Tracking Daily Concentrations of PM_2.5 Chemical Composition in China since 2000

Abstract

PM2.5 chemical components play significant roles in the climate, air quality, and public health, and the roles vary due to their different physicochemical properties. Obtaining accurate and timely updated information on China’s PM2.5 chemical composition is the basis for research and environmental management. Here, we developed a full-coverage near-real-time PM2.5 chemical composition data set at 10 km spatial resolution since 2000, combining the Weather Research and Forecasting–Community Multiscale Air Quality modeling system, ground observations, a machine learning algorithm, and multisource-fusion PM2.5 data. PM2.5 chemical components in our data set are in good agreement with the available observations (correlation coefficients range from 0.64 to 0.75 at a monthly scale from 2000 to 2020 and from 0.67 to 0.80 at a daily scale from 2013 to 2020; most normalized mean biases within ±20%). Our data set reveals the long-term trends in PM2.5 chemical composition in China, especially the rapid decreases after 2013 for sulfate, nitrate, ammonium, organic matter, and black carbon, at the rate of −9.0, −7.2, −8.1, −8.4, and −9.2% per year, respectively. The day-to-day variability is also well captured, including evolutions in spatial distribution and shares of PM2.5 components. As part of Tracking Air Pollution in China (), this daily-updated data set provides large opportunities for health and climate research as well as policy-making in China.

Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data

“…Their spatial patterns are particularly similar to those of 2013–2020 in eastern China, but 16%, 7%, 7%, and 13% higher in population-weighted mean concentrations of SO 4 2– , NO 3 – , NH 4 + , and Cl – ; differently, these four components show significant increasing trends at the annual rates of 0.15, 0.11, 0.07, and 0.03 μg/m 3 ( p < 0.001), respectively. Highly polluted conditions and greater trends were observed in the NCP, associated with the significant increase in total PM 2.5 , mainly due to more anthropogenic emissions of major pollutants caused by the rapid economic growth of the country. , Similar findings were also reported from previous model simulated studies. , Nevertheless, given the absence of historical observations, an independent analysis is needed to validate the reliability of data records estimated for the prior period.…”

“…25,39 Similar findings were also reported from previous model simulated studies. 39,77 Nevertheless, given the absence of historical observations, an independent analysis is needed to validate the reliability of data records estimated for the prior period. hotspots are seen in heavily industrial cities like Shijiazhuang and Zibo, while NO 3 − hotspots were observed in core hub cities like Beijing and Tianjin mainly due to differences in main secondary pollutant emission sources (e.g., heavy industrial production and transportation) in local areas.…”

Long-term exposure to ambient fine particulate matter chemical composition and in-hospital case fatality among patients with stroke in China

“… 20 The TAP data source for PM 2.5 mass had an average out-of-bag cross-validation correlation coefficient (R) of 0.83, and the model cross-validation Rs were 0.70 for , 0.75 for , 0.72 for OM, 0.64 for BC, and 0.75 for , representing overall good consistency with ground measurement of PM chemical components. 19 , 20 …”

“…Daily concentrations of PM 2.5 mass and its dust-free chemical components (BC, OM, , , and ) at 10 × 10 km spatial resolution were obtained from the Tracking Air Pollution [TAP] database in China ( http://tapdata.org.cn/ ). 19 , 20 TAP PM chemical composition data synthesize situ measurements data from ground-based measurements, satellite-based aerosol optical depth estimates, and bottom-up emission inventory, and estimate their daily concentrations using Weather Research and Forecasting (v3.9.1)-Community Multiscale Air Quality (v5.2) chemical transport models in China from 2000 to present. 20 The TAP data source for PM 2.5 mass had an average out-of-bag cross-validation correlation coefficient (R) of 0.83, and the model cross-validation Rs were 0.70 for , 0.75 for , 0.72 for OM, 0.64 for BC, and 0.75 for , representing overall good consistency with ground measurement of PM chemical components.…”

Long-Term Exposure to Fine Particulate Constituents and Vascular Damage in a Population with Metabolic Abnormality in China

“…We also found that OM might exert a positive effect on the development of peripheral atherosclerosis. The formation of OM in the particulate phase was mainly caused by the emission of volatile organic compounds 51,52) . Previous study demonstrated that the removal of organic matters from PM2.…”