Spatial and temporal Modeling of Air Pollutant Source Impacts A Dissertation Presented to The Academic Faculty by Xinxin Zhai In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Civil and Environmental Engineering Georgia Institute of Technology December 2017 COPYRIGHT © 2017 BY XINXIN ZHAI Spatial and temporal Modeling of Air Pollutant Source Impacts Approved by: Dr. James A. Mulholland, Advisor Dr. Armistead G. Russell, Co-advisor School of Civil and Environmental School of Civil and Environmental Engineering Engineering Georgia Institute of Technology Georgia Institute of Technology Dr. Rodney J. Weber Dr. Yongtao Hu School of Earth and Atmospheric Sciences School of Civil and Environmental Georgia Institute of Technology Engineering Georgia Institute of Technology Dr. Heather A. Holmes Department of Physics University of Nevada, Reno Date Approved: 10/30/2017 ACKNOWLEDGEMENTS I sincerely acknowledge all the wonderful people I have met during my graduate study in Georgia Tech. I would like to thank my advisors, Dr. James Mulholland and Dr. Armistead Russell, for their guidance and mentoring in my PhD research. I would also like to thank my committee members, Dr. Heather Holmes, Dr. Yongtao Hu, and Dr. Rodney Webber, for their suggestions and comments to my research, especially Heather for mentoring me at the beginning of my research. I would like to thank all the members in Dr. Mulholland and Dr. Russell groups in past and present for the collaborations, discussions, and companions in work and life. I would also like to thank my family and friends for their support during my graduate studies. I would like to thank my parents for being supportive in the past years while I was away from them and not able to visit a lot. I would like to thank my boyfriend, Yufei Zou, for the companion and the help during the hard times and during all times, and for building a better life together with me than I ever had. I would like to thank my best friends, Tianye and Zhuyun, for listening to me and encouraging me the whole time. I would like to than my friends in Atlanta, Fei, Xiaofei, Ran, Sunni, and many others, for all the encouragement, support, and fun in life. Thank Dobby for coming to my life and brings all the fun and love. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS iii LIST OF TABLES vi LIST OF FIGURES viii LIST OF SYMBOLS AND ABBREVIATIONS xiii SUMMARY xv CHAPTER 1. Introduction 1 CHAPTER 2. Spatial and Temporal Source Apportionment of PM2.5 in Georgia, 2002 to 2013 6 2.1 Introduction 7 2.2 Methods 10 2.2.1 PM2.5 Species Data 10 2.2.2 CMB Model 12 2.2.3 Spatial and Temporal Trend Analysis 14 2.3 Results and Discussion 15 2.3.1 PM2.5 Species Concentration Trends 15 2.3.2 PM2.5 Source Impact Trends 17 2.3.3 Comparison with NEI 23 2.3.4 Spatial Representativeness 25 2.3.5 Limitations 28 2.4 Conclusion 31 2.5 Acknowledgement 31 CHAPTER 3. Spatial PM2.5 Mobile Source Impacts Using a Calibrated Indicator Method 33 3.1 Introduction 34 3.2 Methods 36 3.2.1 Integrated Mobile Source Indicator Method 38 3.2.2 CMAQ-Observation Data Fusion 39 3.2.3 Emissions Data 40 3.2.4 CMB Source Apportionment 41 3.2.5 Spatial PM2.5 Mobile Source Impact Modelling Approach 42 3.2.6 Evaluation 43 3.3 Results and Discussion 44 3.3.1 IMSI Weighting Factors 44 3.3.2 Spatial CMB Annual Averages 45 3.3.3 Spatial PM2.5 Mobile Source Impacts 46 3.3.4 Temporal Trends 47 3.3.5 Evaluation of the IMSI Source Impacts using Daily Observation-Based Estimates 48 3.3.6 Comparison with an alternative IMSI method using local indicators 50 3.4 Conclusion 50 iv 3.5 Acknowledgement 51 CHAPTER 4. Calibrating R-LINE Model Results with Observational Data to Develop Annual Mobile Source Air Pollutant Fields at Fine Spatial Resolution: Application in Atlanta 52 4.1 Introduction 53 4.2 Methods 56 4.2.1 R-LINE Dispersion Model 57 4.2.2 R-LINE Model Calibration 63 4.2.3 Evaluation Methods 65 4.2.4 Regional Scale Models 65 4.3 Results and Discussion 67 4.3.1 R-LINE Model Estimates 67 4.3.2 R-LINE Model Calibration and Evaluation 70 4.3.3 Comparison with Regional Scale Model Results 77 4.3.4 Population-Weighted Concentration 78 4.3.5 Limitations 79 4.4 Conclusion 81 4.5 Acknowledgement 82 CHAPTER 5. Characterization of Traffic Related Pollutant Concentration Gradients Using Air Dispersion Model R-LINE, Passive Sampler Detectors, a Mobile Platform, and Fixed-site Monitors 83 5.1 Introduction 84 5.2 Methods 86 5.2.1 Mobile Platform at Fuzzy Points 86 5.2.2 Passive Sampler Detectors 88 5.2.3 Fixed Monitors 89 5.2.4 Dispersion Model R-LINE 90 5.3 Results and Discussion 91 5.3.1 Near-road Spatial Gradients by PSD and R-LINE 91 5.3.2 FP and Fixed Monitors Comparison 95 5.4 Conclusions 96 5.5 Acknowledgement 97 CHAPTER 6. Conclusion and future work 98 6.1 Conclusions 98 6.2 Future work 101 APPENDIX A. Chapter 2 Supplemental information 103 APPENDIX B. Chapter 3 Supplemental information 114 APPENDIX C. Chapter 4 Supplemental information 122 APPENDIX D. Chapter 5 Supplemental information 135 REFERENCES 137 v LIST OF TABLES Table 1 Power fit regression (y=axb, where y=CMB and x=IMSI) 45 coefficients for calibrating annual average indicators to annual average CMB source impacts. The coefficients (a, b, and R2) are calculated using the linear regression of log-transformed data. Table 2 Bias (NMB), error (NRMSE) and spatiotemporal correlation (RSQ) 48 for mobile source, GV, and DV impacts, on PM2.5 in comparison with CMB source impacts using the IMSI method. Table 3 Correlations (R) between temporal variations of the daily source 49 impacts estimated by the IMSI method and CMB source impacts. Table 4 Stability categories for the meteorology conditions. 61 Table 5 Error and bias from cross validation. 73 Table 6 Selected models for calibrating R-LINE. 76 Population-weighted concentration in 2010 by the raw R-LINE, 79 Table 7 calibrated R-LINE, and regional scale models. PSD locations and their distance to major highways in Atlanta. The 89 Table 8 distances are from monitoring location to the edge of closest highways. For I-75/I-85 connector, I-20, and I-85, three detectors are located on both sides of the roadways. Significant periods (in days) by spectral analysis for all sites and 113 sources. The dust and CC did not converge significant periods around one year. The not available (N.A.) data indicated no significant period around one year converged for that source and Table A 1 site. Table A 2 Distribution of the weighting factors. 120 Table A 3 Weighting factors in Atlanta by Pachon et al. (2012) and in Denver 121 and Huston by Oakes et al. (2014b). Table A 4 Power fit regression (y=axb, where y=CMB) coefficients for 121 calibrating the indicators to CMB source impacts. The Local IMSI method uses power fit regressions of CMB annual averages and fused PM2.5 emissions (x) of total mobile sources, GV, and DV. The coefficients (a, b, and R2) are calculated using the linear regression of log-transformed data. vi NMB and NRMSE for mobile source, GV, and DV impacts, on 121 Table A 5 PM2.5 in comparison with CMB source impacts using Local IMSI method. Table A 6 Annual emissions of 20 counties from mobile sources. 122 Table A 7 Site Information. 122 Table A 8 CMB-GC annual averages at three sites. 122 Table A 9 CO observation annual averages at five sites. 123 Table A 10 NOx observation annual averages at seven sites. 123 Table A 11 PM2.5 annual averages at sites by R-LINE µg/m3. 123 1hr max CO annual averages at sites by R-LINE. 123 Table A 12 Table A 13 RLNE NOx 1hr max ppb. 124 Table A 14 Population weighted exposure for R-LINE, CALIBRATED R- 124 LINE, IMSI, and CMAQ-OBS. Table A 15 Near-road comparison of measured and R-LINE estimated PM2.5, 134 CO and NOx for 2015. Near-road measurements are total, whereas R-LINE estimates are from mobile source only. Table A 16 Concentrations by R-LINE and PSD at the railyard area, rural area, 135 near SDK site, and near the Spaghetti Junction. Units for PM2.5 is µg/m3 and for other pollutants ppb. Table A 17 Pearson correlation coefficient R for R-LINE vs mobile platform 136 FP for all pollutants. vii LIST OF FIGURES Figure 1 Monitors with PM2.5 species concentrations in Georgia. 11 Figure 2 PM2.5 total mass and species concentrations, averaged over 2002 to 15 2013 for each site. Sites are ordered by SEARCH, CSN, and IMPROVE and then county-level population density (2010). Other mass is defined as the total mass minus the sum of the listed species. Ammonium is included in other mass at the two IMPROVE sites. Figure 3 12-year Averages of Source Impacts: the sites ordered same with Fig. 20 2. Sulfate species at the two IMPROVE sites are estimated (See description in Fig. A8 in SI). Figure 4 (a). Annual mobile source impacts at JST (µg/m3) vs. mobile 24 emissions at 20 counties in Metropolitan Atlanta (tons/day) estimated by MOVES based on 2011 National Emissions Inventory; (b).