Regional characteristics of NO2 column densities from Pandora observations during the MAPS-Seoul campaign

Assessing the Spatiotemporal Characteristics, Factor Importance, and Health Impacts of Air Pollution in Seoul by Integrating Machine Learning into Land-Use Regression Modeling at High Spatiotemporal Resolutions

Previous studies have characterized spatial patterns of air pollution with land-use regression (LUR) models. However, the spatiotemporal characteristics of air pollution, the contribution of various factors to them, and the resultant health impacts have yet to be evaluated comprehensively. This study integrates machine learning (random forest) into LUR modeling (LURF) with intensive evaluations to develop high spatiotemporal resolution prediction models to estimate daily and diurnal PM2.5 and NO2 in Seoul, South Korea, at the spatial resolution of 500 m for a year (2019) and to then evaluate the contribution of driving factors and quantify the resultant premature mortality. Our results show that incorporating the random forest algorithm into our LUR model improves the model performance. Meteorological conditions have a great influence on daily models, while land-use factors play important roles in diurnal models. Our health assessment using dynamic population data estimates that PM2.5 and NO2 pollution, when combined, causes a total of 11,183 (95% CI: 5837-16,354) premature mortalities in Seoul in 2019, of which 64.9% are due to PM2.5, while the remaining are attributable to NO2. The air pollution-attributable health impacts in Seoul are largely caused by cardiovascular diseases including stroke. This study pinpoints the significant spatiotemporal variations and health impact of PM2.5 and NO2 in Seoul, providing essential data for epidemiological research and air quality management.

TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO2 Concentrations

Observing the spatial heterogeneities of NO2 air pollution is an important first step in quantifying NOX emissions and exposures. This study investigates the capabilities of the Tropospheric Monitoring Instrument (TROPOMI) in observing the spatial and temporal patterns of NO2 pollution in the continental United States. The unprecedented sensitivity of the sensor can differentiate the fine-scale spatial heterogeneities in urban areas, such as emissions related to airport/shipping operations and high traffic, and the relatively small emission sources in rural areas, such as power plants and mining operations. We then examine NO2 columns by day-of-the-week and find that Saturday and Sunday concentrations are 16% and 24% lower respectively, than during weekdays. We also analyze the correlation of daily maximum 2-m temperatures and NO2 column amounts and find that NO2 is larger on the hottest days (>32°C) as compared to warm days (26°C-32°C), which is in contrast to a general decrease in NO2 with increasing temperature at moderate temperatures. Finally, we demonstrate that a linear regression fit of 2019 annual TROPOMI NO2 data to annual surface-level concentrations yields relatively strong correlation (R 2 = 0.66). These new developments make TROPOMI NO2 satellite data advantageous for policymakers and public health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations.

Spatio-Temporal Variability of Aerosol Optical Depth, Total Ozone and NO2 Over East Asia: Strategy for the Validation to the GEMS Scientific Products

In this study, the spatio-temporal variability of aerosol optical depth (AOD), total column ozone (TCO), and total column NO2 (TCN) was identified over East Asia using long-term datasets from ground-based and satellite observations. Based on the statistical results, optimized spatio-temporal ranges for the validation study were determined with respect to the target materials. To determine both spatial and temporal ranges for the validation study, we confirmed that the observed datasets can be statistically considered as the same quantity within the ranges. Based on the thresholds of R2>0.95 (temporal) and R>0.95 (spatial), the basic ranges for spatial and temporal scales for AOD validation was within 30 km and 30 min, respectively. Furthermore, the spatial scales for AOD validation showed seasonal variation, which expanded the range to 40 km in summer and autumn. Because of the seasonal change of latitudinal gradient of the TCO, the seasonal variation of the north-south range is a considerable point. For the TCO validation, the north-south range is varied from 0.87° in spring to 1.05° in summer. The spatio-temporal range for TCN validation was 20 min (temporal) and 20–50 km (spatial). However, the nearest value of satellite data was used in the validation because the spatio-temporal variation of TCN is large in summer and autumn. Estimation of the spatio-temporal variability for respective pollutants may contribute to improving the validation of satellite products.

Detection of Strong NOX Emissions from Fine-scale Reconstruction of the OMI Tropospheric NO2 Product

Satellite-retrieved atmospheric NO2 column products have been widely used in assessing bottom-up NOX inventory emissions emitted from large cities, industrial facilities, and power plants. However, the satellite products fail to quantify strong NOX emissions emitted from the sources less than the satellite’s pixel size, with significantly underestimating their emission intensities (smoothing effect). The poor monitoring of the emissions makes it difficult to enforce pollution restriction regulations. This study reconstructs the tropospheric NO2 vertical column density (VCD) of the Ozone Monitoring Instrument (OMI)/Aura (13 × 24 km2 pixel resolution at nadir) over South Korea to a fine-scale product (grid resolution of 3 × 3 km2) using a conservative spatial downscaling method, and investigates the methodological fidelity in quantifying the major Korean area and point sources that are smaller than the satellite’s pixel size. Multiple high-fidelity air quality models of the Weather Research and Forecast-Chemistry (WRF-Chem) and the Weather Research and Forecast/Community Multiscale Air Quality modeling system (WRF/CMAQ) were used to investigate the downscaling uncertainty in a spatial-weight kernel estimate. The analysis results showed that the fine-scale reconstructed OMI NO2 VCD revealed the strong NOX emission sources with increasing the atmospheric NO2 column concentration and enhanced their spatial concentration gradients near the sources, which was accomplished by applying high-resolution modeled spatial-weight kernels to the original OMI NO2 product. The downscaling uncertainty of the reconstructed OMI NO2 product was inherent and estimated by 11.1% ± 10.6% at the whole grid cells over South Korea. The smoothing effect of the original OMI NO2 product was estimated by 31.7% ± 13.1% for the 6 urbanized area sources and 32.2% ± 17.1% for the 13 isolated point sources on an effective spatial resolution that is defined to reduce the downscaling uncertainty. Finally, it was found that the new reconstructed OMI NO2 product had a potential capability in quantifying NOX emission intensities of the isolated strong point sources with a good correlation of R = 0.87, whereas the original OMI NO2 product failed not only to identify the point sources, but also to quantify their emission intensities (R = 0.30). Our findings highlight a potential capability of the fine-scale reconstructed OMI NO2 product in detecting directly strong NOX emissions, and emphasize the inherent methodological uncertainty in interpreting the reconstructed satellite product at a high-resolution grid scale.