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Monitoring airborne nanoparticles has a vital role in indoor air quality control due to their hazardous effects on human health. Detecting particles becomes more challenging as their sizes decrease. While research-grade instruments like the scanning mobility particle sizer (SMPS) can provide detailed and useful information, they are not practical for personal use due to their size and cost. This study aims to provide a comparable prediction of the temporal size distribution of ultrafine particles (UFPs, <100nm) using mid-cost measurements from a handheld particle sizer, which is more economical but has a narrower detectable size range. To achieve this, the study builds upon a computational modeling approach based on a mass-balance equation to estimate the time-varying particle size distribution, while accounting for particle evolution processes such as coagulation, deposition, and ventilation. The analytical model for indoor UFPs requires prior information regarding particle dynamic behavior, such as the size-resolved deposition rate and source emission rate. This study estimates, rather than pre-determines, the model parameters required for the temporal prediction of indoor UFP size distribution by applying Bayesian parameter inference with the analytical model of indoor aerosol. The results indicate that the present model reasonably predicts the temporal evolution of particle distributions, comparable to that of the SMPS. Furthermore, this study demonstrates the identifiability of model parameters, considering both the entire and detectable size ranges, through variance-based global sensitivity analysis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Wind farms (WFs) have grown significantly in recent years, especially in the offshore. However, their construction can adversely affect marine life and expose species to microplastics (MPs) pollution, posing a threat to human health through seafood consumption. In order to further understand the impact of WF construction activities on swimming animal resources and the accumulation of MPs ingested by dominant economic species occupying significant ecological niches, this study investigated changes in swimming animal resources over six periods during three years of WF construction in the South Yellow Sea and evaluated MPs ingestion in three economically valuable dominant species (Portunus trituberculatus, Collichthys lucidus, and Coilia nasus) before and after construction. The study found that the most significant negative impacts on swimming animal resources occurred during the early construction stages, with recovery observed as construction finished. By the operational phase in autumn 2021, all indicators had exceeded pre-construction levels from spring 2019, except for biomass, which had not fully recovered. MPs were identified in every biological sample, and the MPs contamination in the gills was higher than that in the gut and skin tissues. The average abundance of MPs and the overall MPs hazard index of the three organisms were higher than those in other areas and pre-construction levels. WF construction in the South Yellow Sea caused short-term negative impacts on swimming animal resources with long-term recovery, but the increased MPs pollution during the construction process requires continuous monitoring and management to achieve sustainable development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Urbanization is rapidly increasing worldwide, leading to rising levels of pollution, one of the major drivers of environmental change; yet little is known about the relationship between urbanization intensity and pollution levels in pollinator taxa. Toxic metals are among the most common contaminants in urban environments, but few data exist on their presence in the flora and fauna of cities in Latin America, one of the world's most urbanized and biologically diverse regions. In this study, we used an urban-rural gradient approach to analyze the relationship between the concentrations of eleven metals present in adult honeybees (Apis mellifera) and the degree of urbanization within twelve landscapes in the metropolitan area of Pachuca, Hidalgo, which forms part of the megalopolis of Mexico City. Metal concentrations were compared with previously reported values contrasting honeybees from urban and rural areas after standardizing urbanization levels among published reports. The concentrations of Ag, Cr, Cu, and Zn in honeybees increased significantly with the degree of urbanization. Urbanization was not found to influence the levels of Al, Ba, Cd, Mn, and Sr in honeybees. The maximum concentrations of six metals in our urban sites (Al, Ba, Cd, Cu, Mn, and Sr) were higher than the maximum values reported for bees in other urban areas. The concentrations of two metals measured in our study (Cr and Zn) were within the range of values previously published for urban areas. Compared to other studies, we did not detect Pb in the body of honeybees. We conclude that the concentrations of Ag, Cr, Cu, and Zn present in honeybees are a quantitative reflection of the degree of urbanization in central Mexico. Our results highlight the need to monitor metal emission sources in this and other areas and investigate their effects on bees and other pollinator taxa., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

A notable greening and warming of the Arctic and Subarctic due to climate change has uncertain implications for the global cycling of mercury (Hg). Snowpacks are dynamic reservoirs for Hg susceptible to solar radiation and wind pumping, with vegetative cover potentially altering Hg photochemistry. However, the impact of northern greening on the transformation of major Hg species and on Hg stocks remain poorly understood. Temporal surface snow and snowpit sampling was conducted under tree canopies and open tundra sites at the boreal-tundra ecotone in Nunavik, Canada. Maximum (mean) concentrations of 69.1 ng/L (8.8 ng/L) total mercury (HgT) and 46.9 ng/L (5.5 ng/L) reactive mercury (HgR) were measured in forest surface snow, with maximums attributed to rapid atmospheric oxidation events. Significant post-depositional reductions were recorded in the bay, tundra, and forest (67-99% HgR) and suggested greater Hg sequestration may occur under tree canopies. Increasing methylmercury (MeHg), HgT, and dissolved organic carbon (DOC) concentrations were detected across a vegetation gradient shifting towards humic-like organic matter. Notably, springtime depth profiles presented an approximate 12-fold greater accumulation of HgT under tree canopies compared to open tundra (p < 0.01), with up to 16-times higher stocks (HgT, MeHg, DOC) at elevated vegetation density (p < 0.05). In the North, increasing vegetation cover and surface warming may favor Hg accumulation and methylation in snowpacks, facilitated by interactions with organic matter, and further enriched by the reduced wind and solar exposure experienced under forest canopies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Fine particulate matter (PM 2.5 ) is a key air quality indicator due to its adverse health impacts. Accurate PM 2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM 2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24 h PM 2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m 3 ) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m 3 ) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m 3 ), while exhibiting a significantly reduced mean bias (MB of -0.3 μg/m 3 vs. -1.5 μg/m 3 for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m 3 , S-MESH outperforms the reanalysis (MB of -7.3 μg/m 3 and -10.3 μg/m 3 respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM 2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The pollution of micro- and mesoplastic (MMP) in the Eastern Indian Ocean (EIO) remains poorly understood. The present study revealed that MMP abundance in nekton from EIO in 2022 (mean: 2.30 ± 0.39 items individual -1 and 1.81 ± 0.54 items g -1 ) was significantly higher than that in 2021 (mean: 1.60 ± 0.22 items individual -1 and 0.80 ± 0.13 items g -1 ). In contrast, MMP abundance in surface water varied insignificantly between 2021 (mean: 0.04 ± 0.01 items m -3 ) and 2022 (mean: 0.05 ± 0.02 items m -3 ). The rise in predominant polymers-polypropylene (PP), rayon (RA), and polyester (PES)-in nekton from 2021 to 2022 may suggest increased pollution from face masks and home textiles along coastal regions. Notable spatial variation in PP and RA between the northeastern and southeastern regions was observed only in nekton, suggesting they are better indicators of MMP spatiotemporal variation than surface water. Shadow driftfish ingested more MMPs than purpleback flying squid and mackerel scad, likely due to its deeper habitat. By simultaneously considering color, composition, and shape, integrated MMP analysis showed insignificant correlation between MMP pollution in surface water and nekton, suggesting that nekton may ingest MMPs through multiple pathways beyond surface water. Risk indices for surface water and nekton reached moderate to upper levels globally, emphasizing the need for continued monitoring in the EIO. Epoxy resin, rubber, and PP + acrylic were identified as the most hazardous polymers, providing a valuable basis for developing effective strategies to mitigate plastic pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Cyanobacterial Harmful Algal Bloom (CyanoHAB) is a global aquatic environmental issue, posing considerable eco-environmental challenges in freshwater lakes. Comprehensive monitoring and accurate prediction of CyanoHABs are essential for their scientific management. Nevertheless, traditional satellite-based monitoring and process-oriented prediction methods of CyanoHABs failed to satisfy this demand due to the limited spatiotemporal resolutions of both monitoring data and prediction results. To address this issue, this paper proposes an upgraded framework for comprehensive monitoring and accurate prediction of CyanoHABs. A collaborative CyanoHAB monitoring network was firstly constructed by integrating space, aerial, and ground-based monitoring means. As a result, CyanoHAB conditions were assessed frequently covering the entire lake, its key areas, and core positions. Furthermore, by overcoming technical limitations associated with high-precision simulation of the growth-drift-accumulation process of CyanoHABs, such as the unclear drifting process of CyanoHABs and the mechanism of its coastal accumulation, the multi-scale CyanoHAB prediction was realized interconnecting the entire lake and its nearshore areas. The implemented framework has been applied in Lake Chaohu for over three years. It provided high-frequency and high-spatial-resolution CyanoHAB monitoring, as well as its multi-scale and accurate simulation. The application of this framework in Lake Chaohu had significantly improved the accuracies of CyanoHAB monitoring, simulation, and early warning. This advancement holds significant scientific value and offers potential for CyanoHAB prevention and control in eutrophic lakes., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Yinguo Qiu reports financial support was provided by Nanjing Institute of Geography and LimnologyChinese Academy of Sciences. Yinguo Qiu reports a relationship with Nanjing Institute of Geography and Limnology Chinese Academy of Sciences that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

This study examines the water quality in the Yaqui Valley in Mexico, a semi-arid region impacted by mining, agriculture, and aquaculture. Contamination sources, health risks and ecological impacts are investigated. Freshwater was found to be contaminated by dissolved As, presumed to result from mining activities in the mountains. Drainage water revealed an overall contamination by dissolved As and by suspended particles enriched with Al, Fe and Mn, associated with runoff processes. Intermittent contamination of drainage water by Cu, K + , NO 3 - and PO 4 3- is attributed to the use of fertilizers or pesticides. In the coastal area, drainage water contains high concentrations of Na, Ca, SO 4 2- and Cl, related to salinization processes, as well as higher concentrations of dissolved As, related to solid/liquid interactions that are enhanced by salinization. This drainage water discharges into the bay, degrading the seawater quality and increasing ecological risks. Outputs of this study can serve as a reference for the protection of this economically important coastal ecosystem. Concerning health risks, this study demonstrates that groundwater is also contaminated by dissolved As, believed to be associated with transfers from the geological basement to the dissolved phase of water, and other major chemicals related to salinization processes. The findings indicate that ingesting the groundwater poses a significant risk to human health with a primary exposure risk associated with dissolved As, particularly among children. This study presents crucial data for the Yaqui population, water managers and researchers, and provides novel insights into the management and mitigation of the identified risks., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Barats reports financial support was provided by National Research Agency (ANR). Gonzalez-Perez reports financial support was provided by Consejo Nacional de Ciencia y Tecnología (CONACYT). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

With the rapid progression of industrialization, the application and release of endocrine disruptors (EDCs), including bisphenol A (BPA), octylphenol and nonylphenol have significantly increased, presenting substantial health hazards. Conventional analytical techniques, such as high-performance liquid chromatography and gas chromatography-mass spectrometry, are highly sophisticated but suffer from complex procedures and high costs. To overcome these limitations, this study introduces an innovative spectral methodology for the simultaneous detection of multiple aquatic multicomponent EDCs. By leveraging chemical machine vision, specifically with convolutional neural network (CNN) models, we employed a long-path holographic spectrometer for rapid, cost-effective identification of BPA, 4-tert-octylphenol, and 4-nonylphenol in aqueous samples. The CNN, refined with the ResNet-50 architecture, demonstrated superior predictive performance, achieving detection limits as low as 3.34, 3.71 and 4.36 μg/L, respectively. The sensitivity and quantification capability of our approach were confirmed through the analysis of spectral image Euclidean distances, while its universality and resistance properties were validated by assessments of environmental samples. This technology offers significantly advantages over conventional techniques in terms of efficiency and cost, offering a novel solution for EDC monitoring in aquatic environments. The implications of this research extend beyond improved detection speed and cost reduction, presenting new methodologies for analyzing complex chemical systems and contributing to environmental protection and public health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Groundwater is the principal water source of drinking and irrigation in the Dawen River Basin of Shandong Province. Thus, its investigations and evaluations are of significant importances. Based on collected groundwater samples, this study employed a combination of the entropy-weighted water quality index(EWQI), Nitrate Pollution Index(NPI) and the human health risk assessment(HHRA) model to evaluate groundwater quality and associated health risks. The combination of EWQI and NPI provides a more refined classification of groundwater quality in the Dawen River Basin. Geostatistical and GIS spatial analysis methods are employed to analyze the spatial characteristics of groundwater quality and its relationship with geomorphology. Results indicate that the region generally enjoys good water quality, with Entropy Water Quality Index (EWQI) values ranging from 20.32 to 302.37, and an average of 70.88. Downstream quality is poorer than upstream, and flat terrains typically exhibit poorer water quality. The major indicators affecting groundwater quality include Na⁺, Cl⁻, SO₄ 2 ⁻, and NO₃⁻. The NPI results show that due to differences in anthropogenic sources, 38.1%, 27.38%, 26.19%, 4.76%, and 3.57% of the groundwater samples are classified into non-polluted, slightly polluted, moderately polluted, significantly polluted, and extremely significantly polluted types, respectively. The HHRA model reveals high potential non-carcinogenic risks for NO₃⁻ and low risks for F⁻ in the study area. The health risks associated with high levels of NO 3 - in the areas surrounding Dongping Lake and Ningyang County are greater than in those other regions and therefore should be a significant concern for public health. Furthermore, this study attempts to combine the EWQI and NPI to categorize groundwater protection and governance statuses into four types: protective, utilizable, preventive, and remedial. This approach addresses the shortcomings in comprehensively identifying water quality types by single evaluation methods and offers valuable insights for distinguishing water quality types under nitrogen pollution conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)