Meteorological and chemical impacts on PM2.5 during a haze episode in a heavily polluted basin city of eastern China

Concentration prediction and spatial origin analysis of criteria air pollutants in Shanghai

Severe air pollution events still occur frequently in Shanghai. In order to predict when Shanghai air quality satisfies the National Ambient Air Quality Standards of China (NAAQSC) and identify potential source areas of criteria air pollutants for the regional joint prevention and control of air pollution, concentration data of PM_2.5, PM₁₀, SO₂, NO₂ and O₃ were collected in 2014-2022 at fourteen monitoring sites across Shanghai and surrounding areas. A first - order rate equation with harmonic regression analysis was employed for time series analysis and concentration prediction. Decreasing concentrations were observed widely over all sites except O₃ and NO₂. It is very likely that the secondary NAAQSC standards for PM_x, and SO₂ would be met by 2025 and O₃ and NO₂ would likely become the critical pollutants that determine air quality level after 2025. Regional transport was predominant for PM_x and SO₂ pollution. A 3D - CWT multisite joint location method was developed to identify their potential source areas at different spatial resolutions. Weighting function correction was assigned via information entropy of endpoint numbers in each cell. A probabilistic parameter WI_PSA was proposed to quantify and normalize the probability that grid cells are source areas in order to achieve fourteen - site joint location, and it was comparable and compatible at different spatial resolutions. Potential source areas of PM_2.5 and PM₁₀ were similar, including Henan, Shandong, Hebei and Anhui, while origin domains of SO₂ mainly covered Henan and Hebei. In all seasons, air pollution that was transported to Shanghai (i.e., PM_x and SO₂) originated mainly from the North China Plain; the contribution of marine sources was neglectable.

Spatiotemporal variation of PM2.5-related preterm birth in China and India during 1990-2019 and implications for emission controls

Preterm birth is the leading threat to neonatal health. The variation of PM_2.5-associated preterm birth in China and India from 1990 through 2019 was estimated in this study. Meanwhile, four mitigation scenarios were proposed, and the corresponding PM_2.5-related preterm birth was projected for 2030. Owing to differences in emission control policies and the effects of various factors (e.g., differences in population-control policies), the PM_2.5 concentration and PM_2.5-associated preterm birth in the two countries presented disparate spatiotemporal characteristics and variation trends during 1990-2019. The 30-year average of annual PM_2.5-associated preterm birth in India was 1018 (95% confidence interval, 718-1289) thousand, which was much larger than in China (280 [196-358] thousand). To fight air pollution, China launched several control strategies in the past two decades, and the nationwide maternal exposure risk dramatically decreased after 2010. In contrast, India's air-pollution control measures and policies have not effectively mitigated the nationwide PM_2.5 pollution. Under current mitigation measures and policies, the projected decrease in maternal exposure risk by 2030 is greater for China than India, and the scope for controlling air pollutant emissions and reducing maternal exposure risk is much large for India. The results of all four scenarios revealed that the annual PM_2.5-associated preterm birth in the two countries is likely to decrease in the future. In particular, if China and India implement more robust emission control strategies than those currently, the number of associated preterm birth is projected to be more than 50% lower in 2030 compared with 2019 rates.

Spatial origin analysis on atmospheric bulk deposition of polycyclic aromatic hydrocarbons in Shanghai

Atmospheric deposition of polycyclic aromatic hydrocarbons (PAHs) onto soil threatens terrestrial ecosystem. To locate potential source areas geographically, a total of 139 atmospheric bulk deposition samples were collected during 2012-2019 at eight sites in Shanghai and its surrounding areas. A multisite joint location method was developed for the first time to locate potential source areas of atmospheric PAHs based on an enhanced three dimensional concentration weighted trajectory model. The method considered spatial and temporal variations of atmospheric boundary layer height and homogenized all results over the eight sites via geometric mean. Regional transport was an important contributor of PAH atmospheric deposition while massive local emissions may disturb the identification of potential source areas. Northwesterly winds were associated with elevated deposition fluxes. Potential source areas were identified by the multisite joint location method and included Hebei, Tianjin, Shandong and Jiangsu to the north, and Anhui to the west of Shanghai. PM and SO₂ data from the national ground monitoring stations confirmed the identified source areas of deposited PAHs in Shanghai.

Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution

PM_2.5 pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM_2.5 concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM_2.5 pollution. The results show that anthropogenic emissions and meteorological conditions contributed about 67% (40.5 μg/m³) and 33% (19.7 μg/m³) to variation in PM_2.5 concentrations, respectively. Specifically, secondary nitrate (SN) had the greatest impact among all sources (about 45%). Hence, we further explore the impacts of the primary sources and meteorological conditions on SN formation. Coal combustion and vehicle emissions significantly contribute to the formation of SN by providing a large number of precursor NO_X. Additionally, the RF-PDP method was further employed to estimate the synergistic effects of primary sources and meteorological conditions on SN formation. The results help reveal strategies to simultaneously reduce SN by controlling primary emissions under suitable meteorological conditions. This work also suggests that the machine learning model can utilize online datasets well and provide a reliable approach for analyzing the causes of PM_2.5 pollution.

Unexpected rise of atmospheric secondary aerosols from biomass burning during the COVID-19 lockdown period in Hangzhou, China

After the global outbreak of COVID-19, the Chinese government took many measures to control the spread of the virus. The measures led to a reduction in anthropogenic emissions nationwide. Data from a single particle aerosol mass spectrometer in an eastern Chinese megacity (Hangzhou) before, during, and after the COVID-19 lockdown (5 January to February 29, 2020) was used to understand the effect lockdown had on atmospheric particles. The collected single particle mass spectra were clustered into eight categories. Before the lockdown, the proportions of particles ranked in order of: EC (57.9%) < K-SN (13.6%) < Fe-rich (10.2%) < ECOC (6.7%) < K-Na (6.6%) < OC (3.4%) < K-Pb (1.0%) < K-Al (0.7%). During the lockdown period, the EC and Fe-rich particles decreased by 42.8% and 93.2% compared to before lockdown due to reduced vehicle exhaust and industrial activity. By contrast, the K-SN and K-Na particles containing biomass burning tracers increased by 155.2% and 45.2% during the same time, respectively. During the lockdown, the proportions of particles ranked in order of: K-SN (39.7%) < EC (38.1%) < K-Na (11.0%) < ECOC (7.7%) < OC (1.2%) < K-Pb (0.9%) < Fe-rich (0.8%) < K-Al (0.6%). Back trajectory analysis indicated that both inland (Anhui and Shandong provinces) and marine transported air masses may have contributed to the increase in K-SN and K-Na particles during the lockdown, and that increased number of fugitive combustion points (i.e., household fuel, biomass combustion) was a contributing factor. Therefore, the results imply that regional synergistic control measures on fugitive combustion emissions are needed to ensure good air quality.

Causes of PM2.5 pollution in an air pollution transport channel city of northern China

To develop effective mitigation policies, a comprehensive understanding of the evolution of the chemical composition, formation mechanisms, and the contribution of sources at different pollution levels is required. PM_2.5 samples were collected for 1 year from August 2016 to August 2017 at an urban site in Zibo, then chemical compositions were analyzed. Secondary inorganic aerosols (SNA), anthropogenic minerals (MIN), and organic matter (OM) were the most abundant components of PM_2.5, but only the mass fraction of SNA increased as the pollution evolved, implying that PM_2.5 pollution was caused by the formation of secondary aerosols, especially nitrate. A more intense secondary transformation was found in the heating season (from November 15, 2016, to March 14, 2017), and a faster secondary conversion of nitrate than sulfate was discovered as the pollution level increased. The formation of sulfate was dominated by heterogeneous reactions. High relative humidity (RH) in polluted periods accelerated the formation of sulfate, and high temperature in the non-heating season also promoted the formation of sulfate. Zibo city was under ammonium-rich conditions during polluted periods in both seasons; therefore, nitrate was mainly formed through homogeneous reactions. The liquid water content increased significantly as the pollution levels increased when the RH was above 80%, indicating that the hygroscopic growth of aerosol aggravated the PM_2.5 pollution. Source apportionment showed that PM_2.5 was mainly from secondary aerosol formation, road dust, coal combustion, and vehicle emissions, contributing 36.6%, 16.5%, 14.7%, and 13.1% of PM_2.5 mass, respectively. The contribution of secondary aerosol formation increased remarkably with the deterioration of air quality, especially in the heating season.

The environmental benefit of Beijing-Tianjin-Hebei coal banning area for North China

In order to achieve the targets specified in the Action Plan for Air Pollution Prevention and Control (APAPPC), a limited coal banning area (10,000 km2) was designated in the heavily polluted Beijing-Tianjin-Hebei region (BTH) for the first time in 2017. PM2.5 and elements were sampled by the network of BTH to evaluate the effectiveness of this policy. This study found that the fine days with PM2.5 < 75 μg m-3 accounted for 74.3% in the autumn and winter of 2017, which was significantly higher than that in 2016 (43%). The heavily polluted days (PM2.5 > 150 μg m-3) also decreased from 32.2% in 2016 to 4.9% in 2017. Arsenic (As) is an important tracer in coal consumption, which can be used to reflect the influence of the establishment of coal banning areas on north China. The cluster analysis of air mass forward trajectory identified that the number of polluted trajectories with PM2.5 and As in 2017 decreased by 47.6% and 49.7%, respectively. Under the implementation of the coal banning policy, the weighted concentration of PM2.5 and As decreased by 94.2 μg m-3 and 5.1 ng m-3 in the coal banning area, 60.9 μg m-3 and 3.4 ng m-3 in the no coal banning area in BTH, respectively. The influence of weighted concentration of PM2.5 and As in coal banning area on North China were 1.6-49.2 μg m-3 and 0.15-2.8 ng m-3, respectively, which was 38.8% and 29.7% lower than 2016. In coal banning area, BTH and other parts of North China, the reduction of the weight concentration of PM2.5 in 2017 accounted for 41.4%, 26.8% and 31.8% of the total reduction, respectively, so was the As in 39%, 26.3% and 34.6%, indicating that setting up a coal banning area scientifically in limited areas can produce remarkable regional benefit.

Spatial Characteristics and Regional Transmission Analysis of PM2.5 Pollution in Northeast China, 2016-2020

Northeast China is an essential industrial development base in China and the regional air quality is severely affected by PM_2.5 pollution. In this paper, spatial autocorrelation, trajectory clustering, hotspot analysis, PSCF and CWT analysis are used to explore the spatial pollution characteristics of PM_2.5 and determine the atmospheric regional transmission pattern for 40 cities in Northeast China from 2016 to 2020. Analysis of PM_2.5 concentration characteristics in the northeast indicates that the annual average value and total exceedance days of PM_2.5 concentration in Northeast China showed a U-shaped change, with the lowest annual average PM_2.5 concentration (31 μg/m³) in 2018, decreasing by 12.1% year-on-year, and the hourly PM_2.5 concentration exploding during the epidemic lockdown period in 2020. A stable PM_2.5 pollution band emerges spatially from the southwest to Northeast China. Spatially, the PM_2.5 in Northeast China has a high degree of autocorrelation and a south-hot-north-cool characteristic, with all hotspots concentrated in the most polluted Liaoning province, which exhibits the H-H cluster pattern and hotspot per year. Analysis of the air mass trajectories, potential source contributions and concentration weight trajectories in Northeast China indicates that more than 74% of the air mass trajectories were transmitted to each other between the three heavily polluted cities, with the highest mean value of PM_2.5 pollution trajectories reaching 222.4 μg/m³, and the contribution of daily average PM_2.5 concentrations exceeding 60 μg/m³ within Northeast China. Pollution of PM_2.5 throughout the Northeast is mainly influenced by short-range intra-regional transport_, with long-range transport between regions also being an essential factor; organized integration is the only fundamental solution to air pollution.

Influencing factors of PM2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ

In this study, distributed lag nonlinear models (DLNM) were built to characterize the non-linear exposure-lag-response relationship between the concentration of PM_2.5 and O₃ and multiple influencing factors, including basic meteorological elements and precursors. Then, a stratified analysis of different years, seasons, pollution levels, and wind direction was conducted. DLNMs and coupled Weather Research and Forecasting Model-Community Multi-scale Air Quality Model (WRF-CMAQ) were used to evaluate PM_2.5 and O₃ changes attributed to meteorological conditions and anthropogenic emissions comparing 2020 with 2016. As DLNMs showed, PM_2.5 pollution was promoted by low wind speed, high temperature, low humidity, and high concentrations of SO₂, NO₂, and O₃, among which NO₂ tended to be the dominant influencing factor. O₃ pollution was promoted by low wind speed, high temperature, low humidity, high concentration of PM_2.5 and low concentration of NO₂, among which temperature tended to be the dominant influencing factor. Moreover, north-south and easterly winds showed the greatest contribution to PM_2.5 and O₃, respectively. Both DLNMs and CMAQ showed that anthropogenic factors alleviated PM_2.5 pollution but aggravated O₃ pollution in 2020 in comparison with 2016, so did meteorological factors, but with smaller impacts. And anthropogenic influences were more evident in heavily polluted seasons for both PM_2.5 and O₃. This research may help understand the influencing factors of PM_2.5 and O₃ and provide scientific guide for abatement policies. Moreover, the good consistency in the results obtained from DLNMs and CMAQ indicated the reliability of the two models.

Variation in PM2.5 sources in central North China Plain during 2017-2019: Response to mitigation strategies

Central North China Plain (NCP) is one of the most important source region of air pollutants over the Beijing-Tianjin-Hebei (BTH) region. The national government has issued abatement measures to improve the air quality in this area from 2017. To examine the effects of control measures, observational analysis on PM_2.5 characteristics was performed in a city of central NCP during 2017-2019 to investigate the variation in mass concentration, chemical composition, and emission source of PM_2.5. Annual PM_2.5 concentration significantly reduced by 16% from 2017 to 2019, implying substantial improvements in air quality. PM_2.5 enriched in autumn-winter seasons was dominated by SNA (sum of sulfate, nitrate and ammonium; ~38%), followed by organic carbon matters (OM; ~24%) and fine soil (FS; ~12%). This chemical composition was different from that in a megacity in NCP (Beijing) where OM accounted for a comparable fraction to SNA. Approximately half of SNA was attributed to nitrate, indicating that SNA changed from sulfate-driven to nitrate-driven, and the considerable effects of coal combustion cutoff, in which sulfate was concentrated. Decreased mass fraction of SNA and increased OM fraction in PM_2.5 were observed in 2018-2019 partly contributed to the decrease in PM_2.5. A progressive increase in the contribution of heterogeneous formed SNA whilst a decrease in OM was observed as the pollution elevated from clean to heavily polluted. Six sources (soil dust, biomass burning, secondary emission, road traffic, coal combustion and industry) were identified by the Positive Matrix Factorization (PMF) model in both years and dominated by secondary aerosols, respectively contributing 39% and 41% to PM_2.5. The decreasing concentrations (with reductions of 17%-61%) of the secondary source, coal combustion, soil dust and biomass burning largely accounted for the reduction in PM_2.5, as a consequence of the recent abatement measures. By contrast, contributions of vehicle-related emissions, similar to the increasing contribution of vehicles at sites in NCP after 2013, should receive increased attention.

City-level air quality improvement in the Beijing-Tianjin-Hebei region from 2016/17 to 2017/18 heating seasons: Attributions and process analysis

With the implementation of clean air strategies, PM_2.5 pollution abatement has been observed in the "2 + 26" cities in the Beijing-Tianjin-Hebei (BTH) region (referred to as the BTH2+26) and their surrounding areas. To identify the drivers for PM_2.5 concentration decreases in the BTH2+26 cites from the 2016/17 heating season (HS1617) to the 2017/18 heating season (HS1718), we investigated the contributions of meteorological conditions and emission-reduction measures by Community Multi-Scale Air Quality (CMAQ) model simulations. The source apportionments of five sector sources (i.e., agriculture, industry, power plants, traffic and residential), and regional sources (i.e., local, within-BTH: other cities within the BTH2+26 cities, outside-BTH, and boundary conditions (BCON)) to the PM_2.5 decreases in the BTH2+26 cities were estimated with the Integrated Source Apportionment Method (ISAM). Mean PM_2.5 concentrations in the BTH2+26 cities substantially decreased from 77.4 to 152.5 μg m^-3 in HS1617 to 52.9-101.9 μg m^-3 in HS1718, with the numbers of heavy haze (daily PM_2.5 ≥150 μg m^-3) days decreasing from 17-77 to 5-30 days. The model simulation results indicated that the PM_2.5 concentration decreases in most of the BTH2+26 cities were attributed to emission reductions (0.4-55.0 μg m^-3, 2.3-81.6% of total), but the favorable meteorological conditions also played important roles (1.9-25.4 μg m^-3, 18.4-97.7%). Residential sources dominated the PM_2.5 reductions, leading to decreases in average PM_2.5 concentrations by more than 30 μg m^-3 in severely polluted cities (i.e., Shijiazhuang, Baoding, Xingtai, and Beijing). Regional source analyses showed that both local and within-BTH sources were significant contributors to PM_2.5 concentrations for most cities. Emission controls in local and within-BTH sources in HS1718 decreased the average PM_2.5 concentrations by 0.1-47.2 μg m^-3 and 0.3-22.1 μg m^-3, respectively, relative to those in HS1617. Here we demonstrate that a combination of favorable meteorological conditions and anthropogenic emission reductions contributed to the improvement of air quality from HS1617 to HS1718 in the BTH2+26 cities.

Key Points in Air Pollution Meteorology

Although emissions have a direct impact on air pollution, meteorological processes may influence inmission concentration, with the only way to control air pollution being through the rates emitted. This paper presents the close relationship between air pollution and meteorology following the scales of atmospheric motion. In macroscale, this review focuses on the synoptic pattern, since certain weather types are related to pollution episodes, with the determination of these weather types being the key point of these studies. The contrasting contribution of cold fronts is also presented, whilst mathematical models are seen to increase the analysis possibilities of pollution transport. In mesoscale, land-sea and mountain-valley breezes may reinforce certain pollution episodes, and recirculation processes are sometimes favoured by orographic features. The urban heat island is also considered, since the formation of mesovortices determines the entry of pollutants into the city. At the microscale, the influence of the boundary layer height and its evolution are evaluated; in particular, the contribution of the low-level jet to pollutant transport and dispersion. Local meteorological variables have a major influence on calculations with the Gaussian plume model, whilst some eddies are features exclusive to urban environments. Finally, the impact of air pollution on meteorology is briefly commented on.

Atmospheric boundary layer turbulence structure for severe foggy haze episodes in north China in December 2016

This paper aims to identify the atmospheric boundary layer turbulence structure and its effect on severe foggy haze events frequently occurring in Northern China. We use data collected from a ground eddy covariance system, meteorology tower, and a PM_2.5 collector in Baoding, China during December 2016. The data shows that 73.5% of PM_2.5 concentration is greater than 100 μg m^-3 with a maximum of 522 μg m^-3. Analyses on vertical turbulence spectrum also reveal that 1) during the pollution period, lower wind can suppress large-scale turbulence eddies, which are more likely inhomogeneous, breaking into small-scale eddies, and 2) the air pollutant scattering effect for radiation could decrease the air temperature near the ground and generate weak vertical turbulence during the daytime. At night, air pollutants suppress the land surface cooling and decrease the air temperature difference as well as the vertical turbulence intensity difference. The vertical turbulence impact analysis reveals that the percentage of large-scale turbulence eddies can also change the atmospheric vertical mixing capacity. During the daytime, the air pollution evolution is controlled by the wind speed and vertical turbulence intensity. While at night, the vertical turbulence is weak and the atmospheric vertical mixing capacity is mainly controlled by the large-scale eddies' percentage. The increased number of large-scale turbulence eddies led by low wind at night could increase the vertical mixing of air pollutants and decrease its concentration near the ground.

A temporal-spatial analysis and future trends of ammonia emissions in China

Excessive anthropogenic activities have led to high-level ammonia loss and volatilization, which is regarded as a key factor in Chinese haze formation. In this study, a comprehensive analysis of ammonia emission estimations is accomplished at both temporal (1980-2016) and spatial (provincial) scales using a mass-balanced model, and emission projections through 2030 are also studied in different development scenarios. The results show that the ammonia emissions increased from 4.7 Tg N yr^-1 in 1980 to 11 Tg N yr^-1 in 2016, which is an approximately 2.4-fold increase. The cropland and livestock emissions are the largest contributors, as most reports show approximately 80% contributions; however, nonagriculture sources of fuel combustion, waste treatment and ammonia escape have grown rapidly in recent years, accounting for 14% in 2016. The spatial differences also reveal the complex heterogeneity in Chinese provinces. In addition, the emission intensities of major agriculture and non-agriculture sources are 0-80 kg N ha^-1 yr^-1 and over 100 kg N ha^-1 yr^-1, respectively, indicating a higher degree of ammonia concentration from non-agriculture emissions, which should attract wide concern. In terms of scenario analysis, emissions would reach 12.8 Tg N yr^-1 in 2030 under the currently developed model and 7.3 Tg N yr^-1 under a series of reduction policies; the spatial analysis also shows that the North China Plain has a 2.1 Tg N yr^-1 reduction potential. The results of this study provide new insights into ammonia emission estimations and a better understanding of the environmental impacts of ammonia emitted from different sources.