Priority sources identification and risks assessment of heavy metal(loid)s in agricultural soils of a typical antimony mining watershed.

Heavy metal(loid) (HM) pollution in agricultural soils has become an environmental concern in antimony (Sb) mining areas. However, priority pollution sources identification and deep understanding of environmental risks of HMs face great challenges due to multiple and complex pollution sources coexist. Herein, an integrated approach was conducted to distinguish pollution sources and assess human health risk (HHR) and ecological risk (ER) in a typical Sb mining watershed in Southern China. This approach combines absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models with ER and HHR assessments. Four pollution sources were distinguished for both models, and APCS-MLR model was more accurate and plausible. Predominant HM concentration source was natural source (39.1%), followed by industrial and agricultural activities (23.0%), unknown sources (21.5%) and Sb mining and smelting activities (16.4%). Although natural source contributed the most to HM concentrations, it did not pose a significant ER. Industrial and agricultural activities predominantly contributed to ER, and attention should be paid to Cd and Sb. Sb mining and smelting activities were primary anthropogenic sources of HHR, particularly Sb and As contaminations. Considering ER and HHR assessments, Sb mining and smelting, and industrial and agricultural activities are critical sources, causing serious ecological and health threats. This study showed the advantages of multiple receptor model application in obtaining reliable source identification and providing better source-oriented risk assessments. HM pollution management, such as regulating mining and smelting and implementing soil remediation in polluted agricultural soils, is strongly recommended for protecting ecosystems and humans.

Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China.

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Standardized framework for assessing soil quality at antimony smelting site by considering microbial-induced resilience and heavy metal contamination.

Antimony smelting activities damage the soil and vegetation surroundings while generating economic value. However, no standardized methods are available to diagnose the extent of soil degradation at antimony smelting sites. This study developed a standardized framework for assessing soil quality by considering microbial-induced resilience and heavy metal contamination at Xikuangshan antimony smelting site. The soil resilience index (SRI) and soil contamination index (SCI) were calculated by Minimum Data Set and geo-accumulation model, respectively. After standardized by a multi-criteria quantitative procedure of modified Nemerow's pollution index (NPI), the integrated assessment of soil quality index (SQI), which is the minimum of SRINPI and SCINPI, was achieved. The results showed that Sb and As were the prominent metal(loid) pollutants, and significant correlations between SQI and SRI indicated that the poor soil quality was mainly caused by the low level of soil resilience. The primary limiting factors of SRI were Fungi in high and middle contaminated areas, and Skermanella in low contaminated area, suggesting that the weak soil resilience was caused by low specific microbial abundances. Microbial regulation and phytoremediation are greatly required to improve the soil quality at antimony smelting sites from the perspectives of pollution control and resilience improvement. This study improves our understanding of ecological effects of antimony smelting sites and provides a theoretical basis for ecological restoration and sustainable development of mining areas.

Musseling through: Mytilus byssal thread production is unaffected by continuous noise.

Anthropogenic low-frequency noise (ALFN) is a rising pollutant in the world oceans. Despite the ubiquity of ALFN, its effect on marine invertebrates is still poorly understood. Here, we tested how continuous low-frequency noise (CLFN), a substantial component of ALFN, affects the byssal thread production of Mytilus, a cosmopolitan genus of mussels with high ecological and economic importance. The effects of acute CLFN exposure and predator cues on byssogenesis by Mytilus spp. were explored in both the presence and absence of predator cues. While predator effluents increased thread production, CLFN had seemingly no effect on thread counts. Further, trends suggested a synergistic effect of CLFN and predator cues. The behavioral indifference of Mytilus spp. toward CLFN could contribute to the observed prevalence of these animals in inherently disturbed habitats. This would partly explain their success in colonizing and persisting on artificial substrata rife with disturbances.

Metal concentrations in fish, crabs, and bivalve molluscs from marine waters adjacent to a multi-metals smelter and refinery.

Metal concentrations were determined in tissues of finfish, crabs, and bivalve molluscs collected from marine waters near Port Pirie, South Australia, the site of a long-standing multi-metals smelter and refinery. A general trend of tissue metal concentrations in order of highest to lowest was observed in bivalves > crabs > finfish. A lead concentration of 158 ± 6.6 mg/kg (wet wt.) was observed in blue mussels (Mytilus galloprovincialis) sampled close to the smelter. Lead concentrations correlated positively with proximity to the smelter in all biota analysed. Similar relationships were observed for cadmium, copper, zinc and selenium in all biota except razorfish (Pinna bicolor; Bivalvia: Pinnidae), which showed no correlation with proximity to the smelter for these metals. Inorganic arsenic concentrations were below the limit of reporting in the majority of the analysed samples, however inorganic arsenic concentrations in blue swimmer crabs (Portunus armatus) and blue mussels correlated with proximity to the smelter. Mercury concentrations in the biota analysed were generally low and showed variable relationships with proximity to the smelter, with no significant correlation observed in finfish and razorfish, a significant positive correlation in blue mussels, and a significant negative correlation in blue swimmer crabs. This is the first major study of metal concentrations in recreationally-targeted marine species near Port Pirie species for more than two decades. Comparison with data from previous studies conducted shows little change in tissue metal concentrations in marine biota near Port Pirie over the past 40 years.

Short-term air pollution and greenness exposures on oxidative stress in urban and peri-urban residents in Beijing: A part of AIRLESS study.

BACKGROUND: Exposure to air pollution has been associated with increased risks of cardiopulmonary diseases, cancer, and mortality, whereas residing near green spaces may reduce the risks. However, limited research explores their combined effect on oxidative stress. METHODS: A total of 251 participants with multi-time measurements were included in the longitudinal-designed study. Personal gaseous air pollutants (CO, NO, NO2, and O3,) and particulate pollution (PM1, PM2.5, and PM10) were measured and followed in two 7-day windows while ambient exposure levels and urine samples were collected simultaneously. Participants' Normalized Difference Vegetation Index (NDVI) was estimated and used to represent greenness exposure. Urinary oxidative stress biomarkers include free malondialdehyde (MDA), total MDA, and 8-hydroxydeoxyguanosine (8-OHdG). Linear mixed-effects models were used to independently and jointly estimate the associations of greenness and air pollution with oxidative stress biomarkers. RESULTS: We found consistent positive associations of personal ozone (O3) exposure with 8-OHdG percent changes, and this association was modified by gender and outdoor activity frequency. Consistent positive associations of personal lag 2-day carbon monoxide (CO) exposure with the percent changes of the three oxidative stress biomarkers were significant. We additionally observed that individuals who lived in greener areas had lower levels of urinary-free and total MDA. Participants in the highest NDVI tertile had 0.38 and 0.46 lower free and total MDA levels, [95 % CI: (-0.70, -0.05) and (-0.78, -0.13)], compared to the lowest NDVI tertile. There was also evidence indicating the modification effects by area, education, and outdoor activity frequency on associations between NDVI exposure and creatinine adjusted free MDA (all Pfor interaction < 0.05). Additional greenness modification effects on personal O3 exposure with urinary 8-OHdG was observed. CONCLUSION: Our study provides biological evidence of the modification effect of the built environment on the impact of air pollution.

Bisphenol A in Water Systems: Risks to Polycystic Ovary Syndrome and Biochar-Based Adsorption Remediation: A Review.

Access to clean and safe water sadly remains an issue in the 21st century. Water reservoirs, whether groundwater or surface water, are routinely contaminated by various harmful Emerging Contaminants (ECs). One of most prevalent pollutants among these pollutants is Bisphenol A, which is classified as an Endocrine Disrupting Compound (EDC). This substance adversely interferes with the endocrine system, primarily by mimicking estrogen, and has been considered a potential contributor to Polycystic Ovary Syndrome (PCOS) with 82.70% of 1,391 women studied showing a positive correlation between BPA exposure and PCOS. PCOS is currently the most prevalent endocrine disorder affecting women of reproductive age; however, its pathogenesis remains unclear, complicating diagnosis and subsequently patient care. In this review, these topics are thoroughly examined, with particular emphasis on biochar, a new promising method for large-scale water purification. Biochar, derived from various organic waste materials, has emerged as a cost-effective substance with remarkable adsorption properties achieving up to 88% efficiency over four cycles of reuse, similar to that of activated carbon. This review interrogates the suitability of biochar for counteracting the issue of EDC pollutants.

Deciphering the ecological impact of azo dye pollution through microbial community analysis in water-sediment microcosms.

The uncontrolled release of untreated dyeing wastewater into aquatic ecosystems poses global environmental risks. It alters native microbial communities and associated ecological processes, often going unnoticed. Therefore, the influence of acid orange 7 dye (AO7) contamination on the natural microbial community was investigated using a water-sediment microcosm. Compared to sterile microcosms, complete dye decolourization in natural microcosms showed microbial communities' significance in combating xenobiotic contamination. Proteobacteria dominated the water community, whereas Firmicutes dominated the sediment. AO7 exposure induced notable shifts in the structural composition of the bacterial community in both water and sediment. Niveispirillum exhibited a marked decrease, and Pseudomonas demonstrated a notable increase. The - 9.0 log2FC in Niveispirillum, a nitrogen-fixing bacterium, from 24.4% in the control to 0.1% post-treatment, may disrupt nutrient balance, plant growth, and ecosystem productivity. Conversely, elevated levels of Pseudomonas sp. resulting from azo dye exposure demonstrate its ability to tolerate and bioremediate organic pollutants, highlighting its resilience. Functional profiling via KEGG pathway analysis revealed differential expression patterns under AO7 stress. Specifically, valine, leucine, and isoleucine degradation pathways in water decreased by 52.2%, and cysteine and methionine metabolism ceased expression entirely, indicating reduced protein metabolism and nutrient bioavailability under dye exposure. Furthermore, in sediment, glutathione metabolism ceased, indicating increased oxidative stress following AO7 infusion. However, C5-branched dibasic acid metabolism and limonene and pinene degradation were uniquely expressed in sediment. Decreased methane metabolism exacerbates the effects of global warming on aquatic ecosystems. Further, ceased-butanoate metabolic pathways reflect the textile dye wastewater-induced adverse impact on ecological processes, such as organic matter decomposition, energy flow, nutrient cycling, and community dynamics that help maintain self-purification and ecological balance in river ecosystems. These findings underscore the critical need for more comprehensive environmental monitoring and management strategies to mitigate ecological risks posed by textile dyes in aquatic ecosystems, which remain unnoticed.

Arsenic and metal levels in snake tissues from Lagoa Santa Karst, Brazil.

Concentrations of one metalloid (As) and eight metals (Cd, Cr, Cu, Hg, Mn, Ni, Pb, and Zn) were determined in tissues (muscle, liver, and kidney) of eight snake species (Bothrops neuwiedi, Crotalus durissus, Dipsas mikanii, Epicrates crassus, Helicops modestus, Micrurus carvalhoi, Oxyrhopus guibei, and Oxyrhopus trigeminus) from Lagoa Santa Karst. Except for Cu and Zn, all other analyzed elements were detected in concentrations within the ranges previously reported for snakes inhabiting polluted areas, emphasizing Hg (specific Hg mean concentrations varied from 0.87 to 9.76 µg g-1 d.w). The highest mean concentrations of all elements except Zn were found in muscle samples of the false corals O. guibei (means ranged from 2.01 [Pb] to 9.76 [Hg]). The highest Zn mean concentration (13.77 µg g-1 d.w) was detected in the kidney of the water snake H. modestus. No significant correlation was found between element concentrations and body size for all species. Significant interorgan differences were observed for As, Cr, Cu, Hg, Mn, Pb, and Zn concentrations in the three tissues in H. modestus. Significant interspecific differences were found in at least one organ for all elements. Significant pairwise differences were found between diet specialist species and between these species and broader diet species, while no significant difference was found between the broader diet species. The bioaccumulation of As and metals in snakes from Lagoa Santa Karst could be associated with natural rock dissolution and erosion processes but also with the wide-scale mining in the region and the increased agriculture and urbanization.

Unraveling the hydrogeochemical characteristics and pollution sources of groundwater in an intensive industrial area, East China.

It is challenging to interpret hydrogeochemical datasets with complex natural and anthropogenic genesis in intensive industrial areas. This paper elucidates the hydrogeochemical characteristics and pollution sources of groundwater in an industrial park, East China, combining the self-organizing map (SOM), hydrochemical graphs, and correlation analysis. The results show that the total dissolved solids of groundwater range from 73.45 to 997.92 mg/L and can be regarded as freshwater. The pH varies greatly from 6.44 to 9.90, most of samples belonging to weakly acidic-weakly alkaline. The groundwater can be classified into five clusters by SOM, representing the non- or least-polluted groundwater (cluster D), high salt groundwater (cluster A), high NH4+-N and HCO3- groundwater (cluster B), high Fe and Mn groundwater (cluster C), and high pH groundwater (cluster E), which were contaminated by industrial salts, historical agriculture activity, industrial reducing substances, and industrial alkali, respectively. The natural evolution of groundwater (cluster D) in the study area is mainly controlled by mineral weathering/dissolution. The contributions of calcite, dolomite, gypsum, halite, and silicate mineral to groundwater solute are 55.8-66.3%, 15.1-18.0%, 9.0-10.7%, 2.5-10.1%, and 2.3-9.4%, respectively, based on the mass conservation. The contaminated groundwaters (all other clusters except for cluster D) have different hydrochemical characteristics associated with the pollution sources. In addition, the relatively reductive environment in quaternary flu-lacustrine sediments favored the formation of high level of Fe, Mn, and NH4+-N in groundwater. This study provides a new insight into the characteristic contaminants and their distributions in groundwater and the associated pollution sources based on the large datasets in an intensive industrial area. The data evaluation methods and results of this study could be useful to the groundwater usage management and pollution control in this area and other industrial areas.

Microplastic abundance and accumulation patterns in eelgrass (Zostera marina L.) meadows throughout coastal Massachusetts, USA.

Microplastics are fast-emerging as another potential threat to already globally declining seagrass ecosystems, but there is a paucity of in situ surveys showing their accumulations. Here, we surveyed multiple Zostera marina L. meadows in 2020 and 2021 across Massachusetts, USA, for microplastic contamination, as well as identified factors related to patterns of accumulation. We found that microplastics were ubiquitous throughout all sites regardless of proximity to human development, with fibers being the most common microplastic type. In addition, we showed that accumulation of microplastics within seagrass meadows was related to epiphytic cover on leaves, plant morphology, and bulk-density in sediments. The results of this study provide the first in situ baseline microplastic concentrations on Z. marina plants and sediments for the temperate western North Atlantic. Additionally, we identify specific biotic and abiotic factors related to patterns of microplastic accumulation in these ecosystems.

Air Pollution and Rheumatoid Arthritis Risk and Progression: Implications for the Mucosal Origins Hypothesis and Climate Change for RA Pathogenesis.

PURPOSE OF REVIEW: The goal of this review paper is to summarize the main research and findings regarding air pollution and its association with the risk and progression of rheumatoid arthritis (RA). RECENT FINDINGS: The most studied components of air pollution included particulate matter of ≤ 2.5 microns in diameter (PM2.5), PM10, carbon monoxide (CO), nitrogen dioxide (NO2), nitric oxide (NOx), sulfur dioxide (SO2), and ozone (O3). In addition, specific occupations and occupational inhalants have been investigated for RA risk. Several studies showed that increased exposure to air pollutants increased the risk of developing RA, particularly seropositive RA. There was evidence of gene-inhalant interactions for seropositive RA risk. Fewer studies have been conducted on RA disease activity and bone erosions. Some studies suggest that patients with RA-associated interstitial lung disease may have worse outcomes if exposed to air pollution. We summarized associations between air pollution and increased RA risk, including RA-associated interstitial lung disease. Relatively few studies investigated air pollution and RA disease activity or other outcomes. These results suggest an important role of air pollution for seropositive RA development and suggest that climate change could be a driver in increasing RA incidence as air pollution increases.

Small microplastics have much higher mass concentrations than large microplastics at the surface of nine major European rivers.

Understanding the fates and impacts of microplastics requires information on their sizes, polymer types, concentrations, and spatial and temporal distributions. Here, we focused on large (LMPs, 500 µm to 5 mm) and small (SMPs, 25 to 500 µm) microplastics sampled with the exact same protocol in nine of the major European rivers during the seven months of the Tara Microplastic Expedition. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and pyrolysis coupled with gas chromatography and mass spectrometry (Py-GC-MS) analyses were used to determine the microplastics contents by number and mass. The median LMP concentration was 6.7 particles m-3, which was lower than those in other regions of the world (America and Asia). The SMP mass concentration was much higher to the LMP concentrations, with SMP/LMP ratios up to 1000 in some rivers. We did not observe a systematic positive effect of urban areas for the two size classes or polymers; this could be explained by the fact that the transport of microplastic is highly heterogeneous in rivers. We believe that this study has important implications for predictive models of plastics distribution and fate in aquatic environments.

Untargeted metabolomic insights into plastisphere communities in European rivers.

Every year, rivers introduce a staggering amount of hundred kilotons of plastic into the Oceans. This plastic is inhabited by microorganisms known as the plastisphere, which can be transferred between different ecosystems through the transport of microplastics. Here, we simulated the microbial colonization of polyethylene-based plastic pellets that are classically used to manufacture large-scale plastic products. The pellets were immersed for 1 month in four to five sampling stations along the river-to-sea continuum of nine of the major European rivers. This study presents the first untargeted metabolomics analysis of the plastisphere, by using ultra high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS). The plastisphere metabolomes were similar in the Rhine and Rhone rivers, while being different from the Tiber and Loire rivers, which showed greater similarity to the Thames and Seine rivers. Interestingly, we found a clear distinction between plastisphere metabolomes from freshwater and marine water in most of the river-to-sea continuum, thus suggesting a complete segregation in plastisphere metabolites that is not consistent with a major transfer of microorganisms between the two contrasted ecosystems. Putative annotations of 189 discriminating metabolites suggested that lipid metabolism was significantly modulated. These results enlightened the relevance of using environmental metabolomic as complementary analysis to the current OMICs analysis.

Exploring the association between atmospheric pollutants and preterm birth risk in a river valley city.

Objective: To investigate the association between exposure to atmospheric pollutants and preterm birth in a river valley-type city and its critical exposure windows. Methods: A retrospective cohort study was used to collect data from the medical records of preterm and full-term deliveries in two hospitals in urban areas of a typical river valley-type city from January 2018 to December 2019. A total of 7,288 cases were included in the study with general information such as pregnancy times, the number of cesarean sections, occupation, season of conception and regularity of the menstrual cycle. And confounding factors affecting preterm birth were inferred using the chi-square test. The effects of exposure to each pollutant, including particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO) and ozone (O3), during pregnancy on preterm birth and the main exposure windows were explored by establishing a logistic regression model with pollutants introduced as continuous variables. Results: Maternal age, pregnancy times, number of births, number of cesarean sections, season of conception, complications diseases, comorbidities diseases, hypertension disorder of pregnancy and neonatal low birth weight of the newborn were significantly different between preterm and term pregnant women. Logistic regression analysis after adjusting for the above confounders showed that the risk of preterm birth increases by 0.9, 0.6, 2.4% in T2 and by 1.0, 0.9, 2.5% in T3 for each 10 µg/m3 increase in PM2.5, PM10, NO2 concentrations, respectively. The risk of preterm birth increases by 4.3% in T2 for each 10 µg/m3 increase in SO2 concentrations. The risk of preterm birth increases by 123.5% in T2 and increases by 188.5% in T3 for each 10 mg/m3 increase in CO concentrations. Conclusion: Maternal exposure to PM2.5, PM10, NO2, CO was associated with increased risk on preterm birth in mid-pregnancy (T2) and late pregnancy (T3), SO2 exposure was associated with increased risk on preterm birth in mid-pregnancy (T2).

Microplastics predominantly affect gut microbiota by altering community structure rather than richness and diversity: A meta-analysis of aquatic animals.

The impacts of microplastics on the gut microbiota, a crucial component of the health of aquatic animals, remain inadequately understood. This phylogenetically controlled meta-analysis aims to identify general patterns of microplastic effects on the alpha diversity (richness and Shannon index), beta diversity, and community structure of gut microbiota in aquatic animals. Data from 63 peer-reviewed articles on the Web of Science were synthesized, encompassing 424 observations across 31 aquatic species. The analysis showed that microplastics significantly altered the community structure of gut microbiota, with between-group distances being 87.75% higher than within-group distances. This effect was significant even at environmentally relevant concentrations (≤1 mg L-1). However, their effects on richness, Shannon index, and beta diversity (community variation) were found to be insignificant. The study also indicated that the effects of microplastics were primarily dependent on their concentration and size, while the phylogeny of tested species explained limited heterogeneity. Furthermore, variations in gut microbiota alpha diversity, beta diversity, and community structure were correlated with changes in antioxidant enzyme activities from the liver and hepatopancreas. This implies that gut microbiota attributes of aquatic animals may provide insights into host antioxidant levels. In summary, this study illuminates the impacts of microplastics on the gut microbiota of aquatic animals and examines the implications of these effects for host health. It emphasizes that microplastics mainly alter the community structure of gut microbiota rather than significantly affecting richness and diversity.

Digital transformation and synergistic reduction in pollution and carbon Emissions--An analysis from a dynamic capability perspective.

Pollution control and carbon emission reduction pose significant challenges for developing countries, and achieving synergistic reductions in pollution and carbon emissions (SRPCs) has emerged as the optimal choice. Digitization, a prevailing trend in the current era, presents new opportunities for realizing the SRPC. We utilize data from Chinese A-share listed companies from 2007 to 2022 to investigate the impact of corporate digital transformation on the SRPC. The research reveals the following. (1) Enterprise digital transformation significantly promotes the SRPC, a conclusion validated through a series of robustness tests. However, the SRPC resulting from digital transformation is characterized by weak synergy, which is primarily observed in the coordinated reduction in air pollution and carbon emissions. (2) This effect is notably stronger for companies near environmental regulatory authorities, those receiving government environmental subsidies, and state-owned enterprises (SOEs). (3) Enhancing managerial collaborative management capabilities, promoting corporate technological innovation capabilities, and alleviating financing pressure are the primary mechanisms at play. This research provides important policy and practical insights for promoting the SRPC in the context of digitization, fostering sustainable development.

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.

The development of Giant reed biochar for adsorption of Basic Blue 41 and Eriochrome Black T. azo dyes from wastewater.

The textile industry is discharging high concentrations of anionic and cationic azo dyes into the nearby environment, which can cause adverse effects on public health, and the aquatic environment. Therefore, this study aimed to develop giant reed biochar and apply for the removal of Basic blue 41 (BB41) and Eriochrome black T (EBT) azo dyes from water. Characterization techniques such as BET surface area analyzer, Fourier-transform infrared spectroscopy (FT-IR), Raman spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermal gravimetric analyzer (TGA) were applied for biochar description. The biochar exhibits a high fixed carbon content (80.4%), a low ash content (3.8%), a large surface area (429.0 m2/g), and good thermal stability. High removal efficiencies of BB41 98.6% and EBT 82.5% were recorded at the specific experimental condition. The experimental data were fitted with the Langmuir isotherm model at R2 0.99 for both dyes whereas the adsorption kinetics revealed the pseudo-second-order kinetics at R2 ∼ 1 and 0.99 for BB41 and EBT, respectively. Furthermore, four regenerations of biochar with adsorption performances of BB41 and EBT dyes were found to be 94.7% and 79.1%, respectively. Finally, this adsorbent can be considered an economically viable material for the removal of synthetic dyes from wastewater systems. In conclusion, the study findings showed that the adsorbent material is promising to apply for water and wastewater treatment but still, the study of adsorption interaction and modifications of the surface functionalities are essential to accommodate multipollutant removal from real water systems.

Optimized air quality management based on air quality index prediction and air pollutants identification in representative cities in China.

With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.