Ambient air quality and subjective stress level using Community Health Survey data in Korea

Abstract

OBJECTIVESAir pollution causes various disease in exposed populations, and can lead to premorbid health effects manifested as both physical and psychological functional impairment. The present study investigated the subjective stress level in daily life in relation to the level of air pollution.METHODSData from the Community Health Survey (2013), comprising 99,162 men, and 121,273 women residing in 253 healthcare administrative districts, were combined with air pollutant concentration modelling data from the Korean Air Quality Forecasting System, and were stratified by subjective stress levels into five strata for multiple logistic regression. Levels of exposure were divided into five quintiles according to the annual concentration of nitrogen dioxide (NO2), and were analyzed using a single-pollutant model using NO2 concentration only, and a multi-pollutant model adjusted for the concentration of particulate matter <10 μm in diameter.RESULTSAnalysis of men and women in various age groups showed the highest odds ratio (OR) for subjective stress level at the highest NO2 concentration quintile in men and women aged 30–64 years (men: 2.91; 95% confidence interval [CI], 2.12 to 4.01; women: 1.82; 95% CI, 1.32 to 2.51). As the NO2 concentration quintile increased, the OR increased. Men showed higher ORs than women in all strata.CONCLUSIONSIn the present study, annual NO2 concentrations were found to be associated with subjective stress levels. This association was especially clear among socioeconomically active men and women aged 30-64 years.

Impacts of ambient air pollution on glucose metabolism in Korean adults: a Korea National Health and Nutrition Examination Survey study

“…Choi et al showed that the spatial distributions of PM 10 , PM 2.5 , and NO 2 with data assimilation over China and the Korean peninsula accurately depicted the observed air quality at the monitoring stations [36]. Further details of data assimilation are available in Choi et al's study, and these data have also been validated in a previous study [36,37].…”

Effects of exposure to ambient air pollution on pulmonary function impairment in Korea: the 2007-2017 Korea National Health and Nutritional Examination Survey

“…First, we only estimated data on the concentrations of specific air pollutants across the country using the KAQFS rather than including real air pollutant measurement concentrations because of the limited number of air-quality monitoring stations. Moreover, the real measurement data cannot fully reflect the air pollution exposure at participants’ residences [ 15 ]. The data assimilation method, similar to the KAQFS, could support simultaneous data fusion for multiple pollutants and accurately represent current air quality [ 30 ].…”

“…The KAQFS included the Sparse Matrix Operator Kernel Emissions version 2.7 and Community Multiscale Air Quality version 4.7.1, which used emission inventories, including the Multi-Resolution Emission Inventory for China, Regional Emission Inventory in Asia, and Clean Air Policy Support System [ 14 ]. Detailed descriptions of the model have been published previously [ 14 , 15 ].…”

“…Data assimilation was applied to the chemical transport model to calculate the concentration of air pollutants [ 15 ]. The air quality in the Korean peninsula was assessed according to grid sizes of 9 km×9 km, with a fine grid size of 3 km×3 km in the metropolitan area of Seoul.…”

“…We used these values to calculate weighted mean values for the 3,470 administrative districts of the residential addresses in the KNHANES. We estimated the daily concentrations of PM 10 (μg/m 3 ), PM 2.5 (μg/m 3 ), SO 2 (parts per billion [ppb]), NO 2 (ppb), CO (ppb), and O 3 (ppb) using the KAQFS data from 2005 to 2017 [ 15 ]. The estimations for air pollution were validated using real measurement data of air quality in the metropolitan area from 2005 to 2017 (PM 2.5 , 2015–2017) in AIRKOREA [ 16 ].…”