Modeling study on the roles of the deposition and transport of PM2.5 in air quality changes over central-eastern China.

The role of PM2.5 (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 PM2.5 and anthropogenic emissions over central-eastern China in cold seasons as well as in different life stages of haze events was unraveled. PM2.5 deposition is spatially distributed differently from PM2.5 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, PM2.5 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 PM2.5 during the removal stages is substantially higher than that in accumulation stages, with most of the PM2.5 being transported southward and deposited to the MLYR and Sichuan Basin region, corresponding to a latitude range of about 24°N-31°N.

Development and application of an automated air quality forecasting system based on machine learning.

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 (PM2.5, PM10, SO2, NO2, O3, 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.

Effect of vegetation seasonal cycle alterations to aerosol dry deposition on PM2.5 concentrations in China.

The effect of vegetation seasonal cycle alterations to aerosol dry deposition on PM2.5 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 PM2.5 concentrations of about 2.4 µg/m3 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 PM2.5 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/m3, 3.4 µg/m3, 3.1 µg/m3, 4.3 µg/m3, and 4.9 µg/m3, 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 PM2.5 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 PM2.5 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 PM2.5 in that region was relatively less than in southern areas of China. These results will contribute to understanding of the underlying mechanisms of PM2.5 enhancement during winter in China.

Impact of Arctic Oscillation anomalies on winter PM2.5 in China via a numerical simulation.

The impact of Arctic Oscillation (AO) anomalies on winter PM2.5 variability in China was investigated using a numerical modeling system (WRF-CUACE). The model results showed that the influence of AO anomalies on winter PM2.5 concentration was mainly concentrated in eastern China, especially in Central China (CEN), Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and Pearl River Delta (PRD) and was mostly consistent with the conclusions of a previous analysis using haze data. Winter PM2.5 concentrations in CEN and BTH increased under abnormally high AO and decreased under abnormally low AO due to the subsequent changes in specific meteorological conditions, such as temperature, wind speed, and boundary layer height. Winter PM2.5 decreased in the YRD and PRD in both abnormally high and low AO years due to more favorable vertical transport conditions and regional transport capacity compared with those of other regions. In addition to meteorological factors, AO anomalies also impacted PM2.5 depositions in winter, with more apparent effects in southern China. It is found that AO had a larger impact on dry deposition than on wet deposition, and dry deposition was a dominant factor affecting PM2.5 concentrations in CEN.

A new parameterization of uptake coefficients for heterogeneous reactions on multi-component atmospheric aerosols.

Based on laboratory studies and field observations, a new parameterization of uptake coefficients for heterogeneous reactions on multi-component aerosols is developed in this work. The equivalent ratio (ER) of inorganic aerosol is used to establish the quantitative relationship between the heterogeneous uptake coefficients and the composition of aerosols. Incorporating the new ER-dependent scheme, the WRF-CUACE model has been applied to simulate sulfate mass concentrations during December 2017 in the Beijing-Tianjin-Hebei region and evaluate the role of aerosol chemical components played in the sulfate formation. Simulated temporal variations and magnitudes of sulfate show good agreement with the observations by using this new scheme. From clean to polluted cases, although both dominant cations and anions increase significantly, the equivalent ratio decreases gradually and is closer to unity, representing the variation of aerosol compositions, which inhibits the heterogeneous uptake of SO2, with the uptake coefficient decreasing from 1 × 10-4 to 5.3 × 10-5. Based on this phenomenon, a self-limitation process for heterogeneous reactions with the increasing secondary inorganic aerosol from clean to polluted cases is proposed.

Removal of PM2.5 and secondary inorganic aerosols in the North China Plain by dry deposition.

The North China Plain (NCP) has experienced heavy air pollution in the past several decades featured by high levels of fine particulate matter (PM2.5). PM2.5 removal from the atmosphere in the NCP by dry deposition was estimated from 1999 through 2013 using the inferential method, which combined PM2.5 air concentrations retrieved from satellite remote sensing and dry deposition velocities (Vd) calculated using a bulk particle dry deposition model. Dry deposition of the three major inorganic ions in PM2.5, namely NH4+ (ammonium), NO3- (nitrate), and SO42- (sulfate), with their concentrations in 2000 and 2010 obtained from WRF-Chem model simulations, were also investigated considering their important roles in PM2.5 formation and ecosystem health. High levels of modeled and satellite-retrieved PM2.5 air concentrations, the secondary inorganic aerosols (the sum of NH4+, NO3-, and SO42-), and their respective deposition fluxes were identified from the southern NCP to Beijing-Tianjin metropolitans. The deposition fluxes derived from the inferential method and WRF-Chem increased considerably in the 2000s due to rising PM2.5 atmospheric levels across the NCP. The enhancement of dry deposition velocities of PM2.5 and three aerosol species in the NCP were associated nicely with increasing vegetation coverage and wind speed. We show that both air concentrations of PM2.5 and secondary inorganic aerosols and rising dry deposition velocities related to extensive afforestation activities contributed to their deposition fluxes and an inclining trend of PM2.5 removal from the atmosphere.

Chemical composition and source apportionment of PM1 and PM2.5 in a national coal chemical industrial base of the Golden Energy Triangle, Northwest China.

As part of the Energy Golden Triangle in northwest China and the largest coal-to-liquids industry in the world, the emission and contamination of fine particles in the Ningdong National Energy and Chemical Industrial Base (NECIB) are unknown. There are also large knowledge gaps in the association of air pollution with coal-to-liquids industry. This paper reports the chemical composition and source apportionment of PM1 and PM2.5 collected at two industrial sites Yinglite (YLT) and Baofeng (BF) from a field campaign during summer 2016 and winter 2017. Major chemical components in PM1 and PM2.5, including carbonaceous aerosols, water-soluble inorganic ions, and metal elements were analyzed. The Positive Matrix Factorization (PMF) model and the ISORROPIA II thermodynamic equilibrium model were used to track possible sources and contributions of these chemical components to the formation of the two fine particles. The results identified four primary sources of the fine particles, including vehicle emissions, biomass burning and waste incineration, the secondary aerosols and coal combustion, and soil dust. The PM1 and PM2.5 concentrations were higher in winter than summer. The summed secondary inorganic and carbonaceous aerosols accounted for 36.1-40.0% of PM2.5 mass. The total mass of chemical components identified in the source apportionment only explained about 64.2 to 72.4% of the PM2.5 mass. These results imply some missing sources in this large-scale coal chemical industry base. A coupled weather forecasting and atmospheric chemistry model WRF-Chem was employed to simulate the PM2.5 mass and concentrations of OC and EC, and to examine the origins of PM2.5 across the NECIB. The modeled concentrations of OC and EC were consistent with the sampled data, but the modeled mass of PM2.5 is lower considerably than the measurements, again suggesting unknown sources of fine particles in this energy industrial base.