Pollution Control Measures Research Articles

Simulation and Analysis of Water Quality Improvement Measures for Plain River Networks Based on Infoworks ICM Model: Case Study of Baoying County, China

The water environment of plain river networks can be self-cleaning to a certain extent, but if the wastewater load exceeds a certain threshold, it can disturb the natural balance and cause water pollution. This underlines the importance of water pollution control measures. However, the development of water pollution control measures requires a large number of hydrological and hydrodynamic parameters and the establishment of corresponding relationships through modelling. Therefore, this study mainly used the Infoworks ICM model to construct a detailed hydrological–hydrodynamic water environment analysis model for the Yundong area of Baoying County, Yangzhou City, China, screened the main pollution source areas and pollution time periods of the typical rivers in the study area, and proposed effective improvement measures according to the actual situation of the study area. The results show that after the synergistic effect of multiple measures, the water quality can reach the Class III standard (GB3838-2002). This study can provide a reference for the water environment management and improvement of the plain river network and has good application prospects.

The Temporal and Spatial Analysis and Assessment of Water Quality in the Tuo River (Suzhou Section) based on the CCME-WQI Method

To investigate the spatiotemporal variations and disparities in water quality within the Suzhou section of the Tuo River, as well as to identify the key pollution factors and influencing elements, this study utilized monitoring data from five water quality monitoring stations along the Tuo River in Suzhou, collected between 2020 and 2022. After analyzing the spatiotemporal variations in water quality indicators, the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) was employed to assess the river's water quality status. The study also examined the causes of water pollution and proposed appropriate pollution control measures. The research results indicate that:(1) Total Nitrogen (TN), Chemical Oxygen Demand (COD), Permanganate Index (CODMn), and Fluoride (F) were identified as the main pollution factors, with COD and TN being the primary pollutants exceeding standard limits, with exceedance rates of 55.66% and 37.14%, respectively;(2)From 2020 to 2022, the concentrations of CODMn, COD, and TN were significantly higher during the flood season compared to the non-flood season, suggesting that non-point source pollution is the major contributor to the pollution load;(3)Temporally, the study area exhibited significant seasonal variations in water quality, with water quality being better during the non-flood season than during the flood season. Spatially, the study area showed notable spatial differences in water quality, with the S1 monitoring station recording the poorest water quality, while the S3 station exhibited the best;(4)The water quality of the Tuo River was significantly influenced by precipitation, with increased rainfall during the flood season exacerbating urban runoff, agricultural non-point source pollution, and internal pollution within the river channels.

Risk hotspots and influencing factors identification of heavy metal(loid)s in agricultural soils using spatial bivariate analysis and random forest

Heavy metal(loid)s (HMs) in agricultural soils not only affect soil function and crop security, but also pose health risks to residents. However, previous concerns have typically focused on only one aspect, neglecting the other. This lack of a comprehensive approach challenges the identification of hotspots and the prioritization of factors for effective management. To address this gap, a novel method incorporating spatial bivariate analysis with random forest was proposed to identify high-risk hotspots and the key influencing factors. A large-scale dataset containing 2995 soil samples and soil HMs (As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, and Zn) was obtained from across Henan province, central China. Spatial bivariate analysis of both health risk and ecological risks revealed risk hotspots. Positive matrix factorization model was initially used to investigate potential sources. Twenty-two environmental variables were selected and input into random forest to further identify the key influencing factors impacting soil accumulation. Results of local Moran's I index indicated high-high HM clusters at the western and northern margins of the province. Hotspots of high ecological and health risk were primarily observed in Xuchang and Nanyang due to the widespread township enterprises with outdated pollution control measures. As concentration and exposure frequency dominated the non-carcinogenic and carcinogenic risks. Anthropogenic activities, particularly vehicular traffic (contributing ∼37.8 % of the total heavy metals accumulation), were the dominant sources of HMs in agricultural soils. Random forest modeling indicated that soil type and PM2.5 concentrations were the most influencing natural and anthropogenic variables, respectively. Based on the above findings, control measures on traffic source should be formulated and implemented provincially; in Xuchang and Nanyang, scattered township enterprises with outdated pollution control measures should be integrated and upgraded to avoid further pollution from these sources.

Biogenic Volatile Organic Compound Emission and Its Response to Land Cover Changes in China During 2001–2020 Using an Improved High‐Precision Vegetation Data Set

AbstractBiogenic volatile organic compounds (BVOCs) are regarded as important precursors for ozone and secondary organic aerosol, mainly from vegetation emissions. In the context of the expanding trend of vegetation greening, the development of high‐precision vegetation data and accurate BVOC emission estimates are essential to develop effective air pollution control measures. In this study, by integrating the multi‐source vegetation cover data, we established a high‐resolution vegetation distribution (HRVD) data set to develop a high spatio‐temporal resolution emission inventory and investigated the impact of different land cover data sets on emission simulation and impact of land cover change on BVOC emissions during 2001–2020. The annual total BVOC emissions in China for 2020 was 15.66 Tg, which were mainly from trees. The emissions simulated by CNLUCC and MODIS data sets were 1.53% and 1.72% higher than those simulated by HRVD data sets, respectively. The spatial distribution of emission differences was consistent with that of land cover differences. The simulated BVOC emissions by the HRVD data set had the best accuracy as they improved the bias between modeling and observation from 69.06% to 65.35% and decreased the underprediction of observations by a factor of 2.13 compared with simulation by MEGAN default vegetation data. The annual BVOC emissions caused by changing vegetation distribution and LAIv (LAI of vegetation covered surfaces) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAIv was the main driver of emission variations. The total OH reactivity of the resulted BVOC emissions increased at a rate of 1.59 s−1 yr−1, with isoprene contributed the most.

Factors affecting the adoption of air pollution control measures across firms in India

Air pollution control has emerged as one of the key concerns of recent times and business firms are one of the significant stakeholders in deteriorating the quality of the natural environment. The purpose of this paper is to analyse the factors affecting the adoption of air pollution control measures across firms and contribute to the existing knowledge on pollution control among firms in the context of an emerging market. The analysis shows a significant association between enterprise characteristics and the adoption of air pollution control measures and about 44.3 percent firms have reported the adoption of air pollution control measures. Further, the marginal effect analysis indicates that firms older in age, larger in size and having shareholders’ equity are more likely to adopt air pollution control measures. Similarly, firms practising lean operations, investing in research & development, availing bank credit, having quality certification and having better capacity utilisation are more likely to adopt air pollution control measures. Finally, firms perceiving obstacles related to business licensing & permits, tax administration, political instability, and business closure due to COVID-19 outbreak are more likely to adopt air pollution control measures. Our study provides useful implications for managers, policymakers and researchers for encouraging air pollution control technologies across business firms. The study also motivates undertaking future research on pollution control measures across various sectors of the economy.

Role of Organic Vapor Precursors in Secondary Organic Aerosol Formation: Concurrent Observations of IVOCs and VOCs in Guangzhou

AbstractSecondary organic aerosol (SOA) formed through the atmospheric transformation of organic vapors constitutes a significant portion of fine particulate matter or PM2.5. While recent laboratory studies underscore the importance of intermediate‐volatility organic compounds (IVOCs) as key precursors to SOA, field observations that recognize the role of both volatile organic compounds (VOCs) and IVOCs in SOA formation remain scarce. In this study, we conducted concurrent measurements of VOCs and IVOCs in ambient air at urban and suburban sites in Guangzhou during a PM2.5 pollution event in winter 2021. The results reveal that between 12:00–15:00 local time, the photochemically adjusted initial concentrations of VOCs at both sites were approximately 7 times higher than that of IVOCs. However, the SOA formation potential (SOAFP) of primary hydrocarbon IVOCs exceeded that of VOCs by over 3–4 times. Receptor modeling results further indicated that while ship emissions contributed to less than 10% of the C2–C22 primary hydrocarbons concentration (VOCs + primary carbonaceous IVOCs), they accounted for the most significant source (approximately 40%) of SOA formation. This study highlights the substantial role of IVOCs in SOA formation and emphasizes the importance of future PM2.5 pollution control measures targeting major IVOCs contributors, such as ship emissions in harbor cities.

Spatiotemporal Variation Characteristics of Ozone and Identification of Key Influencing Factors Based on Random Forest Model： A Case Study of Chuzhou City

The cause of ozone pollution is a complex scientific problem. Studying the spatiotemporal variation characteristics of O3 at different time scales and analyzing the key influencing factors of O3 concentration is of great significance for the precise formulation of urban air pollution control measures and the improvement of urban air quality. Based on the analysis of the spatiotemporal variation characteristics of O3 concentration in Chuzhou City, we studied the 12 ozone-influencing factors of meteorology and pollutants at multiple time scales using Spearman correlation analysis and a random forest model. The results showed that： ① The O3 pollution level of Chuzhou City showed an aggravating trend, and the O3 concentration distribution showed a spatial pattern of "high in the southeast and low in the northwest." ② From February to May, SO2 concentration had a strong impact on the increase in O3 concentration. From June to September, PM2.5 and PM10 were significantly positively correlated with ozone and had a greater impact. ③ Relative humidity, temperature, and wind speed had a significant impact on O3, whereas barometric pressure and hourly rainfall had a weak impact. ④ The O3 pollution mechanism in Chuzhou City changed from "pollutant-controlled" to "meteorology-controlled." ⑤ Among meteorological and pollutant factors, the three influencing factors that had the greatest influence on O3 concentration were temperature, wind speed, and relative humidity, with PM10 concentration, PM2.5 concentration, and SO2 concentration also contributing. All of the above six influencing factors had a significant nonlinear relationship with the O3 concentration.

An evolutionary deep learning model based on XGBoost feature selection and Gaussian data augmentation for AQI prediction

Accurate prediction of air quality is crucial for ensuring the scientific validity and effectiveness of air pollution control measures. This study proposes a combined deep learning (DL) model (XGBoost-GDA-TCN-IMRFO-GRU) for predicting hourly air quality index (AQI) data in four cities. The model integrates Extreme gradient boosting (XGBoost) for feature selection, Gaussian data augmentation (GDA), improved manta ray foraging optimization (IMRFO) algorithm, temporal convolutional network (TCN), and gated recurrent unit (GRU). XGBoost calculates the scores of pollutant gases affecting AQI, selecting the top four important pollutants (PM2.5, PM10, NO2, O3) based on their importance rankings. GDA enhances the robustness of the DL models and addresses the limitations of insufficient and overfitting training datasets. Additionally, the IMRFO algorithm, with two improved strategies, is applied to enhance the GRU model. TCN extracts spatiotemporal features of AQI, while GRU constructs a temporal model for efficient computations. Compared to eleven benchmark models, the proposed model demonstrates superior performance in terms of MAE, RMSE, MAPE, and NSE, achieving high accuracy and optimal prediction performance. Specifically, the XGBoost-GDA-TCN-IMRFO-GRU model reduces RMSE, MAE, and MAPE by 33–60 %, 39–68 %, and 39–66 %, respectively, compared to the TCN model. Therefore, the XGBoost-GDA-TCN-IMRFO-GRU model can provide reliable early warnings for air quality, which is of great significance for air pollution prevention and the sustainable development of society.

Impact of heavy metal hazard perceptions on pollution control intentions: Empirical evidence from rice farmers in China

Heavy metal pollution presents significant challenges to global sustainable development. The treatment of such pollution predominantly relies on government policies, often neglecting farmers' crucial role in the remediation process. Therefore, this study investigates the driving mechanisms behind farmers' willingness to control heavy metal pollution in farmland. By analyzing survey data from 718 rice farmers in China, this study employs the Probit model to quantitatively assess the impact of farmers' perceptions of heavy metal hazards on their inclination to control pollution in farmland. Additionally, it examines the heterogeneity of these impacts between cultivated rice for domestic consumption and rice for export. The findings reveal the following: (1) Approximately 70% of farmers recognize the harmful effects of heavy metals, but only 66.2% of them express willingness to engage in pollution control measures. (2) More awareness regarding heavy metal hazards corresponds to a stronger intention to control pollution. (3) In addition to hazard perception, households with village cadres demonstrate a significantly positive influence on pollution control intention. (4) There are notable disparities in farmers' attitudes towards retained rice (for domestic consumption) and exported rice, with a greater willingness to implement pollution control measures for retained rice. As such, this study provides valuable insights for informing policy development to address the challenges associated with heavy metal pollution control's complex and limited effects. The findings contribute to a better understanding of the Chinese context as a typical developing country, offering analytical cases that promote global sustainable development while leveraging Chinese wisdom.

Study of Water Quality Parameters of Water Samples from Different Places Near Wani, Dist. Yavatmal

: Water is the essential source of life for all living forms and environmental health. All forms of life affected by water directly or indirectly. Water plays an important role in maintaining different activities in human life like metabolism, transport of nutrients, oxygen and wastes to cells and organs. Also it regulates body temperature. It also plays a significant role in prevention of some disease. Therefore water should be clean and free of contaminants to ensure wellness. Water bodies are contaminated by different manmade undesirable products which affects the nature of water. The present work is focused on assessment of water quality of samples through study of some physic-chemical parameters. The water samples are collected from four different locations around Wani area dist. Yavatmal. The comparative investigation of data has been done by correlation analysis of various parameters from different location samples. The results so obtained are interpreted in the light of pollution contamination status of the area and to know about the facts so that pollution control measures can be taken on time at various levels.

Assessment and Prediction of Health and Agricultural Impact from Combined PM2.5 and O3 Pollution in China

Combined PM2.5 and O3 pollution in China has caused negative health impacts on residents and reduced crop yields. The quantitative assessment and prediction of these impacts could provide a scientific basis for policy development. This study assessed the nationwide premature mortality, health effects, and crop damage attributable to PM2.5 and O3 pollution in 2019, and projected the associated health and agricultural losses under a business-as-usual (BAU) scenario for 2025. The economic benefits of improving air quality under different policy scenarios, including the 14th Five-Year Plan (FFP), Secondary Standard Limit (SSL), and Primary Standard Limit (PSL), were also explored. The results showed PM2.5 pollution in 2019 resulted in 246,000 all-cause premature deaths and the economic health loss was RMB 196.509 billion. Similarly, O3 pollution caused 186,300 premature deaths and the economic health loss was RMB 155.807 billion. O3 pollution has led to a loss of 28.5241 million tonnes of crop production and an economic loss of RMB 62.268 billion. Compared with 2019, the avoidable premature deaths from PM2.5 under different scenarios in 2025 were 50,600, 43,000, and 200,300 cases, respectively, exceeding the number of avoided premature deaths from O3 pollution. Compared with the BAU, reducing PM2.5 under different scenarios could generate economic benefits of RMB 70.178 billion, RMB 60.916 billion, and RMB 229.268 billion. Furthermore, the FFP scenario outperformed the SSL in mitigating winter wheat production losses caused by O3 pollution. These results provide important scientific support for the development and evaluation of future comprehensive pollution control measures for PM2.5 and O3.

Enhanced human activities since 20th century in pearl river delta, southern China: Evidence from high-resolution sedimentary records of polycyclic aromatic hydrocarbons and synthetic musks

To understand the environmental impact of anthropogenic activities, the high-resolution temporal and compositional variations of polycyclic aromatic hydrocarbons (PAHs), oxygenated PAHs, and synthetic musks, related to human activities were investigated in a sediment core taken from the Pearl River Estuary, southern China. The temporal trend of the target compounds was evidence of the considerable impact of anthropogenic activities in the Pearl River Delta. Two significant increases of the target compounds levels in the sediment core were aligned with two nodes of key periods of economic development in China, namely, the foundation of People's Republic of China (1950s) and China's accession to the World Trade Organization (2000s). Subsequently, the significant decline of the target compounds in sediments from the 2010s indicated the successful regulation of pollutant discharges and pollution control measures. The increase in the contribution of higher molecular weight PAHs suggested a potential transition from agricultural activities to industrial activities.

Managing technology-critical elements from electronic waste in Small Developing Island States: a burden or an opportunity?

Small Island Developing States (SIDS) face profound challenges from pollution, exacerbated by inadequate infrastructure and resource limitations. This paper examines the longstanding pollution issues plaguing SIDS, highlighting their vulnerability to environmental degradation due to underdeveloped waste management systems and unique oceanographic conditions. The case study of Eleuthera Island in The Bahamas illustrates these challenges vividly, showcasing the island’s struggle with e-waste management. This example reinforces the urgency of integrated strategies to enhance environmental resilience and sustainable development in SIDS. Emerging concerns include the proliferation of technology-critical elements (TCEs) in e-waste, driven by rapid urbanization and tourism growth. Despite their crucial role in technological advancement, TCEs pose significant recycling and management difficulties, particularly in the context of limited waste infrastructure. The paper also highlights the imperative for comprehensive regulatory frameworks and sustainable practices to mitigate pollution impacts and safeguard ocean and human health, particularly by shedding light on legislative gaps and opportunities for enhancing pollution control measures in The Bahamas.

Off the conservation radar: the hidden story of Europe's tiny pea clams (Bivalvia: Sphaeriidae)

AbstractThis study highlights the conservation problems faced by the tiny freshwater bivalves of the family Sphaeriidae, also known as pea, pill, or fingernail clams (or mussels) in Europe. Despite their global distribution, assumed ecological importance, and potential uses, basic knowledge about their taxonomy, biology, and ecology is very limited and much lower than for the larger freshwater bivalve taxa. Immediate scientific priorities are required to fill knowledge gaps regarding their taxonomy, genetic diversity, distribution, reproductive cycle, ecosystem functions, and population trends. Such fundamental knowledge is necessary to identify specific threats and develop appropriate conservation actions. Deploying environmental DNA analysis at a large scale could be a valuable way to fill gaps in distribution and strengthen monitoring in areas where local taxonomic knowledge is lacking. Until taxon-specific management plans can be developed, we recommend that efforts concentrate on the general protection and restoration of wetland habitats, implementing pollution control measures, and managing invasive species. These actions should be complemented by community engagement through citizen science initiatives. Additionally, prioritising data collection to fill existing knowledge gaps and updating conservation statuses (Red Lists) based on comprehensive assessments will be crucial. Implementing these actions will provide a starting point for the broader protection of freshwater ecosystems, thus benefiting pea clams and other interconnected species within these habitats.

Polycyclic aromatic hydrocarbons in fish from four lakes in central and eastern China: Bioaccumulation, pollution characteristics, sources, and health risk assessment

Polycyclic aromatic hydrocarbons (PAHs) are highly concerning environmental pollutants due to their toxicity, persistence, and bioaccumulation. In this paper, concentrations and compositions of 16 United States Environmental Protection Agency (USEPA) priority control PAHs in the fish collected from four lakes in central and eastern China were analyzed. 18 species of fish were collected from four typical lakes, namely Taihu Lake, Danjiangkou Reservoir, Yuncheng Salt Lake, and Nansi Lake. Quantitative analysis of PAHs were carried out with gas chromatograph/mass spectrometer, and 13 out of 16 PAHs were identified, with the main components being pyrene, chrysene, naphthalene, and benzo(b)fluoranthene. The accumulation of PAHs in fish from Taihu Lake, Danjiangkou Reservoir, Yuncheng Salt Lake, and Nansi Lake was 28.75–47.27, 26.60–31.93, 33.56–39.30, and 27.22–43.01 ng·g−1, respectively. The toxic equivalents of high-cyclic PAHs in fish of the four lakes were significantly higher than those of low-cyclic and middle-cyclic PAHs (P < 0.05). In Taihu Lake, Danjiangkou Reservoir, and Nansi Lake, the toxicity equivalents were predominantly contributed by benzo[a] pyrene (BaP), while in Yuncheng Salt Lake, dibenzo(a,h) anthracene (DahA) was the main contributor. The residents in central and eastern China exposed to PAHs had a negligible non-cancer risk (non-carcinogenic risk values <1) and a potential low cancer risk. It was noteworthy that the Pleuronichthys cornutus and Lateolabrax japonicus from Yuncheng Salt Lake could pose carcinogenic risks (>10−4) to humans, with benzo[b]fluoranthene (BbF) having the highest risk contribution rate. Source analysis indicated that the main source of PAHs in fish was combustion sources. BaP, DahA, and BbF could become potential pollutants of concern in the field of ecotoxicology. The results of this study on PAHs bioaccumulation, pollution characteristics, sources and health risks in fish from four lakes would provide a scientific basis for local governments to formulate targeted environmental management policies, pollution control measures, and public health strategies.

Integrated Benefits of Synergistically Reducing Air Pollutants and Carbon Dioxide in China.

China's advancements in addressing air pollution and reducing CO2 emissions offer valuable lessons for collaborative strategies to achieve diverse environmental objectives. Previous studies have assessed the mutual benefits of climate policies and air pollution control measures on one another, lacking an integrated assessment of the benefits of synergistic control attributed to refined measures. Here, we comprehensively used coupled emission inventory and response models to evaluate the integrated benefits and synergy degrees of various measures in reducing air pollutants and CO2 in China during 2013-2021. Results indicated that the implemented measures yielded integrated benefits value at 6.7 (2.4-12.6) trillion Chinese Yuan. The top five contributors, accounting for 55%, included promoting non-thermal power, implementing end-of-pipe control technologies in power plants and iron and steel industry, replacing residential scattered coal, and saving building energy. Measures demonstrating high synergies and integrated benefits per unit of reduction (e.g., green traffic promotion) yielded low benefits mainly due to their low application, which are expected to gain greater implementation and prioritization in the future. Our findings provide insights into the effectiveness and limitations of strategies aimed at joint control. By ranking these measures based on their benefits and synergy, we offer valuable guidance for policy development in China and other nations with similar needs.

Tracing sulfate sources in a tropical agricultural catchment with a stable isotope Bayesian mixing model

Sulfate (SO42−) is an essential anion in drinking water and a vital macronutrient for plant growth. However, elevated sulfate levels can impact ecosystem or human health and could be an important indicator of acid rock drainage or pollution. Therefore, monitoring SO42− sources and transport is important for water quality assessments. This study focused on exploring the sources and transformations of SO42− as well as estimating the proportional contribution of the potential SO42− pollutant sources to groundwater and surface water in a tropical river basin, the Densu River Basin. The study used major ions combined with stable sulfur and oxygen isotope compositions and a Bayesian isotope mixing model, MixSIAR. The major ion characteristics indicate that SO42− concentrations remain stable throughout the rainy and dry seasons but originate from diverse sources. The multi-isotope model (δ34SSO4, δ18OSO4) identified four potential SO42− sources: detergent, precipitation, sewage, and sulfate fertilizer. However, the δ34SSO4 and δ18OSO4 values of the fertilizer source signatures overlapped with those of precipitation and sewage. Nevertheless, the contributions from each source were disentangled using the MixSIAR model, which revealed sewage as the most dominant SO42− pollutant in the Densu Basin, accounting for ~47 % of sulfate in groundwater and ~ 56 % of sulfate in surface water. Sulfate fertilizer (~33 %) was the second most important source after sewage for groundwater, while detergent (~23 %) was the second most important source for surface water. The redox processes of bacterial sulfate reduction and sulfide oxidation were determined to have a minimal impact on the sulfur isotope fractionation within the basin. This study highlights the benefits of combining major ions, sulfur isotopes and the MixSIAR model for identifying sources of sulfate. This approach accounts for uncertainties in source contributions which allows for more robust and reliable apportionment of sulfate sources. The study emphasizes the need for effective waste management and pollution control measures to protect water quality and provides vital guidelines on how to partition sulfate sources on a large catchment scale and evidence for making pollution management decisions on water resources.

Spatial source, simulating improvement, and short-term health effect of high PM2.5 exposure during mutation event in the key urban agglomeration regions in China

Air quality in China has significantly improved owing to the effective implementation of pollution control measures. However, mutation events caused by short-term spikes in PM2.5 in urban agglomeration regions continue to occur frequently. Identifying the spatial sources and influencing factors, as well as improving the prediction accuracy of high PM2.5 during mutation events, are crucial for public health. In this study, we firstly introduced discrete wavelet transform (DWT) to identify the mutation events with high PM2.5 concentration in the four key urban agglomerations, and evaluated the spatial sources for the polluted scenario using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Additionally, DWT was combined with a widely used artificial neural network (ANN) to improve the prediction accuracy of PM2.5 concentration seven days in advance (seven-day forecast). Results indicated that mutation events commonly occurred in the northern regions during winter time, which were under the control of both short-range transportation of dirty airmass as well as negative meteorology conditions. Compared with the ANN model alone, the average band errors decreased by 9% when using DWT-ANN model. The average correlation coefficient (R) and root mean square error (RMSE) obtained using the DWT-ANN improved by 10% and 12% compared to those obtained using the ANN, indicating the efficiency and accuracy of simulating PM2.5, by combining the DWT and ANN. The short-term mortality during mutation events was then calculated, with the total averted all-cause, cardiovascular, and respiratory deaths in the four regions, being 4751, 2554, and 582 persons, respectively. A declining trend in prevented deaths from 2018 to 2020 demonstrated that the pollution intensity during mutation events gradually decreased owing to the implementation of the Three-Year Action Plan to Win the Blue Sky Defense War. The method proposed in this study can be used by policymakers to take preventive measures in response to a sudden increase in PM2.5, thereby ensuring public health.

Environmental forcing and policy synergy: A multidimensional approach in the governance of air pollution and carbon emission

Policy synergies effectively contribute to the integrated management of air pollution and carbon emissions, which is crucial for safeguarding ecosystem stability and public health. This study uses the causal network model of Gaussian process regression to analyze the combined impacts of dynamic and static carbon emission reduction and air quality policies on carbon emissions and air quality. The causal effects of policy measures and their synergistic effects are also examined. The study results indicate: (1) There is significant geographical heterogeneity in the implementation of environmental policies and regional economic development, with the economically developed eastern coastal regions adopting more stringent carbon emission and air pollution control measures, while the western provinces adopt relatively lax environmental policies. (2) The synergistic effect of carbon emission reduction policies and air quality policies exists, and the two types of static policies are substitutable for managing carbon dioxide emissions and air pollution. (3) Policies' forced effect exists, where the exacerbation of environmental problems leads to the formation and implementation of policies. (4) The value added by the secondary industry is a key motivation for forming carbon emission reduction policies and air quality control policies. Additionally, the value added by the secondary industry directly impacts the incidence of respiratory diseases (e.g., tuberculosis). Finally, dynamic and synergistic policy recommendations are proposed based on the study's findings.

Assessment of heavy metal contamination in the surface sediments, seawater and organisms of the Pearl River Estuary, South China Sea

Coastal heavy-metal contamination poses significant risks to marine ecosystems and human health, necessitating comprehensive research for effective mitigation strategies. This study assessed heavy-metal pollution in sediments, seawater, and organisms in the Pearl River Estuary (PRE), with a focus on Cd, Cu, Pb, Zn, As, Hg, and Cr. A notable reduction in heavy metal concentrations in surface sediments was observed in 2020 compared to 2017 and 2018, likely due to improved pollution management and COVID-19 pandemic restrictions. Spatial analysis revealed a positive correlation between elevated heavy-metal concentrations (Cu, Pb, Zn, Cd, and As) and areas with significant human activity. Source analysis indicated that anthropogenic activities accounted for 63 % of the heavy metals in sediments, originating from industrial effluents, metal processing, vehicular activities, and fossil fuel combustion. Cd presented a high ecological risk due to its significant enrichment in surface sediments. Organisms in the PRE were found to be relatively enriched with Hg and Cu, with average As concentrations slightly exceeding the Chinese food-health criterion. This study identified high-risk ecological zones and highlighted Cd as the primary pollutant in the PRE. The findings demonstrate the effectiveness of recent pollution control measures and emphasize the need for ongoing monitoring and mitigation to safeguard marine ecosystems and human health.