Characteristics, formation, and sources of PM2.5 in 2020 in Suzhou, Yangtze River Delta, China

A study on the impact of meteorological and emission factors on PM2.5 concentrations based on machine learning

PM2.5 pollution, a major environmental and health concern, is influenced by a complex interplay of emission sources and meteorological conditions. Accurately identifying these factors and their contributions is essential for effective pollution management. This study applies Positive Matrix Factorization (PMF) to identify primary sources of PM2.5 and uses the Light Gradient Boosting Machine (LightGBM) model, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) to quantitatively assess the impact of meteorological and emission factors on PM2.5 concentrations. SHAP results reveal that meteorological factors contribute 16.6% (5.3 μg/m3) to PM2.5, with humidity being the most influential, while emission sources account for 83.4% (26.8 μg/m3), with secondary particulate matter being the dominant factor. Secondary particulate matter and biomass burning significantly impacted PM2.5 in the first and fourth quarters, while dust sources became more influential in the second quarter, and coal emissions were most prominent in the second and third quarters. Two-dimensional PDP analysis indicated that in the first and fourth quarters, secondary particulate matter concentration increased with air pressure, and the atmospheric oxidation process was more pronounced under high-humidity conditions during the day. Strong transport conditions, with wind direction shifting from north to east, also influenced secondary particulate matter levels. This study demonstrates that the LightGBM model effectively captures the nonlinear relationships between PM2.5 and meteorological and emission factors, providing a reliable approach for analyzing the causes of PM2.5 pollution.

Exploring efficient strategies for air quality improvement in China based on its regional characteristics and interannual evolution of PM2.5 pollution

Fine particulate matter (PM2.5) harms human health and hinders normal human life. Considering the serious complexity and obvious regional characteristics of PM2.5 pollution, it is urgent to fill in the comprehensive overview of regional characteristics and interannual evolution of PM2.5. This review studied the PM2.5 pollution in six typical areas between 2014 and 2022 based on the data published by the Chinese government and nearly 120 relevant literature. We analyzed and compared the characteristics of interannual and quarterly changes of PM2.5 concentration. The Beijing-Tianjin-Hebei region (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) made remarkable progress in improving PM2.5 pollution, while Fenwei Plain (FWP), Sichuan Basin (SCB) and Northeast Plain (NEP) were slightly inferior mainly due to the relatively lower level of economic development. It was found that the annual average PM2.5 concentration change versus year curves in the three areas with better pollution control conditions can be merged into a smooth curve. Importantly, this can be fitted for the accurate evaluation of each area and provide reliable prediction of its future evolution. In addition, we analyzed the factors affecting the PM2.5 in each area and summarize the causes of air pollution in China. They included primary emission, secondary generation, regional transmission, as well as unfavorable air dispersion conditions. We also suggested that the PM2.5 pollution control should target specific industries and periods, and further research need to be carried out on the process of secondary production. The results provided useful assistance such as effect prediction and strategy guidance for PM2.5 pollution control in Chinese backward areas.

Ambient volatile organic compounds in urban and industrial regions in Beijing: Characteristics, source apportionment, secondary transformation and health risk assessment

Field measurements of volatile organic compounds (VOCs) were conducted simultaneously at an urban site and one industrial park site in Beijing in summer. The VOCs concentrations were 94.3 ± 157.8 ppbv and 20.7 ± 8.9 ppbv for industrial and urban sites, respectively. Alkanes and aromatics were the major contributors to VOCs in industrial site, while oxygenated volatile organic compounds (OVOCs) contributed most in urban site. The most abundant VOC species were n-pentane and formaldehyde for industrial site and urban site, respectively. The calculated ozone formation potential (OFP) and OH loss rates (L_OH) were 621.1 ± 1491.9 ppbv (industrial site), 102.9 ± 37.3 ppbv (urban site), 22.0 ± 39.0 s^-1 (industrial site) and 5.3 ± 2.2 s^-1 (urban site), respectively. Based on the positive matrix factorization (PMF) model, solvent utilization I (34.1 %), solvent utilization II (27.9 %), mixture combustion source (19.3 %), OVOCs related source (9.6 %) and biogenic source (9.1 %) were identified in the industrial site, while OVOCs related source (27.8 %), vehicle exhaust (22.1 %), solvent utilization (19.3 %), coal combustion (16.0 %) and biogenic source (14.8 %) were identified in the urban site. The results of O₃-VOCs-NOx sensitivity indicated that O₃ formation were respectively under the VOC-limited and NOx-limited conditions in Beijing urban and industrial regions. Additionally, aromatics accounted remarkable SOA formation ability both in the two sites, and SOA potentials of xylene, toluene and ethylbenzene as the indicator species for the solvent utilization in industrial site were remarkable higher than those obtained in urban regions. The hazard index values in the industrial and urban sites were 1.72 and 3.39, respectively, suggesting a high non-carcinogenic risks to the exposed population. Formaldehyde had the highest carcinogenic risks in the two sites, and the cumulative carcinogenic risks in the industrial site and urban site were 1.95 × 10^-5 and 1.21 × 10^-5, respectively.

Influence of Meteorological Factors and Chemical Processes on the Explosive Growth of PM2.5 in Shanghai, China

In order to explore the mechanism of haze formation, the meteorological effect and chemical reaction process of the explosive growth (EG) of PM2.5 were studied. In this study, the level of PM2.5, water-soluble inorganic ions, carbonaceous aerosols, gaseous precursors, and meteorological factors were analyzed in Shanghai in 2018. The EG event is defined by a net increase of PM2.5 mass concentration greater than or equal to 100 μg m−3 within 3, 6, or 9 h. The results showed that the annual average PM2.5 concentration in Shanghai in 2018 was 43.2 μg m−3, and secondary inorganic aerosols and organic matter (OM) accounted for 55.8% and 20.1% of PM2.5, respectively. The increase and decrease in the contributions of sulfate, nitrate, ammonium (SNA), and elemental carbon (EC) to PM2.5 from clean days to EG, respectively, indicated a strong, secondary transformation during EG. Three EG episodes (Ep) were studied in detail, and the PM2.5 concentration in Ep3 was highest (135.7 μg m−3), followed by Ep2 (129.6 μg m−3), and Ep1 (82.3 μg m−3). The EG was driven by stagnant conditions and chemical reactions (heterogeneous and gas-phase oxidation reactions). This study improves our understanding of the mechanism of haze pollution and provides a scientific basis for air pollution control in Shanghai.