Regional Pollution Research Articles

Mining of dynamic traffic-meteorology-atmospheric pollutant association rules based on Eclat method

With the rapid increase of urban vehicles, the atmospheric compound pollutants, notably PM2.5 and O3, have significantly increased and seriously affected public health. Traffic and meteorological conditions are the primary influencing factors of pollutant concentrations, and their spatial and temporal changes affect the dispersion of pollutants. Increasing use of high-resolution big data offers opportunities to explore these correlations. More extensive quantitative studies are essential for effective air pollution control. This study presents an Eclat algorithm to quantitatively reveal the relationship between traffic, meteorology and pollutants with hourly and 5-minute scale data in the urban area of Guangzhou. We establish a research framework covering temporal pollution analysis, multifactor rule mining, and spatial effects. The results show that PM2.5 and O3 exhibit coordinated trends on the daily scale influenced by traffic flow and meteorology conditions, but on the hourly scale, they are negatively correlated. At the 5-minute scale, synchronized variations occur only during specific periods. This finer scale better identifies association rules for high-concentration pollutant scenarios, and non-roadside sites outperform roadside sites in mining these associations. For example, when humidity is below 37%, atmospheric pressure is 1016.2–1020.3 Pa, wind speed is 1.7–2.6 m/s, and the traffic volume on Jiefang North Road exceeds 635 vehicles every 5 min, there is a 92.86% probability that the PM2.5 concentration at GYQ (a non-roadside monitoring site) will exceed 127 μg/m3. These findings enhance our understanding of how dynamic traffic and meteorological conditions impact atmospheric pollutants and provide a scientific basis for regional collaborative pollution prevention.

Interplay of natural and anthropogenic factors in sediment dynamics and trace element distribution in Güllük Gulf, western Türkiye: A comprehensive geochemical and hydrodynamic analysis

Güllük Gulf, located in the southeastern Aegean Sea, is significantly impacted by diverse human activities, including urban development, tourism, and intensive aquaculture. This study aims to assess the spatial distribution of trace metals in the Gulf's sediments, identify their sources, and evaluate their environmental impacts to inform effective management strategies. It also examines sediment transport patterns, which are crucial in influencing the distribution and concentration of pollutants. Establishing background concentration levels for various elements provides a baseline to assess pollution levels and identify deviations due to anthropogenic impact. Sediment samples were collected from 45 stations and analyzed for grain size, organic carbon, carbonate content, and concentrations of 26 elements. Contamination was assessed using indices such as Enrichment Factor (EF), Geoaccumulation Index (Igeo), Contamination Factor (Cf), Degree of Contamination (Cdeg), Pollution Load Index (PLI), Modified Pollution Index (MPI), and Toxic Risk Index (TRI). Statistical analyses, including Principal Component Analysis (PCA) and Cluster Analysis (CA), were used to identify pollution sources. Results indicated that Güllük Gulf's sediments are predominantly sandy, with significant silt and clay fractions in sheltered bays. Organic carbon content averaged 1.68%, with higher values near urban and aquaculture areas. Trace metal concentrations exhibited notable spatial variability, with elevated levels of Pb, Cu and Zn in specific areas, indicating both natural and anthropogenic sources. PCA identified five significant factors contributing to metal presence, linking them to lithogenic origins, aquaculture activities, and industrial inputs. TRI results showed moderate toxic risk in Asin Bay, primarily due to Ni, As and Cr. The sediment transport model of Güllük Gulf, crucial in understanding regional pollution dynamics, revealed a complex interplay of hydrodynamic forces. The study underscores the significant environmental impact of human activities on sediment quality in Güllük Gulf. Elevated trace metal levels near aquaculture sites emphasize the need for stringent environmental management practices. Effective strategies should include regular monitoring, sustainable aquaculture practices, and stakeholder engagement to mitigate environmental impacts and preserve the Gulf's ecological integrity. This comprehensive approach provides crucial insights for future research and environmental management policies aimed at addressing the complex challenges facing Güllük Gulf.

Long-term nighttime aircraft noise exposure and risk of hypertension in a prospective cohort of female nurses

There is growing interest in cardiometabolic outcomes associated with nighttime noise, given that noise can disturb sleep and sleep disturbance can increase cardiometabolic risk such as hypertension. However, there is little empirical research evaluating the association between nighttime aircraft noise and hypertension risk. In this study, we expand on previous work to evaluate associations between nighttime aircraft noise exposure and self-reported hypertension incidence in the Nurses' Health Studies (NHS/NHSII), two US-wide cohorts of female nurses. Annual nighttime average aircraft sound levels (Lnight) surrounding 90 airports for 1995–2015 (in 5-year intervals) were modeled using the Aviation Environmental Design Tool and assigned to participants’ geocoded addresses over time. Hypertension risk was estimated for each cohort using time-varying Cox proportional-hazards models for Lnight dichotomized at 45 dB (dB), adjusting for individual-level hypertension risk factors, area-level socioeconomic status, region, and air pollution. Random effects meta-analysis was used to combine cohort results. Among 63,229 NHS and 98,880 NHSII participants free of hypertension at study baseline (1994/1995), we observed 33,190 and 28,255 new hypertension cases by 2014/2013, respectively. Although ∼1% of participants were exposed to Lnight ≥45 dB, we observed an adjusted hazard ratio (HR) of 1.10 (95% CI: 0.96, 1.27) in NHS and adjusted HR of 1.12 (95% CI: 0.98, 1.28) in NHSII, comparing exposure to Lnight ≥45 versus <45 dB(A). In meta-analysis, we observed an adjusted HR of 1.11 (95% CI: 1.01, 1.23). These results were attenuated with adjustment for additional variables such as body mass index. Our findings support a modest positive association between nighttime aircraft noise and hypertension risk across NHS/NHSII, which may reinforce the concept that sleep disturbance contributes to noise-related disease burden.

Tropical tropospheric ozone distribution and trends from in situ and satellite data

Abstract. Tropical tropospheric ozone (TTO) is important for the global radiation budget because the longwave radiative effect of tropospheric ozone is higher in the tropics than midlatitudes. In recent decades the TTO burden has increased, partly due to the ongoing shift of ozone precursor emissions from midlatitude regions toward the Equator. In this study, we assess the distribution and trends of TTO using ozone profiles measured by high-quality in situ instruments from the IAGOS (In-Service Aircraft for a Global Observing System) commercial aircraft, the SHADOZ (Southern Hemisphere ADditional OZonesondes) network, and the ATom (Atmospheric Tomographic Mission) aircraft campaign, as well as six satellite records reporting tropical tropospheric column ozone (TTCO): TROPOspheric Monitoring Instrument (TROPOMI), Ozone Monitoring Instrument (OMI), OMI/Microwave Limb Sounder (MLS), Ozone Mapping Profiler Suite (OMPS)/Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI)/Global Ozone Monitoring Experiment 2 (GOME2). With greater availability of ozone profiles across the tropics we can now demonstrate that tropical India is among the most polluted regions (e.g., western Africa, tropical South Atlantic, Southeast Asia, Malaysia and Indonesia), with present-day 95th percentile ozone values reaching 80 nmol mol−1 in the lower free troposphere, comparable to midlatitude regions such as northeastern China and Korea. In situ observations show that TTO increased between 1994 and 2019, with the largest mid- and upper-tropospheric increases above India, Southeast Asia, and Malaysia and Indonesia (from 3.4 ± 0.8 to 6.8 ± 1.8 nmol mol−1 decade−1), reaching 11 ± 2.4 and 8 ± 0.8 nmol mol−1 decade−1 close to the surface (India and Malaysia–Indonesia, respectively). The longest continuous satellite records only span 2004–2019 but also show increasing ozone across the tropics when their full sampling is considered, with maximum trends over Southeast Asia of 2.31 ± 1.34 nmol mol−1 decade−1 (OMI) and 1.69 ± 0.89 nmol mol−1 decade−1 (OMI/MLS). In general, the sparsely sampled aircraft and ozonesonde records do not detect the 2004–2019 ozone increase, which could be due to the genuine trends on this timescale being masked by the additional uncertainty resulting from sparse sampling. The fact that the sign of the trends detected with satellite records changes above three IAGOS regions, when their sampling frequency is limited to that of the in situ observations, demonstrates the limitations of sparse in situ sampling strategies. This study exposes the need to maintain and develop high-frequency continuous observations (in situ and remote sensing) above the tropical Pacific Ocean, the Indian Ocean, western Africa, and South Asia in order to estimate accurate and precise ozone trends for these regions. In contrast, Southeast Asia and Malaysia–Indonesia are regions with such strong increases in ozone that the current in situ sampling frequency is adequate to detect the trends on a relatively short 15-year timescale.

MONITORING OF THE LIVING ENVIRONMENT THROUGH AIR POLLUTANTS IN THE POLOG FIELD OF THE REPUBLIC OF NORTH MACEDONIA

Introduction: The Republic of North Macedonia and especially the field pit of Polog in terms of geographical position and climatic aspect is considered as a place with different variables such as: temperature, humidity, radioactive exposure, hilly-mountainous relief. The Sharr mountain in the west and the dry mountain in the north, limit the Polog field pit in all directions, such as north-west and south-east, causing the living environment to deteriorate in the winter period due to air, water and land pollutants. With special emphasis, we highlight the winter season when we have an alarming state of air pollution, such as certain regions of the Republic of North Macedonia and especially the region of the field pit of Polog with the cities of Tetovo and Gostivar and the surrounding area. Recently, the city of Tetova and its surroundings is considered one of the most polluted cities in the country, region and the largest in the world, then this phenomenon of pollution necessarily leads us to unwanted consequences for the population that lives and operates in this region. As a result of environmental factors such as air pollution and other related factors such as: stress, temperature, food, social genes, mental state and other related factors can lead to disorders in the human body and lead to diseases that attack the nervous and cardiovascular systems , skeletal and thyroid. Many studies talk about the alarming condition of patients who are affected by these diseases, therefore our paper will focus on the effect of these factors above all on the actual state of pollutants in the Polog region of North Macedonia. The purpose of the study: This paper aims to investigate the potential causes that can affect the health of the population of the Polog field pit and especially the city of Tetova and its surroundings. As a potential pollutants of the living environment, the following can be considered: carbon monoxide, carbon dioxide, sulfur dioxide, urban combustion from automobiles, combustion of various physical substances, chemical components from factories, smoke from chimneys from burning oil and coal, combustion from wood in the form of pellets, etc. Material and working method: In the study, fertilized chicken eggs were taken and incubated in the incubator for 21 days of incubation. The eggs were treated with different doses of air pollutant extract at certain stages of embryonic development. Using the stereomicroscope with macroscopic and microscopic method were analyzed ontogenetic changes in certain stages of embryonic development. Research results: The results of the research will give a real insight into the situation of the level of pollution of the living environment in the city of Tetovo and its surroundings and in the field pit of Pollog. The monitoring of the living environment can be followed by the treatment of air pollutants in certain doses in the form of ampoule therapy, and the negative effect of the given dose on the hen's eggs will be observed. All eggs with the highest dose of injection had a stagnated embryonic development, whereas egss with lower dose of injections had underdeveloped embryos. Conclusion: From the results of the research we can conclude that the potential pollutants from air pollutants can lead to consequences for the population living in this region but also for the living world in general. These factors can lead to disorders of the nervous system, cardiovascular, and thyroid disorders and other respiratory diseases. This research will give a real insight into the pollution of the living environment and the possible consequences of this at a local, regional and wider level.

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.

Decoupling Analysis between Socio-Economic Growth and Air Pollution in Key Regions of China

The coordinated development of atmospheric pollution and socio-economic growth plays a core role in the sustainable development of cities and regions. The relationship between socio-economic growth and air pollution can be described using decoupling analysis. The seven key regions of China (168 cities), including Beijing–Tianjin–Hebei and its surrounding areas (BTHSR), the Yangtze River Delta region (YRDR), the Fen-Wei Plain (FWP), the Chengdu–Chongqing region (CCR), the urban agglomeration of the middle reaches of the Yangtze River (MLRYR), the Pearl River Delta region (PRDR), and other provincial capitals and municipalities with specialized plans (OPCCSP) were taken as targets to investigate the spatiotemporal evolution characteristics of AQI values and PM2.5 concentrations from 2014 to 2022. Then, the decoupling relationship between the AQI/PM2.5 and the socio-economic growth index (SEGI) in these key regions was deeply researched by the Tapio decoupling model. The main results were as follows: (1) Although the continuous improvement in air quality was observed in these seven key regions in China, the PM2.5 concentration in the BTHSR and FWP was still higher than 35 μg·m−3. The AQI showed a spatial pattern of high in the north and low in the south, and the distribution of PM2.5 in China was high in the east and low in the west. (2) The decoupling degree between air pollution and socio-economic growth was relatively high in the PRDR and YRDR. In contrast, the degree of decoupling was poor in the FWP and OPCCSP. The decoupling states were primarily influenced by industrial structure, energy consumption, and urbanization. (3) The decoupling of air pollution from socio-economic growth was in a strong decoupling state throughout the majority of the study period, achieving a comparatively ideal decoupling state in 2018. However, the overall decoupling states of the seven regions were not sustainable, and the decoupling stability was relatively poor. During the research period, the decoupling state between socio-economic growth and air pollution changed and was unstable.

Machine learning predictive insight of water pollution and groundwater quality in the Eastern Province of Saudi Arabia

This study presents an innovative approach for predicting water and groundwater quality indices (WQI and GWQI) in the Eastern Province of Saudi Arabia, addressing critical challenges of scarcity and pollution in arid regions. Recent literature highlights the increasing attention towards WQI based on water pollution index (WPI) and GWQI as essential tools for simplifying complex hydrogeological data, thereby facilitating effective groundwater management and protection. Unlike previous works, the present research introduces a novel hybrid method that integrates non-parametric kernel Gaussian learning (GPR), adaptive neuro-fuzzy inference system (ANFIS), and decision tree (DT) algorithms. This approach marks the first application of a non-parametric kernel for groundwater quality pollution index prediction in Saudi Arabia, offering a significant advancement in the field. Through laboratory analysis and the combination of various machine learning (ML) techniques, this study enhances prediction capabilities, particularly for unmonitored sites in arid and semi-arid regions. The study’s objectives include feature engineering based on dependency sensitivity analysis to identify the most influential variables affecting WQI and GWQI, and the development of predictive models using ANFIS, GPR, and DT for both indices. Furthermore, it aims to assess the impact of different data portions on WQI and GWQI predictions, exploring data divisions such as (70% / 30%), (60% / 40%), and (80% / 20%) for training and testing phase, respectively. By filling a critical gap in water resource management, this research offers significant implications for the prediction of water quality in regions facing similar environmental challenges. Through its innovative methodology and comprehensive analysis, this study contributes to the broader effort of managing and protecting water resources in arid and semi-arid areas. The result proved that GPR-M1 exhibited exceptional testing phase accuracy with RMSE = 0.0169 for GWQI. Similarly, for WPI, the ANFIS-M1 achieved high testing predictive skills with RMSE = 0.0401. The results emphasize the critical role of data quality and quantity in training for enhancing model robustness and prediction precision in water quality assessment.

Trace metals in coastal marine sediments: Natural and anthropogenic sources, correlation matrices, and proxy potentials

Rapidly spreading industrialization since the 19th century has led to a drastic increase in trace metal deposition in coastal sediments. Provided that these trace metals have remained relatively immobile after deposition, their sedimentary enrichments can serve as records of local–regional pollution histories. Factors controlling this proxy potential include trace metal geochemistry (carrier-, and host phase affinity), and depositional environmental factors (redox variability, particulate shuttling, organic matter loading, bathymetry). Yet, the relative importance and interactions between these controls are still poorly understood, hampering the reliable use of trace metal-based environmental proxies. By summarizing nine site-specific correlation matrices of 16 metal (loid) s (Pb, Cd, Cu, Zn, Sb, Sn, Ni, As, Tl, V, Mo, U, Re, Fe, Mn, Al), total organic C, and S contents in short sediment cores into a single meta-matrix, we test a novel approach for quickly detecting common and contrasting trace metal enrichment patterns across different study locations. Our meta-matrix shows two trace metal groups, within which positive correlations of e.g., Pb, Cd, Zn, Cu, Sb suggest a primary “anthropogenically sourced” (group I) control, whereas known “redox-sensitive” (group II) trace metals (Mo, U, Re) are characterized by fewer positive correlations. However, some group I metals (Cd, Zn, Cu, Sb) also covary with group II metals, inferring that redox variability may obscure primary anthropogenic signals; Sb even shows advantages over Mo and U under oxic conditions. As a more robust pollution indicator we identified Pb; yet for reconstructing historical Pb atmospheric pollution signals (1970s Pb peak), it is crucial to consider the distance from shore. In near-shore environments, local (fluvial) pollution signals may overprint large-scale (atmospheric) signals. Our findings demonstrate that combining site-specific sedimentary correlation and distribution patterns with a meta-matrix considerably aids the understanding of trace metal sequestration in different coastal sedimentary environments, which thereby improves trace metal proxy reliability.

On-site monitoring and numerical simulation on groundwater flow and pollution plume evolution in a hexavalent-chromium contaminated site

Accurately ascertaining spatiotemporal distribution of pollution plume is critical for evaluating the effectiveness of remediation technologies and environmental risks associated with contaminated sites. This study concentrated on a typical Cr(VI) contaminated smelter being currently remediated using pump-and-treat (PAT) technology. Long-term on-site monitoring data revealed that two highly polluted regions with Cr(VI) concentrations of 162.9 mg/L and 234.5 mg/L existed within the contaminated site, corresponding to previous chromium slag yard and sewage treatment plant, respectively. The PAT technology showed significant removal performance in these highly polluted areas (>160 mg/L) after six months of pumping, ultimately achieving complete removal of the pollutants in these high-pollution areas. Numerical simulation results showed that although the current remediation scheme significantly reduced the Cr(VI) pollution degree, it did not effectively prevent the incursion of the pollution plume into the downstream residential area after 20 years. Additionally, an improved measure involving supplementary pumping wells was proposed, and its remediation effects were quantitatively evaluated. Results indicated that the environmental pollution risk of groundwater downstream could be effectively mitigated by adding pumping wells, resulting in a reduction of the pollution area by 20 % in the case of adding an internal well and 41 % with the addition of external wells after 20 years. The findings obtained in this study will provide an important reference and theoretical guidance for the reliability analysis and design improvement of the PAT remediation project.

Contribution to the study of possible transport of PM10 aerosols in the eastern part of the Czech Republic

The article deals with the assessment of air pollution in the eastern regions of the Czech Republic: Moravia and Silesia, where the limits for the protection of human health for PM and benzo[a]pyrene are significantly exceeded, especially in the north-eastern area. The extent to which this heavily polluted part of Moravia and Silesia affects the central, southern and south-eastern parts of Moravia, i.e. south-eastern part of the Czech Republic, has often been discussed but not proven. The assessment demonstrated the influence and extent of pollutant transport by determining the prevailing daily wind direction. The prevailing NE wind direction results in worse dispersion conditions and higher PM10 concentrations throughout the study area. Conversely, the SW wind direction is a carrier of better dispersion conditions with lower PM10 concentrations in the area. The effect of transport of PM10 pollution in the daily type from the NE direction can be observed at those sites located in the NE of the area of interest from the border with Poland to the Prerov site. In the case of more southerly sites, the methodology used does not allow to determine whether and to what extent they are affected by pollution transport from the northeastern part of Moravia or from neighbouring Poland. This methodology is particularly useful when only 24-h pollutant concentrations are available (not at more detailed, e.g. 1-hour intervals). The internal methodology of the Czech Hydrometeorological Institutefor calculating back trajectories of wind from ground stations is particularly applicable for detecting the origin of short-term episodes with high pollutant concentrations.

Spillover effects of structure-adjustment pollution reduction measures in China's iron and steel industry

The iron and steel industry (ISI) is a significant source of sulfur dioxide and particulate matter pollution in China. Existing research on regional environmental regulation or ISI emission reduction strategies tends to overlook spillover effects and the enterprise perspective. During the heating season, production limitations in ISI are potential policy measures for achieving structural emission reductions in heavily polluted cities in China's Jing-Jin-Ji and surrounding regions. We adopt a bottom-up modeling approach, incorporating effective production time to describe enterprise behavior and establishing a quantitative trade model based on trade theory. By modeling three types of production restriction policies outlined in policy documents, we evaluate the emission reduction effects of structure-adjustment measures using the example of reduced effective production time for steel-producing enterprises in the air pollution transmission channel in the Beijing-Tianjin-Hebei area. The results indicate the following: (1) Reducing the effective production time of ISI enterprises can help decrease domestic production value and total factor productivity in pollution-intensive industries, including but not limited to ISI. It also leads to reduced emissions of various pollutants in the implementation regions. (2) Due to interprovincial trade and input-output linkages, structural reduction measures in certain regions have implications for almost all other provinces' industrial structures. Differences in initial industrial structures, factor endowments, and geographical locations contribute to varying directions and magnitudes of industrial structural changes. Pollution-intensive industries' share tends to increase higher in less developed regions. (3) Our estimated pollution reduction is smaller compared to the literature evaluating clean air policies in similar regions using top-down strategies. This discrepancy arises because we analyze a single policy tool rather than modeling industry-wide emission fluctuations from the top down. Additionally, our modeling approach allows us to examine dynamic changes in comparative advantages. The increase in production scale for certain industries in policy-affected regions partially offsets the decline in pollution emissions. These findings enhance our understanding of structure-adjustment reduction measures' role and highlight their potential advantages and limitations.

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.

Alteration in oxidative stress markers, digestive physiology and gut microbiota of Heteropneustes fossilis and Clarias batrachus exposed to eriochrome black T

Synthetic dyes are the primary cause of water pollution in industrial regions. Azo dyes account for 60–70% of such dyes used in the textile sector due to their numerous beneficial characteristics. Nevertheless, there is a dearth of knowledge regarding the toxicity of Eriochrome Black T (EBT), a widely used azo dye in the textile industry. Therefore, the current study was designed to investigate the effect of EBT exposure on two catfish species, Heteropneustes fossilis and Clarias batrachus. Following 96 h exposure to 1, 10 and 20 mgL−1 of EBT, the MDA content and activities of SOD, CAT and GR exhibited a rising trend. However, as the concentration of EBT increased in both species, GPx showed decreased activity. EBT exposure also altered gut morphometry as well as the three main digestive enzymes activity (increase in lipase and trypsin activity, while decrease in amylase activity). In addition, the exposure of EBT had a significant impact on the gut microbiota of both species. C. batrachus demonstrated the suppression or absence of beneficial gut commensals (Bacillus and Cetobacterium), whereas H. fossilis revealed the proliferation and appearance of beneficial commensal microbes (Bacillus, Bacteroides, Prevotella, and Megashaera). Furthermore, the expansion or absence of these microbial communities indicated that the gut microbiota of both species was involved in dye digestion, immunity and detoxification. Overall, the percent change calculation of all the selected biomarkers, together with gut microbiota analysis, indicates that C. batrachus was more vulnerable to EBT exposure than H. fossilis. The present investigation effectively demonstrated the toxic impact of EBT on fish health by employing oxidative stress markers, digestive enzymes, and the fish gut microbiota as a promising tool for screening the impact of dye exposure on digestive physiology in toxicological research.

Multi-objective Identification Method for Influencing Factors of Soil Heavy Metal Content Change

Identifying the influencing factors of soil heavy metal content changes is the basis for reducing or preventing soil heavy metal pollution. Taking an agricultural experimental field in Changping District of Beijing as an example, the heavy metal content changes in As, Cr, Cu, Ni, Pb, and Zn from 2012 to 2022 were firstly analyzed. Secondly, the influencing factors of the heavy metal content changes were detected based on the geographical detector at the single-target and multi-target levels, respectively. Finally, comparative experiments with the correlation analysis method and existing studies were set up to evaluate the effectiveness of the identification method of influencing factors developed in this study. The results showed that human activity factors have exacerbated the changes in soil heavy metal content in the study area as follows： ① At the single-target level, the land use type was the main influencing factor on the changes in Cr, Cu, and Zn contents, and the annual deposition flux influenced the changes in As. The results of the interaction detection showed that there was an enhancement effect among the factors, and the interaction of the human activity factors dominated for the factor identification. ② The results of the multi-target level detection covered the results of the single-target level detection, which could identify more influencing factors. The land use type affected the changes in Cu, Zn, Cr, Ni, and As, and the changes in As and Zn were influenced by the annual deposition fluxes. ③ The multi-target identification method coupled with geographical detector and principal component analysis could effectively identify the influencing factors of soil heavy metal content changes, which was much more effective than the single soil heavy metal correlation method. The developed multi-target identification method for influencing factors of heavy metal content changes can provide technical support for the regional pollution monitoring and macro-management of soil heavy metals.

Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm.

High-precision evaluations of water environment quality are highly important for improving the accuracy of early warning systems of regional water pollution risk and improving the regional water environment. This paper employs the chimp optimization algorithm (ChOA) to enhance the traditional random forest model, resulting in the chimp optimization algorithm-random forest (ChOA-RF) water quality assessment model for evaluating the Jiansanjiang area in Heilongjiang Province, China. The results show that the overall water environment in Jiansanjiang has the following characteristics: "The water quality of farms in the northwest is poor, and the quality of groundwater is better than that of surface water." Total nitrogen (TN) and total phosphorus (TP) in surface water and ammonium nitrogen (NH3-N), ferrum (Fe), and manganese (Mn) in groundwater are the main pollutants. The TP and TN in surface water and the NH3-N in groundwater exceeded the relevant standards, likely due to the excessive application of chemical fertilizers, especially nitrogen fertilizers. Additionally, Fe and Mn are harmful native substances. According to these findings, targeted improvement strategies, such as reducing nitrogen fertilizer application, plugging well, and increasing the surface water utilization rate, are proposed. Moreover, the ChOA-RF model is compared with the traditional empirical value model and the particle swarm optimization-random forest (PSO-RF) model. The results show that the ChOA-RF model can effectively reduce the root mean square error and mean absolute percentage error and improve the coefficient of determination. The running time and convergence ability are also better than those of the PSO-RF model, which is a more accurate and efficient machine learning model. The model can be used not only for high-precision evaluation of regional water environment quality but also for other machine learning fields.

Evolutionary game analysis and efficiency test of water pollution control driven by emission trading: Evidence from Zhejiang Province, China

Presently, China is actively endorsing the pilot initiative for the remunerative use and trading of emission. By examining the operation and efficacy of emission trading in the context of water pollution control, one can contribute to the advancement and refinement of this system, thereby facilitating the attainment of regional pollution reduction, carbon reduction, and high-quality development objectives. In pursuit of this objective, we develop a theoretical framework for the local government and sewage enterprises evolutionary game of water pollution control, which includes two scenarios without considering and considering emission trading for studying the influencing factors and evolution trajectory of the game subject's. Through the stability analysis, the game interactive mechanism, the difference in evolutionary trajectory, and the response logic of the decision-making body in different situations become clearly visible. Further, the system sensitivity factors are analyzed by solving the partial derivation of the area formula of the phase diagram. And the efficacy of the sewage trading system in water pollution control in Zhejiang Province is empirically examined at the micro level by adopting the trading data of the first pilot area of sewage trading in the country and the case of pollution control in Jinhua City. The research reveals the following conclusions: Under specific circumstances, emission trading can incentivize businesses and even industries to enhance pollution control measures as a whole. The performance and degree of sensitivity factors vary across gaming systems, with public reputation evaluation and central government inspection serving as positive constraints. The initial cost of paid use of emission permits, as a fixed cost component for firms to address pollution, has no effect on the enterprises' behavioral actions to satisfy emission regulations. The findings can furnish local governments with a theoretical foundation and decision support in order to optimize regulatory strategies and enhance pollution control policies.

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.

Characterization and Source Analysis of Heavy Metal(loid)s Pollution in Soil of an Industrial Park in Kunming, China

This study investigated the characteristics and sources of heavy metal(loid) pollution in the soil of a key industrial park in Kunming, China. In total, 60 soil samples (40 from agricultural land and 20 from construction land) were collected from and around the park. The soil pH and contents of Arsenic (As), lead (Pb), copper (Cu), zinc (Zn), cadmium (Cd), mercury (Hg), nickel (Ni), and chromium (Cr) were measured. The contents of the eight heavy metal(loid)s were analyzed using the background values of heavy metal(loid)s in the Kunming soil. The pollution load, geoaccumulation, and Nemero Comprehensive Pollution Indices were used for environmental risk evaluation. Cluster and principal component analyses were used to resolve heavy metal(loid) sources. Cd was enriched in construction and agricultural soils. As, Hg, Cd, Pb, Cu, and Zn exhibited large spatial differentiation and were significantly affected by the external environment. A regional pollution load index of 3.02 indicated overall heavy pollution. The pollution load index for each heavy metal(loid) indicated light pollution. The geoaccumulation index indicated relatively severe As, Cd, Cu, Pb, and Zn pollution. The Nemero Composite Pollution Index value showed that the study area was heavily polluted, with construction land being mildly polluted by Cd, and agricultural land being moderately polluted. The results of the spatial distribution show that there were high levels of contamination in the center. Correlation and principal component analyses showed that the pollution sources of the eight heavy metal(loid)s varied. Hg, Cd, and Pb originate primarily from industrial and agricultural pollution. Traffic sources significantly impacted Cu, Pb, Cd, and Cr. Natural sources are the main sources of Cr, Ni, and Cd. Ni is also affected by industrial sources, whereas Zn and Cu are affected by agricultural pollution. The influences of As, Cd, and Pb on the surface soil in the study area were more serious. Cd is more widely polluted and should be a priority in controlling soil heavy metal(loid)s.

Significance of anthropogenic black carbon in modulating atmospheric and cloud properties through aerosol-radiation interaction during a winter-time fog-haze

The present study investigated the aerosol-radiation-cloud interaction of anthropogenic black carbon (BC) during a severe fog-haze event by utilizing WRF-Chem, multi-satellite and in-situ observations, reanalyses datasets, and HYSPLIT model. The WRF-Chem model adequately captured the regional distribution of aerosol, cloud, and meteorological variables over the study domain. The maximum BC surface concentration (≥7 μg m−3) was predominantly evident over densely populated central and lower Indo-Gangetic Plain (IGP) regions. Although smoke aerosols usually persisted within 4 km above the surface at daylight hours, they reached as high as 6 km during nighttime, specifically over the central IGP regions. The heavily influenced areas by BC aerosols expanded to almost the entire landmass and the continental outflow region upon doubling the anthropogenic emission. The geographical distribution of maximum perturbations in net shortwave radiation flux at the surface closely matched the spatial patterns of the highest BC concentration, indicating a crucial role of BC in directly preventing the incoming sunlight from reaching the surface. Consequently, the reduced solar radiation at the ground might result in substantial surface cooling (between −0.3 and −0.5 °C), eventually prohibiting surface evapotranspiration and diminishing the outgoing sensible and latent heat fluxes. Also, more amplified cooling in the polluted atmosphere can prevent further development of the planetary boundary layer (PBL), increasing particle entrapment near the surface via an ‘aerosol-radiation-PBL’ feedback loop. However, the most striking contrast between the two scenarios (typical and polluted) was the prolonged and intensified warming (0.5–0.8 °C) in the mid-troposphere, causing a remarkable drop in moisture (more than two to three times) and hence burn-off of liquid cloud droplets due to the semi-direct effect of increased BC aerosols. The intense mid-tropospheric heating could remarkably enhance the updraft velocity, supporting the vertical transport of the additional moisture to higher altitudes, forming more ice clouds at night. Therefore, the current study highlighted the urgency of mitigating anthropogenic BC emissions in highly polluted regions since increased emissions could considerably worsen severe fog-haze conditions and influence both liquid water and ice clouds, depending on the atmospheric conditions.