Combined effect of surface PM2.5 assimilation and aerosol-radiation interaction on winter severe haze prediction in central and eastern China

Peng, Yue; Hong, Wang; Zhang, Xiaoye; Wang, Ping; Li, Siting; Liu, Zhaodong; Zhang, Wenjie; Che, Huizheng

doi:10.1016/j.apr.2023.101802

Cited by 3 publications

(1 citation statement)

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“…With the in-depth implementation of the "Air Pollution Prevention Plan" and the "3-year Action Plan to Win the Blue Sky Defense War", ambient air quality in China has improved annually, and the average PM2.5 concentration in the country has decreased significantly [2]. However, there are still some areas where the average annual PM2.5 concentration exceeds the World Health Organization's mediumterm target of 35 µg/m 3 [3]. Long-term exposure to air pollution not only increases the risk of cardiovascular and cerebrovascular diseases [4][5][6][7] but also creates financial pressure due to the consequent medical and health expenditures [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Weighted for Improving the Satellite-Based High-Resolution Ground PM2.5 Estimation Using the Light Gradient Boosting Machine

Yu,

Xi,

et al. 2023

Remote Sensing

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Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult to accurately assess the temporal and spatial changes in PM2.5 levels at a small regional scale. Here, we combined multi-angle implementation of atmospheric correction (MAIAC) aerosol products, ERA5 reanalysis data, etc., to construct an STW-LightGBM model that considers the spatiotemporal characteristics of air pollution and estimate the PM2.5 concentration of China’s surface at 1 km resolution from 2015 to 2020. Our model performed well, and the fitting accuracy of the 10-fold cross-validation between years was 0.877–0.917. The fitting accuracy of the model was >0.85 at different time scales (month, season, and year). The average slope of the regression prediction was 0.9 annually. The results showed that PM2.5 pollution improved from 2015 to 2020. The average PM2.5 concentration decreased by 4.55 μg/m3, and the maximum decrease reached 90.51 μg/m3. The areas with high PM2.5 concentrations were predominantly in the North China Plain, Sichuan Basin, and Xinjiang in the west, and the levels in areas with elevated PM2.5 levels were consistent across most study years. The standard deviation ellipse for PM2.5 in China showed a ‘northeast–southwest’ spatial distribution. From an interannual perspective, the average values of the four seasonal stations in the country showed a downward trend from 2015 to 2020, with the most obvious decline in winter, from 70.67 μg/m3 in 2015 to 46.75 μg/m3 in 2020. Compared to earlier inversion studies, this work provides a more stable and accurate method for obtaining high-resolution PM2.5 data, which is necessary for local air governance and environmental ecological construction at a fine scale.

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