Ground-Level NO₂ Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence

Abstract

Nitrogen dioxide (NO 2 ) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO 2 concentrations over mainland China with full spatial coverage (100%) for the period 2019–2020 by combining surface NO 2 measurements, satellite tropospheric NO 2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO 2 estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) μg/m 3 . The daily seamless high-resolution and high-quality dataset “ChinaHighNO 2 ” allows us to examine spatial patterns at fine scales such as the urban–rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO 2 , especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 μg/m 3 ). During the COVID-19 pandemic, surface NO 2 concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO 2 column, implying that the former can better represent the changes in NO x emissions.

Are House Prices Affected by PM2.5 Pollution? Evidence from Beijing, China

“…Firstly, we vectorized the geographical map of the Beijing region to obtain latitude and longitude information. Then, we constructed the regional inverse distance weight matrix as Equation (5). It assumes that the strength of the spatial effect depends on the distance, and the closer the spatial effect between spatial units is, the stronger the spatial effect.…”

“…Among all air pollutants, fine particulate matter (with an aerodynamic diameter of less than 2.5 μm, PM 2.5 ) is particularly harmful. Long-term exposure to high PM 2.5 loading significantly increases the risk of developing cancer as well as cardiovascular and respiratory diseases [ 4 , 5 ]. In addition, PM 2.5 pollution poses a threat to ecological security [ 6 , 7 , 8 ].…”

Ground-level gaseous pollutants across China: daily seamless mapping and long-term spatiotemporal variations

“…Satellite remote sensing data used here include the daily seamless tropospheric NO2 products (0.25° × 0.25°) generated by first combining OMI/Aura and Global Ozone Monitoring Experiment-2B retrievals (He et al, 2020), and then gap-filling using CAMS tropospheric NO2 simulations via machine learning (Wei et al, 2022b), and MODIS monthly NDVI (0.05° × 0.05°), LandScan TM annual population (POP, 1 km) (Bright et al, 2000), and the SRTM digital elevation model (DEM, 90 m). ERA5-Land (0.1° × 0.1°) and ERA5 global reanalysis (0.25° × 0.25°) provided hourly meteorological fields (Muñoz-Sabater et al, 2021;Hersbach et al, 2020) surface mass concentrations were also included from the MERRA-2 and GEOS-FP global reanalysis every 1 and 3 hours at horizontal resolutions of 0.625° × 0.5° and 0.3125° × 0.25°, respectively.…”

“…The largest downward trends mainly occurred in northern and central China, especially in the BTH (trend = -6.01 and 109 µg/m 3 /yr, p < 0.001, respectively). This is mainly due to the change in fuel for heating from coal to gas widespread across China in winter (Wang et al, 2020), greatly reducing the emissions of precursor gases (Koukouli et al, 2018). Increasing trends were, however, also found in Ningxia and Shanxi in central China.…”

Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region

“…However, the industrial structure in the early 21st century was extensive and mainly depended on energy, raw materials, and labor, resulting in the large release of air pollutants into the atmosphere [ 1 , 2 , 3 ]. These air pollutants exert a notable impact on human health, vegetation growth, and environmental change [ 4 , 5 , 6 ]. It was estimated that the number of fine particulate matter (PM 2.5 )-related deaths in China increased by approximately 390,000 from 2002 to 2017 [ 2 ].…”