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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.

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.

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.

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 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.

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.

Due to the constant expansion of China’s industrial sector, environmental pollution has become a major issue that requires urgent and continuous government intervention. We use a panel of 239 Chinese cities for 2007 – 2019 and a system Generalized Method of Moments (GMM) model to estimate the impact of fiscal expenditures on industrial pollution levels. Our results suggest that local fiscal expenditures have a positive and significant impact on industrial wastewater generation, sulfur dioxide emissions, and smoke and dust pollution levels, as well as on pollution intensity. Conversely, expanding environmental protection initiatives helps significantly improve environmental quality. In addition, government expenditure on education has a negative and statistically significant influence on both industrial wastewater and smoke and dust pollution, while higher spending on research and development (R&D) helps curb sulfur dioxide emissions and pollution intensity. We also demonstrate that green financing initiatives can strengthen the negative relationship between education expenditure and R&D spending on the one hand and pollution level and its intensity on the other. Hence, our results offer suggestions for improving the composition of government expenditures and therefore better controlling pollution levels, which can be achieved by increasing investment in environmental protection, spending more on education and R&D, and promoting the spread of green financing initiatives.

Livestock and poultry (LP) pollution affects water quality of water resources. In this study, spatio-temporal variations in amount, structure, and discharge of LP pollutant in the water source area of the Middle Route of South-to-North Water Diversion Project (MR-SNWDP) in China on the county scale were analyzed. In this regard, the gray water footprint (GWF) was employed as an indicator for quantitative evaluation of LP pollution to measure the impact of these parameters on local water resources. Based on the statistical data for the time period of 2000–2017, the results showed that the total amount of LP farming has steadily increased, except for a slight decrease in the years 2007 and 2014. Also, the counties, Dengzhou (DZ), Neixiang (NX), and Xichuan (XC), are found to be the biggest polluters. The GWF of total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) was calculated to be 12.7, 8.6, and 2.8 billion m 3 in 2017, respectively, with GWF TN  > GWF TP  > GWF COD . The pollution of TN caused by LP has a greater impact on water quality than COD and TP. In 2017, the water pollution level (WPL) of water source area is 0.28, it means LP pollution required 28% of the total local water resources to be diluted. Additionally, the WPL for DZ, NX, and XC was found to be greater than 1, and it is concluded that the water resources of these regions face an environmental threat. Based on the area scale of the water sources, policies and incidence of diseases mainly affected the changes in the number of LP farming. On the county scale, the total amount and structure of LP was affected by factors such as terrain, traffic, economic level, and breeding mode. It is recommended that different policies and disposal methods should be adopted based the LP farming conditions in different cities.

To assess the efficacy of the “Implementation Details of Air Pollution Prevention and Control Action Plan”, the chemical composition of PM2.5 and other pollutants was determined during the winters of 2013–2014 and 2016–2017 at two urban sites in Xiangtan City, Hunan. The concentrations of PM2.5, SO2, and NO2 decreased from 146.0 to 94.5 μg/m3, 75.9 to 33.5 μg/m3, and 80.6 to 55.8 μg/m3, respectively, from winter 2013–2014 to winter 2016–2017. The concentrations of almost all the major chemical components of PM2.5 decreased as well, particularly secondary inorganic aerosols (SIAs). These results indicate that the implementation of the air quality control plan was very effective in improving air quality. Analysis of the data also suggests that SIA formation is likely responsible for high winter PM2.5 pollution and that high relative humidity levels and low wind speed can promote the formation of SIA. A 72-h back trajectory analysis shows that both regional transport and the accumulation of local pollutants under stagnant meteorological conditions promote the occurrence of episodes of high wintertime pollution levels.

Rapid and extensive social and economic development has caused severe soil contamination by heavy metals in China. The spatial distribution, pollution levels, and health risks of metals were identified in an oasis-desert zone of northwest China. The mean concentrations of six heavy metals exceeded their corresponding background contents, and each metal concentration in farmland samples was higher than that in Gobi samples. Moreover, these heavy metals followed a similar spatial pattern and showed significant positive correlations with each other, indicating that they have the same sources. The contamination features of heavy metals and ecological risks were calculated using several quality indicators, and their health risks for population groups were quantified. The results showed that the Gobi and farmland soils were uncontaminated to moderately contaminated by heavy metals, and that farmland pollution was more serious than that of Gobi. The Gobi and farmland soils posed low ecological risks. As a whole, the non-carcinogenic risk which was caused by heavy metals was low for local residents, and the carcinogenic risk was within an acceptable level. Comparatively speaking, children were the more vulnerable population to health risks. The Zn and Cu pollution was relatively serious, and Cr and V were major contributors to health risks.