⋅ 푄푠푤,푑푖푟(푧 = 0) + 푄푠푤,푑 푓(푧 = 0)) ⋅ (1 − 푎푠) 𝑖 (2.16)

The net longwave radiation is calculated by,

푢푛표푏푠 표푏푠 (2.17) 푄푙푤,푛푒푡 = 휎푠푣푓푄푙푤,푛푒푡 + (1 − 휎푠푣푓)푄푙푤,푛푒푡

(2.17)

215

The soil heat flux G0 is calculated by,

푇0 − 푇푘=−1 퐺0 = 휆푠(푘 = −1) (2.18) 0 ⋅ 5훥푧푘=−1

Figure 3.22- Where, Polluti k = -1 as the topmost height level of the soil model, on disper λS = heat transfer coefficient of the soil layer, T0 as the surface temperature, sion Tk= -1 = temperature of the soil layer in the depth k= -1 and simula tion Δzk=-1 = thickness of the topmost grid cell of the soil model (Ali-Toudert, 2005; Huttner, result 2012). from

The turbulent sensible and latent heat fluxes H0 and LE0 in equation (2.15) are Calgar y implemented as functions of the turbulent exchange coefficients between the ground surface and Hospit the lowest grid cell of the atmosphere (Ali-Toudert, 2005; Huttner, 2012). Three-node modelal Implos developed on the basis of research conducted by Terjung and O'Rourke (1980) is used for the ion(2. calculation of the surface temperature of buildings in the later version of ENVI_met (Version18)

4.3) Terjung and O'Rourke's multiple-node transient state model allows the calculation of facade temperatures for an infinite number of nodes in a wall.

216