Separating emission and meteorological contributions to long-term PM<sub>2.5</sub> trends over eastern China during 2000–2018

Abstract

Abstract. The contribution of meteorology and emissions to long-term PM2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM2.5: a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM2.5 and gas precursors in these years. Although emissions dominated the long-term PM2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fenwei Plain, −4.8 % to 4.3 % in the Yangtze River Delta, and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.

Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion

“…A two-stage machine learning model coupled with the synthetic minority oversampling technique (SMOTE) developed in our previous study is used to generate the TAP PM 2.5 data, as presented in Figure . In the first stage, we define a high-pollution indicator to improve the PM 2.5 estimations on highly polluted days, when PM 2.5 are usually underestimated in statistical and machine learning models. , This high-pollution indicator is calculated based on PM 2.5 observation data and describes whether the PM 2.5 observations at each location exceed the monthly mean by two standard deviations.…”

Multi-Year Variation of Ozone and Particulate Matter in Northeast China Based on the Tracking Air Pollution in China (TAP) Data

“…The aim was to build a multiscale, near-real-time aerosol and gaseous pollutant concentration database in China and provide essential support for pollution characteristics analysis. The TAP database was generated using state-of-the-art technology involving machine learning algorithms [ 44 , 45 ]. The TAP data are determined based on the combination of multisource data including ground measurements, satellite aerosol optical parameter retrievals, model simulations, and meteorology field, land use information as well as population, and elevation data by multilayer machine learning models.…”

Trends and Variability of Ozone Pollution over the Mountain-Basin Areas in Sichuan Province during 2013–2020: Synoptic Impacts and Formation Regimes

“…TAP combines the information from ground observations, satellite retrieval, model results from the Community Multiscale Air Quality (CMAQ) model, and other ancillary data as introduced by Geng et al [35] and Xue et al [36]. The TAP PM 2.5 dataset released earlier than the ozone dataset was widely used in PM 2.5 research [37][38][39]. The near real-time MDA8 ozone estimates in China since 2013 were released in March 2021.…”