Exploration and Application of Regulatory PM₁₀ Measurement Data for Developing Long-term Prediction Models in South Korea

Abstract

Many cohort studies have reported associations of individual-level long-term exposures to PM 10 and health outcomes. Individual exposures were often estimated by using exposure prediction models relying on PM 10 data measured at national regulatory monitoring sites. This study explored spatial and temporal characteristics of regulatory PM 10 measurement data in South Korea and suggested PM 10 concentration metrics as long-term exposures for assessing health effects in cohort studies. We obtained hourly PM 10 data from the National Institute of Environmental Research for 2001~2012 in South Korea. We investigated spatial distribution of monitoring sites using the density and proximity in each of the 16 metropolitan cities and provinces. The temporal characteristics of PM 10 measurement data were examined by annual/seasonal/diurnal patterns across urban background monitoring sites after excluding Asian dust days. For spatial characteristics of PM 10 measurement data, we computed coefficient of variation (CV) and coefficient of divergence (COD). Based on temporal and spatial investigation, we suggested preferred long-term metrics for cohort studies. In 2010, 294 urban background monitoring sites were located in South Korea with a site over an area of 415.0 km 2 and distant from another site by 31.0 km on average. Annual average PM 10 concentrations decreased by 19.8% from 2001 to 2012, and seasonal PM 10 patterns were consistent over study years with higher concentrations in spring and winter. Spatial variability was relatively small with 6~19% of CV and 21~46% of COD across 16 metropolitan cities and provinces in 2010. To maximize spatial coverage and reflect temporal and spatial distributions, our suggestion for PM 10 metrics representing long-term

Association between exposure to traffic-related air pollution and pediatric allergic diseases based on modeled air pollution concentrations and traffic measures in Seoul, Korea: a comparative analysis

“…We then computed annual means at sites that met the inclusion criteria. The exclusion criteria used to select sites with representative annual averages were sites with > 91 missing days (25%), > 45 consecutive missing days, or < 10 months without at least one daily average per month [31]. All 37 monitoring sites met the criteria.…”

Web-Based Visualization of Scientific Research Findings: National-Scale Distribution of Air Pollution in South Korea

“…We included these two types of air pollution data to our visualization, in order to present similarities and differences in their spatial and temporal patterns, resulting from the same original data and different spatial coverage. We obtained hourly measurements at regulatory monitoring network sites and their locations in South Korea for 2001-2014 [27]. This data is also downloadable in AirKorea, the website where real-time air pollution conditions are informed based on air quality regulatory monitoring data (https://www.airkorea.or.kr/web/last_amb_hour_data?pMENU_NO=123).…”

“…However, approximately 40% of the districts do not contain any monitoring sites within the area [28]. Using hourly measurements, we computed annual average concentrations at each site, using the inclusion site criteria that excluded temporally or seasonally running sites [27]. We used the annual average concentration, to focus on the spatial patterns of air pollution concentrations rather than daily or seasonal changes.…”

Air Pollution Monitoring Design for Epidemiological Application in a Densely Populated City

“…Using the hourly measurements, we computed daily average concentrations for days with more than 18 hourly measurements (75%), and then computed representative annual averages at all sites. All 37 sites met our site inclusion criteria; at least one daily average per month for more than 9 months, no more than 91 missing days (25%), and less than 45 consecutive missing days [ 11 ].…”