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In this paper, concentrations of some heavy metals in surficial sediments of the International Anzali Wetland were measured, this wetland is located in the northern part of Iran. Sediment pollution levels were examined and analyzed using reliable pollution indices including Pollution Load Index (PLI), Geoaccumulation Index (Igeo) and Enrichment Factor (CF), and finally it was revealed that heavy metal pollution ranged from low to moderate loads in the wetland. According to Sediment Quality Guidelines (SQGs) and Ecological Risk Index (ERI), it was concluded that As and Ni may have significant toxic impacts on aquatic organisms and also according to Effect Range Median (ERM), the toxicity probability of sediments in the Anzali wetland was estimated at 21%.

Estimation of long-term exposure to air pollution levels over a large spatial domain, such as the mainland UK, entails a challenging modelling task since exposure data are often only observed by a network of sparse monitoring sites with variable amounts of missing data. The paper develops and compares several flexible non-stationary hierarchical Bayesian models for the four most harmful air pollutants, nitrogen dioxide and ozone, and PM₁₀ and PM2.5 particulate matter, in England and Wales during the 5-year period 2007–2011. The models make use of observed data from the UK's automatic urban and rural network as well as output of an atmospheric air quality dispersion model developed recently especially for the UK. Land use information, incorporated as a predictor in the model, further enhances the accuracy of the model. Using daily data for all four pollutants over the 5-year period we obtain empirically verified maps which are the most accurate among the competition. Monte Carlo integration methods for spatial aggregation are developed and these enable us to obtain predictions, and their uncertainties, at the level of a given administrative geography. These estimates for local authority areas can readily be used for many purposes such as modelling of aggregated health outcome data and are made publicly available alongside this paper.

In the context of transboundary rivers, which constitute intricate fluvial ecosystems, the persistent threat of heavy metals (HMs) contamination poses significant risks to ecosystem health. In this study, ecotoxicological hazards, governing factors, and the distribution of nine HMs (uranium (U), lead (Pb), cadmium (Cd), nickel (Ni), chromium (Cr), manganese (Mn), iron (Fe), zinc (Zn), and copper (Cu)), as well as sediment characteristics (sand, silt, clay, organic matter, and pH) are assessed within the sediment. The current investigation encompasses the analysis of twenty-seven sediment samples, utilizing inductively coupled plasma mass spectrometry, in the transboundary river basin of Bangladesh, specifically the Teesta River. Notably, the findings underscore the predominance of Cd as a contaminant, responsible for 51.85%, 81.84%, and 100% of the geo-accumulation index, contamination factor, and enrichment factor, respectively. The Teesta River emerges as moderately to highly polluted, with cumulative probabilities of 7.4%, 85.2%, and 7.4% denoting “medium”, “high”, and “priority” pollution levels, respectively. Regions in the upstream and downstream middle sections of the study area exhibit relatively higher pollution levels, particularly in proximity to Kaunia Upazila in the Rangpur district. Ecologically, the potential risk index indicates a low likelihood of ecological impacts at 77.8%, alongside a moderate risk observation of 22.2%. The current results attribute the distribution of these HMs to the pH and organic matter content within the sediment, serving as pivotal factors. To unravel the origins of the HMs, the positive matrix factorization (PMF) model successfully identifies four contributing factors, primarily from geogenic sources. Validation of the PMF model through Spearmen correlation and principal component analysis (PCA) reveals a consistent pattern, affirming its efficacy in this analysis. Within the region, HM sources are identified as originating from anthropogenic activities such as irrigation, industrial discharges, and domestic effluent, in addition to substantial inputs from geogenic sources. Recognizing the transboundary nature of metal pollution, the current study underscores the imperative for continuous and vigilant monitoring, coupled with the implementation of robust management practices. The interplay of both anthropogenic and geogenic factors necessitates a comprehensive approach to effectively and sustainably combat HM contamination. •Levels of 9 HMs in the sediments of the Teesta River was compared.•Pollution status and potential ecological risks of HMs were determined.•Impacts of pH and organic matter on the deposition of HMs in sediments were explored.•Sources of HMs and partitioned using PCA and PMF models were identified.

Industrial wastewater is the largest contributor of toxic pollutants and third-largest contributor of nutrients to bodies of water in China, and understanding the characteristics of such pollution is important for water pollution control. In this study, the industrial gray water footprint (GWF) of each industry sector in China’s 31 provinces in 2015 was calculated to identify the pollution characteristics of industrial wastewater discharge and determine how to efficiently allocate investment to pollution reduction. We show that the total industrial GWF of China was 300 billion m 3 in 2015 and that the major pollutants were petroleum pollutant (PP), ammonia nitrogen (NH 3 -N), volatile phenol (VP), and chemical oxygen demand (COD). The water pollution level (WPL) was higher than 1 in Ningxia, Shanxi, Hebei, Tianjin, Shanghai, Henan, and Shandong, indicating that industrial pollution exceeded the carrying capacity of local water bodies in these seven regions. Given equivalent total investment, a scenario that takes the total reduction of the industrial GWF weighted by the WPL in each region as the investment target can better allocate funds to control industrial wastewater pollution in regions with high WPLs relative to a scenario in which investment targets the reduction of the unweighted total industrial GWF. For further industrial GWF reduction in regions with high WPLs, it is crucial to adjust the industrial structure and to upgrade relevant technologies.

This paper studies the firm-level relationship among productivity, decision to export, and environmental performance. The emerging theoretical and empirical literature suggests that trade has an important role in determining firms' heterogeneity: increased openness to trade induces a real-location effect that increases within-industry efficiency, thereby linking firms' decisions to export and adopt newer (and cleaner) technology. We argue that this framework provides the following empirically-relevant predictions: there is an inverse relationship between firm productivity and pollution emissions per unit output; exporting firms have lower emissions per unit output; and larger firms have a lower emission intensity. To examine these implications empirically, we have assembled a uniquely detailed dataset of the U.S. manufacturing industry for the years 2002, 2005, and 2008 by matching facility-level air emission data from the U.S. Environmental Protection Agency with the facility's economic characteristics contained in the National Establishment Time Series database. The strategy is to first estimate a facility-level total factor productivity parameter as a plant-specific fixed effect. We then investigate how this estimated productivity parameter correlates with emission intensity on a pollutant-by-pollutant basis. Our empirical findings support the hypotheses suggested by the conceptual model. For each criteria air pollutant considered, we find a significant negative correlation between estimated facility productivity and emission intensity. Conditional on a facility's estimated productivity and other controls, exporting facilities have significantly lower emissions per value of sales than non-exporting facilities in the same industry. We also find that plant size is negatively and significantly related to emission intensity for all pollutants.

One of the rivers in the Gamo Zone is the Kulfo River, which empties into Chamo Lake. Most people use river water for recreational and agricultural purposes. However, a combination of human and natural factors, including farming practices, weathering, wastewater discharge, and rock-water interactions, are degrading the river water quality. The present research made an attempt to investigate the pollution status of river water by Comprehensive Pollution Index (CPI) and identify the potential pollution sources using Principal Component Analysis (PCA). Consequently, the Comprehensive Pollution Index (CPI) value ranged from (6.2–10.2), indicating that the pollution status during the rainy season was in the severely polluted category. In contrast, there was a range in the pollution levels during the Belg season, from moderately polluted (1.96) to severely polluted (4.07). Conversely, according to Heavy Metal Pollution Index (HPI), both sampling events’ pollution levels were in the high pollution class, with values ranging from (100.8 to 930). The principal component analysis identifies two components (PC1 and PC2) for rainy (68.32% and 31.63%) and Belg season (75.157% and 24.82%) from the total variance. So, in addition to natural processes like soil erosion and rock weathering, the most likely sources of pollution are inflow of residential and commercial waste, leachate, and agricultural wastes. Therefore, to reduce pollution in the Kulfo River, integrated waste water and watershed management is crucial.

This study presents a comprehensive approach for predicting pollution levels on high-voltage insulators in the Eastern Region of Saudi Arabia using five machine learning techniques integrated with the Improved Harris Hawks Optimizer (IHHO). The focus is on accurately estimating two key pollution metrics: Equivalent Salt Deposit Density and Normalized Salt Deposit Density. IHHO was used for hyperparameter optimization to guarantee that all proposed models function adequately. The prediction model is constructed from one year’s worth of environmental data, which includes temperature, humidity, wind speed, solar radiation, elevation, dew point, precipitation, line voltage, and service period. Of all evaluated models, the hybrid IHHO-XGBoost model performed best, with an R 2 of 0.993, a MAE of 0.0002, an RMSE of 0.00015, a MedAE of 3.563 × 10⁻ 5 , an EV of 0.992, and an Adj R 2 of 0.9923 with tenfold cross-validation. Model validation using Taylor diagram analysis confirmed a high degree of agreement between predicted and actual values. Furthermore, application of the SHAP (SHapley Additive Explanation) technique revealed that the most important predictors were wind speed, temperature, line voltage, and solar radiation. In addition, the results were compared to the results of other benchmark models to improve model explained accuracy and trustworthiness. Not only did the IHHO-XGBoost model best others in accuracy, it equally enhanced understanding of the environmental and operational factors that cause insulator contamination. These predictive capabilities support more effective condition monitoring and maintenance planning, ultimately contributing to improved reliability of electrical grid infrastructure in harsh environments.

The freshwater environment of watersheds in the Tibetan Plateau is bound with the safety of the Asian Water Tower. In this study, nitrogen (N) and phosphorus (P) loads delivered to freshwater and the associated gray water footprint (GWF) in the agriculture, tourism, domestic life, and industrial sectors were estimated to assess the seasonal and annual characteristics of the water pollution levels (WPLs) in the Lhasa River Basin from 2006 to 2018, and WPL calculations were compared with actual water quality measurements from 2017 to 2018. We found that more than 90% of the GWF came from anthropogenic sources. From the perspective of the whole basin, domestic life was the largest contributor to both N-related GWFs (52%) and P-related GWFs (50%), followed by agriculture for N-related GWFs (32%) and tourism industry for P-related GWFs (30%). The N emissions into the freshwater environment exceeded the maximum assimilation capacity of the watersheds in individual years at both seasonal and annual scales, while P emissions were completely within the pollution assimilative capacity. Besides, we found the serious N pollution near irrigation areas at the seasonal scale (WPL = 2.7 and TN = 1.11 mg/L). The prosperity of tourism has led to a tenfold increase in N-related GWFs and a fivefold increase in P-related GWFs for the tourism industry near the Lhasa city. The strict top-down unified management for ecological environmental protection in plateaus may be an effective method.

This study investigated the relationships between benthic macroinvertebrates and environmental variables in the Vam Co River system in southern Vietnam. Field surveys were conducted four times from May to November 2023 to evaluate seasonal variations in macroinvertebrate diversity and water quality. The river, characterized by moderate pollution levels, elevated nutrient concentrations, and substrates mainly composed of fine and coarse sand, significantly influences the distribution and abundance of benthic macroinvertebrates. A total of 32 species were identified, with bivalves and crustaceans being the most prevalent groups during the sampling period. Benthic macroinvertebrate densities ranged from 28 to 167 individuals per square meter, reflecting habitat quality linked to the substrate type. Biodiversity, assessed using the Shannon–Wiener index (H’), varied from 1.51 to 2.94, whereas the average tolerance score per individual (ATSPI) ranged from 37 to 51, indicating moderate-to-good ecological health. Species richness was positively associated with pH, total suspended solids (TSS), and dissolved oxygen (DO), suggesting that these factors support diverse communities. Conversely, ATSPI scores, which indicate pollution tolerance, were positively correlated with biochemical oxygen demand (BOD5), total nitrogen (T_N), and total phosphorus (T_P) and negatively correlated with pH, total suspended solids (TSS), and DO. These findings demonstrate the importance of benthic macroinvertebrates as bioindicators of river health and underscore the need for ongoing integrated monitoring and adaptive management to promote the sustainable conservation of the Vam Co River system.

Adjusting Polish law to EU standards, many studies were started in the 1990s on the harmfulness of presumably contaminating elements (PCE) to the environment and the quality of plants intended produced for food purposes. For this reason, in 1987, a unique microplate experiment was established on natural soils artificially contaminated with copper, zinc, lead and cadmium oxides (up to the pollution level of class I, II and III). The soils were diversified in terms of pH (through liming), organic matter content (through the addition of brown coal) and the grain size composition of the humus level (Ap) (strong clay sand and light silt clay). After 14 years from the introduction of different rates of metals into the top layer (0–30 cm) of the two soils studied, relatively large movement of heavy metals in the soil profile occurred. The amount of leached metals depended mainly on the rate of a given element. The more contaminated was the soil was, the heavier the metals that leached to lower genetic levels of soil. Contaminated soils always had a higher concentration of individual metals in Et than in Bt level. The content of the tested metals in the Et layer was determined in HCl (1 mol·dm−3) and compared to the humus level. Only at the soil depth below 50 cm (Bt), the content of the studied metals’ forms was much lower than in the surface levels. The calculated mobility coefficients of the tested metals determined in 1 M HCl indicate a larger movement of the tested metals in lighter soils than in medium soils. The highest displacement coefficients were obtained for cadmium, while the lowest were for lead. An increase in mobility was obtained alongside an increase in soil contamination with the heavy metals studied. By analyzing the mobility coefficients (heavy metal increase in the Bt and Et layers), they can be ranked in the following decreasing sequence: on light soils: Cd > Cu > Zn > Pb and on medium soils: Cd > Zn > Pb > Cu.