Source apportionment of PM10 and PM2.5 air pollution, and possible impacts of study characteristics in South Korea.

INTRODUCTION: Studies of source apportionment (SA) for particulate matter (PM) air pollution have enhanced understanding of dominant pollution sources and quantification of their contribution. Although there have been many SA studies in South Korea over the last two decades, few studies provided an integrated understanding of PM sources nationwide. The aim of this study was to summarize findings of PM SA studies of South Korea and to explore study characteristics. METHODS: We selected studies that estimated sources of PM10 and PM2.5 performed for 2000-2017 in South Korea using Positive Matrix Factorization and Chemical Mass Balance. We reclassified the original PM sources identified in each study into seven categories: motor vehicle, secondary aerosol, soil dust, biomass/field burning, combustion/industry, natural source, and others. These seven source categories were summarized by using frequency and contribution across four regions, defined by northwest, west, southeast, and southwest regions, by PM10 and PM2.5. We also computed the population-weighted mean contribution of each source category. In addition, we compared study features including sampling design, sampling and lab analysis methods, chemical components, and the inclusion of Asian dust days. RESULTS: In the 21 selected studies, all six PM10 studies identified motor vehicle, soil dust, and combustion/industry, while all 15 PM2.5 studies identified motor vehicle and soil dust. Different from the frequency, secondary aerosol produced a large contribution to both PM10 and PM2.5. Motor vehicle contributed highly to both, whereas the contribution of combustion/industry was high for PM10. The population-weighted mean contribution was the highest for the motor vehicle and secondary aerosol sources for both PM10 and PM2.5. However, these results were based on different subsets of chemical speciation data collected at a single sampling site, commonly in metropolitan areas, with short overlap and measured by different lab analysis methods. CONCLUSION: We found that motor vehicle and secondary aerosol were the most common and influential sources for PM in South Korea. Our study, however, suggested a caution to understand SA findings from heterogeneous study features for study designs and input data.

Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities.

OBJECTIVES: Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 µm in diameter (PM10) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. METHODS: We obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R(2)) statistics were computed. RESULTS: Mean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 µg/m(3) (standard deviation=2.40 and 9.51 µg/m(3), respectively). Cross-validated R(2) values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R(2) values of zero. The national model produced a higher crossvalidated R(2) (0.36) than those for the city-specific models. CONCLUSIONS: In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics.