[Environ. Sci. Technol. 2022] Tracking Daily Concentrations of PM_2.5 Chemical Composition in China since 2000 [Link] [PDF]

Shigan Liu, Guannan Geng*, Qingyang Xiao, Yixuan Zheng, Xiaodong Liu, Jing Cheng, and Qiang Zhang

PM_2.5 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 PM_2.5 chemical composition is the basis for research and environmental management. Here, we developed a full-coverage near-real-time PM_2.5 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 PM_2.5 data. PM_2.5 chemical components in our data set are in good agreement with the available observations (correlation coefficients range from 0.64 to 0.75 at monthly scale from 2000 to 2020 and from 0.67 to 0.80 at daily scale from 2013 to 2020; most normalized mean biases within ±20%). Our data set reveals the long-term trends in PM_2.5 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 PM_2.5 components. As part of Tracking Air Pollution in China (TAP; http://tapdata.org.cn), this daily updated data set provides large opportunities for health and climate research as well as policy-making in China.

[Environ. Sci. Technol. 2021] Tracking PM_2.5 and O₃ Pollution and the Related Health Burden in China 2013-2020 [Link] [PDF]

Qingyang Xiao, Guannan Geng*, Tao Xue*, Shigan Liu, Cilan Cai, Kebin He, and Qiang Zhang

Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn), we characterized the spatiotemporal variations in PM_2.5 and O₃ exposures and quantified the long- and short-term exposure related premature deaths during 2013–2020 with respect to the two-stage clean air actions (2013-2017 and 2018-2020). We find a 48% decrease in national PM_2.5 exposure during 2013–2020, although the decrease rate has slowed after 2017. At the same time, O₃ pollution worsened, with the average April–September O₃ exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM_2.5 concentration. It is also noticed that with smaller PM_2.5 reduction, the avoided long-term exposure associated deaths in 2017–2020 (13%) was greater than that in 2013–2017 (9%), because the exposure–response curve is nonlinear. As a result of the efforts in reducing PM_2.5-polluted days with the daily average PM_2.5 higher than 75 μg/m³ and the considerable increase in O₃-polluted days with the daily maximum 8 h average O₃ higher than 160 μg/m³, deaths attributable to the short-term O₃ exposure were greater than those due to PM_2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM_2.5 and O₃ are urgently needed.

[Atmos. Chem. Phys. 2022] Spatiotemporal continuous estimates of daily 1 km PM_2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework [Link] [PDF]

Qingyang Xiao, Guannan Geng*, Shigan Liu, Jiajun Liu, Xia Meng, and Qiang Zhang

High spatial resolution PM_2.5 data covering a long time period are urgently needed to support population exposure assessment and refined air quality management. In this study, we provided complete-coverage PM_2.5 predictions with a 1 km spatial resolution from 2000 to the present under the Tracking Air Pollution in China (TAP, http://tapdata.org.cn, last access: 3 October 2022) framework. To support high spatial resolution modeling, we collected PM_2.5 measurements from both national and local monitoring stations. To correctly reflect the temporal variations in land cover characteristics that affected the local variations in PM_2.5, we constructed continuous annual geoinformation datasets, including the road maps and ensemble gridded population maps, in China from 2000 to 2021. We also examined various model structures and predictor combinations to balance the computational cost and model performance. The final model fused 10 km TAP PM_2.5 predictions from our previous work, 1 km satellite aerosol optical depth retrievals, and land use parameters with a random forest model. Our annual model had an out-of-bag R² ranging between 0.80 and 0.84, and our hindcast model had a by-year cross-validation R² of 0.76. This open-access, 1 km resolution PM_2.5 data product, with complete coverage, successfully revealed the local-scale spatial variations in PM_2.5 and could benefit environmental studies and policymaking.

[Environ. Sci. Technol. 2021] Tracking Air Pollution in China: Near Real-Time PM_2.5 Retrievals from Multisource Data Fusion [Link] [PDF]

Guannan Geng, Qingyang Xiao, Shigan Liu, Xiaodong Liu, Jing Cheng, Yixuan Zheng, Tao Xue, Dan Tong, Bo Zheng, Yiran Peng, Xiaomeng Huang, Kebin He, and Qiang Zhang*

Air pollution has altered the Earth’s radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM_2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM_2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R² of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM_2.5 data allow us to track the day-to-day variations in PM_2.5 concentrations over China in a timely manner. The long-term records of PM_2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM_2.5 data are publicly available through our website for sharing with the research and policy communities.