Industrial Pollution Sources Research Articles

Spatial pattern, source apportionment, and source-oriented health risk quantifying of heavy metals in farmland soils of southern China.

The contamination of heavy metal has permeated many parts of China, especially in densely populated and industrialized southern China. This study focused on the degree of pollution in farmland soil heavy metals (HMs), and its spatial distribution characteristics and source apportionment. Meanwhile, we conducted an evaluation of the health risks attributed to soil HMs and analyzed the factors that impact them. We found that the distribution of five heavy metals is mainly concentrated in the east-central and southern parts of the study area. Specifically, Cd and Hg have high levels of pollution and present potential ecological risks. The pollution sources of five HMs were analyzed utilizing positive matrix factorization. The results revealed that the contribution of different sources keeps the following order: natural source (42.42%), agricultural activities (29.93%), industrial pollution source (20.49%), and atmospheric deposition pollution (7.16%). The non-carcinogenic risks to residents were acceptable, whereas the carcinogenic risks were relatively high. Children and the elderly are more vulnerable to the negative effects of Cr, As. Using structural equation modeling, we found soil property is a vital factor affecting soil contamination, with the soil organic matter and cation exchange capacity having a relatively greater impact on heavy metals pollution. Our study provides some data reference and guidance for soil ecological protection and restoration.

Enhanced Assessment of Water Quality and Pollutant Source Apportionment Using APCS-MLR and PMF Models in the Upper Reaches of the Tarim River

To evaluate the pollution sources and dynamics of the upper reaches of the Tarim River, 10 typical sampling points were selected, and 23 water quality parameters from 2020 to 2022 were analyzed using one-way analysis of variance, the comprehensive Water Quality Identification Index (WQI), and Principal Component Analysis (PCA). The pollution status, sources, and contribution rates of water quality were investigated using the Absolute Principal Component-Multiple Linear Regression Model (APCS-MLR) and Positive Matrix Factorization (PMF). The results indicated that the water quality parameters of dissolved oxygen (DO), chemical oxygen demand (CODMn), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP), fluoride ions (F-), and ammonia-nitrogen (NH3-N) in the upper reaches of the Tarim River exceed standards, with noticeable spatial variations observed for each parameter. The water quality evaluation grades in the upper reaches of Tarim River primarily indicate “moderate” and “good” levels, with DO, TN, NH3-N, and electrical conductivity (EC) being the key parameters influencing variations in water quality. The source analysis results from APCS-MLR and PMF yielded similar outcomes, identifying six potential pollution sources. Among these, soil weathering, livestock and poultry breeding, and agricultural activities exhibited higher contribution rates. Specifically, the contribution rates for these sources according to APCS-MLR were 44.11%, 19.63%, and 11.67%, respectively; while according to PMF they are 24.08%, 17.88%, and 27.54%, respectively. Furthermore, industrial pollution sources contributed at a rate of 6.01% according to APCS-MLR, while urban living sources contributed at a rate of 2.13%. However, based on PMF analysis, the contribution rates for industrial pollution sources increased significantly to 16.71%. Additionally, APCS-MLR identified natural sources as contributing at a rate of 16.45%, whereas PMF suggested that a combination of agricultural activities and natural sources contributed at a lower rate of only 9.52%. In conclusion, the water quality within the upper reaches of the Tarim River is predominantly satisfactory. Nonetheless, localized pollution, primarily attributable to human activities, presents a substantial challenge. These observations provide critical insights into improving and protecting the fragile water quality of the Tarim River.

Used in Urban Area for Landscape Planning and Design Spatial and Temporal Variations in Chromium (Cr) Concentrations in Picea orientalis L.

This study investigates the spatial and temporal variations in chromium (Cr) concentrations in Picea orientalis L., across different directions (north, east, south, and west) and plant organs (outer bark, inner bark, and wood) in a forested region. The research, conducted over eight age periods spanning 1980 to 2020, aimed to assess the effectiveness of Picea orientalis L. as a biomonitor for Cr pollution. The highest Cr concentrations were observed in the east direction, particularly in the inner bark and wood, while the lowest levels were found in the west. The study was conducted in an urban area near the industrial zone and highway, as well as in forested regions. A total of 100 trees were selected for the study, with samples collected from three different organs: outer bark (OB), inner bark (IB), and wood. Samples were taken from each organ at breast height (approximately 1.3 meters above the ground) to ensure consistency. The sampling covered various age periods, specifically 1980–2020, to analyze temporal changes in Cr concentrations. Statistical analysis revealed significant variations in Cr concentrations across most directions and periods, with notable increases during certain periods, especially in the west direction. These variations can be attributed to several factors, including the proximity to industrial sources of pollution, which typically release higher levels of chromium into the environment. The eastern direction likely experiences greater exposure to these emissions due to prevailing wind patterns and urban runoff, leading to increased accumulation in Picea orientalis L. Additionally, seasonal changes, temperature fluctuations, and soil characteristics may influence the bioavailability of chromium, affecting its uptake by the tree. The results suggest that Picea orientalis L. can effectively reflect Cr pollution levels, with significant directional and temporal variations that highlight the influence of these environmental factors on Cr accumulation. This study underscores the potential of Picea orientalis L. as a valuable tool for monitoring and managing Cr pollution in forested environments.

Assessing Groundwater Vulnerability to Pollution in a Rapidly Urbanizing River Basin Using a Modified DRASTIC Land Use–Lineament Density Method

ABSTRACTGroundwater quality assessment is crucial for ensuring safe drinking water, protecting public health, and maintaining sustainable water resources for agricultural and industrial uses. The Awash River basin faces significant groundwater quality challenges due to rapid population growth, high urbanization, large‐scale irrigation, and industrial pollution. The main objective of this study is to evaluate the intrinsic vulnerability of aquifers to pollution in the Awash Basin and identify hotspots requiring urgent intervention using a modified DRASTIC overlay analysis method. In addition to the seven parameters considered in the generic DRASTIC overlay analysis (depth to the water table, recharge, aquifer media, slope, soil media, vadose zone, and hydraulic conductivity), we incorporated land use/land‐cover (LULC) and lineament density (LD) distributions (DRASTIC‐LU‐LD). This modification allowed us to produce more realistic groundwater vulnerability maps for the basin. To identify the most influential parameters in the overlay, sensitivity analysis was conducted using Map Removal Sensitivity Analysis (MRSA) and Single Parameter Sensitivity Analysis (SPSA). Initially, the generic DRASTIC index in the area ranges from 69 to 181, categorizing the area into four vulnerability zones: very low (21%), low (51%), medium (27%), and high (1%). After incorporating LU and LD, the index values ranged from 90 to 240. Based on the percentage of the total area studied, the inclusion of LU decreased the very low and low vulnerability zones from 72% to 44% and the inclusion of LD increased the high‐ and very‐high‐vulnerability zones from 14% to 27%. The areas most vulnerable to groundwater pollution are in the western (upper Awash), middle Awash, and northwestern regions, particularly in city centers where groundwater abstraction is significant. These high‐vulnerability zones coincide with municipal, industrial, and agricultural pollution sources. The vadose zone parameter has the highest impact in both MRSA and SPSA, with a variation index value of 3.08% and a mean effective weight of 27.93%, respectively. By identifying areas vulnerable to groundwater pollution, this study provides a valuable basis for informed decision‐making and the development of effective strategies for protecting groundwater from urban, industrial, and agricultural pollution sources.

Inter- and intra-annual carbon isotope fluctuations in Pinus sylvestris L. tree rings whole wood and cellulose in north-eastern Lithuania

In temperate regions trees typically exhibit growth sensitivity to climatic conditions during the growth season. Annual tree ring growth increments correlate with a variety of environmental factors. As an index of water use efficiency, δ13C is a preferred proxy to allow accurate interpretation of environmental factors critical for the tree growth, including changes in climate patterns, variation in the ambient temperature and precipitation. We assumed that isotopic differences within individual tree rings are likely to produce seasonal isotope fluctuations in the chronology that might be interpreted as response to environmental impacts. To verify the assumption, we measured δ13C in annual tree rings of Pinus Sylvestris L. split into 13 intra-annual segments each and checked δ13C correlations with temperature, precipitation, the number of sunshine hours and relative air humidity. For the investigation, we chose a site in north-eastern Lithuania, Zarasai, located in boreal latitude and remote from industrial pollution sources. The methodology of the research was based on high coherence of δ13C chronologies measured in the whole wood and α-cellulose extracted by means of two different methods. The experiment produced strong δ13C correlations with hydrometeorological parameters, especially in the earlywood formed in June

Use of Machine Learning and Indexing Techniques for Identifying Industrial Pollutant Sources: A Case Study of the Lower Kelani River Basin, Sri Lanka

With the recent acceleration in urbanisation and industrialisation, industrial pollution has severely impacted inland water bodies and ecosystem services globally, causing significant restrains to freshwater availability and myriad damages to benthic species. The Kelani River Basin in Sri Lanka, covering only ~3.6% of the land but hosting over a quarter of its population and many industrial zones, is identified as the most polluted watershed in the country. This study used unsupervised learning (UL) and an indexing approach to identify potential industrial pollutant sources along the Kelani River. The UL results were compared with those obtained from a novel Industrial Pollution Index (IPI). Three latent variables related to industrial pollution were identified via Factor Analysis of monthly water quality data from 17 monitoring stations from 2016 to 2020. The developed IPI was validated using a Long Short-Term Memory Artificial Neural Network model (NSE = 0.98, RMSE = 0.81), identifying Cd, Zn, and Fe as the primary parameters influencing river pollution status. The UL method identified five stations with elevated concentrations for the developed latent variables, and the IPI confirmed four of them. Based on the findings from both methods, the industrial zones along the Kelani River have emerged as a likely source of pollution in the river’s water. The results suggest that the proposed method effectively identifies industrial pollution sources, offering a scalable methodology for other river basins to ensure sustainable water resource management.

Modeling and simulation for the sustainable recovery of aromatics (BTX) from petrochemical industrial wastewater.

Petrochemical wastewater is a major industrial source of pollution that produces a variety of toxic organic and inorganic pollutants, naturally present or added during the process. These pollutants are a serious threat to the soil, water, environment, and human being due to their complex and hazardous nature. Glycols such as monoethylene glycol (MEG), diethylene glycol (DEG), triethylene glycol (TEG), and aromatics (BTX-benzene, toluene, and xylene) are the most common organic impurities present in petrochemical wastewater. The objective of this paper is to recover aromatics and water from petrochemical industrial wastewater. The reclamation process is used to remove inorganic impurities such as heavy metals Fe, Zn, Pb, Mn, Al, Ni, As, Cr, Cu, Cd, and K and salts. In the present work, 1% sodium bi-carbonate (NaHCO3) is used to precipitate the inorganic impurities present in the wastewater at 40°C atmospherically. Aspen Hysys simulation software is used for modeling and simulation for the treatment process using NRTL (non-random-two-liquid) thermodynamic model. The process generated from Aspen Hysys is validated with lab experiments. To support global sustainable development, this study is focused on reducing, reusing, and recycling separation techniques such as centrifuge separation and vacuum distillation have been used. The characterization of regenerated water was performed using ICP-OES (inductively coupled plasma-optical emission spectroscopy) to determine the reduction in heavy metals. It was found that > 99.5% of heavy metals were removed. The regeneration of these aromatics is necessary for economic and environmental reasons so that it can be reused to avoid its disposal in and contamination of natural environments.

Association between point sources of industrial pollution and child lead levels

Association between point sources of industrial pollution and child lead levels

Photocatalytic nanohybrid UV-light-driven PVDF/GO-NiFe@SiO2 membrane coupled with bentonite adsorption and ozonation process for a sustainable textile wastewater treatment

The textile industries face significant environmental challenges due to the presence of complex pollutant dyes in its wastewater, making it one of the world's major sources of industrial water pollution. Numerous studies have been employed to overcome this problem, including physical, chemical, and biological treatments. However, most of those methods still suffered to achieve higher dye removal performance. This study provides new insight regarding textile wastewater treatment containing indigo red as the main colorant by carrying out a hybrid process combining bentonite adsorption, ozonation, and photocatalytic membrane filtration. For the membrane, we propose a modified polyvinylidene fluoride (PVDF) membrane incorporated with graphene oxide (GO) and nickel-iron doped silica (NiFe@SiO2) photocatalyst, which is named PVDF/GO-NiFe@SiO2 membrane. The membrane was employed under different sets of filtrations (dark and under ultraviolet irradiation) to treat natural dye wastewater from textile industry effluent. Further improvement was achieved when UV light irradiation was subjected to the process. This sequential adsorption-ozonation-membrane process under UV exposure exhibited improved dye removal from 55.11 % to 97.22 %. The results show that the proposed hybrid process successfully improved the membranes' antifouling behavior, thus boosting the membranes' performance and durability. From a broader perspective, our work promotes new insights for the integration of other physical, chemical, and biological treatment methods to improve the performance and durability of membrane-based processes, particularly for dye wastewater treatment.

Preparation of eggshell-based materials and their adsorption performance on Fe(III) in water*

Abstract If there is too much trivalent iron in the body, it will disrupt the balance of osmotic pressure inside the body, and its heavy metal properties will denature proteins. Therefore, effective management of industrial pollution source wastewater is very important for environmental protection and human’s health. Calcium carbonate particles have a good ability to adsorb trivalent iron in wastewater. The aim is to prepare porous calcium carbonate by a template method using eggshell powder, hydrochloric acid, sodium stearate, and anhydrous sodium carbonate solution as the raw materials and research the adsorption properties of porous calcium carbonate to trivalent iron. Porous calcium carbonate was prepared by a template method using eggshell powder, hydrochloric acid, sodium stearate, and anhydrous sodium carbonate solution as the raw materials. The morphology and adsorptive properties of Fe (III) of the porous calcium carbonate were analyzed by scanning electron microscope (SEM) and UV-visible spectrophotometer (UV). The best value was found between different temperatures, time, and pH. The results verified that the maximum amount of adsorption was obtained when calcination was 400°C, pH was 5, time was 60 min, and the input amount was 20 mg. The adsorption of porous calcium carbonate to Fe (III) is chemical and has a better adsorption effect.

Novel amino-ethyl carboxymethyl cellulose crosslinked ampholyte hydrogel development for Methyl orange removal from waste water

In the modern era, with the rapid growth of various industries, the issues of energy crisis and environmental pollution have garnered increasing attention. One significant source of industrial pollution is printing and dyeing wastewater. This wastewater often contains dyes that have aromatic structures and azo groups, such as Methyl orange (MO), which are both toxic and difficult to degrade. If these dyes are released into the wastewater stream without any treatment, they can have adverse effects on ecological balance and human health. Therefore, it is crucial to identify suitable treatment strategies to efficiently remove dyes from wastewater systems before discharge. In this study, the Methyl orange (MO) azo dye has been removed from dyes-contaminated wastewater, for the first time, using a novel amino-ethyl carboxymethyl cellulose crosslinked ampholyte hydrogel (AECMC). Different characterization methods, including FTIR, TGA, and DSC were used to characterize the generated AECMC compounds. The water absorption and cationic exchange capacities were assessed. Factors affecting the MO anions adsorption including MO concentration, adsorption pH, temperature, time, adsorbent dose, and agitation speed have been investigated. Moreover, the kinetics of the adsorption process was assessed by the use of three models: pseudo-first-order, Pseudo-second-order, and Elovich. Moreover, the mechanism of the adsorption process was monitored using the Intraparticle diffusion and Boyd models. Additionally, the adsorption isotherm was examined using established models such as Langmuir, Freundlich, and Temkin isotherms. The thermodynamic characteristics of the MO adsorption process have been investigated at various adsorption temperatures using the Van't Hoff model. The results obtained from the study indicate that the process of MO adsorption adhered to the Pseudo-second-order kinetic model, the Langmuir isotherm model was found to be applicable, and spontaneous and exhibited an endothermic character. In conclusion, the developed novel amino-ethyl carboxymethyl cellulose crosslinked ampholyte hydrogels (AECMC) have successive in the removal of the MO anionic dye from contaminated wastewater.

COMPARATIVE ANALYSIS OF AIR QUALITY IN THE NORTHERN AND SOUTHERN DISTRICTS OF KARAKALPAKSTAN

Karakalpakstan, a region in northwest Uzbekistan, is known for its unique culture and ecological challenges due to its arid climate and industrial activities. Air quality in the region is a growing concern, as pollution from various sources poses risks to public health and the environment. This article aims to provide a comparative analysis of air quality in the Northern and Southern districts of Karakalpakstan, highlighting differences in pollutant levels and potential factors contributing to these variations. By examining air quality in these two distinct regions, we can better understand the impact of industrial, traffic, and natural sources of pollution on the local environment and explore potential solutions to improve air quality for residents in Karakalpakstan.

A framework model to integrate sources and pathways in the assessment of river water pollution

Metal and nutrient pollution, soil erosion, and alterations in climate and hydrology are prevalent issues that impact the water quality of riverine systems. However, integrated approaches to assess and isolate causes and paths of river water pollution are scarce, especially in the case of watersheds impacted by multiple hazardous activities. Therefore, a framework model for investigating the multiple sources of river water pollution was developed. The chosen study area was the Paraopeba River basin located in the Minas Gerais, Brazil. Besides multiple agriculture, industrial, and urban pollution sources, this region was profoundly affected by the rupture of the B1 tailings dam (in January 2019) at the Córrego do Feijão mine, resulting in the release of metal-rich waste. Considering this situation, thirty-nine physicochemical and hydromorphological parameters were examined in the Paraopeba River basin, in the 2019–2023 period. The analysis involved various statistical techniques, including bivariate and multivariate methods such as correlation analysis, principal component analysis, and clustering. The Paraopeba River was mainly impacted by metal contamination resulting from the dam collapse, whereas nutrient contamination, mainly from urban and industrial discharges, predominantly affected its tributaries. Additionally, the elevated concentrations of aluminum, iron, nitrate, and sulfate in both main river and tributaries can be attributed to diffuse and point source pollution. In terms of hydromorphology and soil type, the interaction between woody vegetation and erosion-resistant soils, especially latosols, contributes to the stability of riverbanks in the main river. Meanwhile, in the tributaries, the presence of neosols and sparse vegetation in urbanized areas promoted riverbank erosion potentially amplifying pollution. While the study was conducted in a particular watershed, the findings are based on a methodology that can be applied universally. Hence, the insights on surface water quality from this research can be a valuable resource for researchers studying watersheds with diverse pollution sources.

Identification of pollution source and prediction of water quality based on deep learning techniques

Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.

Accurate identification of sludge contamination sources by classification-based PMF and machine learning with consideration of sewer network distribution differences

The application of source identification such as PMF for large-scale pollution source analysis frequently produces ambiguous outcomes. In this study, we utilized a classification-based method to accurately track key pollution sources in the sludge. In the study, we categorized the wastewater treatment plants into two groups: T1 and T2, according to the pipeline network. T1 sewage treatment plants are the main sewage plants in urban areas, covering a large area and connected to industrial wastewater treatment plants for secondary treatment. T2 sewage treatment plants are typically smaller in size and usually responsible for treating sewage in rural or township areas. The PMF analysis indicates that industrial pollution sources contribute 3.4 times more to T1 sludge than to T2 sludge, making industrial pollution the primary factor causing the disparity. The application of Random Forest and Adaboost based on pollutant concentrations for classification and fitting of sludge resulted in the identification of the main pollutants: Zn, Cu, Ni, and Cyanide, which align with characteristic pollutants from the electroplating industry. The GIS analysis shows a significant correlation between the distance of wastewater treatment plants with abnormal environmental risk and electroplating industrial parks, all within a 20 km radius. Indeed, when conducting large-scale pollution source identification studies, utilizing classification-based analysis can effectively improve the accuracy of pollution source identification, leading to more valuable analysis results.

ABOUT HEAVY METAL ACCUMULATION POTENTIAL OF SOME EVERGREEN TREES IN GREEN SPACES OF YEREVAN (ARMENIA)

This article presents the final research on the study the metal accumulation potential of two evergreen species Thuja occidentalis L. and Juniperus virginiana involved in the green infrastructure of Yerevan-Armenia’s capital city. We studied the condition of both evergreen species, seasonal variations of heavy metal contents in their needle at the end of summer and winter, and the bio concentration factor of plants. The decreased longevity shows that both studied evergreen species are inappropriate for planting in the streets with heavy traffic or close to industrial pollution sources.

Assessment of Heavy Metals Contamination and Potential Ecological Risk in Surface Sediments of the El Bey Wadi, Tunisia

ABSTRACT Sebkhat Soliman included in the Ramsar Convention is a wetland of international importance. This area is of great ecological interest considering the quality and the richness of the flora and the diversity of the avifauna. This site receives a large amount of water through the EL Bey River. In order to assess the levels of contamination, concentrations of six heavy metals, including Cr, Cu, Pb, Ni, Mn, and Zn, were examined in 25 surface sediment samples from El Bey Wadi. These concentrations, which were expressed in mg/kg of dry weight, showed that Pb ranged from 5.52 to 41.66, Zn from 26.94 to 1471.89, Cu from 91.97 to 260.05, Ni from 9.35 to 140.45, Cr from 25. It’s important to note that the highest metal concentrations have been found in site 5 which is frequently exposed to local industrial pollutant sources and in sites that are frequently inundated by river water that is carrying away industrial waste from the cities of Grombalia and Bou-Argoub. The mean EF values for Cu, Mn and Ni were between 3 and 5 in the sediments of the El Bey River, suggesting moderate enrichment, while the mean EF values for Pb, Zn and Cr indicating respectively moderate to severe enrichment and very severe enrichment. CF values for Cr, Cu and Zn were > 6 in sediments taken from the fifth site, which denotes a very high contamination by these metals. At site 5, PLI value (4.09), reflecting that its sediment is heavily polluted. Hierarchical cluster analysis-based complementary statistical methods have demonstrated that Mn is lithogenic and other analyzed metals are anthropogenic.

Batı Anadolu Kıyısındaki Deniz Suyunda Eser Element Konsantrasyonlarının Belirlenmesi

In this study, we examined seawater from Izmir Bay, Turkey for its trace element content. Three stations were chosen to carry out our mission. The sample collection process took place between November 2019 and May 2020. We focused on pH levels, dissolved oxygen, total dissolved solids, salinity, conductivity values, and trace elements (Ag, Al, Ba, Cd, Co, Cr, Cu, Fe, Li, Mn, Ni, Pb, Sr, and Zn) concentrations. Trace element analysis was done using an ICP-OES. Trace element concentrations were as follows: Ag (0.4 mg/l), Al (0.47 mg/l), Cu (0.82 mg/l), Li (0.81 mg/l), Pb (0.065 mg/l), and Sr (8.06 mg/l). Meanwhile, Ba, Cd, Co, Cr, Fe, Mn, and Ni levels were below the measurable limits. Our results are generally high when compared with the literature. Industrial pollution sources (cement, metallurgy, iron and steel industries, electric arc furnaces (EAF-Steel), coal combustion residuals (CCRs), and petrol refineries) all cause marine pollution because they leak trace elements into the sea, which consequently accumulate over time.

Spatial distribution, ecotoxicity and human health assessment of nutrients and heavy metals in river sediment under urbanization – A study case of Yitong River, China

ABSTRACT We investigated the contents and distributions of nutrients and heavy metals in sediment from the Yitong River. The main aims were to analyze heavy metals speciation and evaluate their bioavailability, in order to assess the risks they pose to both ecosystem and human health. The average total nitrogen (TN), total phosphorus (TP), organic matter (OM) and Cd, Cr, Cu, Ni, Pb and Zn contents were 1747.69, 895.58, 75103, 0.19, 75.64, 33.61, 47.17, 56.65 and 159.91 mg kg−1, respectively. Results from acid-extraction fraction analysis showed that the proportions of Cd and Zn ranged from 3.78% to 30.20% and 5.31% to 30.74%, respectively. Health risk assessment model showed the risk index values of children and adults were 0.412 and 0.059, respectively, indicating a minimal threat to human health. Multivariate statistical analyses revealed that TN and TP may have similar sources and could be attributed to combined sewer overflows, while Cr, Cu, Ni and Zn were primarily derived from industrial pollution sources. Toxicity bioassay tests demonstrated that the majority of sediment samples were classified as non-toxic. However, 25% of the samples showed slight toxicity towards Vibrio qinghaiensis Q67, while 7% of the samples exhibited slight toxicity towards Daphnia magna. Based on these findings, it is necessary to implement pollution control measures and conduct assessments of aquatic ecological risks in sediments from urban rivers. These results can be used to develop effective strategies for the management and governance of aquatic sediments.

Non-Negligible Ecological Risks of Urban Wetlands Caused by Cd and Hg on the Qinghai-Tibet Plateau, China.

The Huangshui National Wetland Park (HNWP) is a unique national wetland park in a city on the Qinghai-Tibetan Plateau, containing three zones: Haihu, Beichuan, and Ninghu. In this study, a total of 54 soil samples (18 sampling points with depths of 0-10 cm, 10-20 cm, and 20-30 cm) were collected in these three zones, and the contents of heavy metals (Cr, Cd, Cu, Hg, Ni, Pb, Zn, and As) of each sample were determined. The ecological risk of eight kinds of heavy metals was evaluated by using the geo-accumulation index (Igeo), and the ecological risk-controlling effect of the Xining urban wetlands on heavy metals was explored by comparative analysis, and the possible sources of heavy metals in the soil were analyzed via correlation analysis and principal component analysis (PCA). The results revealed that the total heavy metal concentration order was Haihu > Beichuan > Ninghu zone. As and Cu presented vertical accumulation characteristics in the surface and lower horizon, respectively. Cr, Cd, Hg, Ni, Pb, and Zn accumulated downwards along the depth. On the spatial scale, the enrichments of Cd and Hg brought non-negligible ecological risks in plateau urban wetlands. The results of PCA indicated that soil heavy metals mainly came from compound sources of domestic and atmospheric influences, traffic pollution sources, and industrial pollution sources. The study has revealed that human activities have inevitable negative impacts on wetland ecosystems, while the HNWP provides a significant weakening effect on heavy metal pollution.