Title Page
Contents
요약문 8
Abstract 10
Chapter 1. Introduction 18
1.1. Introduction 18
1.2. Reference 22
Chapter 2. Overview 26
2.1. Community Multiscale Air Quality model 26
2.1.1. Gas-phase chemistry and aerosol dynamics 28
2.2. Description of GMAF system 31
2.3. Modification of CMAQ 34
2.3.1. N₂O₅ uptake coefficient 34
2.3.2. pcVOC emission scale factor 35
2.3.3. Vertical mixing height 36
2.3.4. Below-cloud scavenging 38
2.4. References 41
2.5. Nomenclature 47
Chapter 3. Characteristics of multi-day PM₂.₅ episodes from 2015 to 2021 in Seoul 48
3.1. Abstract 48
3.2. Introduction 49
3.3. Methods and materials 51
3.3.1. Overview of automatic PM₂.₅ monitoring station and the area of interest 51
3.3.2. Measurement of semi-continuous PM₂.₅ and PM₂.₅ chemical components 52
3.3.3. Indicators 53
3.4. Results and discussion 55
3.4.1. Selecting the multi-day PM₂.₅ episodes in Seoul 55
3.4.2. Meteorological characteristics during the multi-day PM₂.₅ episodes 56
3.4.3. Chemical characteristics of PM₂.₅ 60
3.4.4. SOR and NOR 64
3.5. Long-range Transport 65
3.5.1. Analysis of temporal variation 65
3.5.2. Coefficient of divergence 69
3.5.3. Correlation coefficient between NOR and temperature 71
3.6. Summary 72
3.7. References 73
Chapter 4. Formation of HNO₃ over the Yellow Sea and its impact on particulate nitrate concentrations in Seoul 78
4.1. Abstract 78
4.2. Introduction 79
4.3. Methods 81
4.3.1. Study areas and periods 81
4.3.2. Model configuration 82
4.3.3. Monitoring methods 84
4.3.4. Model performance evaluation metrics 85
4.3.5. Chemistry of HNO₃ gas and particulate nitrate 86
4.3.6. N₂O₅ uptake coefficient 88
4.3.7. Process analysis tool of CMAQ 91
4.4. Results and Discussion 92
4.4.1. Monitoring results 92
4.4.2. Effect of organic coating thickness on nitrate formation 96
4.4.3. Comparison of the predicted concentrations with the observed concentrations 98
4.4.4. Calculated spatial distribution of selected chemical species concentrations 101
4.4.5. NH₃, HNO₃, nitrate concentration distribution in the cross-section from China to Korea during the PM₂.₅ episode 110
4.4.6. HNO₃ production during the episode 114
4.5. Conclusion 116
4.6. References 118
Chapter 5. Modeling of organic aerosol in Seoul using CMAQ with AERO7 123
5.1. Abstract 123
5.2. Introduction 124
5.3. Materials and methods 126
5.3.1. Monitoring of carbonaceous aerosol and estimation of the secondary organic aerosol concentration 126
5.3.2. Model simulation and performance evaluation 128
5.3.3. Atmospheric aerosol chemistry 130
5.3.4. Emissions 135
5.4. Results and discussion 137
5.4.1. Model evaluation on OC and EC prediction 137
5.4.2. Model evaluation on SOC prediction 141
5.4.3. Modeling of the seasonal behavior of organic aerosol compositions 146
5.4.4. Organic matter to organic carbon ratio 148
5.5. Summary and conclusions 150
5.6. References 152
5.7. Supplementary materials 159
Appendix A. Modeling of secondary organic aerosol with various pcVOC scale factor 161
Chapter 6. Conclusion 165
6.1. Summary and conclusion 165
6.2. Future work 166
6.3. Reference 168
Table 2-1. Nudged variables and nudging coefficients in the GMAF. 32
Table 3-1. Episode period, duration hour, and PM₂.₅ mass concentrations averaged over 25 urban monitoring stations located in Seoul during the five... 56
Table 3-2. Average temperature, relative humidity, and wind speed during the five multi-day PM₂.₅ episodes in Seoul. 58
Table 3-3. Nitrate to sulfate molar ratio, ammonium to sulfate molar ratio, and DSN in the SU site during the five multi-day PM₂.₅ episodes and non-episode. 63
Table 3-4. SOR and NOR values estimated for the SU site during the five multi-day PM₂.₅ episodes. 64
Table 3-5. Periods, average PM₂.₅ concentration, peak PM₂.₅ concentration of PM₂.₅ episodes in the BN site, and time-lag between time of the peak PM₂.₅... 68
Table 3-6. Coefficient of divergence between the SU and BN sites during the five multi-day PM₂.₅ episodes in Baengnyeong and Seoul. 70
Table 3-7. Correlation coefficients between SOR and relative humidity, and NOR and temperature in Seoul. 71
Table 4-1. Mean temperature, relative humidity, wind speed, and prevalent wind direction during the multi-day PM₂.₅ episodes that occurred in 2018 and 2019 in... 82
Table 4-2. Statistical analysis of the model performance in predicting particulate nitrate concentrations with varying organic coating thicknesses at the SU site in... 97
Table 5-1. Names and chemical properties of the EPOA and OPOA surrogate species at 298K. 131
Table 5-2. Names and chemical properties of the ASOA surrogate species at 298K. 132
Table 5-3. Names and chemical properties of the BSOA surrogate species at 298K. 134
Table 5-4. Annual average concentrations of observed and modeled OC, EC, and PM₂.₅ and model performance metrics for OC, EC, and PM₂.₅ at the BG and BN sites. 138
Table 5-5. The intercept and slope with a 95% confidence interval calculated by the Deming linear regression, standard errors of the regression, and the... 142
Table 5-6. The intercept and slope with a 95% confidence interval calculated by the Deming linear regression, standard errors of the regression, and the... 143
Table 5-7. Model performance metrics for POC and SOC at the BG and BN sites. 145
Table A-1. Average observed and modeled OC and SOC concentrations in Seoul, correlation coefficients (R), normalized mean bias (NMB), normalized mean... 162
Figure 2-1. Schematic diagram of CMAQ (from CMAQ user's guide). 27
Figure 2-2. Overview of GMAF system. 33
Figure 2-3. Observed and predicted monthly mean PM₂.₅ concentrations in Seoul with PWF of 0, 0.4, and 1. 35
Figure 2-4. Temporal variations of observed PM₂.₅ concentration, modeled PM₂.₅ concentration with Kz,min of CMAQ version 5 (Kzz_531). and that of CMAQ... 37
Figure 2-5. Daily observed and modeled PM₂.₅ concentrations in Bulgwang supersite and Seoul. 'base_run' means modeled PM₂.₅ concentration with... 40
Figure 3-1. Plots of wind rose during the five multi-day PM₂.₅ episodes. 57
Figure 3-2. Time series of wind speed and wind direction during the five multi-day PM₂.₅ episodes in Seoul. 'WS' and 'WD' denote wind speed and wind... 59
Figure 3-3. Temporal variations of concentrations of PM₂.₅ and PM₂.₅ chemical compositions in the SU site. The purple shade means the multi-day PM₂.₅ episode periods. 61
Figure 3-4. Mass percentages of PM₂.₅ chemical components during the five multi-day PM₂.₅ episodes and non-episode. 62
Figure 3-5. Temporal variations of PM₂.₅ mass concentrations in the BN and SU sites during the five multi-day PM₂.₅ episodes. The blue lines mean the time lag... 66
Figure 3-6. Temporal variations of PM₂.₅ mass concentrations in the BN and SU sites during the two short-term PM₂.₅ episodes. The purple lines mean a time lag... 68
Figure 4-1. Model domain with locations of weather stations in SU and PM₂.₅ supersites in BN, SU, GJ, and US. 84
Figure 4-2. Relative coating thicknesses (ℓ/Rp) as a function of organic aerosol (OA) volume fractions.[이미지참조] 89
Figure 4-3. Uptake coefficients of N₂O₅ corresponding to the coating thickness (γN₂O₅,coating) as a function of aerosol diameters and coating thicknesses at the...[이미지참조] 90
Figure 4-4. PM₂.₅ mass concentrations and particulate species concentrations measured at the SU supersite in January 2018. 92
Figure 4-5. Mass fractions of PM₂.₅ chemical compositions measured at the SU supersite in January 2018. 93
Figure 4-6. Hourly variations of PM₂.₅, nitrate, and NO₂ concentrations monitored at the four selected PM supersites in January 2018. 95
Figure 4-7. Predicted nitrate concentrations with varying organic coating thickness in estimating N₂O₅ uptake coefficient and comparison with the... 96
Figure 4-8. Comparison between model results and observations of wind speed, PM₂.₅, NO₂, and O₃ at the SU site in January 2018. 100
Figure 4-9. Wind velocity vectors and spatial variation of concentrations of NO₂ and O₃ at selected daytime and nighttime during the episode. 102
Figure 4-10. Spatial distribution of ground-level concentrations of NH₃, particulate ammonium, HNO₃, and particulate nitrate at selected daytime and... 104
Figure 4-11. Wind velocity vectors and spatial variation of concentrations of NO₂ and O₃ at selected daytime and nighttime during the non-episode. 107
Figure 4-12. Spatial variation of concentrations of NH₃, particulate ammonium, HNO₃, and particulate nitrate at selected daytime and nighttime during the non-episode. 108
Figure 4-13. NH₃, HNO₃, and nitrate concentrations along the cross-section from "a" to "e" as depicted in Figure 4-12 (d). 111
Figure 4-14. Wind speed and directions in the Yellow Sea and percent contribution of HNO₃ gas formed in the Yellow Sea to the particulate nitrate... 113
Figure 4-15. HNO₃ production rate in the sub-areas of China, Yellow Sea, and Korea during the daytime and nighttime of the episode. 115
Figure 4-16. Conversion rate of NO₂ to HNO₃ in the sub-areas of China, Yellow Sea, and Korea during the daytime and nighttime of the episode. 116
Figure 5-1. The study area and the selected PM supersite locations: (a) outer domain, (b) inner domain, (c) BG supersite, and (d) BN supersite. 126
Figure 5-2. Monthly average VOC emissions in the Korea model domain. (a) POA and (b) AVOCs and BVOCs. 136
Figure 5-3. Daily variations of OC, EC, and PM₂.₅ concentrations (μg/m³) from observation and model in BG supersite. 139
Figure 5-4. Daily variations of OC, EC, and PM₂.₅ concentrations (μg/m³) from observation and model in BN supersite. 140
Figure 5-5. Monthly average SOC to POC concentration ratios (SOC/POCs) from observations and modeling. 'Observation' denotes SOC/POCs calculated... 144
Figure 5-6. Monthly mass composition in Seoul. (a) OA, (b) POA, (c) ASOA, and (d) BSOA. The AIVPO1, ASVOO1, ASVOO2, ASVOO3, AAVB2, AAVB3,... 146
Figure 5-7. OM to OC ratios (OM/OCs) in Seoul: (a) seasonal variation and (b) diurnal variation. In Figure 5-7 (a), the OM/OCs of BSOA in the cool months... 149
Figure 5-S1. Monthly Deming regression analysis results in BG supersite. 159
Figure 5-S2. Monthly Deming regression analysis results in BN supersite. 160
Figure A-1. Temporal variations of observed and modeled SOC concentrations. '0.0-pcVOC', '0.0387-pcVOC', and '0.0155-pcVOC' mean 0.0 mole-pcVOC/g-POA, 0.0387 mole-pcVOC/g-POA, and 0.0155 mole-pcVOC/g-POA, respectively. 163
Figure A-2. Monthly observed and (a) modeled SOC/POC ratios and (b) modeled OM/OC ratios in Seoul. 164