Pollution Intensity Research Articles

Determining an Optimal Combination of Meteorological Factors to Reduce the Intensity of Atmospheric Pollution During Prescribed Straw Burning

Currently, large-scale burning is an important straw disposal method in most developing countries. To execute prescribed burning while mitigating air pollution, it is crucial to explore the maximum possible range of meteorological changes. This study conducted a three-year monitoring program in Changchun, a core agricultural area in Northeast China severely affected by straw burning. The data included ground-level pollutant monitoring, ground-based polarized LiDAR observations, and ground meteorological factors such as planetary boundary layer height (PBLH), relative humidity (RH), and wind speed (WS). Using response surface methodology (RSM), this study analyzed key weather parameters to predict the optimal range for emission reduction effects. The results revealed that PM2.5 was the primary pollutant during the study period, particularly in the lower atmosphere from March to April, with PM2.5 rising sharply in April due to the exponential increase in fire points. Furthermore, during this phase, the average WS and PBLH increased, whereas the RH decreased. Univariate analysis confirmed that these three factors significantly impacted the PM2.5 concentration. The RSM relevance prediction model (MET-PM2.5) established a correlation equation between meteorological factors and PM2.5 levels and identified the optimal combination of meteorological indices: WS (3.00–5.03 m/s), RH (30.00–38.30%), and PBLH (0.90–1.45 km). Notably, RH (33.1%) emerged as the most significant influencing factor, while the PM2.5 value remained below 75 μg/m3 when all weather indicators varied by less than 20%. In conclusion, these findings could provide valuable meteorological screening schemes to improve planned agricultural residue burning policies, with the aim of minimizing pollution from such activities.

International pellet watch; origins and monitoring of oil markers in surface sediments and plastic resin pellets in southern Caspian Sea, Iran.

Oil pollution in marine environments, particularly along the southern coasts of the Caspian Sea, has become a serious issue due to anthropogenic activities such as shipping, tourism, fishing, and urban development. This study aimed to assess the concentration and origin of PAHs, n-alkanes, hopanes, and steranes in sediment samples (coastal area and coastal line) and resin pellets collected from 30 stations along the southern shores of the Caspian Sea (Golestan, Mazandaran, and Gilan provinces). The results showed that PAHs concentrationranged from 530.4 to 9201ng/g-dw in coastal area sediments, 753 to 10,528ng/g-dw in coastal line sediments, and 5920.7 to 16,548ng/g-pellet in resin pellets. The average total concentration of alkanes ranged from 251 to 7059μg/g-dw in coastal area sediments, 273 to 8401.6μg/g-dw in coastal line sediments, and 1967 to 22,258μg/g-pellet in resin pellets. The total terpene concentration in coastal area sediments was 60.5 to 537.5ng/g-dw, in coastal line sediments 120.6 to 811.3ng/g-dw, and in resin pellets 227 to 2886.5ng/g-pellet. Additionally, the highest concentrationwere observed in port-related areas (Bandar Anzali, Amirabad, and Astara), while the lowest concentrationwere found in recreational areas. Resin pellets generally exhibited higher concentrationof all investigated compounds compared to sediments. The classification of PAH pollution intensity based on global criteria indicated moderate to very high contamination in sediments and high to very high contamination in resin pellets. The determination of pollution levels based on the concentration of n-alkanes in southern Caspian Sea sediments revealed very high and considerable pollution. The categorization of hopane compounds' pollution intensity in resin pellets, according to International Pellet Watch classification, indicated a lack of pollution in all samples. The primary source of all compounds was identified as petrogenic.

Influencing Mechanism of Precipitation Pollution Intensity in Qinhuai River Basin Based on Random Forest

Analyzing the distribution characteristics of precipitation pollution intensity in the basin and identifying the main factors affecting the precipitation pollution intensity are the important basis for realizing the accurate management of diffused pollution. Based on the surface water quality data from four typical sections of the main stream of Qinhuai River Basin and rainfall data collected from 2021 to 2022, the distribution characteristics of precipitation pollution intensity in the basin were analyzed, and representative natural and social factors were selected to construct models of the precipitation pollution intensity of ammonia nitrogen （NH4+-N）, permanganate index, and total phosphorus （TP） based on random forest algorithm. Additionally, the main driving factors of precipitation pollution intensity were identified, and the influencing mechanism was analyzed. The results showed that rainfall was the main driving factor for the water quality exceeding the standard in the Qinhuai River Basin, and the proportion of rainfall events within 3 days of when the water quality of the sections in the study area exceeded the standard was between 61.4% and 97.4%. The precipitation pollution intensity of NH4+-N and TP showed a gradual increase from upstream to downstream. NH4+-N was the primary pollutant in the midstream Qiqiaoweng section and the downstream Sanchahekou section, with the precipitation pollution intensity ranging from 0.12 to 2.98 and from 0.31 to 3.84, respectively. The precipitation pollution intensity of permanganate index in the upstream Yangqiao section was the largest, ranging from 0.41 to 1.35, and the precipitation pollution intensity of the Jiangjundadao section, which is located on the channel of diverted water, was at a lower level compared with that of the other three sections. Water diversion was an important influencing factor for the reduction of precipitation pollution intensity of NH4+-N and permanganate index （P<0.01）； the proportion of built-up area was significantly positively correlated with the precipitation pollution intensity of NH4+-N and TP； the proportion of cultivated area was significantly positively correlated with the precipitation pollution intensity of permanganate index, and the proportion of forest land area was significantly negatively correlated with the precipitation pollution intensity of these three indicators. Under the conditions of light rain, moderate rain, and heavy rain, the rainfall was significantly positively correlated with the precipitation pollution intensity of each indicator, and the precipitation pollution intensity remained high under the condition of torrential rain. The post-rain pollution of NH4+-N and TP mainly originated from the middle and lower reaches of the study area, which was significantly affected by regional characteristics such as the percentage of built-up area and water diversion （P<0.01）, and the post-rain pollution of permanganate index mainly originated from the upstream area with a wide distribution of cultivated land area and was significantly affected by flow （P<0.01）.

Investigating resource productivity: the ecological potential of printed electronics as alternative to PCB

Abstract Printed electronics are gaining momentum as an alternative to etched electronics. Production of printed electronics requires less energy, water and raw materials and its flexible, transparent, stretchable and breathable potential allows for numerous new applications. While still in its infancy, printed electronics have the potential to become a permanent low-cost and efficient substitute for conventional production methods. However, despite promising assets of the printed electronics technology, few quantitative environmental analysis have been performed comparing printed electronics to the conventional printed circuit board (PCB). To fill this gap in the literature, this research evaluates and compares PCB circuitry production to that of a common printing technology; screen printing. A combination of a resource productivity and pollution intensity evaluation and a material input and output analysis is performed. Performance is evaluated by gathering data from existing literature, expert consultancy and own calculations on production data and product weight. Printed electronics were evaluated according to a worst-case scenario and PCB to a best-case scenario in terms of resource consumption. The findings reveal substantial disparities in resource productivity and pollution intensity between printed electronics and PCB. Printed electronics demonstrate superiority across all indicator ratios in comparison to PCB as a consequence of - for example - a zero-water production process, or a printed solution which is 16.14 times lighter in terms of weight. This research thereby contributes to informing the electronics sector about a promising alternative to PCB from the perspective of resource efficiency and pollution intensity. As such, our research results provide a lever for future research investigating the scalability and profitability of the implementation of this technology.

Spatio-temporal analysis of extreme air pollution and risk assessment.

Extreme air pollution poses global health and environmental threats, necessitating robust policy interventions. This study first analyses the surface mass concentration of major aerosols (such as black carbon, organic carbon, dust, sea salts, and sulphates) to estimate global PM2.5 concentrations from 1980 to 2023. The developed model-estimated PM2.5 database was validated against data from 526 cities worldwide, showing strong accuracy, with RMSE, r, and R2 values of 7.47μg/m³, 0.87, and 0.75, respectively. The motivation arises from the need to understand whether recent pollution increases are driven by rising emissions or natural variability, given the significant impacts on life and property. To assess both short-and long-term pollution trends, magnitudes, and risks, we proposed twelve novel extreme pollution indices, which comprehensively characterize the spatial and temporal variations in pollution. The highest PM2.5 concentrations were observed in regions near the Saharan Desert, reaching up to 90,000μg/m³. However, significant PM2.5TOT (total pollution) concentrations were also found in the Indo-Gangetic Plain (IGP) and eastern China, ranging from 20,000 to 40,000μg/m³. Persistent pollution burdens North Africa for approximately 350 days annually, while the IGP and eastern China experience extreme pollution for over 200 days yearly. Other pollution indices highlight the intensity and frequency of pollution in regions such as North Africa, IGP, Eastern Russia, Western USA, and Eastern China, revealing critical regional air quality challenges. Our analysis identifies cities in low-income and middle-income countries, such as New Delhi, Lahore, Dhaka, and Dammam, as being at extreme risk scores above 90 out of 100. Meanwhile, cities like Ghaziabad, Chongqing, Kolkata, Mumbai, and East London fall into the high-risk category, scoring between 60 and 80. Conversely, most cities in the EU, USA, and Canada are at very low risk, a result of the effective implementation of strategic air pollution norms and policies. The study promotes a phased approach for low- and middle-income regions, emphasizing achievable air quality standards, low-cost monitoring, targeted interventions, urban greening, public awareness, and innovative financing for improvements.

Small and medium-sized banks and pollution emissions from industrial enterprises

Drawing upon the theory of comparative advantage of small and medium-sized banks, this paper employs pollution data from industrial enterprises in China spanning 2000 to 2014. Utilizing the establishment of urban commercial bank branches in counties as a quasi-natural experiment, we investigate the impact of the development of small and medium-sized banks on industrial enterprises' pollutant emissions and the underlying mechanisms of this effect. Our findings reveal that the establishment of urban commercial bank branches in counties significantly reduces wastewater and sulfur dioxide emissions from enterprises, a result that remains robust across various tests. Further heterogeneity analyses show that this pollution reduction effect is more pronounced for small and medium-sized, more finance-constrained, start-up firms, and firms located in areas with low carbon pilots and higher levels of marketisation. The analysis of the impact mechanism suggests that the establishment of urban commercial banks achieves this reduction by increasing enterprise output, thereby diluting pollution intensity, and fostering green technological innovation, which enhances pollution treatment capabilities. Additionally, our research highlights that government intervention significantly influences the pollution reduction effect of urban commercial banks and the mode of this influence.

Environmental judicature and firm productivity: Evidence from a quasi-natural experiment.

The impact of environmental governance on firm productivity has been widely discussed, but few studies have examined the function of environmental judicature. Using the establishment of environmental courts as a quasi-natural experiment, this paper examines the relationship between environmental judicature and firm productivity. Our findings show that environmental courts will reduce firm productivity in the short term, chiefly owing to the increased environmental violation costs, environmental reputation costs and environmental compliance costs. The conclusion remains robust after mitigation of heterogeneous treatment effects, PSM-DID estimation, placebo tests and IV estimation. This negative effect is more pronounced for firms located in regions with high legal scores and low government intervention, for firms located in the eastern region, for firms with weak market power, and for firms with high pollution intensity. In addition, our further analysis suggests that environmental courts would improve the long-term firm productivity and regional green productivity, indicating that strengthening environmental judicature are conducive to firms' sustainable growth and regions' green transformation in the long run.

Import competition and pollution abatement: Firm-level evidence from China

The relationship between international trade and environmental has garnered considerable attention. However, research on developing economies’ experiences remains scant. This paper investigates the impact of import competition on pollution emissions of firms in China, the largest developing country, utilizing micro-firm survey data from 2000 to 2012. Our study reveals that import competition significantly curtails firms’ SO2 emission intensity. The employment of DID and 2SLS strategies corroborates the robustness of our results. Furthermore, we find that import competition bolsters firms’ technical efficiency, leads to alterations in product mix and industry features, and improved pollution treatment efficiency, culminating in diminished pollution intensity. However, there is no evidence that import competition has encouraged firms to prioritize green innovation and the use of clean energy. We also introduce a novel approach to identify ‘pollution leakage effects’. When this effect is taken into account, the pollution abatement effect of import competition still exists.

Does enterprise green innovation contribute to the carbon emission reduction? Evidence from China

Green innovation serves as a driving force for enterprises to enhance their competitiveness and may offer an effective pathway for reducing carbon emissions. This study, using data on green innovation and carbon emissions from publicly listed companies between 2000 and 2022, examines the impact of green innovation on enterprise carbon emissions and its specific transmission channels. The findings reveal that: 1) Green innovation significantly reduces enterprise carbon emissions, and this inhibitory effect remains robust across a series of stability tests. 2) Mechanistically, green innovation primarily decreases carbon emissions through improvements in energy efficiency and the specialization of enterprise management. 3) Compared to the application for green utility model patents, green innovation through the application for green invention patents is more effective in reducing enterprise carbon emissions. The carbon emission reduction effect of green innovation is found to be more pronounced in non-state-owned enterprises compared to state-owned enterprises. Furthermore, when comparing the pollution intensity and technological complexity of industries, the suppressive effect of green innovation on carbon emissions is stronger for enterprises operating in highly polluting and technologically complex industries. The findings of this study provide empirical evidence to support the promotion of green innovation for facilitating the green and low-carbon transition of manufacturing enterprises in developing countries.

Mapping of heavy metal pollution density and source distribution of campus soil using geographical information system

In this study, the pollution intensity, spatial distribution, and index-based risk distribution in campuses, which are a small prototype of cities, were mapped and the sources of heavy metals in the soil were investigated. Soil samples were taken from 9 different points from the Aksaray University Central campus, which was determined as the study area. It has been determined that the pH value in the collected soil samples varies between 8.7 and 11.0. This situation created an effect on reducing the accumulation and mobility of heavy metals in the soil. When the study area was evaluated based on the geo-accumulation index, Pb heavy metal was much denser in the places indicated as circulation areas and where students were actively present. Based on the pollution load index, it was concluded that 75% of the study area was moderately/highly polluted, and the rest consisted of unpolluted soils. Pearson correlation analysis and APCS-MLR analyses conducted to determine the source distribution showed that the contributions of natural sources, mixed sources of industrial and traffic activities, agricultural activity-based sources, and other sources were 57.49%, 21.44%, 12.67%, and 8.40%, respectively. Pb is mainly related to the mixed sources of industrial and traffic activities. Therefore, to clear up its long-term impact on the accumulation of heavy metals in the soil, it is important to conduct continuous heavy metal monitoring in the soil throughout the campus.

PM2.5 reduces the daytime/nighttime urban heat island intensity over mainland China

PM2.5 pollution severely threatens people's living environment and health, significantly affect urban heat island (UHI). In this study, in-situ observed data were combined with satellite data via refined pollution intensity classification methods, and the relationships between PM2.5 and canopy/surface UHI (CUHI/SUHI) were analyzed. PM2.5 reduced the UHI in nearly all the environments, with more pronounced effects during clear-sky daytime and winter (-0.33 °C for the SUHI) and less pronounced effects at all-sky nights and in summer. For the former, PM2.5 primarily diminishes the UHI intensity (UHII) by weakening incident radiation; this radiative forcing effect is exacerbated by the combination of PM2.5 and high humidity, which is further amplified by winter pollution peaks. At night, the UHII primarily influenced by the initial temperature conditions during the day. Less intake of surface energy on high-pollution days, which in turn affects longwave radiation from the surface/canopy at night. Additionally, PM2.5 has been found to significantly influence UHI through its interaction with potential influential factors and its filtering effect (different PM2.5 levels correspond to varying conditions of these factors). This study with new insights into the impact of PM2.5 on UHI could aid in the design of strategies to improve urban heat and pollution environments.

Shields against pollution: phytoremediation and impact of particulate matter on trees at Wigry National Park, Poland

This study examines the impact of airborne particulate matter (PM) and associated trace elements (TEs) on deciduous and coniferous trees at the edge of Wigry National Park in northeast Poland, focusing on pollution levels and the potential for phytoremediation. Researchers measured PM concentrations in the air and on the leaves of Picea abies, Quercus robur, and Corylus avellana, along with photosynthetic indicators (Fv/Fm ratio and performance index). The study found significant differences in pollution intensity across areas with varying levels of human activity. P. abies, an evergreen species, accumulated the highest PM levels (>200 μg/cm2), while Q. robur had the highest accumulation among deciduous trees (>50 μg/cm2). Trace elements such as Fe, Cu, Zn, Sr, and Cd were detected, with C. avellana being the most efficient in accumulating Cd (up to 7.5 mg/kg). The accumulation of pollutants correlated with reduced photosynthetic efficiency in trees closest to pollution sources. The findings suggest that strategically planting specific tree species can help mitigate air pollution in national parks and protect sensitive vegetation. Future research should explore the long-term effects of PM on forest health and the role of different species in phytoremediation.

Bottom-up discrete systematic modelling for analysis and prediction of future trends for land-sea environmental pollution systems

Addressing the challenge of marine environmental pollution requires accurate forecasting systems, yet current methods relying on artificial intelligence and big data face limitations due to insufficient and inconsistent marine data. This paper proposes an integrated bottom-up prediction system featuring a grey modeling module and an optimization module. The grey modeling module introduces a novel nonlinear nested systematic grey model, which employs multi-structure discretization to capture intricate and variable system changes by leveraging the dual nonlinearity of cumulative generated sequences and time trends. Nested solutions minimize prediction errors through discrete recursive equations with underlying-upper structures. The optimization module utilizes an efficient bio-inspired meta-heuristic algorithm, the Coati Optimization Algorithm, which balances global and local search strategies. The accuracy and robustness of the proposed model are enhanced by optimizing its nonlinear parameters through comparative analysis with other algorithms and conducting a sensitivity analysis on the number of iterations. The proposed prediction system is validated in representative Chinese provinces (Zhejiang, Shandong, and Hainan), demonstrating high prediction accuracy for both land-based and marine environmental pollution systems, with mean absolute percentage errors below 8% in both training and test sets. Projections indicate that by 2030, land-based source pollution intensities in these provinces will decrease by approximately 5.71%, 0.16%, and 1.64%, respectively, while marine pollution indices are expected to decline by 3.28%, 4.86%, and 2.89%. This study provides a valuable predictive tool for addressing the complexity, variability, and uncertainty of marine environmental pollution, while contributing to the promotion of land-sea integration and the protection of marine ecosystems.

LES of blockage of thermal radiation to the pool surface in large double pool fires.

Thermal radiation blockage to the pool surface plays a major role in assessing the fire growth and heat feedback to the pool surface and thereby intensity of pollution to the environment. In this work, large eddy fire simulations are performed to quantify the thermal radiation blockage to the pool surface of 0.6 m n-heptane double pool fires (DPF). The interspace between the two pools is varied from 0 to 0.6 m. The results of the air entrainment show that the considered double pool configuration is radiation dominated irrespective of the separation distances between the pools. The predicted heat feedbacks are in good agreement with the experimental results. A radiation influencing zone (RIZ) is introduced based on the percentage of the radiation contribution from the flame. RIZ is directly proportional to the flame height. Based on the opacity through the RIZ, the thermal radiation blockage is calculated. The blockage of radiation from standalone fire to double fire increased to 20%. The calculated radiative heat flux to the pool surface is in good agreement with the reported measurements. In addition, the maximum deviation between calculated and measured heat fluxes of the studied DPF is 6.8%. Further, the accuracy of the present methodology is shown better than the reported literature estimations.

Composition of the Low Molecular Weight Metabolome of Potamogeton perfoliatus (Potamogetonaceae) as an Indicator of the Transformation of the Ecological State of the Littoral Zone

The composition and nature of changes in the low-molecular-weight metabolome (NM) of Potamogeton perfoliatus L., growing in 6 biotopes of Lake Ladoga with different types of the anthropogenic load has been analyzed. According to the research results, it was found that the total number of low molecular weight organic compounds (LMWOCs) in the P. perfoliatus NM composition is directly dependent on anthropogenic load, which is well marked by the development of cyanobacteria. The greater the intensity of pollution or eutrophication of waters, or the higher the number of cyanobacteria, the lower the total number of LMWOCs and their concentration. A strongly pronounced dependence of the total concentrations of groups of NM compounds on the anthropogenic disturbance of the biotope and the concentration of cyanobacteria was revealed. A decrease in the number, relative amount, total concentration of carboxylic acids, number and content of unsaturated fatty acids, and, at the same time, an increase in the composition and content of phenols and the total content of aldehydes and ketones depends on an increase in anthropogenic pressure. The specific composition of NM of pierced pondweed depends on its response to biotic and abiotic factors of the aquatic environment, including anthropogenic ones. The revealed features of the change in the composition of P. perfoliatus NM make it possible to use it as an integral indicator of the anthropogenic impact on the littoral biotopes of water bodies and the deterioration of their ecological state.

Sustainable Urban Development: A Comprehensive Model for Assessing Health Risks from Compounded Pollution in Xi’an

Global climate change and rapid urbanization have brought unprecedented environmental challenges, especially in rapidly expanding cities like Xi’an, posing potential challenges to sustainable development if not accompanied by adequate greenery, thoughtful layouts, and pollution control measures. The increasing frequency and intensity of urban air pollution (e.g., PM2.5, PM10, O3) and extreme climate events (e.g., heatwaves) have heightened health risks for residents. Existing studies mostly focus on single pollution or climate factors, neglecting the compounded effects of these factors. To fill this research gap, this study presents a health risk assessment model, specifically by analyzing the compounded effects of heatwaves and air pollution. By integrating hazard, exposure, and vulnerability factors using the entropy weight–TOPSIS method, the results reveal significant spatial differences in health risks across various regions of Xi’an. The findings provide valuable guidance for urban planners and policymakers to better address environmental hazards, reduce health risks, and promote sustainable urban development.

Beautifying urban environment: Smart city construction and sustainable pollution control in China

Faced with the environmental pollution challenges brought by urbanization, smart city construction (SCC) offers a practical and viable pathway for environmental protection. This study treats China's SCC policy as a quasi-natural experiment, utilizing balanced panel data from 285 cities across the country spanning from 2003 to 2019. Through the multi-period Difference-in-Differences (DID) model, the influence of smart city initiatives on urban pollution emissions is systematically examined. Results demonstrate that SCC effectively control urban pollution emissions, leading to a 1.46% decrease in pollution levels in pilot cities. These findings are robust to various tests, including parallel trends analysis, placebo tests, Propensity Score Matching (PSM)-DID, alternative pollution calculation methods, pollution intensity adjustments, control for other policy interferences, and sample adjustments, alongside endogeneity tests using instrumental variables. SCC achieves pollution control through mechanisms that enhance urban technological innovation, facilitate industrial restructuring, and talent agglomeration and financial agglomeration. Heterogeneity analysis reveals a more pronounced impact in the eastern area, large cities, higher administrative levels, cities with superior digital infrastructure, and those with higher human capital levels and pollution severity. This research explores a practical approach towards achieving sustainable pollution control by optimizing technology, industrial structure, and human resources.

The Characteristics and Traceability Analysis of the Overflow Pollution During the Flood Season in an Urban Area

The issue of combined sewer overflow (CSO) triggered by rainfall has become a significant obstacle to the improvement of water environment quality. This study conducted a long-term monitoring of three types of rainwater outlets, i.e., combined sewer overflows (Test-CSO), separated sewer outlets (Test-SSO), and partially separated sewer outlets (Test-PSSO), to reveal the characteristics of overflow pollution and trace its sources by monitoring the pollutants from different underlying surfaces across various urban functional areas. The results showed that the major pollutants in overflow events exhibited the following order: COD ≥ TSS > TN > TAN > TP. Rainwater elevated COD and TSS in the Test-CSO, while reducing nitrogen and phosphorus concentrations by dilution. The Test-PSSO experienced varying degrees of overflow pollution, primarily due to the sewer sediment. A negative relationship between the rainfall and peak time of overflow pollution was observed. The traceability analysis indicated the overall pollution intensity exhibited the following order: residential areas > industrial parks > commercial areas. In addition to commercial areas, the pollution intensity across underlying surfaces generally exhibited the following order: roofs > roads > grasslands. The roof runoff was an important source of pollutants for overflow pollution, and TSS and COD were the major contributors. Notably, grasslands had a buffering effect on pollutants and pH.

Characteristics of iron-sulfur metabolism and acid-producing microorganisms in groundwater contaminated by acid mine drainage in closed coal mines

The generation of acid mine drainage (AMD) from mining activities poses significant challenges to humanity due to its intense pollution and difficult management. Despite the hostile conditions presented by AMD, a plethora of microorganisms thrive in this environment. In this study, we employ metagenomic techniques and microbial diversity analysis to systematically categorize microorganisms. Specifically, we identify Sulfur-iron metabolizing microorganisms associated with AMD, encompassing 6 phyla and 1149 genera. Functional predictions and pathogenicity analyses are conducted on this microbial community. Furthermore, we specifically emphasize the study of acid-producing microorganisms. This research marks the first discussion on the scope and structure of acid-producing microorganisms., comprising 1 phylum, 39 genera, and 1393 species. Our research outcomes comprehensively depict the microbial diversity in AMD, unveiling their influence on the physicochemical properties of AMD pollution. This study contributes valuable insights to addressing environmental challenges arising from AMD and aids in its remediation.

Unveiling Environmental Crime Trends and Intensity in the EU Countries Through a Sustainability Lens

AbstractEnvironmental crime poses a significant threat to global ecosystems, biodiversity, and human well-being, encompassing activities such as pollution, illegal waste disposal, trade in protected species, and natural resource degradation. Understanding the dynamics of these crimes is essential for devising effective mitigation strategies and promoting sustainability at micro, meso, and macro level. The aim of the study is to examine environmental crimes in the EU, focusing on their current trends, patterns, and Intensity, as well as the impact on sustainability across its three dimensions. Fourteen EU countries environmental crime data from 2016 to 2021were analysed to reveal variations and trends. Geographic information systems (GIS) utilized to identify countries with elevated environmental crime rates, emphasizing the need for interventions in pollution control, waste management, and resource conservation. Results indicate high environmental pollution intensity in Italy, Sweden, and Denmark. Moreover, Italy emerges as the epicentre of illegal waste dumping, with notable cases also present in Belgium and France. Additionally, France, Slovakia, and Italy show alarming levels of illegal wildlife trade. Furthermore, France and Sweden exhibit the highest density of natural resource degradation cases. In addition, the study unveiled a positive correlation ($$\:b>0$$) between environmental pollution, trade in protected species, and natural resource degradation with population growth, while waste disposal exhibited a negative correlation ($$\:b<0$$). Moreover, trade in protected species and resource degradation correlated negatively ($$\:b<0$$) with the poverty ratio. Addressing environmental crimes aligns with the pursuit of green justice, recognizing the interconnectedness of environmental, social, and economic factors. The findings offer valuable insights for policymakers, environmentalists, and communities, guiding interventions towards a sustainable and resilient future.