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The role of PM 2.5 (particles with aerodynamic diameters ≤ 2.5 µm) deposition in air quality changes over China remains unclear. By using the three-year (2013, 2015, and 2017) simulation results of the WRF/CUACE v1.0 model from a previous work (Zhang et al., 2021), a non-linear relationship between the deposition of PM 2.5 and anthropogenic emissions over central-eastern China in cold seasons as well as in different life stages of haze events was unraveled. PM 2.5 deposition is spatially distributed differently from PM 2.5 concentrations and anthropogenic emissions over China. The North China Plain (NCP) is typically characterized by higher anthropogenic emissions compared to southern China, such as the middle-low reaches of Yangtze River (MLYR), which includes parts of the Yangtze River Delta and the Midwest. However, PM 2.5 deposition in the NCP is significantly lower than that in the MLYR region, suggesting that in addition to meteorology and emissions, lower deposition is another important factor in the increase in haze levels. Regional transport of pollution in central-eastern China acts as a moderator of pollution levels in different regions, for example by bringing pollution from the NCP to the MLYR region in cold seasons. It was found that in typical haze events the deposition flux of PM 2.5 during the removal stages is substantially higher than that in accumulation stages, with most of the PM 2.5 being transported southward and deposited to the MLYR and Sichuan Basin region, corresponding to a latitude range of about 24°N-31°N., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2022. Published by Elsevier B.V.)

The effect of vegetation seasonal cycle alterations to aerosol dry deposition on PM 2.5 concentrations (hereafter referred as the VSC effect) in China was investigated using a numerical modelling system (WRF/CUACE). Two simulation experiments using the vegetation parameters in particle dry deposition schemes typical for January and July revealed an absolute increase in surface PM 2.5 concentrations of about 2.4 μg/m 3 and a 5.5% relative increase in China (within model domain 2). The effect in non-urban areas was more significant than that in urban areas. The increases in PM 2.5 concentrations in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Sichuan Basin (SCB), and Central China (CC) were calculated as 1.9 μg/m 3 , 3.4 μg/m 3 , 3.1 μg/m 3 , 4.3 μg/m 3 , and 4.9 μg/m 3 , respectively, corresponding to relative increases of 2.9%, 4.5%, 5.4%, 5.8%, and 5.9%. These results demonstrate that the effect of decreased particle dry deposition due to reduced vegetation in southern areas was stronger, which was partially attributed to the increased vegetation cover and more significant seasonal changes in those regions. Furthermore, the increased PM 2.5 concentrations caused by the VSC effect were transported from north to south via the winter northerly winds, which weakened the effect in North China Plain and enhanced the effect in parts of central and southern China, such as the south of CC. Although the surface PM 2.5 concentration was relatively high in North China Plain, the effects of the northerly wind and relatively small dry deposition velocity meant that the removal of PM 2.5 in that region was relatively less than in southern areas of China. These results will contribute to understanding of the underlying mechanisms of PM 2.5 enhancement during winter in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

As one of the most concerned issues in modern society, air quality has received extensive attentions from the public and the government, which promotes the continuous development and progress of air quality forecasting technology. In this study, an automated air quality forecasting system based on machine learning has been developed and applied for daily forecasts of six common pollutants (PM 2.5 , PM 10, SO 2 , NO 2 , O 3 , and CO) and pollution levels, which can automatically find the best "Model + Hyperparameters" without human intervention. Five machine learning models and an ensemble model (Stacked Generalization) were integrated into the system, supported by a knowledge base containing the meteorological observed data, pollutant concentrations, pollutant emissions, and model reanalysis data. Then five-year data (2015-2019) of Beijing, Shanghai, Guangzhou, Chengdu, Xi'an, Wuhan, and Changchun in China, were used as an application case to study the effectiveness of the automated forecasting system. Based on the analysis of seven evaluation criteria and pollution level forecasts, combined with the forecasting results for the next 3-days, it is found that the automated system can achieve satisfactory forecasting performance, better than most of numerical model results. This implied that the developed system unveils a good application prospect in the field of environmental meteorology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Burning sacrificial offerings, a Chinese custom, can cause severe air pollution at some festivals, especially in urban areas. For example, during the Hanyi Festival (Festival) in 2020, the PM 2.5 concentrations in Lanzhou, China, peaked at 20:00 (mean: 161 μg/m3; max: 353 μg/m3) and remained high until 22:00. To eliminate the impact of meteorological variation, estimate the change in PM 2.5 emission due to the incineration of sacrificial offerings (ISO), and assess the effectiveness of relevant controls, a random forest (RF) approach was utilized to quantify the meteorologically normalized contribution of ISO emissions (C I S O _ M N ) to the PM 2.5 concentration during the Festival from 2015 to 2020 in Lanzhou. The maximum hourly C I S O _ M N (C I S O _ M N m a x ) during the Festival in 2017 at national sites in urban areas (mean: 11 μg/m3) was lower than that in other years (mean: 12–66 μg/m3) due to strict controls at the end of the year as the Air Pollution Prevention and Control Action Plan was implemented. Nevertheless, the C I S O _ M N m a x (mean: 74 μg/m3; max: 339 μg/m3) during the Festival in 2019 exhibited the highest contribution rate (mean: 60%; max: 87%) to PM 2.5 at microsites in urban areas in the six years. The C I S O _ M N m a x at six national sites from 2015 to 2020 showed increasing trends, as was also observed at the microsites from 2017 to 2020, whether the sites were in urban or rural areas, indicating that the controls did not have persistent effects. Health effects attributed to ISO-related PM 2.5 pollution in Lanzhou Valley were found to have increased mainly during the 2019 and 2020 Festivals compared with those in 2017 and 2018. Although ISO impacts increased as a whole, some areas suffering severe effects from ISO emissions exhibited considerably decreased rates of C I S O _ M N m a x , indicating the effectiveness of control measures in these areas. Therefore, good management, persistent controls, and modification of related habits are recommended to alleviate ISO pollution. [Display omitted] • The magnitude and spatiotemporal scale of the incineration of sacrificial offerings (ISO) impact on PM 2.5 were investigated. • The meteorologically normalized contribution of ISO-related emissions to PM 2.5 was determined via random forest models. • The ISO control policies performed well during the 2017 Hanyi Festivals but did not have persistent effects. • The results have implications for air quality management during similar sacrificial festivals. [ABSTRACT FROM AUTHOR]