Elsevier

Environmental Pollution

Volume 226, July 2017, Pages 21-29
Environmental Pollution

National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea

https://doi.org/10.1016/j.envpol.2017.03.056Get rights and content

Highlights

  • We developed a national point-wise exposure prediction approach for PM10 and NO2 in South Korea.

  • Our models had moderate or good predictive performance.

  • Our approach will allow national exposure assessments and health analyses.

Abstract

The limited spatial coverage of the air pollution data available from regulatory air quality monitoring networks hampers national-scale epidemiological studies of air pollution. The present study aimed to develop a national-scale exposure prediction model for estimating annual average concentrations of PM10 and NO2 at residences in South Korea using regulatory monitoring data for 2010. Using hourly measurements of PM10 and NO2 at 277 regulatory monitoring sites, we calculated the annual average concentrations at each site. We also computed 322 geographic variables in order to represent plausible local and regional pollution sources. Using these data, we developed universal kriging models, including three summary predictors estimated by partial least squares (PLS). The model performance was evaluated with fivefold cross-validation. In sensitivity analyses, we compared our approach with two alternative approaches, which added regional interactions and replaced the PLS predictors with up to ten selected variables. Finally, we predicted the annual average concentrations of PM10 and NO2 at 83,463 centroids of residential census output areas in South Korea to investigate the population exposure to these pollutants and to compare the exposure levels between monitored and unmonitored areas. The means of the annual average concentrations of PM10 and NO2 for 2010, across regulatory monitoring sites in South Korea, were 51.63 μg/m3 (SD = 8.58) and 25.64 ppb (11.05), respectively. The universal kriging exposure prediction models yielded cross-validated R2s of 0.45 and 0.82 for PM10 and NO2, respectively. Compared to our model, the two alternative approaches gave consistent or worse performances. Population exposure levels in unmonitored areas were lower than in monitored areas. This is the first study that focused on developing a national-scale point wise exposure prediction approach in South Korea, which will allow national exposure assessments and epidemiological research to answer policy-related questions and to draw comparisons among different countries.

Introduction

There has been increasing evidence supporting the association between long-term exposure to air pollution and human health (Pope and Dockery, 2006, Hoek et al., 2013). Most cohort studies of long-term air pollution and health have particularly focused on traffic-related air pollutants such as particulate matter less than or equal to 10 or 2.5 μm in diameter (PM10 or PM2.5), nitric oxide (NO), and nitrogen dioxide (NO2) (Beelen et al., 2014, Kaufman et al., 2016, Laden et al., 2006, Pope et al., 2004).

One of the most important challenges faced by these studies has been the unavailability of individual-level air pollution measurements. Measurements are only available at limited numbers of monitoring sites. To overcome this limitation, many cohort studies have adopted exposure prediction models based on statistical approaches and regulatory air quality monitoring data, and estimated air pollution concentrations at participants’ homes. The most common statistical modeling approaches employed have been regression methods, namely, land use regression and geostatistical techniques such as kriging (Hoek et al., 2008, Jerrett et al., 2005). Land use regression explains the spatial variability of air pollution using predictors, and kriging additionally incorporates spatial correlation structures. These models often considered hundreds of geographic variables, computed in the Geographic Information System (GIS), as predictors representing local or regional pollution sources. Either a limited number of selected variables or a few summary predictors estimated by dimension reduction were included (Eeftens et al., 2012, Sampson et al., 2011). In addition to geographic variables, other studies improved the prediction models by including satellite imagery data or air quality model outputs as predictors in the models (Kloog et al., 2011, Lindstrom et al., 2013, van Donkelaar et al., 2015 Young et al., 2016).

Although these exposure prediction models have mostly been developed for city areas (Eeftens et al., 2012, Hoek et al., 2008; Keller et al., 2015), some studies have expanded the modeling domains to a national scale while retaining the high spatial resolution (Hystad et al., 2011, Knibbs et al., 2014 Novotny et al., 2011; Sampson et al., 2013, Vienneau et al., 2010). There are large areas without regulatory air quality monitoring sites in the vicinity, which hamper the assessment of air pollution concentrations that the people residing in such areas are exposed to. This limitation also prevents resulting health analyses using the existing national-scale health data from large cohorts or government-generated databases. Developing national exposure prediction models would support exposure assessments and health analyses on a national scale to provide important policy-related information, such as national patterns of air pollution and health risks and areas that need to be targeted for air pollution reduction and that contain vulnerable populations.

Most national exposure prediction models have been developed in North America and Western Europe (Hystad et al., 2011; Novotny et al., 2011; Sampson et al., 2013, Vienneau et al., 2010). National prediction models that operate in different areas can provide insights into the differences and similarities between countries. The aim of this study was to develop a national-scale exposure prediction approach with fine-scale spatial variability using universal kriging and dimension-reduced predictors for estimating annual average concentrations of PM10 and NO2 during 2010 in South Korea. We selected the year 2010 for our study, as there were sufficient numbers of monitoring sites available and a quinquennial population and housing census, a data source of a large portion of geographic variables, was conducted this year (Yi et al., 2016). To gain insights into the performance of our prediction models, we compared our primary approach with two alternative exposure prediction approaches. Using our exposure prediction models, we also investigated the distributions of the predicted PM10 and NO2 concentrations across residential census output areas to assess population exposure levels.

Section snippets

Regulatory air quality monitoring data

We obtained hourly PM10 and NO2 concentrations measured at the 294 regulatory air quality monitoring sites operating in 2010 in South Korea (48 million people in 100,033 km2) from the National Institute of Environmental Research (Fig. 1). The South Korean regulatory air quality monitoring network consists of four types of monitoring site: urban background, urban roadside, regional background, and national background (MOE, 2011). Urban background sites were established for monitoring air

Regulatory monitoring data

Out of the 277 regulatory monitoring sites inlcuded in our models, about 40% of the sites were located in seven metropolitan cities (Table 1). The median distance between the closest regulatory monitoring sites was 3.14 km, with shorter distances in metropolitan cities (median = 2.85 km) than in provinces (3.99 km).

The mean annual average concentrations of PM10 and NO2 for 2010 across 277 regulatory monitoring sites in South Korea were 51.63 μg/m3 (SD = 8.58) and 25.64 ppb (11.05), respectively

Discussion

To the best of our knowledge, this is the first study to develop a national-scale exposure prediction approach that estimates annual average air pollution concentrations at individual residential locations in South Korea. Our national prediction approach based on universal kriging and PLS regression gave modest and good model performances for PM10 and NO2, respectively. The PLS predictors of PM10 and NO2 concentrations were positively correlated with geographic variables representing traffic,

Conclusions

Assessment of air pollution concentrations in South Korea, with its heavily urban-focused population exposed to high air pollution levels, could provide new scientific knowledge regarding exposure prediction and health analyses. Our national-scale exposure prediction models for estimating individual-level concentrations of PM10 and NO2 is the first such study carried out for South Korea, which will allow national-scale epidemiological studies to assess national patterns of risks attributable to

Funding

This research was primarily supported by the Basic Science Research Program through National Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A6A3A04059017); and additional support was provided by the Environmental Health Action Program funded by the Korea Ministry of Environment (MOE) (2014001360003, 2014001360002).

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