Pollution Scenarios Research Articles

Factors affecting the different growth rates of PM2.5：Evidence from composition variation, formation mechanisms, and importance analysis of water-soluble inorganic ions with case study in northern China

PM2.5 affects air quality, therefore, understanding the mechanism of PM2.5 growth is essential to figure out mitigation measures. Hourly real-time concentrations of water-soluble inorganic ions (WSIIs), including anions and cations, in fine particulate matter (PM2.5) were measured in Baoji, northwest China. During the winter monitoring period, the concentrations of PM2.5 and most WSIIs exhibited similar trends. Mass proportions of SNA [i.e., sulfate (SO42-), nitrate (NO3-), ammonium (NH4+)] in PM2.5 gradually increased with air deterioration, while equivalent ratios of anions to cations also increased. The heterogeneous aqueous reactions and/or gas-phase homogeneous reactions promoted the formation of secondary inorganics, especially during the haze events. Rapid transformations of primary gaseous precursors to secondary pollutants could lead to the substantial formation of SO42- and NO3-. In terms of particle growth rate, the mass proportions of SNA in PM2.5 decreased from General Growth (GG) to Explosive Growth (EG) events. Furthermore, the particle growth rates did not coincide with the pollution levels, while it occurred most frequently during the Transition Period, instead of the Polluted Period. The diurnal variation of SNA at different PM2.5 growth rates has been discussed. The results of the Random Forest (RF) model showed that RH was an important factor for EG of PM2.5, while low RH was a reliable reason for the relatively low mass proportion of SNA. The results of this study could advance our understanding of particle growth and provide scientific evidence to support the establishment of unique air quality control measures under different pollution scenarios in Fenwei Plain, China.

A multifunctional coconut shell biochar modified by titanium dioxide for heavy metal removal in water/soil and tetracycline degradation

Heavy metals and tetracycline pollute the environment and endanger human health. This work prepared a multifunctional coconut shell biochar (Ti-XBC) through NaHCO3 activation and TiO2 modification. The modified coconut shell biochar was characterized by various methods. Ti-4BC exhibited excellent removal performance for heavy metals. The maximum adsorption capacity of Ti-4BC on Cd2+, Pb2+, Cr3+, and Cr6+ reached 104.2, 136.8, 101.2, 112.4 mg/g in water. The adsorption of Ti-4BC for four metal ions had a competitive effect in the binary metal system. It also reduced the mobility of four heavy metal ions in soil. Density functional theory (DFT) calculations were employed to explain the mechanism of heavy metal removal. Additionally, the degradation performance and mechanism of Ti-4BC towards tetracycline were investigated. The results indicated that the maximum degradation rate was 99.4%. Overall, this work developed a multifunctional material capable of both removing heavy metals and degrading antibiotics, addressing the issue of single-function in biochar. This provided a novel approach for tackling complex and diverse environmental pollution scenarios.

EHT and environmental justice at EPA Victoria: Aspirations and regulatory realities

Abstract EPA Victoria (Australia) is a statutory authority that regulates polluting activities and advises other Victorian government departments on relevant policy settings. In 2016, a public health function was installed at EPA Vic to provide emergency health messaging during pollution events, review research evidence of environmental-health relationships and assess industry-contracted health risk assessments of polluting entities. An Environmental Health Tracking Network (EHTN) is under development to support these activities. The EHTN is an agile data and analytical infrastructure for translating expertise across an expanding multidisciplinary network of public health researchers, EPA operational staff and public sector policy makers. Its current interactive dashboards calculate health impacts and associated costs of air pollution scenarios, model land-use influences on land and water contamination and identify coincidences of sociodemographic-, urban environment- and pollution exposure-disadvantage in a high spatial resolution environmental justice (EJ) framework. Regulatory leverage of EHTN intelligence, however, requires legislated EJ thresholds and/or legally tractable costing of industry-externalised human health impacts. Thus, to establish triggers for more stringent conditions on polluting activities and promote equity of interventions and regulations, novel regionally and EJ specific health risk functions are required. Considering these ambitions and challenges, the EHTN at EPA Vic is focussed on 1) identifying and characterising vulnerable populations at high spatial resolution, 2) estimating associated exposure-health effect size modifications using primary epidemiological methods and 3) applying these to health impact and cost assessments of pollution and waste. We seek defensible grounds on which to impose more stringent standards for polluters and redress entrenched social, health and economic disadvantage. Speakers/Panelists Hanna Tolonen THL, Helsinki, Finland

Microplastics–biofilm in aquatic ecosystem: Formation, pollutants complexation, greenhouse gas emission and ecotoxicology

The omnipresent microplastics (MPs) have gradually become a significant environmental problem due to its adverse consequences for ecological systems. MPs serve as substrates for biofilms colonization, which enhances adsorption of harmful contaminants on MPs surface in the aquatic ecosystem. The present study provides a critical discussion on the mechanism involved in MPs–biofilm formation, microbial colonization and the robust factors influencing the process in the aquatic ecosystem. Subsequently, the impact of MPs–biofilm on adsorption of inorganic and organic contaminants is explored. The ecological significance of MPs–biofilm associated pollutant complex for promoting greenhouse gases (GHGs) emissions from aquatic ecosystem is extensively discussed for understanding the climatic risk. Furthermore, the discussion is extended over ecotoxicological impact of MPs–biofilm on aquatic biodiversity and humans. The protective extracellular polymeric substances secreted by colonised bacteria over MPs during biofilm formation creates sticky MPs surface for heteroaggregates formation with swift adsorption of chemical compounds and microorganisms. MPs with functional aromatic groups facilitate the bacterial adhesion on the surface, but affect formation of biofilm. Alternatively, MPs–biofilm promotes the Mn and Fe hydrous oxides formation that can co–precipitate with heavy metal ions and facilitate in remediation measures. However, MPs biodegradation generates GHGs emission per unit mass, comparably more from freshwater than marine ecosystem. Considering the toxicity, MPs–biofilm induces the oxidative response in fishes, causing painful death and thus, destroys aquatic biodiversity. This study will be useful to address MPs–biofilm associated pollution scenario via trace, test and treat strategy involving future engineering research framework for ecological restoration.

Robust regenerable metal-selenide-chitosan photocatalyst for the effective removal of Bromothymol Blue (BB) from wastewater

Water scarcity has been a crucial debate in recent years regarding the critical scenario of water pollution. The water body is continuously contaminated by organic effluents of textile industries, including pigmented dye pollutants. To tackle water bodies contamination, there is a need to develop an eco-friendly and efficient method for removing toxic dyes. Herein, ternary metal selenide nanocomposites of barium nickel selenide (NBSe-NPs) were synthesized by the solvothermal method supported by chitosan microsphere (NBSe-NPs-CM). Recovery of the catalyst was convenient by capping nanoparticles in the microsphere to maintain effective stability, biocompatibility, and well-designed surface coating. FTIR spectrum verified nanocomposite synthesis and chitosan microsphere (NBSe-CM) formation. SEM observations of nanocomposites and NBSe-CM indicated an average size of 13.78 nm and 253 μm, respectively. The presence of barium, nickel, and selenium elements in the NBS-NPs was verified by EDX analysis. The nanocomposites had a crystallite size of 15.73 nm. The photocatalyst exhibited a narrow bandgap of only 1.3 eV based on Tauc's plot. In addition, the synthesized microsphere demonstrated an efficient photocatalytic degradation (97 %) of Bromothymol Blue dye within 100 min under optimized operating conditions (pH of 6.0, dye concentration of 40 ppm, catalyst dosage of 0.25 g). The photocatalysis process followed the pseudo-first-order kinetics. The repeatability studies showed a slight decline in the catalyst's efficiency after four successive cycles. The DFT study shows that the NBSe-CM is energetically stable with more considerable negative binding energy, and the dye molecule interacts more strongly with the NBSe-CM surface. The findings highlight the exceptional characteristics of the newly designed ternary-metal-selenide-containing chitosan-microspheres for degrading dye contaminants from textile effluents.

Microplastic pollution scenario and its effects on public health and ecosystem: a Bangladesh perspective

Microplastic pollution scenario and its effects on public health and ecosystem: a Bangladesh perspective

Development and optimization of an Artificial Neural Network (ANN) model for predicting the cadmium fixation efficiency of biochar in soil

This study addresses the issue of soil cadmium pollution and the common problem of data missing during the training of artificial neural network models. A substantial amount of characteristic data regarding the immobilization of cadmium in soil through biochar was collected from the literature. Subsequently, data was organized and analyzed using correlation matrix analysis and feature importance analysis. The results revealed that the feature importance of biochar (52%) was relatively significant. A novel approach is introduced, involving a non-deterministic optimization technique based on linear interpolation to supplement the missing data within the dataset. The completed dataset effectively preserves the distributional characteristics of the original data, thereby enhancing the training performance of the neural network model. Through three phases, namely data incompleteness, data supplementation, and genetic algorithm optimization, the neural network model was progressively refined. The outcome of this iterative process is a high-performance artificial neural network model capable of predicting the efficiency of biochar in immobilizing cadmium in soil. The significance of these findings lies in their potential to advance the application of machine learning in environmental science, particularly in addressing complex pollution scenarios.

Mining of dynamic traffic-meteorology-atmospheric pollutant association rules based on Eclat method

With the rapid increase of urban vehicles, the atmospheric compound pollutants, notably PM2.5 and O3, have significantly increased and seriously affected public health. Traffic and meteorological conditions are the primary influencing factors of pollutant concentrations, and their spatial and temporal changes affect the dispersion of pollutants. Increasing use of high-resolution big data offers opportunities to explore these correlations. More extensive quantitative studies are essential for effective air pollution control. This study presents an Eclat algorithm to quantitatively reveal the relationship between traffic, meteorology and pollutants with hourly and 5-minute scale data in the urban area of Guangzhou. We establish a research framework covering temporal pollution analysis, multifactor rule mining, and spatial effects. The results show that PM2.5 and O3 exhibit coordinated trends on the daily scale influenced by traffic flow and meteorology conditions, but on the hourly scale, they are negatively correlated. At the 5-minute scale, synchronized variations occur only during specific periods. This finer scale better identifies association rules for high-concentration pollutant scenarios, and non-roadside sites outperform roadside sites in mining these associations. For example, when humidity is below 37%, atmospheric pressure is 1016.2–1020.3 Pa, wind speed is 1.7–2.6 m/s, and the traffic volume on Jiefang North Road exceeds 635 vehicles every 5 min, there is a 92.86% probability that the PM2.5 concentration at GYQ (a non-roadside monitoring site) will exceed 127 μg/m3. These findings enhance our understanding of how dynamic traffic and meteorological conditions impact atmospheric pollutants and provide a scientific basis for regional collaborative pollution prevention.

Extracting regional and temporal features to improve machine learning for hourly air pollutants in urban India

India is suffering from severe particulate matter (PM, including PM2.5 and PM10) pollution, while limited ground observations are insufficient to support a comprehensive understanding of its health risks. Machine learning (ML) has the potential to improve the estimation of PM distribution and exposure efficiently. Regional transport as well as accumulation and dispersion processes of PM and its components, which have significant impacts on PM concentrations, are crucial when building ML models, especially for sparsely observed regions like India. Here, geographic and temporal-rolling weighting methods were used to separately extract regional and temporal features for improving the performance of the ML model. The incorporation of temporal and regional features into the ML model significantly improved ML model performance, with root mean square error (RMSE) reduced by 21 % and 19% for PM2.5 and PM10 estimation, as well as an improvement in model underestimation for the heavy pollution scenarios. The spatial-temporal model shows out-of-sample test CV coefficients of determination (R2) of 0.87 and 0.88 for hourly PM2.5 and PM10. The ML model predicts an annual nationwide concentration of 68.3 μg/m3 for PM2.5 with a north (high, especially in Indo-Gangetic Plain) to south (low) distribution, which is consistent with high satellite aerosol optical depth (AOD) values. Boundary layer height is identified as the main meteorological factor influencing PM2.5 concentrations in winter. Characterizing the regional transport and cumulative dispersion processes of pollutants by extracting features can help in machine learning training, and this method can be further improved and applied to other studies.

The influence of river basin nitrogen pollution sources and their transport on microbial community structure

The characterization of variations in riverine microbiota that stem from contaminant sources and transport modes is important for understanding biogeochemical processes. However, the association between complex anthropogenic nitrogen pollution and bacteria has not been extensively investigated owing to the difficulties faced while determining the distribution of nitrogen contaminants in watersheds. Here, we employed the Nitrogen and Oxygen Stable Isotope Tools alongside microbiological analysis to explore microbial characteristics and their responses to complex nitrogen pollution patterns. Significant changes in microbial communities were observed in watersheds with different complex nitrogen pollution sources migrating. Complex nitrogen sources were closely associated with microbiota. The main sources of nitrogen pollution in the dry season watershed were Manurc and Sewage (M&S, 54.2%). M&S exhibited reduced microbial diversity, high number of denitrification groups, and increased nitrogen cycling compared with others. However, Soil Nitrogen sources (SN, 59.3%) in the watershed during the wet season were the main contributors of nitrogen in the watershed. Eluviation allows SN to increased microbial community similarity, in addition to facilitating microbial immobilization processes. Overall, this study expands our knowledge regarding microbial biogeochemistry in catchments and beyond by linking specific nitrogen pollution scenarios to microorganisms.

Source apportionment and emission projections of heavy metals from traffic sources in India: Insights from elemental and Pb isotopic compositions

The study investigates the sources of metals in urban road dusts using elemental concentration and Pb isotopic ratios. The elemental concentrations are also utilized to determine the present heavy metal emissions as well as projected emissions till 2045. Bayesian mixing model for source apportionment highlights the significant contributions of both exhaust and non-exhaust sources to the metal-enriched urban road dusts, with each contributing approximately 40 %. Emission analysis reveals that India’s projected electric vehicle (EV) penetration may not be sufficient to suppress the metal emissions from vehicular exhausts. Further challenge is posed by high metal concentrations in the non-exhaust sources, that dominates the emission of some metals compared to exhaust sources. If the metal concentrations remain unchanged, the emission analysis predicts alarming increases in total emissions from all the exhaust and non-exhaust sources by 174 %, 176 %, 163 % and 184 % for Ni, Cu, Zn and Pb, respectively, from 2022 to 2045. Thus, it is crucial to reduce the metal concentrations in traffic emission sources and also impose better regulatory measures to improve the urban metal pollution scenario.

Preparation of Fe-doped coffee ground biochar and activation of PMS for chloroquine phosphate removal

Iron-doped biochar catalyst (Fe-CGB) was synthesized by embedding rust into coffee grounds using a high-temperature water bath. Fe-CGB was then used with activated peroxymonosulfate (PMS) to treat chloroquine phosphate (CQP) contaminated wastewater. The presence of biochar facilitates the cycling between Fe(III) and Fe(II) species, thereby augmenting the activation efficacy of PMS. The Fe-CGB/PMS system demonstrated exceptional catalytic degradation efficacy, achieving complete removal of CQP within a mere 30 min, with a reaction rate constant of 0.1501 min−1, surpassing the unmodified coffee grounds biochar by a factor of 47. Notably, under extreme pH conditions and in the presence of complex inorganic ions, CQP was entirely eliminated within 60 min. Mechanistic investigations revealed that biochar-mediated promotion of the Fe(III)/Fe(II) cycle amplifies the activation efficiency of PMS, leading to the generation of abundant sulfate radicals (SO4·−) and hydroxyl radicals (·OH), which facilitate pollutant decomposition. Furthermore, Fe-CGB exhibited notable stability across multiple cycles, with minimal iron ion leaching and facile separation from the solution. Subsequently, the degradation effects of 18 organic pollutants were evaluated, and their potential as effective treatment methods for multiple pollution scenarios were explored in combination with theoretical calculations. Overall,this study provides insights into PMS activation as well as waste management.

Influence of cephalexin on cadmium adsorption onto microplastic particles in water: Human health risk evaluation

This paper explores the impact of environmental factors on the adsorption of cadmium (Cd) and cephalexin (CEX) onto polyethylene (PE) microplastics. The study focused on Cd adsorption behavior on microplastics (MPs) of various sizes, revealing that particles sized 30–63 μm exhibited the highest adsorption capacity compared to other sizes. Cd sorption was significantly influenced by initial pH and salinity levels. Experimental data closely matched both the Langmuir (R2 > 0.91) and Freundlich (R2 > 0.92) isotherms. Cd adsorption onto PE particles was greater than CEX adsorption, with the maximum Cd uptake capacity measured at 1.8 mg/g. FTIR analysis indicated that Cd and CEX adsorption onto MPs was likely governed by physical interactions, as no new functional groups were detected post-uptake. The desorption rates of Cd and CEX from PE microplastics were evaluated in various liquids, including aqueous solution, tap water, seawater, and synthetic gastric juice. The health risks associated with Cd, in combination with MPs and CEX, for both children and adults were assessed in groundwater and aqueous solutions. This study offers scientific insights and guidelines for examining the environmental behavior, migration, and transformation of microplastics and their related ecological risks in scenarios of combined pollution.

Resource partitioning influences levels of toxic trace elements in sympatric tropical seabirds

Morphologically similar species that occupy resource-limited environments tend to differ in their ecological traits in order to coexist, which may result in differential exposure to environmental threats. For instance, partitioning of feeding resources may influence contaminant exposure and bioaccumulation in marine predators through different diets or foraging habitats. Here, we sampled three tropical seabird species breeding in sympatry in the southwestern Atlantic Ocean for blood and feather trace element concentrations (As, Hg, Cd, Pb), and assessed their foraging ecology with bio-logging (GPS tracks and time-depth recorders), analysis of regurgitated prey, and carbon and nitrogen stable isotope mixing models. Red-billed tropicbirds (Phaethon aethereus), brown (Sula leucogaster) and masked (S. dactylatra) boobies differed in their preferred foraging locations, the range of foraging trips, diving parameters and diets. In addition, interspecific differences were detected in blood or feathers for all trace elements analyzed, suggesting influence of the differences observed in diet and space use. Red-billed tropicbirds had the largest foraging range over the continental shelf and over the slope, suggesting lower exposure to continental sources of metals. Brown and masked boobies had higher Hg concentrations than tropicbirds, higher δ15N values (a proxy for trophic level), and δ15N correlated with Hg levels, suggesting biomagnification of Hg along the food chain. Nonetheless, red-billed tropicbirds showed comparable levels of As and Cd in blood or feathers, and higher levels of Pb in both tissues in comparison to boobies, which may suggest overall exposure of seabirds in the region, through their diets and foraging areas. Resource partitioning is critical for allowing coexistence of different seabird species in shared breeding sites where they act as central-place foragers. Nonetheless, in scenarios of environmental pollution, these species-specific strategies lead to differential bioaccumulation, and thus distinct effects of pollution on populations are expected.

The native submerged plant, Hydrilla verticillata outperforms its exotic confamilial with exposure to polyamide microplastic pollution: Implication for wetland revegetation and potential driving mechanism

Microplastic pollution and biological invasion, as two by-products of human civilization, interfere the ecological function of aquatic ecosystem. The restoration of aquatic vegetation has been considered a practical approach to offset the deterioration of aquatic ecosystem. However, a lack of knowledge still lies in the species selection in the revegetation when confronting the interference from microplastic pollution and exotic counterpart. The present study subjected the native submerged species, Hydrilla verticillata and its exotic confamilial, Elodea nuttallii to the current and future scenarios of polyamide microplastic pollution. The plant performance proxies including biomass and ramet number were measured. We found that the native H. verticillata maintained its performance while the exotic E. nuttallii showed decreases in biomass and ramet number under severest pollution conditions. The restoration of native submerged plant such as H. verticillata appeared to be more effective in stabilizing aquatic vegetation in the scenario of accelerating microplastic pollution. In order to explore the underlying driving mechanism of performance differentiation, stress tolerance indicators for plants, sediment enzymatic activity and sediment fungal microbiome were investigated. We found that polyamide microplastic had weak effects on stress tolerance indicators for plants, sediment enzymatic activity and sediment fungal diversity, reflecting the decoupling between these indicators and plant performance. However, the relative abundance of sediment arbuscular mycorrhizal fungi for H. verticillata significantly increased while E. nuttallii gathered “useless” ectomycorrhizal fungi at the presence of severest polyamide microplastic pollution. We speculate that the arbuscular mycorrhizal fungi assisted the stabilization of plant performance for H. verticillata with exposure to the severest polyamide microplastic pollution.

A comprehensive scenario of heavy metals pollution in the rivers of Bangladesh during the last two decades.

For decades, rivers have been used for transporting pollutants loaded with heavy metals (HMs) causing severe pollution in downstream.The current study aimed to review the levels and sources of 10 HMs, viz. As, Pb, Cd, Cr, Fe, Mn, Cu, Co, Ni, and Zn in the surface water of the rivers in Bangladesh. The PRISMA criteria were used to conduct a systematic review of the available literature published between 2001 and 2020, and thus a total of 55 documents were finally selected for review. The mean concentration of each HM exceeding the threshold limits as per World Health Organization (WHO), the United States Environmental Protection Agency (USEPA), and Department of Environment (DoE), Bangladesh standards were higher in the last decade (2011-2020) than in the previous one (2001-2010). Most HM concentrations in water were found above the threshold limits in three divisions (Dhaka, Rajshahi, and Chattogram). The Buriganga River in Dhaka has been the top polluted river in Bangladesh. Among the 10 HMs, six metals (As, Pb, Cd, Cr, Fe, and Mn) exceeded the limit set by WHO, USEPA, and DoE in all three seasons, where mean values of most of the HMs were found to be the highest in the summer season. Statistical analyses identified possible sources of HMs such as natural weathering, electroplating, fertilizers and pesticides, mining and manufacturing, textiles, coal mining and burning, batteries, and paint industries. Strong legislations and regulations, awareness programs, continuous monitoring, and comprehensive research are urgently needed to control riverine HMs pollutionin Bangladesh.

Chronic effects of exposure to polyethylene microplastics may be mitigated at the expense of growth and photosynthesis in reef-building corals

The causes of the physiological effects of microplastic pollution, potentially harming reef-building corals, are unclear. Reasons might include increased energy demands for handling particles and immune reactions. This study is among the first assessing the effects of long-term microplastic exposure on coral physiology at realistic concentrations (200 polyethylene particles L−1). The coral species Acropora muricata, Pocillopora verrucosa, Porites lutea, and Heliopora coerulea were exposed to microplastics for 11 months, and energy reserves, metabolites, growth, and photosymbiont state were analyzed. Results showed an overall low impact on coral physiology, yet species-specific effects occurred. Specifically, H. coerulea exhibited reduced growth, P. lutea and A. muricata showed changes in photosynthetic efficiency, and A. muricata variations in taurine levels. These findings suggest that corals may possess compensatory mechanisms mitigating the effects of microplastics. However, realistic microplastic concentrations only occasionally affected corals. Yet, corals exposed to increasing pollution scenarios will likely experience more negative impacts.

Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin

This study evaluates the long-term accuracy of six daily aerosol optical depth (AOD) datasets derived from the Moderate Resolution Imaging Spectroradiometers (MODIS) Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), combined DT and DB datasets (DTB) and MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms and the Multi-angle Imaging SpectroRadiometer (MISR) Version 23, by comparing them with ground-based AOD observations in 18 Aerosol Robotic Network (AERONET) sites over the Amazon basin between 2000 and 2022. The AOD retrieval accuracy was also assessed under varying seasons, aerosol loading, particle size, elevation and land cover type. Overall results showed that Terra and Aqua-MODIS DB and MISR algorithms perform better, with about 88% of AOD retrievals within the expected error range. However, MISR presented a systematic high negative bias. MODIS DT 3 km overestimated AOD values and indicated unsatisfactory retrieval accuracy in almost all scenarios. MODIS and MISR retrievals showed improved performance under low aerosol loading, whereas Aqua-MODIS MAIAC under intermediate conditions (AOD>0.2). MISR indicated a large dependence on a coarse mode-dominated environment with considerable negative bias. Terra-MODIS MAIAC retrievals exhibited a smaller dependence for aerosol particle size distribution, while Terra-MODIS DB performed better for fine mode and Aqua-MODIS DB for coarse dominance. MAIAC also performed well under polluted scenarios in the dry season in contrast to the majority of satellite-based AOD retrievals, which showed good agreement under clean background conditions in the wet season. Almost all satellite-based retrievals showed higher uncertainty under extremely high altitudes (>3000 m) followed by non-satisfactory correlation and significant biases. Additionally, almost all satellite-based AOD retrievals indicated poor performance as vegetation coverage increases. On the contrary, MISR showed higher accuracy under forest type. In general, MODIS and MISR retrieval accuracy demonstrated distinct levels of dependencies that require further improvement in the Amazon Basin.

Heavy metals in surface sediments of Lake Patria wetland, southern Italy, and environmental risk

The study investigated the presence of heavy metals in the surface sediments of one of the most important wetlands in south Italy, Lake Patria, and the related environmental risk. Surface sediments were analyzed for particle size and shape, total organic carbon and As, Cd, Cr, Cu, Fe, Hg, Ni, Pb and Zn levels. Mean total organic carbon was high, 3.1 %, with a peak of 5.9 % near the input of freshwater channel whereas sediments appeared to be mainly composed of sand and/or gravel and significantly polluted by metals especially Cr, Cu and Zn. Data from heavy metals spatial distribution, Pearson correlation, principal component analysis and geochemical indices suggested dominant anthropogenic source for most metals. Mean Igeo values of Cr, Cu and Ni were in the range of 4.6–7.3 of strongly polluted class while their relative mean enrichment factors were largely exceeding the threshold of the anthropic pollution, 1.5. A very high pollution situation was also outlined when considering the Cf indices, with Cr, Cu and Ni possessing mean values from an order of magnitude of ten to hundred times higher the very high polluted class, 6 ≤ Cf. On the overall, the integrated indices of modified contamination degree, pollution load and ecological risk revealed a serious pollution scenario. The mean comprehensive ecological hazard index was fivefold the most critical limit, ≥600, in almost all the lagoon and especially in the locations close to channel.

Effects of tire wear particles on the water retention of soils with different textures in the full moisture range

Tire wear particles (TWPs) are significant contributors to microplastic pollution in the environment, yet there is limited scientific information concerning their impact on soil hydraulic properties. This study aimed to investigate the impact of TWPs at different concentrations (1, 4, 8, and 16% of the air-dried mass of packed soil samples, w/w) on the water retention curves (WRC) of southern California soils with five different textures (clay, clay loam, silt loam, sandy loam, and loamy sand). The concentrations of 8% and 16% were selected to represent extreme pollution scenarios that might occur near highway corridors. High-resolution water retention data, spanning from saturation to oven dryness, were generated using HYPROP™ and WP4C dew point meter instruments. We also developed WRC scaling equations based on the quantity of TWPs. The bulk density of the samples decreased as the TWP concentration in soils increased. The inclusion of very high concentrations of TWPs (8% and 16% w/w) led to a significant reduction in soil moisture content in the intermediate and dry ranges across various soil textures. However, at the same moisture range, adding 1% TWPs had a minimal impact on soil moisture reduction, while the influence of the 4% TWPs concentration treatment was noticeable only in loamy sand and partially in clay loam soils. Additionally, the overall plant available water decreased with increasing TWP concentrations, except for the clay soil. The texture-specific scaling models exhibited promising performance, with RMSE values ranging from 0.0061 to 0.0120 cm3 cm−3. When bulk density was included as an additional input predictor to construct a single scaling model for all textures, the RMSE increased. Nevertheless, it still indicated a good fit ranging from 0.007 to 0.024 cm3 cm−3, highlighting the suitability of simple scaling for identifying WRC in TWPs-polluted soils, particularly for practical purposes. The findings of this study can contribute to a better understanding and quantification of the impact of TWPs on soil hydrology.