Regional Pollution Control Research Articles

Research on the Impact Effects of the Thermal Power Industry and Other High-Haze-Pollution Industries on the Atmospheric Environment

As industrialization accelerates, China’s industrial development pace has been rapidly increasing. However, this growth has been accompanied by an increase in high-pollution and high-emission industries, leading to the release of a significant amount of air pollutants and exacerbating haze pollution nationwide. This article utilizes the spatial dynamic Durbin model and panel threshold regression model to analyze the impact of the thermal power industry and other high-haze-pollution industries on atmospheric environmental quality. The results indicate a negative correlation between the thermal power industry and other high-haze pollution industries and atmospheric environmental quality. There is a spatial spillover effect of the thermal power industry and other high-haze-pollution industries on air pollution. Environmental regulations have a single-threshold characteristic in their impact on atmospheric quality in the thermal power industry and other high-haze-pollution industries, as does green technology innovation. Additionally, cumulative rainfall has a significant single-threshold effect on the atmospheric environmental quality in regions with the thermal power industry and other high-haze-pollution industries. The article suggests policies for severely polluted areas, including reducing high-haze-pollution enterprises, optimizing industrial structures rationally, strengthening regional cooperation, enhancing regional haze pollution prevention and control coordination mechanisms, increasing the intensity of environmental regulations, utilizing the threshold effect of environmental regulations, promoting green technological innovation and application in heavily polluted areas, and exploring options to improve air pollution through increased rainfall. These recommendations aim to provide reference points for China to further optimize its industrial structure and comprehensively manage haze pollution.

Spatial Distribution and Mechanisms of Groundwater Hardness in the Plain Area of Tangshan City, China

Groundwater resources play a critical role in meeting the agricultural, industrial, and domestic water demands of Tangshan, a key industrial city in China. However, with the acceleration of urbanization and the overextraction of groundwater, issues related to groundwater quality have become increasingly apparent. Notably, groundwater hardness has steadily increased over the years, posing risks to human health and elevating industrial water treatment costs. This study analyzed the spatial distribution characteristics and causes of groundwater hardness using 214 groundwater quality samples collected in 2022 from the plain area of Tangshan City, employing inverse distance weighting (IDW), Gibbs diagrams, ion ratios, mineral saturation indices, and Pearson correlation analysis. The results indicate that, in horizontal distribution, high-hardness groundwater is predominantly concentrated in the southern coastal plain area, with hardness gradually decreasing from south to north. Vertically, shallow groundwater in the coastal plain exhibits significantly higher hardness than deep groundwater, with a non-compliance rate of 94.12%, while deep groundwater hardness remains markedly lower. Mid-depth groundwater (60–300 m) in the alluvial plain exhibits elevated hardness, primarily attributed to mineral dissolution and agricultural irrigation return flow. The spatial distribution pattern of groundwater hardness across the study area is predominantly governed by hydrogeochemical processes and hydrochemical environmental factors, with cation exchange adsorption and evaporation–concentration processes identified as the dominant influences. The analysis of ion sources indicates that Ca2+ and Mg2+, the primary contributors to groundwater hardness in the area, are mainly derived from the weathering and dissolution of carbonate minerals, sulfate minerals, and cation exchange processes. Therefore, an in-depth investigation into the spatial distribution and driving factors of groundwater hardness in Tangshan can provide a scientific basis for regional water resource management, pollution control, and water quality optimization. Such research also supports the development of sustainable groundwater management and optimization strategies.

Does the Air Pollution Joint Prevention and Control Policy Work: Evidence from China

Developing countries face significant challenges in managing regional air pollution. While previous studies show the benefits of the Air Pollution Joint Prevention and Control (AJPC) policy, there needs to be more understanding of its implementation challenges. Using data from the Beijing-Tianjin-Hebei (BTH) region in China and applying a tripartite evolutionary game (TEG) model along with a difference-in-differences (DID) method, our study talks about the effectiveness of AJPC policy. We find that the AJPC policy has positively impacted air quality. However, its effectiveness varies across regions and periods, making it difficult to achieve the original goals. Additionally, we identified challenges to implementing the policy, such as free-riding from governance spillover, the instability of central government regulation, and the limited policy-carrying capacity of micro-entities. Our findings highlight the complexity of regional pollution control and provide critical insights for refining pollution management strategies in developing countries, offering valuable guidance for policymakers aiming to enhance regional air quality control.

Transforming air pollution management in India with AI and machine learning technologies

A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

The new region demarcation framework for implementing the joint prevention and control of groundwater pollution: A case study in western of Bohai Bay, China

The joint groundwater pollution prevention and control (GPPC) strategy has been extensively implemented to address the coastal region groundwater pollution challenges in China. However, regional groundwater pollution control and treatment efficiency cannot achieve the expected results due to the lack of regional priority control orders and accurate restoration levels. Thus, this study developed a new region demarcation framework method for delineating GPPC zones, in tandem with a comprehensive pollution index method, the DRASTIC model, source apportionment. To validate the new methodological framework, a case study of groundwater pollution in Qinhuangdao, the western of Bohai Bay, China, was implemented to calculate pollution prevention and control zoning. In total, 340 groundwater samples from shallow aquifers with 9 target pollutants (NO3-, NO2-, NH4+, As, Cd, Cr, Cu, Pb, and Ni) were selected as the dataset for GPPC regionalization. The results showed that GPPC zoning further clarified the direction of groundwater pollution protection and management in Qinhuangdao. Compared to the traditional method, the new GPPC zoning better reflects groundwater mobility characteristics and pollution transport and enrichment patterns in terms of groundwater functional integrity and delineation. This new regional demarcation framework method contributes to providing support for the fine division of groundwater pollution zoning and precise pollution control for groundwater resource management in China.

The impact of evolving synoptic weather patterns on multi-scale transport and sources of persistent high-concentration ozone pollution event in the Yangtze River Delta, China

High-concentration ozone pollution pose threats to ecosystems and human health. However, there is limited research on the impact of the alternating evolution of synoptic weather patterns (SWPs) on the multi-scale transport processes and sources of ozone. From June 14 to 18, 2018, a rare consecutive ozone pollution plagued in Hefei and broader Yangtze River Delta region (YRD). This study investigates the meteorological factors and sources using in-situ observational data and WRF-Chem model simulations. Analysis reveals a northeastern low-pressure system moving from north to south generated a cold front. This moving cold front facilitated the vertical transport of warm air masses carrying high-concentration ozone originating from North China. Subsequently, Ozone-rich air masses (ORMs) were transported over the YRD, influenced by the eastward movement of the Mongolian high-pressure system. Based on WRF-Chem model with NOx tagging mechanisms and WRF-FLEXPART backward simulations, it is confirmed that a notable atmospheric transport originated from North China region (NCR) to Hefei, especially on June 15. As the Mongolian high-pressure weakens and shifts east-southward, it carried ORMs generated by NOx emissions from the YRD, accumulating over the sea within the range of 120°E to 126°E and 25°N to 30°N. Both WRF-chem model results and TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) Chemistry Reanalysis dataset Version 2 (TCR-2) revealed the existence of ORMs in this geographic range. Subsequently, the ORMs carried out to sea by the weakened high-pressure system were reintroduced inland, influenced by southeast winds brought about by the peripheral circulation of typhoon “Gaemi”. In summary, the alternating evolution of SWPs significantly influences multi-scale ozone transport from both the NCR and the YRD regions, making substantial contributions to this prolonged episode. These findings offer valuable insights for improving regional ozone pollution prevention and control mechanisms.

Spatial and temporal characteristics of non-point source pollution and risks in the drinking water source area: A case study of Heihe River in Xi’an

Climate change and human activities have posed challenges to the availability and quality of drinking water resources. In water-stressed regions of northern China, pollution in water source areas has significantly impacted public health and hindered socio-economic development. This study focuses on an important water source area in Xi’an city and utilizes high-resolution multi-source integrated remote sensing images from Google Earth and Sentinel-2 remote sensing data to interpret land use information in the water source area. The Analytic Hierarchy Process (AHP) and Non-Point Source Pollution Risks Index (NPPRI) method were employed to assess non-point source pollution risks and analyze their spatial distribution characteristics. Additionally, water quality data from 2011 to 2021 in the water source area were combined to analyze the impact of land use changes on water quality. The results indicate that most water quality indicators meet Class II standards, with total nitrogen being the main pollutant exceeding the standards, primarily originating from non-point sources such as domestic sewage, livestock, and agricultural fields within the region. The overall non-point source risk within the water source area is relatively low. The NPPRI is influenced by three factors: the proportion of non-point source risk areas within the first-level protection zone, the second-level protection zone, and the small watershed. High-risk level small watersheds are the main areas affected by non-point source pollution and should be given priority in management efforts. Therefore, based on the spatial distribution of risk factors, regional pollution prevention and control measures should be implemented to mitigate water source risks and ensure drinking water security. The research findings can provide data support for the refined management of water source area risks.

Air Pollution Prediction using Attention Module With CNN -OptBiLSTM

<p>Predicting air pollution using environmental data assessment parameters becomes increasingly significant amid growing fears about climate change and the sustainability of urban areas. The use of sophisticated deep learning (DL) methods to model the intricate relation among these variables represents a promising area of research. However, current approaches have not effectively taken advantage of the temporal features derived from spatiotemporal correlations among air quality prediction systems, leading to poor long-term predictions. This work presents an AM (attention module) with CNN (convolutional neural network)-OptBiLSTM (optimal bidirectional long- and short-term memory) for AQIP (air quality index prediction). Here, the optimal process is carried out by the WSA (white shark algorithm). The analysis is demonstrated on the dataset and achieved better MSE and MAE values of 0.72 and 0.532 respectively. The developed model has the potential for application to other air pollutants. This proposed AM-CNN-OptBiLSTM has the capacity to substantially improve information services related to air quality prediction for the public. In addition, it provides support for regional pollution control and early warning systems.</p>

Regional transport characteristics of PM2.5 pollution events in Beijing during 2018–2021

Although air pollutant emissions have sharply reduced in recent years, the occurrence of PM2.5 pollution events remains an intractable environmental problem in Beijing, and regional transport is the key influence factor. However, it has been difficult to identify regional transport characteristics and the main contributors to pollution events in recent years. In this study, the relative contribution of regional transport was quantified (61.3%) in PM2.5 pollution events during 2018-2021 by the Community Multiscale Air Quality model embedded with the Integrated Source Apportionment Model (CMAQ-ISAM). The four regions with the largest fractional contributions to Beijing for all events were Shandong (7.7%), South Hebei (7.3%), Baoding (6.2%), and Langfang (5.8%). Pollution events were classified into the following types based on regional transport directions: local, southwest (SW), southeast (SE), south-mixed (SM), and others. Based on the transport distance, the SW, SE, and SM types can be subdivided into SW-short, SW-long, SE-short, SE-long, SM-short, SM-long distance from southwest, SM-long distance from southeast, and SM-long distance from southwest and southeast. SE-long was regarded as the most important type, with the highest relative frequency (20%). The transport directions were related to the southwest wind at 925 hPa and southeast wind at 1000 hPa in the south of the Beijing–Tianjin–Hebei (BTH) region, and the distance was mainly controlled by wind strength. The wind-field difference can be attributed to the low-pressure and high-pressure systems that control the BTH region. The results suggest that regional joint pollution control should be optimized based on the transport type.

Does environmental decentralization improve industrial ecology? Evidence from China's Yangtze River Economic Belt

The rational division of environmental management powers between governments is the institutional basis for promoting the transformation of industrial ecology (IE). Based on panel data of 105 prefecture-level cities in the Yangtze River Economic Belt (YREB) of China from 2006 to 2020, this study analyzes the spatial and temporal evolution characteristics of IE and environmental decentralization (ED) in the YREB by constructing evaluation index systems. The spatial econometric model is used to explore the effects of ED on IE under economic growth pressure (EGP) and officials’ tenure (LT). The results indicate that regional differences in IE in the YREB exhibit a fluctuating downward trend and that the imbalance between regions is the leading cause of overall differences. Furthermore, IE presents a significant spatial positive correlation. ED has a restraining effect on IE and is regulated by EGP and LT. Moderate EGP can mitigate the negative effects of ED on IE, while LT intensifies the negative effects of ED on IE. Heterogeneity exists in the relationship between ED and IE in regional locations and pollution control pressure areas. These results are significant for developing a modern industrial system, formulating differentiated environmental protection strategies, and improving the government performance appraisal system.

Ecological risk and spatial distribution, sources of heavy metals in typical purple soils, southwest China

The identification and quantification of the ecological risks, sources and distribution of heavy metals in purple soils are essential for regional pollution control and management. In this study, geo-accumulation index (Igeo), enrichment factor (EF), pollution index (PI), potential ecological risk index (RI), principal component analysis (PCA) model and geographical detector (GD) were combined to evaluate the status, ecological risk, and sources of heavy metals (HMs) in soils from a typical purple soil areas of Sichuan province. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in purple soil were 7.77, 0.19, 69.5, 27.9, 0.077, 30.9, 26.5 mg/kg and 76.8 mg/kg, and the Igeo, EF and RI of topsoil Hg and Cd in designated area was the highest, and the average contents of Hg and Cd in topsoil were obviously greater than respective soil background value in Sichuan province and purple soil. The hot spots for the spatial distribution of 8 HMs were mainly focused in the southwest and northeast of the designated area, and there were also significant differences for 8 HMs distribution characteristics in the profile soil. Cu comes from both anthropogenic and natural sources, Zn, Ni and Cr mainly come from natural sources, but As, Pb, Hg and Cd mainly derived from human activities. GD results showed that soil texture (X18), altitude (X4), total nitrogen (TN), clay content (X3), sand content (X2) and silt content (X1) had the greatest explanatory power to 8 HMs spatial differentiation.This study provides a reference for understanding the status and influencing factors of HM pollution in typical purple soil, and lays a theoretical foundation for the environmental treatment of purple soil in China.

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Value-added tax credit refund and environmental protection investment: Evidence from Chinese heavy-polluting enterprises

Heavy-polluting enterprises play a crucial role in regional pollution control and carbon reduction efforts. This study adopts Finance and Taxation [2018] No. 70 as a quasi-natural experiment, employing a difference-in-differences model to examine the impact of the value-added tax credit refund (VATCR) policy on the environmental protection investment (HEEPI) of 510 heavy-polluting enterprises. The results indicate that the VATCR policy significantly increases HEEPI. After the policy implementation, enterprises facing stricter environmental regulations, possessing higher potential, situated in economically developed regions, and larger in scale exhibit a greater inclination toward HEEPI. Alleviating financing constraints, enhancing core business activities, and reducing operating costs represent potential pathways for augmenting enterprises’ environmental investment. These findings are important for China in dealing with the relationship between business and government in environmental governance and are a valuable reference for other countries.

Chemical composition, sources and formation mechanism of urban PM2.5 in Southwest China: a case study at the beginning of 2023

Abstract. Despite significant improvements in air quality in recent years, the Sichuan Basin (SCB) is still facing frequent haze pollution in winter, and the causes of severe haze formation have not yet been fully investigated. In this study, the chemical components of PM2.5 (i.e., particulate matter with an aerodynamic diameter of less than 2.5 µm) in a typical pollution period at the beginning of 2023 in Chengdu, a megacity in the SCB, were characterized by bulk-chemical and single-particle analysis, and the PM2.5 sources and formation mechanism of pollution were analyzed. The average mass concentration of PM2.5 during the study period was 95.6 ± 28.7 µg m−3. Organic matter (OM) was the most abundant component (35.3 %), followed by nitrate (22.0 %), sulfate (9.2 %) and ammonium (7.8 %). The individual aerosol particles were classified into five categories, i.e., mineral, OM, secondary inorganic aerosol (SIA), soot and fly ash/metal particles, and most of them were in the state of being internally mixed. The entire observation period could be divided into two non-pollution periods (NP-1 and NP-2) and two haze periods (Haze-1 and Haze-2). With the evolution of pollution, the bulk-chemical and single-particle analysis exhibited similar characteristics, suggesting that Haze-1 was mainly caused by pollutants related to fossil fuel combustion, especially local mobile sources, while Haze-2 was triggered by the rapidly increasing secondary pollutants, which mainly came from regional transmission. The PM2.5 sources included dust (8.5 %), biomass burning (3.5 %), coal combustion (15.4 %), industrial processes (6.5 %), vehicular emissions (25.6 %) and secondary sources (40.5 %). Analysis of Weather Research and Forecasting model with Chemistry (WRF-Chem) model results showed that the average contributions of local sources and regional transmission to pollution in Chengdu were the same (50 % vs. 50 %). In addition, the source composition and WRF-Chem simulation results in different periods confirmed our analysis of the formation mechanisms of the two haze events. This study confirms that further significant reductions in PM2.5 in Chengdu are still needed, and more effective policies for local emission reduction or joint prevention and control of regional air pollution will be necessary in the future.

Source apportionment based on EEM-PARAFAC combined with microbial tracing model and its implication in complex pollution area, Wujin District, China

Further improving the quality of surface water is becoming more difficult after the control of main point-sources, especially in the complex pollution area with mixed industrial and agricultural productions, whereas the pollution source apportionment might be the key to quantify different pollution sources and developing some effective measures. In this study, a technical framework for source apportionment based on three-dimensional fluorescence and microbial traceability model is developed. Based on screening of the main environmental factors and their spatiotemporal characteristics, potential pollution sources have been tentatively identified. Then, the pollution sources are further tested based on the analysis of fluorescence excitation-emission matrix (EEM) and the similarity of fluorescence components in surface water and potential pollution sources. At the same time, the correlation between microbial species and pollution sources is constructed by analyzing the spatiotemporal characteristics of microbial composition and the response of main species to environmental factors. Therefore, pollution source apportionment is quantified using PCA-APCS-MLR, Fast Expectation-maximization for Microbial Source Tracking (FEAST), and Bayesian community-wide culture-independent microbial source tracking (SourceTracker). PCA-APCS-MLR could not effectively distinguish the contributions of different industrial sources in the complex environment of this study, and the contribution of unknown sources was high (average 39.60%). In contrast, the microbial traceability model can accurately identify the contribution of 7 pollution sources and natural sources, effectively reduce the proportion of unknown sources (average of FEAST is 19.81%, SourceTracker is 16.72%), and show better pollution identification and distribution capabilities. FEAST exhibits a more sensitive potential in source apportionment and shorter calculation time than SourceTracker, thus might be used to guide the precise regional pollution control, especially in the complex pollution environments.

Multi-scale response relationship between water quality of rivers entering lakes from different pollution source areas and land use intensity: a case study of the three lakes in central Yunnan.

As the main supply source of lakes, the water quality of the rivers entering the lakes directly determines the water safety and sustainable development of the lakes. Human activities are the direct cause of changes in the water quality of rivers entering lakes, and land use intensity is the direct manifestation of human activities on the land surface. Although significant progress has been made in studying the relationship between land use changes and water quality in lakes, there is still a lack of research on exploring the relationship between land use intensity and water quality at multiple scales, especially in comparative studies of different pollution source areas. To address this problem, this study used Pearson's correlation analysis and land use intensity index method to explore the response relationship between river water quality and land use intensity at different spatial and temporal scales and different pollution source areas using three lakes in central Yunnan as examples. The results showed that land use intensity was generally positively correlated with water quality, but the response relationship between land use intensity and different water quality indicators was significantly different at different scales and for different pollution source areas. Compared to non-urban areas, the impact of land use intensity on water quality is more significant in urban areas. Compared to the rainy season, the correlation between CODNa, TP, and NH3-N values and land use intensity is stronger during the dry season, while the correlation between COD, TN, and land use intensity is weaker during the dry season. When viewed at different scales, different water quality indicators have different scale effects, but overall, the larger the scale, the stronger the correlation. Therefore, in the work of lake water environmental governance, it is necessary to consider comprehensively from multiple scales and perspectives and adopt measures that are more suitable for regional water pollution prevention and control.

Unveiling the impact of digital industrialization on synergistic governance of pollution and carbon reduction in China: a geospatial perspective.

The impact of digital industrialization on regional pollution control and carbon reduction in China is an area that remains largely unexplored despite being a new driving force in promoting high-quality economic development. This study constructs a combined system synergy model to measure the synergistic governance effect of regional pollution and carbon reduction in China from 2011 to 2020 and then estimatesthe direct impact and spatial spillover effect using a spatial dual-weight model. Our findings indicate that digital industrialization has a greater impact on regional pollution reduction and carbon reduction as geographical distance decreases, with the spillover effect with close geographical relationships being higher than that of adjacent. Furthermore, the heterogeneity analysis reveals that the added value of digital technology and services has a significantly positive effect, while the spatial spillover effect of the added value of digital infrastructure is significantly negative. Finally, our mechanism judgements prove digital industrialization canimpact the level of regional co-governance of pollution and carbon reduction through source prevention, process control, and end-treatment. Our study provides a factual basis for further promoting China's environmental pollution control and carbon reduction behavior and offers a method to use different spatial weights in depth.

Pollution characteristics of peroxyacetyl nitrate in karst areas in Southwest China

In recent years, the issue of photochemical pollution in the Guangxi region of China has escalated considerably. However, there remains a notable dearth of related research in this area. Peroxyacetyl nitrate (PAN), recognized as a reliable indicator of photochemical pollution, was the focus of our study. This study marks the inaugural observation of PAN levels in Guilin, a renowned world tourist destination situated in a typical karst region, during 1–31 October 2021. Throughout this observation period, the average volume concentration of PAN ranged from 0.087 to 2.559 ppb, which was 3.61 times higher than the South China background site of the Nanling. Combined with meteorological factors and potential source analysis, the causes of a typical high-value PAN process were explored during 24–29 October. The results showed that, during this high-value PAN event, pollution primarily originated from the horizontal transport of polluted air masses and the descent of high-altitude air masses from Hunan Province in the northeast direction. Additionally, the meteorological conditions, including high temperatures, intense radiation, and low humidity, fostered local PAN formation. Notably, traffic emissions emerged as the primary source of PAN's locally generated precursor volatile organic compounds. Furthermore, we estimated the background concentration of O3 to be approximately 20.347 ppb based on PAN monitoring data, constituting 44.4% of the total O3 levels in Guilin City. This study offers valuable insights for addressing and mitigating photochemical pollution in southern Chinese cities, while providing a theoretical foundation for regional pollution control efforts.摘要桂林是世界著名的地处喀斯特地区的旅游胜地, 其光化学污染问题日益严重.过氧乙酰硝酸酯(PAN)被认为是光化学污染的可靠指标, 也是本研究的重点. 本研究于2021年10月首次观测了桂林的PAN的浓度为0.087–2.559 ppb, 同时探讨了PAN典型高值过程的成因. 此次污染主要来源于东北方向污染气团的水平和高空输送, 同时, 高温, 强辐射和低湿度等气象条件也促进了本地PAN的形成. 本研究同时估算了桂林市的O3背景浓度为20.347 ppb.这项研究为城市的光化学污染控制工作提供了理论基础.

Analysis of severe ozone-related human health and weather influence over China in 2019 based on a high-resolution dataset.

Ozone pollution in 2019 in China is particularly severe posing a tremendous threat to the health of Chinese inhabitants. In this study, we constructed a more reliable and accurate 1-km gridded dataset for 2019 with as many sites as possible using the inverse distance weight interpolation method to analyze spatiotemporal ozone pollution characteristics and health burden attributed to ozone exposure from the perspective of different diseases and weather influence. The accuracy of this new dataset is higher than other public datasets, with the coefficient of determination of 0.84 and root-mean-square error of 8.77 ppb through the validation of 300 external sites which were never used for establishing retrieval methods by the datasets mentioned-above. The averaged MDA8 (the daily maximum 8 h average) ozone concentrations over China was 43.5 ppb, and during April-July, 83.9% of total grids occurred peak-month ozone concentrations. Overall, the highest averaged exceedance days (60 days) and population-weighted ozone concentrations (55.0 ppb) both concentrated in central-eastern China including 9 provinces (only 11.4% of the national territory); meanwhile, all-cause premature deaths attributable to ozone exposure reached up to 142,000 (54.9% of national total deaths) with higher deaths for cardiovascular and respiratory, and the provincial per capita premature mortality was 0.27~0.44‰. The six most polluted weather types in the central-eastern China are in order as follows: westerly (SW and W), cyclonic, northerly, and southerly (NW, N, and S) types, which accounts for approximately 73.2% of health burden attributed to daily ozone exposure and poses the greatest public health risk with mean daily premature deaths ranging from 466 to 610. Our findings could provide an effective support for regional ozone pollution control and public health management in China.

Spatio-temporal variation of O3 concentration and exposure risk assessment in key regions of China, 2015–2021

In recent years, the increasing Ozone (O3) concentration has become a significant concern for human life and health. In this study, O3 concentration data at 10 km resolution and population density data at 1 km resolution for China were utilized. Methods such as the Global Moran's I index, Getis-Ord Gi* index, and population exposure risk index, to analyze the spatial-temporal variation, meteorological influences, aggregation characteristics, exposure risk levels and health impact of O3 in China and six key regions from 2015 to 2021. The results revealed an increasing trend with a bimodal fluctuation pattern, peak in May and August. Only Pearl River Delta (PRD) showed the highest in autumn, five other key regions were highest O3 concentration in the summer. And the spatial autocorrelation in China is obvious among regions. Among the five meteorological factors, temperature and sunshine duration predominantly influenced O3 concentration in China. Relative humidity was the main factor affecting O3 concentration in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Sichuan Basin (SCB), while precipitation had the greatest impact on FenWei Plain (FWP) and Hexi Corridor (HXC) and exhibited a positive correlation. Wind speed in PRD showed a strong negative correlation with O3 concentration. The Ri exhibited significant differences between the eastern and western sides of the Hu Huanyong line, demonstrating a distribution pattern of "high in the east and low in the west". Through this research, we aim to provide valuable insights for public health policies and the prevention and control of regional air pollution.