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A regional online chemical weather model, Weather Research and Forecasting (WRF)/China Meteorological Administration Unified Atmospheric Chemistry Environment (CUACE), is used to assess the contributions of cloud chemistry to the SO2 and sulfate levels in typical regions of China. Upon comparison with several time series of in situ cloud chemical observations on Mountain Tai in Shandong Province of China, the CUACE cloud chemistry scheme is found to reasonably reproduce the observed cloud consumption of H2O2, O3, and SO2 and the production of sulfate, and it is consequently used in the regional assessment of a heavy pollution episode and monthly average of December 2016. During the cloudy period in the heavy pollution episode, sulfate production increased by 60 %–95 % and SO2 production reduced by over 80 %. The cloud chemistry mainly affects the middle and lower troposphere below 5 km as well as within the boundary layer, and it contributes significantly to the SO2 reduction and sulfate production in central-east China. Among these four typical regions in China, the Sichuan Basin (SCB) is the most affected by the cloud chemistry, with an average SO2 abatement of about 1.0–10.0 ppb and sulfate increase of about 10.0–70.0 µg m−3, followed by the Yangtze River Delta (YRD) and the southeast of the North China Plain (NCP), where SO2 abatement is about 1.0–5.0 ppb and sulfate increase is about 10.0–30.0 µg m−3. However, the cloud chemistry contributions to the Pearl River Delta (PRD) and the northwest of the NCP are not significant due to lighter pollution and less water vapor than the other regions.

A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e., PM

The role of PM

Haze pollution in recent decades varies largely with both pollutant emissions and meteorological conditions. Using the discrete wavelet transform (DWT) method, we separate these two influences on haze variations in southern China in the time series of haze observations from 1981 to 2011. This helps us to identify the meteorological influence on interannual variation in haze occurrences in southern China and thus observe a teleconnection between the thermal forcing of sea surface temperature (SST) in the central and eastern Pacific and wintertime haze occurrences in southern China (R = − 0.51, p < 0.05). The total haze days in winter is highest among all seasons over southern China and the climotological mean of number of winter haze days is 7.5 days for the region. Compared with the normal winters, the regional mean of the number of haze days in southern China is reduced by ~ 5 days in the winters with above-normal Niño3.4 SST (during El Niño phases), but increased by ~ 4 days in the winters with below-normal Niño3.4 SST (during La Niña phases). In the warm SST winters, the cumulative consequences of strong winds, more precipitation, and a more unstable atmosphere with an “upper colder and lower warmer” vertical pattern leading to more ascendance can all hinder haze formation, whereas in the cold SST winters, opposite meteorological conditions are favorable to haze formation. These meteorological conditions induced by anomalous SST make wintertime haze pollution in southern China vary from year to year to a large extent. This study suggests a strong sensitivity of winter haze occurrences in southern China to the viability of the SST in the central and eastern Pacific.

The definition of haze day is still controversial, and there are different data sources for haze data in China. This study analyzes the uncertainty of haze data in China, which is a prerequisite for studying haze trends. The overall trends defined by different approaches were relatively consistent, but the average values were different. The annual number of haze days defined by visibility and relative humidity was higher than the number defined by manual observation. The correlation coefficient between the two datasets for the whole country was 0.92, and ranged from 0.7 to 0.9 for different regions. The similarity between the trends of haze days by different approaches was greater in the Yangtze River Delta region and the Pearl River Delta region than in the Southwest Region. Limited impact of relative humidity on the trends of haze days was detected in China and different regions. The influence of station screening was also investigated. Station screening had no effect on the annual average number of haze days and the long-term trend. Based on the above analysis, the haze data defined by visibility and relative humidity were chosen to study the spatial and temporal distributions for China. In terms of seasons, haze days were most frequent in winter and least frequent in summer. Except for the Northwest region, the number of haze days increased noticeably after 1980. Our results can further deepen the understanding of the characteristics of haze pollution, and support the study of the causes of haze pollution in China.

The effect of vegetation seasonal cycle alterations to aerosol dry deposition on PM

This study developed a next‐generation atmospheric aerosol/chemistry‐climate model, the BCC_AGCM_CUACE2.0. Then, the performance of the model for nitrate was evaluated, and the nitrate direct radiative forcing (DRF) and effective radiative forcing (ERF) due to aerosol‐radiation interactions were simulated for the present day (2010), near‐term future (2030), and middle‐term future (2050) under the Representative Concentration Pathway 4.5, 6.0, and 8.5 scenarios relative to the preindustrial era (1850). The model reproduced the distributions and seasonal changes in nitrate loading well, and simulated surface concentrations matched observations in Europe, North America, and China. Current global mean annual loading of nitrates was predicted to increase by 1.50 mg m−2 relative to 1850, with the largest increases occurring in East Asia (9.44 mg m−2), Europe (4.36 mg m−2), and South Asia (3.09 mg m−2). The current global mean annual ERF of nitrates was −0.28 W m−2 relative to 1850. Due to global reductions in pollutant emissions, the nitrate ERF values were predicted to decrease to −0.17, −0.20, and −0.24 W m−2 in 2030 and −0.07, −0.18, and −0.19 W m−2 in 2050 for Representative Concentration Pathway 4.5, 6.0, and 8.5 relative to 1850, respectively. Although global mean nitrate values showed a declining trend, future nitrate loading remained high in East Asia and South Asia.

A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e. PM

A multi-model simulation system for street level circulation and pollutant tracking (S-TRACK) has been developed by integrating the Weather Research and Forecasting (WRF), the Computational Fluid Dynamics (CFD) and the Flexible Particle (FLEXPART) models. The winter wind environmental characteristics and the potential impact of a traffic source on nearby sites (about 300 to 400 m) in Jinshui district of Zhengzhou, China are analyzed with the system. It is found that the existence of buildings complicates the structure of the wind fields. The wind speed inside the building block is smaller than the background wind speed due to the dragging effect of dense buildings. Ventilation is better when the dominant airflow is in the same direction as the building layout. Influenced by the building layout, local circulations show that the windward side of the building is mostly the divergence zone and the leeward side is mostly the convergence zone, which is more obvious for high buildings and influencing air pollution transport at the street-level. Using the traffic source (line source) on a road within a city block, the system was applied to investigate the potential impact of a line source on specific sites under the influence of the streel-level circulations. The potential contribution ratio was estimated by the method of residence time analysis and to a particular site found to vary with the height of the site with a peak not at the ground but on a certain height. The results of the study are helpful to understand the characteristics of wind environment and effect of traffic emissions in the area, which is important to improve urban living environment and control air pollution.

Air stagnation modulates the frequency and duration of haze events. Based on meteorological and environmental observation data during 1980 to 2013, the present study analyzed the relationship between the interannual variations of Sea Surface Temperature (SST) over tropical central-eastern Pacific and the number of winter haze days (WHD) over the Southern China (SC) region. The potential preceding signal of Sea Surface Temperature anomalies (SSTAs) in Nino3.4 region associated with ENSO can be used as a predictor of haze occurrences in winter. Results indicate that the detrended WHD in the SC is significantly correlated (r = −0.55) with the contemporary SSTAs in Nino3.4 region. The winters with warm Nino3.4 SSTAs in (El Nino) tend to be accompanied with less haze events in the SC, resulting from more local precipitation and enhanced mid-level winds, which helps to build an unstable condition that is conductive for the decrease of haze occurrence. The precursory signal of WHD variability can be detected in the tropical central-eastern Pacific SST, which is amplified since August to winter season. Based on the August–October mean SSTAs in Nino3.4 and three other identified predictors, we developed a seasonal prediction model of the WHD using RF regression method. The model accounted for 90% of the total variance of the WHD in the SC and ranked the SSTAs in Nino3.4 as the most important predictor. This implies that the SSTAs tropical Pacific play a significant role in the variability of WHD in the SC. Since the predictors can be readily monitored in real time, the model provides a real time forecast tool and could brighten the prospects for seasonal forecast of haze anomalies in vulnerable regions such as the SC.

Open bio-mass burning plays an important role in the formation of heavy pollution events during harvest seasons in China by releasing the trace gases and particulate matters into the atmosphere. A better understanding of spatial-temporal variations of open bio-mass burning in China is required to assess its impacts on the air quality and especially on the heavy haze pollution. The MODIS fire spots data and the calculated burned areas were used in this research, which shows the varying number of fire spots in China from 2013 to 2017, with the highest in 2014 and the lowest in 2016. Meanwhile, the fire spots were found mainly concentrated in three key periods (March–April, June and October–November) and two zones (Zone 1 and Zone 2) with inter-annual variations of burned areas. In addition, the contribution of major vegetation types burning was studied, the cropland occupied the largest proportion of burned area of more than 70% in any period time, followed by forest. Finally, from the perspective of climate and human activities, the causes of inter-annual variations were discussed. By comparing the average temperature and precipitation in the two zones from 2013 to 2017, it was found that the burned forest area is positively correlated with the average temperature of the zones and negatively correlated with the average precipitation. Meanwhile, the relationship between the El Nino events and the bio-mass burning was discussed.

The Sichuan Basin (SB), covering 260,000 km2 immediately to the east of the Tibetan Plateau (TP) with a large drop exceeding 3000 m in elevation over a short horizontal distance, is a region of high haze pollution in China. The terrain effects of this unique deep basin on haze pollution have not been well investigated. During 14–19 January 2014, heavy haze pollution engulfed the SB with high PM2.5 concentrations. By using the WRF-Chem model, the topographic effects on haze pollution in the SB and the underlying mechanisms were investigated by two sets of simulation with and without the basin topography. The simulation results show that the SB topography lead to an increase of 48 μg m−3 in the basin-averaged surface PM2.5 contributing about 44% to the PM2.5 concentrations during heavy haze pollution, indicating an important role of the basin topography in worsening air pollution. The surface PM2.5 level enhancements varied spatially from 0 to 30 μg m−3 in the eastern basin to 60–120 μg m−3 in the western basin, respectively corresponding to 0–20% and 50–70% relative to the concentrations simulated without the SB topographic condition, reflecting the stronger topographic effect of the TP in the western SB. The topographic effects intensify the haze pollution via reducing wind speed, raising air temperature and humidity in the lower troposphere, as well as dropping boundary layer height in the basin. Resulted from the impact of TP on mid-latitude westerly winds, a typical leeside vortex of immediately upstream TP over the basin was developed with accentuating PM2.5 accumulations in the basin, implying a strong influence of TP on air quality in the SB.

A framework was developed to quantitatively assess the contribution of meteorology variations to the trend of fine particular matter (PM2.5) concentrations and to separate the impacts of meteorology from the control measures in the trend, based upon the Environmental Meteorology Index (EMI). The model-based EMI realistically reflects the role of meteorology in the trend of PM2.5 and is explicitly attributed to three major factors: deposition, vertical accumulation and horizontal transports. Based on the 2013–2019 PM2.5 observation data and re-analysis meteorological data in China, the contributions of meteorology and control measures in nine regions of China were assessed separately by the EMI-based framework. Monitoring network observations show that the PM2.5 concentrations have declined by about 50 % on the national average and by about 35 % to 53 % for various regions. It is found that the nationwide emission control measures were the dominant factor in the declining trend of China PM2.5 concentrations, contributing about 47 % of the PM2.5 decrease from 2013 to 2019 on the national average and 32 % to 52 % for various regions. The meteorology has a variable and sometimes critical contribution to the year-by-year variations of PM2.5 concentrations, 5 % on the annual average and 10 %–20 % for the fall–winter heavy pollution seasons.

In this study, National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analysis data and meteorological observation data from 2013 to 2017 were used to evaluate the impact of seasonal changes and different circulation classifications on the dynamical downscaling simulation results of Weather Research and Forecasting (WRF) in the Pearl River Delta (PRD) region. The results show that the dynamical downscaling method can accurately simulate the time variation characteristics of the near-surface meteorological field and the hit rates of a 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction are 92.66%, 93.98%, 26.78%, and 76.78%, respectively. The WRF model slightly underestimates the temperature and relative humidity, and overestimates the wind speed and precipitation. For precipitation, the WRF model can better simulate the variation characteristics of light rain and heavy rain, with the probability of detection are 0.59 and 0.69, respectively. For seasonal factors, the WRF model can conduct a perfect simulation in autumn and winter, followed by spring, while summer is vulnerable to extreme weather, so the result of the simulation is relatively poor. The circulation type is an important parameter of downscaling assessment. When the PRD is controlled by high pressure, the simulated results of WRF are good, and when the PRD is affected by low pressure or extreme weather, the simulation results are relatively poor.

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.

The impact of Arctic Oscillation (AO) anomalies on winter PM

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.

A framework was developed to quantitatively assess the contribution of meteorology variations in the trend of particular matter (PM) concentrations and to separate the impacts of meteorology from the control measures in the trend, based upon an Environmental Meteorology Index (EMI). The model-based index EMI realistically reflects the role of meteorology in the trend of PM and can also be attributed into three major factors: deposition, vertical accumulation and horizontal transports. Based on the 2013–2019 PM2.5 observation data and re-analysis meteorological data in China, the contributions of meteorology and control measures in nine regions of China were assessed separately by the EMI-based framework. Monitoring network observations show that the PM2.5 concentrations have been declined about 50 % on national average and about 35 % to 53 % for various regions. It is found that the nation-wide emission control measures were the dominant factor in the declining trend of China PM2.5 concentrations, contributing to about 47 % of the PM2.5 decrease from 2013 to 2019 on the national average and 32 % to the 52 % for various regions. The meteorology has a variable and sometimes critical contribution to the year by year variations of PM2.5 concentrations, 5 % on annual average and 10–20 % for the fall-winter heavy pollution seasons.

The development of chemical transport models with advanced physics and chemical schemes could improve air-quality forecasts. In this study, the China Meteorological Administration Unified Atmospheric Chemistry Environment (CUACE) model, a comprehensive chemistry module incorporating gaseous chemistry and a size-segregated multicomponent aerosol algorithm, was coupled to the Weather Research and Forecasting (WRF)-Chem framework using an interface procedure to build the WRF/CUACE v1.0 model. The latest version of CUACE includes an updated aerosol dry deposition scheme and the introduction of heterogeneous chemical reactions on aerosol surfaces. We evaluated the WRF/CUACE v1.0 model by simulating PM2.5, O3, and NO2 concentrations for January, April, July, and October (representing winter, spring, summer, and autumn, respectively) in 2013, 2015, and 2017 and comparing them with ground-based observations. Secondary inorganic aerosol simulations were also evaluated through a simulation of a heavy haze pollution event during 9–15 January 2019 in the North China Plain. The model well captured the variations of PM2.5, O3, and NO2 concentrations in all seasons in eastern China. However, it is difficult to accurately reproduce the variations of air pollutants over Sichuan Basin, due to its deep basin terrain. The sulfate and nitrate simulations are substantially improved by introducing heterogenous chemical reactions into the CUACE model (change in bias from −95.0 % to 4.1 % for sulfate and from 124.1 % to 96.0 % for nitrate). The development of the WRF/CUACE v1.0 model represents an important step towards improving air-quality modelling and forecasts in China.

Fine particulate matters (PM2.5) and relative humidity (RH) in the ambient atmosphere are the leading anthropogenic and natural factors changing atmospheric horizontal visibility. Based on the analysis of environmental and meteorological data observed over 2013&ndash, 2019 in Nanjing, an urban area in East China, this study investigated the influences of PM2.5 and RH on atmospheric visibility changes over recent years. The visibility had significantly negative correlations with the PM2.5 concentrations and RH changes. The nonlinear relationships existed between PM2.5 concentrations and visibility, as well as between RH and visibility, with the inflection points in the atmospheric visibility changes. The PM2.5 inflection concentrations were 81.0 &mu, g m&minus, 3, 76.0 &mu, 3, 49.0 &mu, 3, and 33.0 &mu, 3, respectively, for the RH ranges of RH &lt, 60%, 60% &le, RH &lt, 80%, 80% &le, 90%, and RH &ge, 90%, indicating that the improvement of visibility with reducing PM2.5 concentrations could be more difficult under the humid meteorological condition. The visibility changes were most sensitive to PM2.5 concentrations in the RH range of 60&ndash, 80% in this urban area of East China. The relative contributions of natural factor RH and anthropogenic factor PM2.5 to variations of wintertime atmospheric visibility were quantified with 54.3% and 45.7%, respectively, revealing an important role of natural factor RH in the change of atmospheric visibility in the urban area of East Asian monsoon region.

Facing serious air pollution problems, the Chinese government has taken numerous measures to prevent and control air pollution. Understanding the sources of pollutants is crucial to the prevention of air pollution. Using numerical simulation method, this study analysed the contributions of the total local emissions and local emissions from different sectors (such as industrial, traffic, resident, agricultural, and power plant emissions) to PM2.5 concentration, backward trajectory, and potential source regions in Tangshan, a typical heavy industrial city in north China. The impact of multi-scale meteorological conditions on source apportionment was investigated. From October 2016 to March 2017, total local emissions accounted for 46.0% of the near-surface PM2.5 concentration. In terms of emissions from different sectors, local industrial emissions which accounted for 23.1% of the near-surface PM2.5 concentration in Tangshan, were the most important pollutant source. Agricultural emissions were the second most important source, accounting for 10.3% of the near-surface PM2.5 concentration. The contributions of emissions from power plants, traffic, residential sources were 2.0%, 3.0%, and 7.2%, respectively. The contributions of total local emissions and emissions from different sectors depended on multi-scale meteorological conditions, and static weather significantly enhanced the contribution of regional transport to the near-surface PM2.5 concentration. Eight cluster backward trajectories were identified for Tangshan. The PM2.5 concentration for the 8 cluster trajectories significantly differed. The near-surface PM2.5 in urban Tangshan (receptor point) was mainly from the local emissions, and another important potential source region was Tianjin. The results of the source apportionment suggested the importance of joint prevention and control of air pollution in some areas where cities or industrial regions are densely distributed.

Air pollutant emissions play a determinant role in deteriorating air quality. However, an uncertainty in emission inventories is still the key problem for modeling air pollution. In this study, an updated emission inventory of coal-fired power plants (UEIPP) based on online monitoring data in Jiangsu Province of East China for the year of 2012 was implemented in the widely used Multi-resolution Emission Inventory for China (MEIC). By employing the Weather Research and Forecasting model with Chemistry (WRF-Chem), two simulation experiments were executed to assess the atmospheric environment change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that power plant emissions of PM2.5, PM10, SO2, and NOx were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs (non-methane volatile organic compounds) were higher in UEIPP relative to those in MEIC, reflecting a large discrepancy in the power plant emissions over East China. In accordance with the changes in UEIPP, the modeled concentrations were reduced for SO2 and NO2, and increased for most areas of primary OC, BC, and CO. Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced. This was reflected by increased oxidizing agents, e.g., O3 and OH, thus directly strengthening the chemical production from SO2 and NOx to sulfate and nitrate, respectively, which offset the reduction of primary PM2.5 emissions especially on haze days. This study indicates the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with implications on air quality and environmental changes.