To provide a nationwide representative dataset for the study on health impact of air pollution, we combined the data from the Korea National Health and Nutrition Examination Survey with the daily air quality and weather data by matching the date of examination and the residential address of the participants. The database of meteorological factors and air quality as sources of exposure data were estimated using the Community Multiscale Air Quality model. The linkage dataset was merged by three ways; administrative district, si-gun-gu (city, county, and district), and geocode (in latitude and longitude coordinate units) based on the participants’ residential address, respectively. During the study period, the exposure dataset of 85,018 individuals (38,306 men and 46,712 women) whose examination dates were recorded were obtained. According to the definition of exposure period, the dataset was combined with the data on short-term, mid-term, and long-term exposure to air pollutants and the meteorological indices. Calculation of the daily merged dataset’s average air pollution linked by si-gun-gu and geocode units showed similar results. This study generated a daily average of meteorological indices and air pollution exposure dataset for all regions including rural and remote areas in Korea for 11 years. It is expected to provide a platform for the researchers studying the health impact of air pollution and climate change on the representative population and area, which may facilitate the establishment of local health care plans by understanding the residents’ health status at the local as well as national level.

The accurate monitoring and analysis of PM-2.5 are critical for improving air quality and formulating public health policies. However, environmental data often contain missing values due to equipment failures, data collection errors, or extreme weather conditions, which can hinder reliable analysis and predictions. This study evaluates the performance of various missing data imputation methods for PM-2.5 data in Seoul, Korea, using scenarios with artificially generated missing values during high- and low-concentration periods. The methods compared include FFILL, KNN, MICE, SARIMAX, DNN, and LSTM. The results indicate that KNN consistently achieved stable and balanced performance across different temporal intervals, with an RMSE of 5.65, 9.14, and 9.71 for 6 h, 12 h, and 24 h intervals, respectively. FFILL demonstrated superior performance for short intervals (RMSE 4.76 for 6 h) but showed significant limitations as intervals lengthened. SARIMAX performed well in long-term scenarios, with an RMSE of 9.37 for 24 h intervals, but required higher computational complexity. Conversely, deep learning models such as DNN and LSTM underperformed, highlighting the need for further optimization for time-series data. This study highlights the practicality of KNN as the most effective method for addressing missing PM-2.5 data in mid- to long-term applications due to its simplicity and efficiency. These findings provide valuable insights into the selection of imputation methods for environmental data analysis, contributing to the enhancement of data reliability and the development of effective air quality management policies.

Public concern about the adverse health effects of air pollution has grown rapidly in Korea, and there has been increasing demand for research on ways to minimize the health effects of air pollution. Integrating large epidemiological data and air pollution exposure levels can provide a data infrastructure for studying ambient air pollution and its health effects. The Korean Genome and Epidemiology Study (KoGES), a large population-based study, has been used in many epidemiological studies of chronic diseases. Therefore, KoGES cohort data were linked to air pollution data as a national resource for air pollution studies. Air pollution data were produced using community multiscale air quality modeling with additional adjustment of monitoring data, satellite-derived aerosol optical depth, normalized difference vegetation index, and meteorological data to increase the accuracy and spatial resolution. The modeled air pollution data were linked to the KoGES cohort based on participants’ geocoded residential addresses in grids of 1 km (particulate matter) or 9 km (gaseous air pollutants and meteorological variables). As the integrated data become available to all researchers, this resource is expected to serve as a useful infrastructure for research on the health effects of air pollution.

Air 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. Data 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. Analysis 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. In 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.

Accurate prediction of PM_2.5 concentrations is essential for public health management, especially in areas affected by long-range pollutant transport. This study presents a hybrid model combining convolutional long short-term memory (ConvLSTM) and deep neural networks (DNNs) to enhance PM_2.5 forecasting in Seoul, South Korea. The hybrid model leverages ConvLSTM’s ability to capture spatiotemporal dependencies and DNN’s strength in feature extraction, enabling it to outperform standalone CMAQ and DNN models. For the T1 forecast (6 h averages), the ConvLSTM-DNN model exhibited superior performance, with an RMSE of 7.2 µg/m³ compared to DNN’s 8.5 µg/m³ and CMAQ’s 10.1 µg/m³. The model also maintained high categorical accuracy (ACC) and probability of detection (POD) for critical PM_2.5 levels while reducing false alarms (FARs), particularly in bad and very bad events. Although its performance decreases over extended forecast periods, the ConvLSTM-DNN model demonstrates its utility as a robust forecasting tool. Future work will focus on optimizing the network structure to improve long-term forecast accuracy.