Exploring the regional pollution characteristics and meteorological formation mechanism of PM2.5 in North China during 2013-2017

Multi-Scale Meteorological Impact on PM2.5 Pollution in Tangshan, Northern China

Tangshan, a major industrial and agricultural center in northern China, frequently experiences significant PM2.5 pollution events during winter, impacting its large population. These pollution episodes are influenced by multi-scale meteorological processes, though the complex mechanisms remain not fully understood. This study integrates surface PM2.5 concentration data, ground-based and upper-air meteorological observations, and ERA5 reanalysis data from 2015 to 2019 to explore the interactions between local planetary boundary layer (PBL) structures and large-scale atmospheric processes driving PM2.5 pollution in Tangshan. The results indicate that seasonal variations in PM2.5 pollution levels are closely linked to changes in PBL thermal stability. During winter, day-to-day increases in PM2.5 concentrations are often tied to atmospheric warming above 1500 m, as enhanced thermal inversions and reduced PBL heights lead to pollutant accumulation. Regionally, this aloft warming is driven by a high-pressure system at 850 hPa over the southern North China Plain, accompanied by prevailing southwesterly winds. Additionally, southwesterly winds within the PBL can transport pollutants from the adjacent Beijing-Tianjin-Hebei region to Tangshan, worsening pollution. Simulations from the chemical transport model indicate that regional pollutant transport can contribute to approximately half of the near-surface PM2.5 concentration under the unfavorable synoptic conditions. These findings underscore the importance of multi-scale meteorology in predicting and mitigating severe wintertime PM2.5 pollution in Tangshan and surrounding regions.

Quantified the influence of different synoptic weather patterns on the transport and local production processes of O3 events in Pearl River Delta, China

Regional ozone (O3) pollution in the Pearl River Delta (PRD) region has become a topic of discussion in recent years. The occurrence of regional O3 pollution are influenced by local emissions and cross-regional transportation. In this study, we identified the predominant synoptic patterns that were associated with regional O3 pollution from August to November in 2015-2021 using the Lamb-Jenkinson classification technique. All synoptic types were divided into four major categories of NE-type, C-type, S-type and A-type, which accounted for 42 %, 25 %, 18 % and 15 % of the total number of regional O3 pollution days, respectively. The weather conditions for each synoptic pattern were described by using MERRA-2 datasets. Then a rapidly method was established to quantify the contribution of cross-regional processes to high O3 concentration in different synoptic patterns over the PRD through the WRF-Flexpart model. The NE-type weather condition was characterized by a relatively large wind speed with a significant cross-regional transport contribution of 35.8 %. The A-type weather condition had moderate surface wind speed with the stable weather condition, resulting in a lower cross-region transport contribution of 27.7 %. Under controlled by C-type, the stagnant weather condition caused by low-pressure systems on its periphery, would suppress diffusion of O3. As a result, the regional O3 pollution in the PRD were mostly attributed to locally (87.9 %) with minimal cross-regional transport (12.1 %). The S-type weather condition was mainly associated with the West Pacific Subtropical High and the surface equalization pressure field, accompanied by low wind speed. Therefore, the considerable (minor) contribution of local production (cross-regional transport) of 83.3 % (16.7 %) to O3 pollution in the PRD is a consequence of the stagnation weather condition.

High aerosol loading over the Bohai Sea: Long-term trend, potential sources, and impacts on surrounding cities

Air pollution over the oceans has received less attention compared to densely populated urban areas of continents. The Bohai Sea, a semi-enclosed sea in northern China, is surrounded by thirteen industrial cities that have experienced significant improvements in air quality over the past decade. However, the changes in air pollution over the Bohai Sea and its impacts on surrounding cities remain poorly understood. To address this, this study investigated the evolution of air pollution and its chemical composition in the Bohai Sea over four decades, utilizing satellite remote sensing data, reanalysis datasets, emissions inventories, and statistical modeling. Historically, the region has suffered from severe air pollution, resulting from a combination of continental emissions and marine inputs (e.g., sea salt, ports and maritime vessel activities). The aerosol optical depth (AOD) over the sea was higher than the mean levels observed in its surrounding coastal cities. Statistically, 45% of the air masses reaching the Bohai Sea are associated with natural sources (dust- and marine-rich), while the remainder carry anthropogenic pollutants from continental regions. With the exception of Cangzhou city, these coastal cities suffer from air pollutants originating from the Bohai Sea. Cities in the northern region of the sea, spanning from Tianjin to Yingkou, are particularly impacted. The majority of the surrounding cities are affected by a large proportion of anthropogenic aerosol types transported through air masses from the Bohai Sea, including those from biomass burning and industrial activities. These findings emphasize the considerable influence of human-induced sources in the Bohai Sea on neighboring urban areas. Furthermore, being a maritime region, natural sources like sea salt and dust from the sea may also exert a discernible impact on the neighboring environment.

Observation of black carbon in Northern China in winter of 2018-2020 and its implications for black carbon mitigation

Enhanced observations of BC in hotspot regions with a high temporal resolution are critical to refining our BC mitigation strategies, which are co-directed by air-quality and climate goals. In this work, the temporal variation and emission sources of BC in Shijiazhuang, Northern China, during the winter of 2018-2020 were investigated on the basis of multi-wavelength Aethalometer BC observations. The average BC concentrations decreased from 9.13 ± 6.63 μg/m³ in the winter of 2018 to 3.51 ± 2.48 μg/m³ in the winter of 2020. The BC source attributions derived from the Aethalometer model showed that the BC concentrations in Shijiazhuang in the winter of 2018 were mainly contributed by biomass burning (53 %). In contrast, during the winter of 2019 and 2020, fossil fuel combustion (BC_ff) exhibited higher contributions, and higher BC concentrations attributed to greater BC_ff contributions. Potential source contribution function (PSCF) analysis suggested that local emissions in Shijiazhuang and transport from highly industrialized regions like central Shanxi and southern Hebei contributed significantly to BC in Shijiazhuang. Concentration weighted trajectory (CWT) analysis revealed that the BC contributions from source regions decreased successively from the winter of 2018 to the winter of 2020. Our results also implied an air quality/climate co-benefit effect of enforcing multi-scale air-quality improvement regulations. Yet, it is still worth noting that some of the measures in favor of reducing BC emissions contradict the measures for reducing CO₂. The synergies of BC to air quality and climate should be considered and addressed by policymakers with the aim of realizing a sustainable environment.

Quantifying the Spatiotemporal Heterogeneity of PM2.5 Pollution and Its Determinants in 273 Cities in China

Fine particulate matter (PM_2.5) pollution brings great negative impacts to human health and social development. From the perspective of heterogeneity and the combination of national and urban analysis, this study aims to investigate the variation patterns of PM_2.5 pollution and its determinants, using geographically and temporally weighted regression (GTWR) in 273 Chinese cities from 2015 to 2019. A comprehensive analytical framework was established, composed of 14 determinants from multi-dimensions, including population, economic development, technology, and natural conditions. The results indicated that: (1) PM_2.5 pollution was most severe in winter and the least severe in summer, while the monthly, daily, and hourly variations showed "U"-shaped, pulse-shaped and "W"-shaped patterns; (2) Coastal cities in southeast China have better air quality than other cities, and the interaction between determinants enhanced the spatial disequilibrium of PM_2.5 pollution; (3) The determinants showed significant heterogeneity on PM_2.5 pollution-specifically, population density, trade openness, the secondary industry, and invention patents exhibited the strongest positive impacts on PM_2.5 pollution in the North China Plain. Relative humidity, precipitation and per capita GDP were more effective in improving atmospheric quality in cities with serious PM_2.5 pollution. Altitude and the proportion of built-up areas showed strong effects in western China. These findings will be conductive to formulating targeted and differentiated prevention strategies for regional air pollution control.

Incorporation and improvement of a heterogeneous chemistry mechanism in the atmospheric chemistry model GRAPES_Meso5.1/CUACE and its impacts on secondary inorganic aerosol and PM2.5 simulations in Middle-Eastern China

Heterogeneous chemistry is considered one of the critical pathways of secondary inorganic aerosol (SIA) productions. In this study, a heterogeneous chemistry mechanism is incorporated into the atmospheric chemistry model GRAPES_Meso5.1/CUACE. Varying uptake coefficient schemes of SO₂ and NO₂ are compared and the equivalent ratio of inorganic aerosol (ER)-dependent scheme for SO₂ and relative humidity (RH)/ER-dependent scheme for NO₂ are used to form the improved heterogeneous chemistry. Focusing on a severe haze episode in Middle-Eastern China, the impacts of heterogeneous mechanism on SIA and PM_2.5 composition are investigated based on the updated model. Study results show that the differences in RH or ER uptake coefficients result in obvious differences in sulfate and nitrate concentrations, especially during the severe pollution period, because the ER schemes restrict the excessive production of sulfate and nitrate under high RH effectively by including the self-limitation of heterogeneous reactions, which shows better performance in capturing the magnitude and temporal variations of surface SIA and PM_2.5. Normalized mean bias of sulfate, nitrate, ammonium, and PM_2.5 in megacity Beijing decreases from -27.0, -28.3, -58.2, and -26.3 to 1.0, -2.2, -47.2, and -16.5 %, respectively. And the fractions of sulfate, nitrate, ammonium, and organics during the polluted period change from 13.7, 19.3, 6.9, and 60.1 to 16.5, 23.0, 7.6, and 52.9 %, respectively, which is more consistent with the observation (16.0, 23.2, 14.1, and 46.7 %). SIA and PM_2.5 simulations in another megacity Shanghai have the similar improvements. The modeled SIA by heterogeneous processes contributes 11.7 % of total PM_2.5 in Beijing and 22.5 % in Shanghai. That is 13.5 % in the Chinese megalopolis Beijing-Tianjin-Hebei and 19.8 % in Yangtze-River-Delta, indicating a considerable contribution of heterogeneous pathways to haze pollution. This work indicates the importance of detailed and reasonable heterogeneous schemes for better SIA and haze/fog prediction in the atmospheric chemistry model.

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

Chemical characterization, source apportionment, and health risk assessment of PM2.5 in a typical industrial region in North China

To clarify the chemical characteristics, source contributions, and health risks of pollution events associated with high PM_2.5 in typical industrial areas of North China, manual sampling and analysis of PM_2.5 were conducted in the spring, summer, autumn, and winter of 2019 in Pingyin County, Jinan City, Shandong Province. The results showed that the total concentration of 29 components in PM_2.5 was 53.4 ± 43.9 μg·m^-3, including OC/EC, water-soluble ions, inorganic elements, and metal elements. The largest contribution was from the NO₃^- ion, at 14.6 ± 14.2 μg·m^-3, followed by organic carbon (OC), SO₄^2-, and NH₄⁺, with concentrations of 9.3 ± 5.5, 9.1 ± 6.4, and 8.1 ± 6.8 μg·m^-3, respectively. The concentrations of OC, NO₃^-, and SO₄^2- were highest in winter and lowest in summer, whereas the NH₄⁺ concentration was highest in winter and lowest in spring. Typical heavy metals had higher concentrations in autumn and winter, and lower concentrations in spring and summer. The annual average sulfur oxidation rate (SOR) and nitrogen oxidation rate (NOR) were 0.30 ± 0.14 and 0.21 ± 0.12, respectively, with the highest SO₂ emission and conversion rates in winter, resulting in the SO₄^2- concentration being highest in winter. The average concentration of secondary organic carbon in 2019 was 2.8 ± 1.9 μg·m^-3, and it comprised approximately 30% of total OC. The concentrations of 18 elements including Na, Mg, and Al were between 2.3 ± 1.6 and 888.1 ± 415.2 ng·m^-3, with Ni having the lowest concentration and K the highest. The health risk assessment for typical heavy metals showed that Pb poses a potential carcinogenic risk for adults, whereas As may pose a carcinogenic risk for adults, children, and adolescents. The non-carcinogenic risk coefficients for all heavy metals were lower than 1.0, indicating that the non-carcinogenic risk was negligible. Positive matrix factorization analysis indicated that coal-burning emissions contributed the largest fraction of PM_2.5, accounting for 35.9% of the total. The contribution of automotive emissions is similar to that of coal, at 32.1%. The third-largest contributor was industrial sources, which accounted for 17.2%. The contributions of dust and other emissions sources to PM_2.5 were 8.4% and 6.4%, respectively. This study provides reference data for policymakers to improve the air quality in the NCP.

Spatiotemporal variations of PM2.5 pollution and its dynamic relationships with meteorological conditions in Beijing-Tianjin-Hebei region

Identifying the effects of meteorological conditions on PM_2.5 pollution is of great significance to explore methods to reduce atmospheric pollution. This study attempts to analyze the spatiotemporal variations of PM_2.5 pollution and its dynamic nexus with meteorological factors in the Beijing-Tianjin-Hebei (BTH) region from 2015 to 2020 using standard deviation ellipse (SDE) and panel vector autoregressive (PVAR) model. The results indicate that: (1) In 2015-2020, PM_2.5 pollution decreased significantly, indicating air pollution control policies in China have taken effect; Also, it showed a cumulative effect, or there was the path dependence of air pollution. (2) PM_2.5 pollution presented a distribution pattern from northeast to southwest, while the directionality of air pollution has weakened. Based on SDE, PM_2.5 pollution in Cangzhou can reflect the average level in the BTH; (3) Meteorological conditions exhibited a lagged and sustained effect on PM_2.5 pollution. Specifically, the effects of meteorological factors on PM_2.5 presented disequilibrium over time. In the long run, precipitation and temperature mainly showed negative impacts on PM_2.5 pollution, while wind speed, relative humidity and sunshine duration aggravated PM_2.5 pollution in the BTH. This study contributes to extending the study on the spatiotemporal evolution of PM_2.5 pollution and its links with meteorological conditions.

Mortality burden due to long-term exposure to ambient PM2.5 above the new WHO air quality guideline based on 296 cities in China

OBJECTIVE

Quantifying the spatial and socioeconomic variation of mortality burden attributable to particulate matters with aerodynamic diameter ≤ 2.5 µm (PM2.5) has important implications for pollution control policy. This study aims to examine the regional and socioeconomic disparities in the mortality burden attributable to long-term exposure to ambient PM2.5 in China.

METHODS

Using data of 296 cities across China from 2015 to 2019, we estimated all-cause mortality (people aged ≥ 16 years) attributable to the long-term exposure to ambient PM2.5 above the new WHO air quality guideline (5 µg/m3). Attributed fraction (AF), attributed deaths (AD), attributed mortality rate (AMR) and total value of statistical life lost (VSL) by regional and socioeconomic levels were reported.

RESULTS

Over the period of 2015-2019, 17.0% [95% confidence interval (CI): 7.4-25.2] of all-cause mortality were attributable to long-term exposure to ambient PM2.5, corresponding to 1,425.2 thousand deaths (95% CI: 622.4-2,099.6), 103.5/105 (95% CI: 44.9-153.3) AMR, and 1006.9 billion USD (95% CI: 439.8-1483.4) total VSL per year. The AMR decreased from 120.5/105 (95% CI: 52.9-176.6) to 92.7/105 (95% CI:39.9-138.5) from 2015 to 2019. The highest mortality burden was observed in the north region (annual average AF = 24.2%, 95% CI: 10.8-35.1; annual average AMR = 137.0/105, 95% CI: 60.9-198.5). The highest AD and economic loss were observed in the east region (annual average AD = 390.0 thousand persons, 95% CI: 170.3-574.6; annual total VSL = 275.6 billion USD, 95% CI: 120.3-406.0). Highest AMR was in the cities with middle level of GDP per capita (PGDP)/urbanization. The majority of the top ten cities of AF, AMR and VSL were in high and middle PGDP/urbanization regions.

CONCLUSION

There were significant regional and socioeconomic disparities in PM2.5 attributed mortality burden among Chinese cities, suggesting differential mitigation policies are required for different regions in China.

Nonlinear influence of winter meteorology and precursor on PM2.5 based on mathematical and numerical models: A COVID-19 and Winter Olympics case study

Air pollution during the COVID-19 epidemic in Beijing and its surrounding regions has received substantial attention. We collected observational data, including air pollutant concentrations and meteorological parameters, during January and February from 2018 to 2021. A statistical and a numerical model were applied to identify the formation of air pollution and the impact of emission reduction on air quality. Relative humidity, wind speed, SO₂, NO₂, and O₃ had nonlinear effects on the PM_2.5 concentration in Beijing, among which the effects of relative humidity, NO₂, and O₃ were prominent. During the 2020 epidemic period, high pollution concentrations were closely related to adverse meteorological conditions, with different parameters having different effects on the three pollution processes. In general, the unexpected reduction of anthropogenic emissions reduced the PM_2.5 concentration, but led to an increase in the O₃ concentration. Multi-scenario simulation results showed that anthropogenic emission reduction could reduce the average PM_2.5 concentration after the Chinese Spring Festival, but improvement during days with heavy pollution was limited. Considering that O₃ enhances the PM_2.5 levels, to achieve the collaborative improvement of PM_2.5 and O₃ concentrations, further research should explore the collaborative emission reduction scheme with VOCs and NO_x to achieve the collaborative improvement of PM_2.5 and O₃ concentrations. The conclusions of this study provide a basis for designing a plan that guarantees improved air quality for the 2022 Winter Olympics and other international major events in Beijing.

Air stagnation in China: Spatiotemporal variability and differing impact on PM2.5 and O3 during 2013-2018

In recent years, winter PM_2.5 and summer O₃ pollution which often occurred with air stagnation condition has become a major concern in China. Thus, it is imperative to understand the air stagnation distribution in China and elucidate its impact on air pollution. In this study, three air stagnation indices were calculated according to atmospheric thermal and dynamics parameters using ERA5 data. Two improved indices were more suitable in China, and they displayed similar characteristics: most of the air stagnant days were found in winter, and seasonal distributions showed substantial regional heterogeneity. During stagnation events, flat west or northwest winds at 500 hPa and high pressure at surface dominated, with high relative humidity (RH) and temperature (T), weak winds in most regions. The pollutants concentrations on stagnant days were higher than those on non-stagnant days in most studied areas, with the largest difference of the 90th percentiles of maximum daily 8-h average (MDA8) O₃ up to 62.2 μg m^-3 in Pearl River Delta (PRD) and PM_2.5 up to 95.8 μg m^-3 in North China Plain (NCP). During the evolution of stagnation events, the MDA8 O₃ concentrations showed a significant increase (6.0 μg m^-3 day^-1) in PRD and a slight rise in other regions; the PM_2.5 concentrations and the frequency of extreme PM_2.5 days increased, especially in NCP. Furthermore, O₃ was simultaneously controlled by temperature and stagnation except for Xinjiang (XJ), with the average growth rate of 19.5 μg m^-3 every 3 °C at 19 °C-31 °C. PM_2.5 was dominated by RH and stagnation in northern China while mainly controlled by stagnation in southern China. Notably, the extremes of summer O₃ (winter PM_2.5) pollution was most associated with air stagnation and T at 25 °C-31 °C (air stagnation and RH >50%). The results are expected to provide important reference information for air pollution control in China.

Vertical evolution of black and brown carbon during pollution events over North China Plain

The vertical distribution of carbonaceous aerosol impacts climate change, air quality and human health, but there is a lack of in-situ vertical observations of black (BC) and brown carbon (BrC). Thus, the characteristic of vertical profiles of BC concentration, particle number concentration (PNC), O₃ concentration and optical absorption of BC and BrC were observed in a suburban site over North China Plain, where heavy pollution of PM_2.5 and O₃ always occurred in winter and summer, respectively. In winter, during a heavy pollution episode, the BC and PNC was near uniformly distributed within mixing layer (ML) (15.2 ± 6.7 μg m^-3 and 678 ± 227 p cm^-3, respectively) and decreased with altitude above the ML. The BC heating rate reached about 0.13 K h^-1 during the heaviest pollution day. In summer, the BC concentration (2.9 ± 1.3 μg m^-3) in ML during the middle O₃ pollution events was higher than that (1.7 ± 0.6 μg m^-3) during the light O₃ pollution. The light absorption coefficients of BC at 880 nm and BrC at 375 nm measured in the early morning were lower than that in the daytime, and the contribution of BrC to total light absorption of carbonaceous aerosols was in the range of 27-47%. In addition, BC was effectively transported to high altitude than BrC in the daytime. The light absorption of secondary BrC in the daytime was higher 10-20% than that in the early morning. Simultaneously, the contribution of secondary BrC to the total BrC light absorption at 375 nm was range from 32% to 68% within 1000 m.

Spatiotemporal characteristics of NO2, PM2.5 and O3 in a coastal region of southeastern China and their removal by green spaces

Understanding the spatio-temporal characteristics of air pollutants is essential to improving air quality. One aspect is the question of whether green spaces can reduce air pollutant concentrations. However, previous studies on this issue have reported mixed results. This study analyzed the spatio-temporal characteristics of NO₂, PM_2.5 and O₃ in Fujian Province, Southeast China in 2015. In order to reduce uncertainties in the conclusions drawn, the effects landscape metrics describing green spaces have on air pollutants have been analyzed using Pearson correlation analysis at six different spatial scales for the four seasons, considering the influence of meteorological conditions. The results show that PM_2.5 and O₃ are major pollutants whose relative importance varies with the seasons. Significant differences in pollutant concentrations were observed in suburban and urban areas, highlighting the importance of ensuring a reasonable spatial distribution of monitoring stations. Moreover, significant correlations between air pollutants and green space landscape patterns during the four seasons were found, revealing increased air pollutant concentrations with increasing landscape fragmentation and reduced connectivity and aggregation. This probably indicates that interconnected green spaces have the potential to improve air quality. Utilizing green space function regulations can alleviate NO₂ and PM_2.5 pollution effectively, but it is still difficult to reduce O₃ concentrations because green spaces are likely to not only serve as sinks for O_3, but can also promote O₃ formation.

Capture of decline in spring phytoplankton biomass derived from COVID-19 lockdown effect in the Yellow Sea offshore waters

The Yellow Sea, characterized as a high-productivity ecosystem, is considered to be significantly attributable to high nutrient supply via atmospheric deposition. We observed a significant decline in phytoplankton biomass (~30%) over the Yellow Sea during February-May 2020 (period of COVID-19 lockdown effect) compared to the same period in 2015-2019 (period of no effect of COVID-19 lockdown). Several possible factors, such as variations in irradiance, vertical mixing, and river discharges, were not major contributors. Through the analysis of transportation and the constituents of atmospheric pollutants from Northern China (main source regions) to the Yellow Sea, we suggest that the decline in phytoplankton biomass over the Yellow Sea is mainly attributed to decreased atmospheric nutrient deposition due to the COVID-19 lockdown effect, because of decreased anthropogenic emissions in Northern China. Thus, attention should be focused on the Yellow Sea ecosystem response to increasing anthropogenic activities by lifting the COVID-19 lockdown restrictions.

Double high pollution events in the Yangtze River Delta from 2015 to 2019: Characteristics, trends, and meteorological situations

We investigated the spatial distribution and trend of double high pollution (DHP), in which the daily average concentration of fine particulate matter (PM_2.5) was above 75 μg/m³ and the daily maximum 8-hour average ozone (MDA8 O₃) concentration was above 160 μg/m³, in the Yangtze River Delta (YRD) region during 2015-2019, along with the meteorological and chemical characteristics during DHP and differences compared to high O₃ pollution (HOP) and high PM_2.5 pollution (HPP). In the YRD, Shanghai had the highest frequency of DHP at 7.6%, while Anhui had the least (2.1%). DHP mostly occurred in the northwest and along the Yangtze River in the east of the YRD, especially in spring (April) and autumn (October). MDA8 O₃ level was relatively higher during DHP than HOP, while PM_2.5 level was relatively higher during HPP than DHP. In 2015-2019, the total number of DHP events decreased in the YRD, but the changes in PM_2.5 and O₃ concentrations showed great spatial variations. DHP was often associated with a weak pressure field, under meteorological conditions with east winds, temperatures of 18.7-26.1 °C, relative humidity of 65.7-77.1%, sea level pressure of 1008.2-1019 hPa, wind speed of 1.4-2.4 m/s, and visibility of 3.1-7.5 km. Water-soluble ions (NO₃^-, NH₄⁺, and SO₄^2-) were the dominant components of PM_2.5 during DHP at Nanjing and Changzhou City in 2019. Although the fraction of those ions during DHP and HPP were similar, the secondary conversion of NO₂ and SO₂ was stronger in HPP. The concentrations of those ions were lowest in HOP, with a higher fraction of sulfate than the other two types of pollution. The conversion of SO₂ to sulfate was easier to occur than that of NO₂ to nitrate under all the polluted conditions in the two cities.

Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities

PM_2.5 pollution has gradually attracted people's attention due to its important negative impact on public health in recent years. The influence of anthropogenic emission factors on PM_2.5 concentrations is more complicated, but their relative individual impact on different emission sectors remains unclear. With the aid of the geographic detector model (GeoDetector), this study evaluated the impacts of anthropogenic emissions from different sectors on the PM_2.5 concentrations of major cities in China. The results indicated that the influence of anthropogenic emissions factors with different emission sectors on PM_2.5 concentrations exhibited significant changes at different spatial and temporal scales. Residential emissions were the dominant driver at the national annual scale, and the NO_X of residential emissions explained 20% (q = 0.2) of the PM_2.5 concentrations. In addition, residential emissions played the leading role at the regional annual scale and during most of the seasons in northern China, and ammonia emissions from residents were the dominant factor. Traffic emissions play a leading role in the four seasons for MUYR and EC in southern China, MYR and NC in northern China, and on a national scale. Compared with primary particulate matter, secondary anthropogenic precursors have a more important effect on PM_2.5 concentrations at the national or regional annual scale. The results can help to strengthen our understanding of PM_2.5 pollution, improve PM_2.5 forecasting models, and formulate more precise government control policy.

Evaluation and Bias Correction of the Secondary Inorganic Aerosol Modeling over North China Plain in Autumn and Winter

Secondary inorganic aerosol (SIA) is the key driving factor of fine-particle explosive growth (FPEG) events, which are frequently observed in North China Plain. However, the SIA simulations remain highly uncertain over East Asia. To further investigate this issue, SIA modeling over North China Plain with the 15 km resolution Nested Air Quality Prediction Model System (NAQPMS) was performed from October 2017 to March 2018. Surface observations of SIA at 28 sites were obtained to evaluate the model, which confirmed the biases in the SIA modeling. To identify the source of these biases and reduce them, uncertainty analysis was performed by evaluating the heterogeneous chemical reactions in the model and conducting sensitivity tests on the different reactions. The results suggest that the omission of the SO2 heterogeneous chemical reaction involving anthropogenic aerosols in the model is probably the key reason for the systematic underestimation of sulfate during the winter season. The uptake coefficient of the “renoxification” reaction is a key source of uncertainty in nitrate simulations, and it is likely to be overestimated by the NAQPMS. Consideration of the SO2 heterogeneous reaction involving anthropogenic aerosols and optimization of the uptake coefficient of the “renoxification” reaction in the model suitably reproduced the temporal and spatial variations in sulfate, nitrate and ammonium over North China Plain. The biases in the simulations of sulfate, nitrate, ammonium, and particulate matter smaller than 2.5 μm (PM2.5) were reduced by 84.2%, 54.8%, 81.8%, and 80.9%, respectively. The results of this study provide a reference for the reduction in the model bias of SIA and PM2.5 and improvement of the simulation of heterogeneous chemical processes.

Evaluation and Influencing Factors of Industrial Pollution in Jilin Restricted Development Zone: A Spatial Econometric Analysis

Winning the battle against pollution and strengthening ecological protection in all respects are vital for promoting green development and building a moderately prosperous ecological civilization in China. Using the entropy weight method, this paper establishes and evaluates a comprehensive industrial pollution index that contains and synthesizes six major industrial pollutants (wastewater, COD, waste gas, SO2, NOx, and solid waste) in the 2006–2015 period. Subsequently, this paper studies the spatiotemporal characteristics and influencing factors of industrial pollution via the Moran index and spatial econometric analysis. The empirical results indicate that (1) the temporal evolution of the industrial pollution index is characterized by an overall trend of first decreasing and then increasing. (2) The industrial pollution index of each county has certain geographical disparities and significant spatially polarized characteristics in 2006, 2009, 2012, and 2015. (3) The Moran test shows that there is a relatively significant spatial autocorrelation of the industrial pollution index among counties and that the geographical distribution of the industrial pollution index tends to show clustering. (4) Spatial regression models that incorporate spatial factors better explain the influencing factors of industrial pollution. The economic development level, technological progress, and industrialization are negatively correlated with industrial pollution, while population density and industrial production capacity are positively correlated. (5) Consequently, as relevant policy recommendations, this paper proposes that environmental cooperation linkage mechanisms, environmental protection credit systems, and green technology innovation systems should be established in different geographical locations to achieve the goals of green county construction and sustainable development.

A grey spatiotemporal incidence model with application to factors causing air pollution

The factors causing air pollution in China has caused extensive concern, but there are still many problems in the grey incidence model of identifying air pollution factors. The results produced by the existing grey incidence models are not stable when the order of objects in a given panel data is changed. In order to improve the reliability and uniformity of the grey incidence model, a new grey incidence model, called the grey spatiotemporal incidence model, abbreviated as the GSTI model, is designed in this paper. In the proposed model, the spatiotemporal data which can represent the spatial relationship among different objects rather than the three-dimensional panel data are defined. In addition, the new model includes two procedures. Firstly, the trend coefficient is used to measure the positive and negative connections between two data sequences. Secondly, the measurement coefficient is utilized to calculate the size of grey incidence degree. Subsequently, five properties of the GSTI model are discussed. To demonstrate its practicability and compatibility, the novel model is utilized to identify south Jiangsu province's main factors causing air pollution according to monthly data for 2018. The abundant comparison shows the applicability and superiority of the model in the identification of air pollution factors and the construction of grey incidence model.

Deciphering wintertime air pollution upon the West Lake of Hangzhou, China

The West Lake of Hangzhou, a world famous landscape and cultural symbol of China, suffered from severe air quality degradation in January 2015. In this work, Random Forest (RF) and Recurrent Neural Networks (RNN) are used to analyze and predict air pollutants on the central island of the West Lake. We quantitatively demonstrate that the PM2.5 and PM10 were chiefly associated by the ups and downs of the gaseous air pollutants (SO2, NO2 and CO). Compared with the gaseous air pollutants, meteorological circumstances and regional transport played trivial roles in shaping PM. The predominant meteorological factor for SO2, NO2 and surface O3 was dew-point deficit. The proportion of sulfate in PM10 was higher than that in PM2.5. CO was strongly positively linked with PM. We discover that machine learning can accurately predict daily average wintertime SO2, NO2, PM2.5 and PM10, casting new light on the forecast and early warning of the high episodes of air pollutants in the future.

Policy-driven changes in the health risk of PM2.5 and O3 exposure in China during 2013-2018

China issued a series of control measures to mitigate PM_2.5 pollution, including long-term (i.e., Air Pollution Prevention and Control Action Plan, APPCAP) and short-term (emergency measures in autumn and winter) acts. However, the O₃ concentration increased significantly as PM_2.5 levels sharply decreased when these measures were implemented. Therefore, the policy-driven positive/negative health effects of PM_2.5/O₃ need to be comprehensively estimated. The health impact function (HIF) is applied to evaluate the health burden attributable to long- and short-term PM_2.5 and O₃ exposure. The results show that the PM_2.5 concentration decreased by 42.95% in 74 cities, whereas O₃ pollution is increased by 17.56% from 2013 to 2018. Compared with 2013, the number of premature deaths attributable to long- and short-term PM_2.5 exposure decreased by almost 5.31 × 10⁴ (95% confidence interval [CI]: 2.87 × 10⁴-4.71 × 10⁴) (10.13%) and 3.00 × 10⁴ (95% CI: 1.66 × 10⁴-4.39 × 10⁴) (72.49%), respectively, in 2018. In contrast, O₃-attributable deaths, increased by 1.98 × 10⁴ (95% CI: 0.31 × 10⁴-3.59 × 10⁴) (130.57%) and 0.91 × 10⁴ (95% CI: 0.50 × 10⁴-1.33 × 10⁴) (76.16%) for long- and short-term exposure, respectively. The number of avoidable deaths attributed to PM_2.5 reduction is larger than the level of premature deaths related to increasing O₃. Although annual mean PM_2.5 concentrations have fallen rapidly, the benefits of reducing long-term exposure are limited, whereas the deaths associated with acute exposure decrease more significantly due to the reduction of heavy-pollution days by implementing emergency measures. The results show appreciable effectiveness in protecting human health and illustrate that synchronous control of PM_2.5 and O₃ pollution should be emphasized.

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

Synergetic impacts of precursory climate drivers on interannual-decadal variations in haze pollution in North China: A review

North China suffers from severe haze pollution and has received widespread attentions since the winter of 2012. In addition to human activities, climate variability also plays an important role, particularly in the interannual-decadal variations in the number of haze days in North China (HD_NC). Many previous studies separately explored numerous preceding climate drivers, including Arctic sea ice, Eurasia snow and soil moisture, sea surface temperature in Pacific and Atlantic and forcing of Tibetan Plateau, but lacked assessment and analysis of the joint effects. In this study, we reviewed their impacts on HD_NC and associated physical mechanisms. Beyond that, the synergetic effects were newly revealed by the observations and numerical experiments with fixed emissions. The preceding signals explained approximately 66% of the interannual-decadal variations in HD_NC by exciting teleconnection patterns in winter and influencing the local dispersion conditions in North China. Furthermore, some future research directions were identified, such as the subseasonal variations in HD_NC, subseasonal-seasonal prediction of haze by numerical climate models, and changing relationships between HD_NC and climate conditions.

Effects of China's current Air Pollution Prevention and Control Action Plan on air pollution patterns, health risks and mortalities in Beijing 2014-2018

Beijing is one of the most polluted cities in the world. However, the "Air Pollution Prevention and Control Action Plan" (APPCAP), introduced since 2013 in China, has created an unprecedented drop in pollution concentrations for five major pollutants, except O₃, with a significant drop in mortalities across most parts of the city. To assess the effects of APPCAP, air pollution data were collected from 35 sites (divided into four types, namely, urban, suburban, regional background, and traffic) in Beijing, from 2014 to 2018 and analyzed. Simultaneously, health-risk based air quality index (HAQI) and district-specific pollution (PM_2.5 and O₃) attributed mortality were calculated for Beijing. The results show that the annual PM_2.5 concentration exceeded the Chinese national ambient air quality standard Grade II (35 μg/m³) in all sites, ranging from 88.5 ± 77.4 μg/m³ for the suburban site to 98.6 ± 89.0 μg/m³ for the traffic site in 2014, but was reduced to 50.6 ± 46.6 μg/m³ for the suburban site, and 56.1 ± 47.0 μg/m³ for the regional background in 2018. O₃ was another most important pollutant that exceeded the Grade II standard (160 μg/m³) for a total of 291 days. It peaked at 311.6 μg/m³ in 2014 for the urban site and 290.6 μg/m³ in 2018 in the suburban site. APPCAP led to a significant reduction in PM_2.5, PM₁₀, NO₂, SO₂ and CO concentrations by 7.4, 8.1, 2.4, 1.9 and 80 μg/m³/year respectively, though O₃ concentration was increased by 1.3 μg/m³/year during the five-years. HAQI results suggest that during the high pollution days, the more vulnerable groups, such as the children, and the elderly, should take additional precautions, beyond the recommendations currently put forward by Beijing Municipal Environmental Monitoring Center (BJMEMC). In 2014, PM_2.5 and O₃ attributed to 29,270 and 3,030 deaths respectively, though in 2018 their mortalities were reduced by 5.6% and 18.5% respectively. The highest mortality was observed in Haidian and Chaoyang districts, two of the most densely populated areas in Beijing. Beijing's air quality has seen a dramatic improvement over the five-year period, which can be attributable to the implementation of APPCAP and the central government's determination, with significant drops in the mortalities due to PM_2.5 and O₃ in parallel. To further improve air quality in Beijing, more stringent regulatory measures should be introduced to control volatile organic compounds (VOCs) and reduce O₃ concentrations. Consistent air pollution control interventions will be needed to ensure long-term prosperity and environmental sustainability in Beijing, China's most powerful city. This study provides a robust methodology for analyzing air pollution trends, health risks and mortalities in China. The crucial evidence generated forms the basis for the governments in China to introduce location-specific air pollution policy interventions to further reduce air pollution in Beijing and other parts of China. The methodology presented in this study can form the basis for future fine-grained air pollution and health risk study at the city-district level in China.

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.

The climate impact on atmospheric stagnation and capability of stagnation indices in elucidating the haze events over North China Plain and Northeast China

In this study, the spatial pattern and temporal evolution of PM_2.5 over North China Plain (NCP) and Northeast China (NEC) during 2014-2018 was investigated. The annual mean PM_2.5 shows clear decreasing trends over time, but the seasonal mean PM_2.5 as well as the seasonal total duration and frequency of haze days shows large inter-annual fluctuation. Based on the atmospheric stagnation index (ASI), this study examined the correlation between ASI and haze events over NCP and NEC. Detailed analysis indicates that location dependency exists of ASI in the capability of capturing the haze events, and the ability is limited in NCP. Therefore, we first propose two alternative methods in defining the ASI to either account for the lag effect or enlarge the threshold value of wind speed at 500 hPa. The new methods can improve the ability of ASI to explain the haze events over NEC, though marginal improvement was achieved in NCP. Furthermore, this study constructed the equation based on the boundary layer height and wind speed at 10-meter, apparently improving the ability in haze capture rate (HCR), a ratio of haze days during the stagnation to the total haze days. Based on a multi-model ensemble analyses under Representative Concentration Pathway (RCP) 8.5, we found that by the end of this century, climate change may lead to increases in both the duration and frequency of wintertime stagnation events over NCP. In contrast, the models predict a decrease in stagnant events and the total duration of stagnation in winter over NEC.

How aerosol transport from the North China plain contributes to air quality in northeast China

Northeast China (NEC) has unique climate characteristics and emission sources; continued urbanisation has aggravated regional pollution. The in situ observation data concerning PM_2.5, visibility, surface meteorological elements and synchronous aerosol vertical extinction profiles obtained from ground-based Lidar were investigated to better understand local and regional particulate pollution in NEC. The WRF (3.7.1)/CAMx (6.40) model was employed to quantitative investigate the contribution of regional transport to PM_2.5 in Shenyang. The results suggested that PM_2.5 increased significantly from 9 to 14 January over NEC and the Northern China (NC), with monthly PM_2.5 highest in Shijiazhuang and Baoding of NC about 145.2 ± 88.9 and 136.8 ± 83.1 μg m^-3, respectively. The distribution of SO₂ and NO₂ for PM_2.5 implied SO₂ was more influence on PM_2.5 in NEC, while NO₂ has larger impact on PM_2.5 in NC. The significant increasing of relative humidity (RH) and temperatures exhibited in the pollution indicate water vapor and warm air flow during the transport. The development of the southwest airflow was conducive to pollutant transport across the Beijing-Tianjin-Hebei (or Jing-Jin-Ji) megalopolis to NEC, and together with the local emissions in NEC to affect air quality. The modelling results pointed out that contribution of regional transport to PM_2.5 in Shenyang was about 80.12% at 00:00 LT in 10 January, of which the contribution of BTH was about 61.52%; the total regional contribution to PM_2.5 in Shenyang reaching 60.70% at 02:00 LT on 13 January including 34.56% contributed by BTH region. Aerosol vertical extinction indicated the particle layer appeared in the near-surface and in the upper atmospheric layer from 0.5 to 1.0 km following the development of transport event. The findings of this study can facilitate a comprehensive understanding of the local and regional air pollution in NEC and helpful for national environment pollution controls and improvement.

Seasonal and long-term trends of sulfate, nitrate, and ammonium in PM2.5 in Beijing: implication for air pollution control

Particulate matter pollution in Beijing is a serious environmental problem. In response to this, the Beijing government has implemented comprehensive emission reduction measures in recent years. To assess the effectiveness of these measures, the seasonal and long-term trends in chemical compositions of PM_2.5 in Beijing have been studied based on daily samples collected from July 2015 to April 2016 and literature investigation (2000-2014). Results show that the concentrations of sulfate, nitrate, and ammonium in PM_2.5 have significant seasonal variations, which are related to the changes in meteorological conditions and emission intensities. In addition, the long-term data display that the concentrations of sulfate, nitrate, and ammonium have significantly decreased between 2013 and 2016, which are consistent with the reduction in PM_2.5 levels (~ 11.2 μg/m³ per year). The declines could not be interpreted by the meteorological factors. It suggests that the air pollution control measures in Beijing (2013-2016), especially the decreasing consumption of coal, can effectively decrease the mass concentration of fine particles. To further improve the air quality, similar measures should be adopted in the areas around Beijing. These air pollution control measures taken in Beijing can provide invaluable guidance for mega-cities in China and other developing countries to decrease their PM_2.5 concentration and reduce health risk from particulate pollution.