Statistical source identification of metals in groundwater exposed to industrial contamination

Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review

The growing number of contaminated sites across the world pose a considerable threat to the environment and human health. Remediating such sites is a cumbersome process with the complexity originating from the need for extensive sampling and testing during site characterization. Selection and design of remediation technology is further complicated by the uncertainties surrounding contaminant attributes, concentration, as well as soil and groundwater properties, which influence the remediation efficiency. Additionally, challenges emerge in identifying contamination sources and monitoring the affected area. Often, these problems are overly simplified, and the data gathered is underutilized rendering the remediation process inefficient. The potential of artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) to address these issues is noteworthy, as their emergence revolutionized the process of data management/analysis. Researchers across the world are increasingly leveraging AI/ML/DL to address remediation challenges. Current study aims to perform a comprehensive literature review on the integration of AI/ML/DL tools into contaminated site remediation. A brief introduction to various emerging and existing AI/ML/DL technologies is presented, followed by a comprehensive literature review. In essence, ML/DL based predictive models can facilitate a thorough understanding of contamination patterns, reducing the need for extensive soil and groundwater sampling. Additionally, AI/ML/DL algorithms can play a pivotal role in identifying optimal remediation strategies by analyzing historical data, simulating scenarios through surrogate models, parameter-optimization using nature inspired algorithms, and enhancing decision-making with AI-based tools. Overall, with supportive measures like open-data policies and data integration, AI/ML/DL possess the potential to revolutionize the practice of contaminated site remediation.

Combined Multivariate Statistical Techniques and Water Quality Index (WQI) to Evaluate Spatial Variation in Water Quality

In present study, Water Quality Index (WQI) has been assessed of the Rawal Lake which is a major source of drinking water for people in the Federal Capital, Islamabad, and its adjacent city Rawalpindi in Pakistan. For this, the principal component analysis (PCA) and WQI were applied as an integrated approach to quantitatively explore difference based on spatial variation in 11 water quality parameters of the five major feeding tributaries of the Rawal Lake, Pakistan. The results of temperature in water, total dissolved solids, pH, electrical conductivity, chlorides and sulfates were well within the allowable World Health Organisation's (WHO) limits. However, the heavy metals like cadmium and lead were above permissible limits by the WHO in tributaries of Bari Imam and Rumli. Moreover, this has been proven by the Pearson correlation which suggested strong positive correlation (0.910*) between lead and cadmium. The results of present study were subjected to statistical analysis, i.e., PCA which gave three major factors contributing 96.5% of the total variance. For factor 1, pH, TDS, alkalinity, chlorides, sulfates and zinc have highest factor loading values (>0.60) and presented that these parameters were among the most significant parameters of first factor. As per the WQI results, the water was categorised in two major classes indicating that water of Bari Imam and Rumli is highly contaminated with heavy metals and totally unsuitable for drinking purposes. Based on the results of the present study, it is suggested to make heavy metals consideration as an integrated component in future planning for maintaining water quality of the Rawal Lake and its tributaries.

Spatial distribution, sources and health risk assessment of heavy metals in topsoil around oil and natural gas drilling sites, Andhra Pradesh, India

Soils are usually the interface between human activity and environmental components that must be conserved and protected. As a result of rising industrialization and urbanization, activities such as exploration and extraction operations lead to the release of heavy metals into the environment. This study presents distribution of six heavy metals (As, Cr, Cu, Ni, Pb and Zn) in 139 top soil samples collected in and around oil and natural gas drilling sites at a sampling density of 1 site/12 km2. The results indicated the concentration ranged from 0.1 to 16 mg/kg for As, 3-707 mg/kg for Cr, 7-2324 mg/kg for Cu, 14-234 mg/kg for Ni, 9-1664 mg/kg for Pb, and 60-962 mg/kg for Zn. The contamination of soil was estimated on the basis of Index of geo accumulation (Igeo), enrichment factor (Ef), and contamination factor (Cf). Further, spatial distribution pattern maps indicated that the pollution levels for Cu, Cr, Zn, and Ni were higher around drilling sites of the study area relative to other regions. Using exposure factors for the local population and references from the USEPA's integrated database, potential ecological risk indices (PERI) and health risk assessments were made. The hazard index (HI) values of Pb (in adults) and Cr, Pb (in children) exceeded the recommended limit of HI = 1, indicating the non-carcinogenic risks. Total carcinogenic risk (TCR) calculations revealed Cr (in adults) and As, Cr (in children) levels in soils exceeded the threshold value of 1.0E - 04, indicating significant carcinogenic risk due to high metal concentrations in the study area. These results may assist in determining the soil's present state and its effect due to extraction strategies used during drilling process and initiate few remedial techniques, particularly for proper management strategies in farming activities to decrease point and non-point source of contamination.

Evaluation of Heavy Metal Contamination in Soil Samples around Rampal, Bangladesh

Rising soil pollution has recently emerged as a significant global issue as a result of increased industrialization, urbanization, and inadequate waste management. In Rampal Upazila, soil contamination with heavy metals resulted in a significant deterioration of quality of life and life expectancy, so the study's goal is to appraise the level of heavy metal contamination in soil samples. Inductively coupled plasma-optical emission spectrometry was used to identify 13 heavy metals (Al, Na, Cr, Co, Cu, Fe, Mg, Mn, Ni, Pb, Ca, Zn, and K) from 17 soil samples that were collected at random from Rampal. Enrichment factor (EF), geo-accumulation index (I_geo), contamination factor (CF), pollution load index, elemental fractionation, and potential ecological risk analysis were used to evaluate the level of pollution and sources of metal. The average concentration of heavy metals implies that they are below in the permissible limit except for Pb. Environmental indices also showed the same result for Pb. The ecological risk index (RI) for six elements-Mn, Zn, Cr, Fe, Cu, and Pb-is 26.575. For investigating the behavior and origin of elements, multivariate statistical analysis was also applied. From the EF, Na, Cr, Fe, and Mg are in the anthropogenic region, and Al, Co, Cu, Mn, Ni, Ca, K, and Zn are minorly polluted, but Pb is highly contaminated in the Rampal area. The geo-accumulation index exhibits that Pb is slightly contaminated but others are not, while CF shows no contamination in this region. From the ecological RI, the value which is below 150 is called uncontaminated, which indicates that our studied area is ecologically free. There are various classifications of heavy metal contamination in the study area. Therefore, regular monitoring of soil pollution is required, and the public awareness needs to be raised to ensure a safe environment.

Multivariate analysis of accumulation and critical risk analysis of potentially hazardous elements in forage crops

Potentially hazardous element (PHE) contamination of aquifers is an issue of global concern, as this not only affects soil and plants but also exerts a negative impact on livestock. The current study assessed the extent of PHE (cadmium, copper, nickel, and lead) contamination of groundwater, soil, and forage crops in Shorkot, Punjab, Pakistan. Low concentrations of PHEs, particularly Cd and Cu, were found in drinking water which remained below detection limits. The concentrations of Ni and Pb in water samples were 0.1 and 0.06 mg L^-1, respectively. Calculated risk indices showed that there was a high carcinogenic and non-carcinogenic risk to livestock (sheep and cow/buffalo) from the ingestion of Ni- and Pb-contaminated water. Soil irrigation with contaminated water resulted in PHE accumulation (Cd: 0.4 mg kg^-1, Cu: 16.8 mg kg^-1, Ni: 17.6 mg kg^-1, Pb: 7.7 mg kg^-1) in soil and transfer to forage crops. The potential impact of PHE contamination of the groundwater on fodder plants was estimated for animal health by calculating the average daily dose (ADD), the hazard quotient (HQ), and the cancer risk (CR). While none of the PHEs in forage plants showed any carcinogenic or non-carcinogenic risk to livestock, a high exposure risk occurred from contaminated water (HQ: 12.9, CR: 0.02). This study provides baseline data for future research on the risks of PHE accumulation in livestock and their food products. Moreover, future research is warranted to fully understand the transfer of PHEs from livestock products to humans.

Removal of hexavalent chromium ions using micellar modified adsorbent: isothermal and kinetic investigations

Hexavalent chromium is a very poisonous oxyanion and has had a negative impact on human health. This study assessed the viability of removing chromium(vi) using micellar modified adsorbents. In this study, chromium(vi) was removed from locally accessible wheat bran using separate applications of anionic sodium dodecyl sulfate (SDS) and cationic cetyltrimethylammonium bromide (CTAB) surfactants. The initial chromium content (5–12 ppm), pH (2–12), adsorbent dose (1–6 g/100 mL), agitation time (15–240 min), agitation speed (50–300 rpm), and temperature (15–50 °C) were all varied in the adsorption investigation. Pseudo first-order and second-order kinetic models were utilized to analyze the kinetic investigation. To determine thermodynamic parameters, the van't Hoff relationship was used. The maximum result for chromium(vi) uptake was obtained as 87.7%, 83.5% and 98.9% for WB, SDS-mWB, and CTAB-mWB, respectively, at an agitation time of 240 min (i.e., 4 h), temperature (i.e., 25–30 °C), agitation speed (150 rpm). However, both WB and CTAB-mWB derives metal ion removal at lower pH levels (2–4), whereas SDS-mWB requires a pH between 4 and 6 for maximum percentage removal of Cr(vi). The equilibrium data of WB and SDS-mWB were modeled by the Langmuir adsorption isotherm, while the data of CTAB-mWB fitted well in the Freundlich isotherm model. The kinetic analysis of WB, SDS-mWB, and CTAB-mWB revealed that the pseudo-second-order kinetic model provides a thorough explanation for each of these adsorbents. It was found that CTAB-mWB can preferably be used for the removal of chromium(vi) due to its high affinity with adsorbate molecules and adsorption capacity.

In this investigation, anionic sodium dodecyl (SDS) and cationic cetyltrimethylammonium bromide (CTAB) surfactants were separately applied to locally accessible wheat bran to remove chromium(vi).

Impacts of the linear flowing industrial wastewater on the groundwater quality and human health in Swabi, Pakistan

The present study aimed to probe the extent and mobility of contamination in wastewater and its impact on groundwater and human health in the Swabi region in Pakistan. Representative samples (n = 86) were collected from both wastewater streams and groundwater in an analogous environmental setting. The result showed that pH, color, hardness, alkalinity, chemical oxygen demand, chloride, suspended solids, total dissolved solids, Cr, Cd, Pb, Cu, Fe, Mg, Na, Ca, and K in industrial wastewater were higher than the Pak-EPA (Pakistan Environmental Protection Agency) and the United State Environmental Protection Agency (US-EPA) devised standards. In groundwater, the concentration (μg L^-1) of trace elements, namely, Cd (1.16), Pb (17.4), Fe (12426), Mn (320), Mg (129784), Na (33630), Ca (177944), and K (9558) was significantly higher than the WHO (World Health Organization) acceptable level, showing decreasing tendency with increasing distance from the industrial zone. The study perceived that wastewater caused permanent hardness, while groundwater hardness was decreased from permanent to temporary at a distance from industries. Integrated health risk assessment revealed that Cu, Zn, and Co may cause low risk, Na, Mn, Ni, Pb, and Cr cause medium risk, whereas Cd, Fe, Mg, Ca, and K may cause a high health risk. Moreover, the average daily intake of Fe, Mn, Mg, Na, Ca, and K was comparably higher than Cr, Cd, Pb, Ni, Cu, Zn, and Co in both adults and children. The mode of occurrence of contaminants in groundwater was due to the leaching of contaminated wastewater and the oxidation of metals. Furthermore, carbonates, chloride, and SAR (sodium adsorption ratio) precipitation have a key role in groundwater contamination and influencing the natural water quality. The study concluded that the health problems in the surrounding areas were due to the use of contaminated water for drinking and household purpose. The study suggests filtering the drinking water and treating the wastewater before releasing it into the environment.

Heavy metals assessment in water, soil, vegetables and their associated health risks via consumption of vegetables, District Kasur, Pakistan

The consumption of contaminated vegetables has a great impact on human health. Due to this fact, we conduct the study to estimate the heavy metals in groundwater, soil, and vegetables by using the atomic adsorption spectroscopy (AAS) and find out the health risk using THQ and TCR caused by using these vegetables. The mean concentrations of As (0.015–0.40 mg/L), Cd (0.02–0.029 mg/L), Co (0.31–0.38 mg/L), Cr (1.02–1.09 mg/L), Cu (2.14–2.17 mg/L), and Hg (0.01–0.04 mg/L) are high in groundwater from threshold values given by WHO. The mean concentrations of As (22.17–23.14 mg/kg), Cd (4.21–4.54 mg/kg), Cu (21.24–24.36 mg/kg), and Pb (32.12–33.48 mg/kg) are high in soil samples from threshold values given by WHO. The mean concentrations of As, Cd, Pb, Cr, Fe, Hg, and Mn values exceeded the recommended values with concentration ranges: 1.75–4.56, 0.41–0.67, 2.12–3.12, 1.44–4.56, 87.12–135.25, 2.09–2.64, and 33.41–129.32 mg/Kg, respectively. The vegetable sample’s average concentration of heavy metals was in decreasing order cabbage ˃ brinjal ˃ okra ˃ tomato. The EDI values for As, Co, and Hg calculated for both adults and children is high. The target hazard quotients (THQ) for As, Co, and Hg are greater than the threshold value by consuming vegetables, which indicated the health risk for both adults and children. Similarly, HI due to tomato, cabbage, okra, and brinjal’s consumption is ˃ 1, with HI values 8.1975, 15.3077, 8.7312, and 10.2306, respectively. This advised the possible health effect in this area by using these vegetables. Target Cancer risk (TCR) exposed the adverse cancer risk persuaded by As, Cr, and Hg as their values exceeded the normal range by USEPA by consumption of these vegetables. This study concluded that vegetables imply the total health risk on local people, and regular monitoring of heavy metals is strongly suggested in this region.

Appraisal of groundwater quality and associated risks in Mansa district (Punjab, India)

Mansa district in Malwa region of South-West Punjab has gained significant attention due to elevation in number of patients suffering from diverse diseases especially cancer and consumption of contaminated groundwater could be one of the possible reasons. The present study reports the assessment of 59 groundwater samples from Mansa district by evaluating physicochemical characteristics, potentially toxic element (PTE) contamination and associated health implications followed by analysis of water quality status using various indices. Multivariate statistics were applied for source identification of PTEs in groundwater. The study revealed occurrence of PTEs with mean (μg L^-1) dominance order of As (650.8) > U (104.14) > Zn (55.3) > Fe (34.4) > Hg (8.3) > Mn (5.1) > Cu (4.1) > Cr (2.7) > Pb (2.4). One hundred and 71.19% groundwater samples were found to be seriously contaminated with As and U, respectively, and posing high cancer risks to local residents via ingestion. Higher hazard indices of 16.64 and 12.85 for children and adults, respectively, indicated high non-carcinogenic health risks to both population groups but children were observed to be more vulnerable. Correlation analysis showed positive correlations of U with total dissolved solids (TDS), fluoride (F^-) and total alkalinity (TA). Principal component analysis (PCA) and cluster analysis (CA) revealed the contribution of both geogenic (weathering of rocks) and anthropogenic sources (overuse of agrochemicals in agricultural lands and release of inefficiently treated industrial effluents) for deteriorating the groundwater quality of study area. The study counsels the inhabitants to consume treated groundwater as ingestion route was identified as the primary route of exposure.

Urinary profiles of selected metals and arsenic and their exposure pathway analysis in four large floodplains of Pakistan

In context of fragile geological conditions and rapid urbanization, element exposure via dietary (food, water) and non-dietary (dust, soil) routes into human population at different land use settings is a major concern in the Indus floodplains (FPs) of Pakistan. In current study, several important trace elements including arsenic (As), chromium (Cr), manganese (Mn), cobalt (Co), cadmium (Cd), nickel (Ni), copper (Cu) and lead (Pb) were analyzed in the paired human urine, food, water and dust samples collected from main FPs of Pakistan. Daily intake estimation and regression analysis were used to evaluate the relationships between internal exposure, exposure routes of studied trace elements and different land use settings. High concentrations of urinary As, Cr, Cu, Mn, and Cd were detected in the general male population of the studied floodplains (FPs). Moreover, the levels (μg/L) of urinary As increased gradually from FP1 (12.8), FP2 (18), FP3 (61) to FP4 (71). Regression analysis showed that As contaminated water was correlated with elevated urinary As concentrations in FP3 and FP4, and water Cr and Mn was significantly associated with urinary Cr and Mn concentrations in FP2. Moreover, the associations of food Mn and urinary Mn were found in FP1. Over all, cumulative estimated daily intake (EDI) values from water, dust and food from all the flood plains showed that Mn had the highest values (6.6, 9.2, 14.4 μg/kg/day) followed by water As (1.98 μg/kg/day), dust Cu (1.5 μg/kg/day) and Pb (1.7 μg/kg/day). Studied floodplains were moderately to highly polluted in terms of studied trace elements (As, Cr, Cu, Mn, and Cd) contamination especially in FP3 and FP4. The results will contribute to improve the knowledge and information on current exposure of Pakistani male adults to the different contaminants.

Spatiotemporal characterization of dissolved trace elements in the Gandaki River, Central Himalaya Nepal

A comprehensive investigation was conducted on trace elements (TEs) in the glacier-fed Gandaki River Basin, Central Himalayas Nepal. A total of 93 water samples were collected from 31 locations in pre-monsoon, monsoon and post-monsoon seasons in 2016 to evaluate the concentrations of TEs. Multivariate statistical techniques such as analysis of variance, cluster analysis, principal component analysis, and correlation analysis were applied to investigate the spatiotemporal variations and identify the major sources of the TEs. The results classified most of the TEs into two groups. Group 1 including Cs, Li, Ni, Rb, Sc, Sr, Tl, U and V were from geogenic sources, while Group 2 including Cd, Co, Cr, Cu, Pb, Ti, Y and Zn were impacted by anthropogenic activities. Group 1 showed lower concentrations in the lower-middle and downstream segments with higher precipitation and the highest concentrations during pre-monsoon. In contrast, Group 2 demonstrated higher concentrations in the densely populated lower segments with more agriculture and industries, and the highest concentrations during the post-monsoon season. Comparing to the drinking water limits suggested that special attention should be paid to the elevated concentrations of Zn and As. The results of the study provide a basic guideline for future environmental protection in the Himalaya.

A new indexing approach for evaluating heavy metal contamination in groundwater

Three indexing methods, namely heavy metal pollution index (HPI), contamination index (C_d) and heavy metal evaluation index (HEI), are commonly used for heavy metal evaluation in groundwater. These methods have several limitations. In HPI, 14 out of 15 groundwater samples collected in the study area of Nalagarh valley, Himachal Pradesh, India qualify for drinking purposes with their values varying between 10.73 and 107.50 (critical limit = 100), while in C_d, the same number of samples (>90%) are rejected as their values (C_d = 1.31-37.87) exceed the critical limit of 3. HEI varies from 10.31 to 46.87 with a mean of 26.06, but since it does not have a defined critical limit, quality assessment depends on worker's discretion. It thus becomes very confusing as to which indexing method to use. To overcome this dilemma, a very simple indexing method called 'heavy metal contamination index (HCI)' has been developed on the basis of assigning weight to each heavy metal parameter. A new classification system with six distinct water classes of different uses too has been proposed considering the regulatory limits, human health risk and toxicity of the violator parameters. Regression analysis confirms that HCI has larger number of significantly correlated key parameters compared to the other three indices. Chemometric techniques confirm that Cr, Cu, Fe, Mn and Zn are derived from lithogenic inputs and As, Cd, Ni and Pb from anthropogenic sources. HCI when integrated with Cluster Analysis gives the best possible results in identifying factors that influence the various water classes.

Groundwater vulnerability and trace element dispersion in the Quaternary aquifers along middle Upper Egypt

Association of trace metal concentrations in water is problematic; however, its information is scarce and sometimes contradicted. This work presents variations in dissolved major constituents and trace element concentrations along the quaternary aquifers located in middle Upper Egypt (Minia and Assiut governorates). A total of 205 groundwater samples from these aquifers were collected. Auxiliary parameters (pH, alkalinity, and conductivity), major cations (Ca²⁺, Mg²⁺, Na⁺, and K⁺), dominant anions (HCO₃^-, SO₄^2-, Cl^-, and NO₃^-), and trace element (B, Fe, Cu, Mn, Ni, Pb, Cd, and Cr) concentrations were measured in all samples. Univariate (correlation coefficient and scatter matrix) analysis was employed combined with multivariate (principal coordinates analysis) analysis to identify the chemical characteristics of groundwater that are responsible for generating most of the variability within the dataset. Also, hierarchical cluster analysis was applied to classify the geochemical origin of the groundwater constituents. The results indicate that the groundwater pollution is mainly due to water-rock interactions, including aquifer matrix dissolution, redox reaction of trace metals, input from wastewater, and agricultural fertilizers.

Amelioration effect of chromium-tolerant bacteria on growth, physiological properties and chromium mobilization in chickpea (Cicer arietinum) under chromium stress

In this study, chromium (Cr)-tolerant bacteria were test for their efficiency in alleviating Cr stress in Cicer arietinum plants. On the basis of 16S rRNA gene analysis, the isolates were identified belonging to genus Stenotrophomonas maltophilia, Bacillus thuringiensis B. cereus, and B. subtilis. The strains produced a considerable amount of indole-3-acetic acid in a medium supplemented with tryptophan. The strains also showed siderophore production (S2VWR5 and S3VKR17), phosphorus production (S1VKR11, S3VKR2, S3VKR16, and S2VWR5), and potassium solubilization (S3VKR2, S2VWR5, and S3VKR17). Furthermore, the strains were evaluated in pot experiments to assess the growth promotion of C. arietinum in the presence of chromium salts. Bacterization improved higher root and shoot length considerably to 6.25%-60.41% and 11.3%-59.6% over the control. The plants also showed increase in their fresh weight and dry weight in response to inoculation with Cr-tolerant strains. The accumulation of Cr was higher in roots compared to shoots in both control and inoculated plants, indicating phytostabilization of Cr by C. arietinum. However, phytostabilization was found to be improved manifold in inoculated plants. Apart from the plant attributes, the amendment of soil with Cr and Cr-tolerant bacteria significantly increased the content of total chlorophyll and carotenoids, suggesting the inoculant's role in protecting plants from deleterious effects. This work suggests that the combined activity of Cr-tolerant and plant growth-promoting (PGP) properties of the tested strains could be exploited for bioremediation of Cr and to enhance the C. arietinum cultivation in Cr-contaminated soils.

Street dust heavy metal pollution implication on human health in Nicosia, North Cyprus

The consequence of carcinogenic and non-carcinogenic health risks of the heavy metal concentrations in street dust of North Cyprus is yet to be reported. This study is aimed at investigating the concentration of six different heavy metals' concentration explicitly: As, Cu, Zn, Ni, Cr, and Pb, along leading highways in Nicosia. The result obtained was analyzed using an X-ray fluorescent machine. Multivariate and statistical methods were applied for the data analysis using xlstat MS-excel; furthermore, index of geo-accumulation (Igeo) and human health risk assessment using exposure pathways as defined by United State Environmental Protection Agency (USEPA) pollution mode were also used for level assessment and health risk implications. The average (M ± SD) concentrations of the metals in the dust are as follows: As (17.48 ± 1.53 mg/kg), Cu (51.86 ± 8.60 mg/kg), Cr (321.14 ± 8.20 mg/kg), Pb (35.62 ± 8.54 mg/kg), Ni (64.79 ± 8.72 mg/kg), and Zn (136.13 ± 30.85 mg/kg). Variation coefficient, Vc, and principle component analysis (PCA) suggested that As, Cr, Ni, and Pb have same source of pollution emission from both natural and anthropogenic activities, Zn from traffic emission while Cu from natural source. However, the result was compared with other nearby towns bordering North Cyprus; all the metal shows similar pattern of pollution with the exception of Cr which is 5 and 11 times higher than street dust of Amman (Jordan) and Tokat (Turkey), respectively. Additionally, Igeo result has the following decreasing order: Zn > Cr > Pb > Ni > Cu > As and also revealed that the As, Cu, and Ni have originated from natural source. Cr has mix source: one from traffic and the other one from atmospheric deposition. Also, Pb is emitted from industrial pollution, whereas 80% of Zn are from traffic-related emissions. The non-carcinogenic health risk (hazard quotient (HQ) and hazard index (HI)) follows the order Cr > As>Ni > Pb > Zn > Cu for children and adults. It is found that the HI of As, Cu, Ni, Pb, and Zn is less one; hence, the street dust does not exhibit non-carcinogenic health risk. But that of Cr content is greater than one, with HI values of Cr 1.44E+02 and 1.55E+01 for children and adults, respectively. The result for carcinogenic health risk (total cancer risk (TCR)) has the following order: Pb (1.42E-05) > Cr (4.81E-09) > (Ni 1.35E-09) > As (1.96E-10). With all the values less than threshed hole limit of TCR ≥ 10^-4, street dust does not possess carcinogenic health risk for the entire values of six heavy metals considered in this work.

Identification of groundwater pollution sources in a landfill site using artificial sweeteners, multivariate analysis and transport modeling

In this study, sources of groundwater pollution in a landfill site were identified, using artificial sweeteners as chemical tracers, multivariate statistical analysis and a quantitative analysis of the groundwater flow system through particle tracking and transport modeling. The study area, located in northern Italy, hosts an older unlined landfill and a newer lined municipal solid waste landfill placed downstream of the former. Groundwater, surface water, treated wastewater, and leachate samples were collected in March 2017 for analysis of the artificial sweeteners saccharin, cyclamate, acesulfame and sucralose together with major cations and anions, inorganic nitrogen compounds, total phosphorus, COD and some further parameters. The interpretation of the results suggests that two main leachate leaks/spills are affecting the study area. The first one concerns leachate probably spilling out of the leachate collection system serving the younger lined landfill, the other one involves leachate from the older unlined landfill that also seems to affect an area downstream of the lined landfill. Direct leachate leaks from the lined landfill seem unlikely, although they cannot be definitively excluded. This work underlines the importance of a multi-methods approach, which integrates here chemical tracers, multivariate analysis and transport modeling, for assessing groundwater pollution sources generated from complex landfill sites, where multiple and different sources may exist. In particular, this work highlights how artificial sweeteners can be used for tracing leachate plumes from landfills. The methodology applied in this study can have a broad applicability also in other polluted landfill sites worldwide.

Improvement in fluoride remediation technology using GIS based mapping of fluoride contaminated groundwater and microbe assisted phytoremediation

Fluoride (F) in groundwater is a major issue of water pollution. Geo-statistical analysis of groundwater quality in Newai Tehsil, (India) has been done in order to identify the possible spatial distribution of water quality parameters and to assess the spatial dependence of water properties with the help of principal component analysis (PCA) structure. Two types of maps (spatial map and principal component map) of groundwater quality have been developed. A field experiment was conducted to investigate the effect of different Fluoride (F) concentration combined with Pseudomonas fluorescens (P.F) on Prosopis juliflora plant. The field design was used as completely randomized block design with three replicates. Study revealed that parameters were found to be positively and highly correlated with principal component. Low and high values (with their acceptable limit) have also been displayed over the each spatial map. Plants treated with P. fluorescens showed the highest F uptake in root, shoot and leaves tissues were 33.14, 19.41 and 15.15 mg kg^-1 after 120 days, respectively. Both total bioaccumulation factor (BF) and translocation factor (TF) were obtained above one i.e., 1.06 and 1.04, this confirmed the high accumulation and translocation of F in plant tissues. The F uptake efficiency of plant was enhanced to 67.7% and plant biomass was increased upto 57.03%. According to the available literature, this is the first spatial field study for the remediation of F polluted soil through P. fluorescens. The present study will be beneficial for researchers working towards further improvement of F phytoremediation technology. Also, GIS based study can be very useful for decision maker's exploration of groundwater to understand the potential of present research work on fluoride contamination.

Nonnegative tensor factorization for contaminant source identification

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. Here, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources. The characterization of groundwater mixing processes is a challenging but very important task critical for any environmental management project aiming to characterize the fate and transport of contaminants in the subsurface and perform contaminant remediation. This task typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. Additionally, the application of inverse methods may introduce biases in the analyses through the assumptions made in the model development process. Here, we substitute the model inversion with unsupervised ML analysis. The ML analysis does not make any assumptions about underlying physical and geochemical processes occurring in the aquifer. Our ML methodology, called NTFk, is capable of identifying (1) the unknown number of groundwater types (contaminant sources) present in the aquifer, (2) the original geochemical concentrations (signatures) of these groundwater types and (3) spatial and temporal dynamics in the mixing of these groundwater types. These results are obtained only from the measured geochemical data without any additional site information. In general, the NTFk methodology allows for interpretation of large high-dimensional datasets representing diverse spatial and temporal components such as state variables and velocities. NTFk has been tested on synthetic and real-world site three-dimensional datasets. The NTFk algorithm is designed to work with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

Contaminant source identification using semi-supervised machine learning

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. The NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

Seasonal and Spatial Variability of Anthropogenic and Natural Factors Influencing Groundwater Quality Based on Source Apportionment

Globally, groundwater resources are being deteriorated by rapid social development. Thus, there is an urgent need to assess the combined impacts of natural and enhanced anthropogenic sources on groundwater chemistry. The aim of this study was to identify seasonal characteristics and spatial variations in anthropogenic and natural effects, to improve the understanding of major hydrogeochemical processes based on source apportionment. 34 groundwater points located in a riverside groundwater resource area in northeast China were sampled during the wet and dry seasons in 2015. Using principal component analysis and factor analysis, 4 principal components (PCs) were extracted from 16 groundwater parameters. Three of the PCs were water-rock interaction (PC₁), geogenic Fe and Mn (PC₂), and agricultural pollution (PC₃). A remarkable difference (PC₄) was organic pollution originating from negative anthropogenic effects during the wet season, and geogenic F enrichment during the dry season. Groundwater exploitation resulted in dramatic depression cone with higher hydraulic gradient around the water source area. It not only intensified dissolution of calcite, dolomite, gypsum, Fe, Mn and fluorine minerals, but also induced more surface water recharge for the water source area. The spatial distribution of the PCs also suggested the center of the study area was extremely vulnerable to contamination by Fe, Mn, COD, and F⁻.

Prevalence of exposure of heavy metals and their impact on health consequences

Even in the current era of growing technology, the concentration of heavy metals present in drinking water is still not within the recommended limits as set by the regulatory authorities in different countries of the world. Drinking water contaminated with heavy metals namely; arsenic, cadmium, nickel, mercury, chromium, zinc, and lead is becoming a major health concern for public and health care professionals. Occupational exposure to heavy metals is known to occur by the utilization of these metals in various industrial processes and/or contents including color pigments and alloys. However, the predominant source resulting in measurable human exposure to heavy metals is the consumption of contaminated drinking water and the resulting health issues may include cardiovascular disorders, neuronal damage, renal injuries, and risk of cancer and diabetes. The general mechanism involved in heavy metal-induced toxicity is recognized to be the production of reactive oxygen species resulting oxidative damage and health related adverse effects. Thus utilization of heavy metal-contaminated water is resulting in high morbidity and mortality rates all over the world. Thereby, feeling the need to raise the concerns about contribution of different heavy metals in various health related issues, this article has discussed the global contamination of drinking water with heavy metals to assess the health hazards associated with consumption of heavy metal-contaminated water. A relationship between exposure limits and ultimate responses produced as well as the major organs affected have been reviewed. Acute and chronic poisoning symptoms and mechanisms responsible for such toxicities have also been discussed.

Modeling of Cr contamination in the agricultural lands of three villages near the leather industry in Kasur, Pakistan, using statistical and GIS techniques

Kasur is one of the hubs of leather industry in the Punjab, Pakistan, where chrome tanning method of leather processing is extensively being used. Chromium (Cr) accumulation levels in the irrigation water, soil, and seasonal vegetables were studied in three villages located in the vicinity of wastewater treatment plant and solid waste dumping site operated by the Kasur Tanneries Waste Management Agency (KTWMA). The data was interpreted using analysis of variance (ANOVA), clustering analysis (CA), and principal component analysis (PCA). Interpolated surface maps for Cr were generated using the actual data obtained for the 30 sampling sites in each of the three villages for irrigation water, soil, and seasonal vegetables. The level of contamination in the three villages was directly proportional to their distance from KTWMA wastewater treatment plant and the direction of water runoff. The highest level of Cr contamination in soil (mg kg^-1) was observed at Faqeeria Wala (37.67), intermediate at Dollay Wala (30.33), and the least in Maan (25.16). A gradational variation in Cr accumulation was observed in the three villages from contaminated wastewater having the least contamination level (2.02-4.40 mg L^-1), to soil (25.16-37.67 mg kg^-1), and ultimately in the seasonal vegetable crops (156.67-248.33 mg kg^-1) cultivated in the region, having the highest level of Cr contamination above the permissible limit. The model used not only predicted the current situation of Cr contamination in the three villages but also indicated the trend of magnification of Cr contamination from irrigation water to soil and to the base of the food chain. Among the multiple causes of Cr contamination of vegetables, soil irrigation with contaminated groundwater was observed to be the dominant one.

PAHs behavior in surface water and groundwater of the Yellow River estuary: Evidence from isotopes and hydrochemistry

Large-scale irrigation projects have impacted the regional surface-groundwater interactions in the North China Plain (NCP). Given this concern, the aim of this study is to evaluate levels of PAH pollution, identify the sources of the PAHs, analyze the influence of surface-groundwater interactions on PAH distribution, and propose urgent management strategies for PAHs in China's agricultural areas. PAH concentrations, hydrochemical indicators and stable isotopic compositions (δ¹⁸O and δ²H) were determined for surface water (SW) and groundwater (GW) samples. PAHs concentrations in surface water and groundwater varied from 11.84 to 393.12 ng/L and 8.51-402.84 ng/L, respectively, indicating mild pollution. The seasonal variations showed the following trend: PAHs in surface water at the low-water phase > PAHs in groundwater at the low-water phase > PAHs in surface water at the high-water phase > PAHs in groundwater at the high-water phase. Hydrochemical and δ¹⁸O value of most groundwater samples distributed between the Yellow River and seawater. The mean value of mixture ratio of the Yellow River water recharge to the groundwater was 65%, few anomalous sites can reach to 90%. Surface-groundwater interactions influence the spatial distribution of PAHs in the study area. In light of the ongoing serious pollution, management practices for source control, improved control technologies, and the construction of a monitoring network to warn of increased risk are urgently needed.

Groundwater quality in Ghaziabad district, Uttar Pradesh, India: Multivariate and health risk assessment

This study aimed to assess the quality of groundwater and potential health risk due to ingestion of heavy metals in the peri-urban and urban-industrial clusters of Ghaziabad district, Uttar Pradesh, India. Furthermore, the study aimed to evaluate heavy metals sources and their pollution level using multivariate analysis and fuzzy comprehensive assessment (FCA), respectively. Multivariate analysis using principle component analysis (PCA) showed mixed origin for Pb, Cd, Zn, Fe, and Ni, natural source for Cu and Mn and anthropogenic source for Cr. Among all the metals, Pb, Cd, Fe and Ni were above the safe limits of Bureau of Indian Standards (BIS) and World Health Organization (WHO) except Ni. Health risk in terms of hazard quotient (HQ) showed that the HQ values for children were higher than the safe level (HQ = 1) for Pb (2.4) and Cd (2.1) in pre-monsoon while in post-monsoon the value exceeded only for Pb (HQ = 1.23). The health risks of heavy metals for the adults were well within safe limits. The finding of this study indicates potential health risks to the children due to chronic exposure to contaminated groundwater in the region. Based on FCA, groundwater pollution could be categorized as quite high in the peri-urban region, and absolutely high in the urban region of Ghaziabad district. This study showed that different approaches are required for the integrated assessment of the groundwater pollution, and provides a scientific basis for the strategic future planning and comprehensive management.

Toxic Metal Pollution in Pakistan and Its Possible Risks to Public Health

Environmental pollution has increased many folds in recent years and in some places has reached levels that are toxic to living things. Among pollutant types, toxic heavy metals and metalloids are among the chemicals that pose the highest threat to biological systems (Jjemba 2004). Unlike organic pollutants, which are biodegradable, heavy metals are not degraded into less hazardous end products (Gupta et al. 2001). Low concentrations of some heavy metals are essential for life, but some of them like Hg, As, Pb and Cd are biologically non-essential and very toxic to living organisms. Even the essential metals may become toxic if they are present at a concentration above the permissible level (Puttaiah and Kiran 2008). For example, exposure to Zn and Fe oxides produce gastric disorder and vomiting, irritation of the skin and mucous membranes. Intake of Ni, Cr, Pb, Cd and Cu causes heart problems, leukemia and cancer, while Co and Mg can cause anemia and hypertension (Drasch et al. 2006). Similarly, various studies indicated that overexposure to heavy metals in air can cause cardiovascular disorders (Miller et al. 2007; Schwartz 2001), asthma (Wiwatanadate and Liwsrisakun 2011), bronchitis/emphysema (Pope 2000), and other respiratory diseases (Dominici et al. 2006).

Co-occurrence of arsenic and fluoride in the groundwater of Punjab, Pakistan: source discrimination and health risk assessment

The present study discusses elevated groundwater arsenic (As) and fluoride (F(-)) concentrations in Mailsi, Punjab, Pakistan, and links these elevated concentrations to health risks for the local residents. The results indicate that groundwater samples of two areas of Mailsi, Punjab were severely contaminated with As (5.9-507 ppb) and F(-) (5.5-29.6 ppm), as these values exceeded the permissible limits of World Health Organization (10 ppb for As and 1.5 ppm for F(-)). The groundwater samples were categorized by redox state. The major process controlling the As levels in groundwater was the adsorption of As onto PO4 (3-) at high pH. High alkalinity and low Ca(2+) and Mg(2+) concentrations promoted the higher F(-) and As concentrations in the groundwater. A positive correlation was observed between F(-) and As concentrations (r = 0.37; n = 52) and other major ions found in the groundwater of the studied area. The mineral saturation indices calculated by PHREEQC 2.1 suggested that a majority of samples were oversaturated with calcite and fluorite, leading to the dissolution of fluoride minerals at alkaline pH. Local inhabitants exhibited arsenicosis and fluorosis after exposure to environmental concentration doses of As and F(-). Estimated daily intake (EDI) and target hazard quotient (THQ) highlighted the risk factors borne by local residents. Multivariate statistical analysis further revealed that both geologic origins and anthropogenic activities contributed to As and F(-) contamination in the groundwater. We propose that pollutants originate, in part, from coal combusted at brick factories, and agricultural activities. Once generated, these pollutants were mobilized by the alkaline nature of the groundwater.

Use of statistical and GIS techniques to assess and predict concentrations of heavy metals in soils of Lahore City, Pakistan

Soils from different land use areas in Lahore City, Pakistan, were analyzed for concentrations of heavy metals-cadmium (Cd), chromium (Cr), nickel (Ni), and lead (Pb). One hundred one samples were randomly collected from six land use areas categorized as park, commercial, agricultural, residential, urban, and industrial. Each sample was analyzed in the laboratory with the tri-acid digestion method. Metal concentrations in each sample were obtained with the use of an atomic absorption spectrophotometer. The statistical techniques of analysis of variance, correlation analysis, and cluster analysis were used to analyze all data. In addition, kriging, a geostatistical procedure supported by ArcGIS, was used to model and predict the spatial concentrations of the four heavy metals-Cd, Cr, Ni, and Pb. The results demonstrated significant correlation among the heavy metals in the urban and industrial areas. The dendogram, and the results associated with the cluster analysis, indicated that the agricultural, commercial, and park areas had high concentrations of Cr, Ni, and Pb. High concentrations of Cd and Ni were also observed in the residential and industrial areas, respectively. The maximum concentrations of both Cd and Pb exceeded world toxic limit values. The kriging method demonstrated increasing spatial diffusion of both Cd and Pb concentrations throughout and beyond the Lahore City area.

Study of Selected Metals Distribution, Source Apportionment, and Risk Assessment in Suburban Soil, Pakistan

Composite soil samples collected from suburban areas were analyzed for Cd, Co, Cr, Cu, Fe, Mn, Pb, Sr, and Zn by atomic absorption spectrophotometry. Based on pseudototal metal analysis, Fe, Mn, Sr, and Zn were the prevailing metals while Cd, Co, Cr, and Pb were the least participants. However, based on bioavailability, Cd, Co, Pb, and Sr were easily leachable and might pose adverse effects to soil biota. In ecological risk assessment, contamination factor demonstrated moderate contamination by Co, Sr, and Zn and high contamination by Cd, Cu, and Pb; geoaccumulation index indicated heavy to extreme contamination by Cd and heavy contamination by Pb; enrichment factor revealed significant enrichment by Co, Cr, Cu, Mn, Sr, and Zn and extreme enrichment by Cd and Pb. Substantial human inputs for Cd, Co, Cr, Cu, Mn, Pb, Sr, and Zn were also revealed by principal component analysis in the examined soil. Overall the study area was found to be contaminated at considerable/high degree.

Pollution status of Pakistan: a retrospective review on heavy metal contamination of water, soil, and vegetables

Trace heavy metals, such as arsenic, cadmium, lead, chromium, nickel, and mercury, are important environmental pollutants, particularly in areas with high anthropogenic pressure. In addition to these metals, copper, manganese, iron, and zinc are also important trace micronutrients. The presence of trace heavy metals in the atmosphere, soil, and water can cause serious problems to all organisms, and the ubiquitous bioavailability of these heavy metal can result in bioaccumulation in the food chain which especially can be highly dangerous to human health. This study reviews the heavy metal contamination in several areas of Pakistan over the past few years, particularly to assess the heavy metal contamination in water (ground water, surface water, and waste water), soil, sediments, particulate matter, and vegetables. The listed contaminations affect the drinking water quality, ecological environment, and food chain. Moreover, the toxicity induced by contaminated water, soil, and vegetables poses serious threat to human health.

Dissolved concentrations, sources, and risk evaluation of selected metals in surface water from Mangla Lake, Pakistan

The present study is carried out for the assessment of water quality parameters and selected metals levels in surface water from Mangla Lake, Pakistan. The metal levels (Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb, Sr, and Zn) were determined by flame atomic absorption spectrophotometry. Average levels of Cd, Co, Cr, Ni, and Pb were higher than the allowable concentrations set by national and international agencies. Principal component analysis indicated significant anthropogenic contributions of Cd, Co, Cr, Ni, and Pb in the water reservoir. Noncarcinogenic risk assessment was then evaluated using Hazard Quotient (HQ_ing/derm) and Hazard Index (HI_ing/derm) following USEPA methodology. For adults and children, Cd, Co, Cr, and Pb (HQ_ing > 1) emerged as the most important pollutants leading to noncarcinogenic concerns via ingestion route, whereas there was no risk via dermal contact of surface water. This study helps in establishing pollutant loading reduction goal and the total maximum daily loads, and consequently contributes to preserve public health and develop water conservation strategy.

Trace metal in surface water and groundwater and its transfer in a Yellow River alluvial fan: evidence from isotopes and hydrochemistry

Metals are ubiquitous in the environment. The aim of sustainable management of the agro-ecosystem includes ensuring that water continues to fulfill its function in agricultural production, cycling of elements, and as a habitat of numerous organisms. There is no doubt that the influence of large-scale irrigation projects has impacted the regional surface-groundwater interactions in the North China Plain (NCP). Given these concerns, the aim of this study is to evaluate the pollution, identify the sources of trace metals, analyze the influence of surface-groundwater interactions on trace metal distribution, and to propose urgent management strategies for trace metals in the agriculture area in China. Trace metals, hydrochemical indicators (EC, pH, concentrations of Na(+), K(+), Mg(2+), Ca(2+), Cl(-), SO4(2-), and HCO3(-)) and stable isotopic composition (δ(18)O and δ(2)H) were determined for surface water (SW) and groundwater (GW) samples. Trace metals were detected in all samples. Concentrations of Fe, Se, B, Mn, and Zn in SW exceeded drinking water standards by 14.8%, 29.6%, 25.9%, 11.1%, and 14.8% higher, respectively, and by 3.8%, 23.1%, 11.5%, 11.5%, and 7.7% in GW. The pollution of trace metals in surface water was more serious than that in groundwater, and was also higher than in common irrigation areas in NCP. Trace metals were found to have a combined origin of geogenic and agriculture and industrial activities. Their distribution varied greatly and exhibited a certain relationship with the water flow direction, with the exception of a number of singular sites. Hydrochemical and environmental isotopic evidence indicates surface-groundwater interactions influence the spatial distribution of trace metal in the study area. Facing the ongoing serious pollution, management practices for source control, improved control technologies, and the construction of a monitoring net to warn of increased risk are urgently needed.

Cr-resistant rhizo- and endophytic bacteria associated with Prosopis juliflora and their potential as phytoremediation enhancing agents in metal-degraded soils

Prosopis juliflora is characterized by distinct and profuse growth even in nutritionally poor soil and environmentally stressed conditions and is believed to harbor some novel heavy metal-resistant bacteria in the rhizosphere and endosphere. This study was performed to isolate and characterize Cr-resistant bacteria from the rhizosphere and endosphere of P. juliflora growing on the tannery effluent contaminated soil. A total of 5 and 21 bacterial strains were isolated from the rhizosphere and endosphere, respectively, and were shown to tolerate Cr up to 3000 mg l(-1). These isolates also exhibited tolerance to other toxic heavy metals such as, Cd, Cu, Pb, and Zn, and high concentration (174 g l(-1)) of NaCl. Moreover, most of the isolated bacterial strains showed one or more plant growth-promoting activities. The phylogenetic analysis of the 16S rRNA gene showed that the predominant species included Bacillus, Staphylococcus and Aerococcus. As far as we know, this is the first report analyzing rhizo- and endophytic bacterial communities associated with P. juliflora growing on the tannery effluent contaminated soil. The inoculation of three isolates to ryegrass (Lolium multiflorum L.) improved plant growth and heavy metal removal from the tannery effluent contaminated soil suggesting that these bacteria could enhance the establishment of the plant in contaminated soil and also improve the efficiency of phytoremediation of heavy metal-degraded soils.

Heavy-metal levels in feathers of cattle egret and their surrounding environment: a case of the Punjab Province, Pakistan

Levels of 10 heavy metals in cattle egret chick feathers, prey, and surrounding soils from three heronries in Punjab Province, Pakistan, were assessed by atomic absorption spectrophotometry. Mean levels of cadmium (Cd), iron (Fe), chromium (Cr), and lithium (Li) in feathers, manganese (Mn), cobalt (Co), and nickel (Ni) in prey, and lead (Pb), iron (Fe), chromium (Cr), zinc (Zn), cobalt (Co), and lithium (Li) in soils were significantly different among Trimun Headworks, Shorkot, and Mailsi heroneries. Mean levels of Pb (43.10 μg/g), Cr (35.77 μg/g), Co (18.34 μg/g), Cu (0.20 μg/g), and Ni (0.22 μg/g) in feathers were significantly greater at Mailsi, and Mn (3.07 μg/g), Zn (18.83 μg/g), and Li (1.5 μg/g) levels were significantly greater at Shorkot. Multivariate analysis identified that some metals, such as Fe, Zn, and Li, in feathers were either associated with natural sources or with human-related activities, whereas Ni, Cr, Pb, Cd, Cu, Co, and Mn were correlated mainly with anthropogenic processes. Alarming levels were recorded for Cr, Pb, and Cd in feathers that were above threshold levels that may affect cattle egret flighting capacity and reproduction, thus leading to their population decline in Punjab Province. The results of this study provide evidence for the potential of feathers of cattle egret to be used as a biomonitor for the local heavy-metal contamination.

Dissolved heavy metals in the Tigris River (Turkey): spatial and temporal variations

Multivariate statistical techniques, such as analysis of variance, cluster analysis (CA), correlation analysis, principal component analysis (PCA), and factor analysis (FA), were applied to determine the spatial and temporal variations of dissolved heavy metals in the Tigris River at 7 different sites spread over the river stretch of about 500 km during the period of February 2008 to January 2009. The results indicated that Fe, Cr, and Ni were the most abundant elements in the river water, whereas Cd and As were the less abundant. Cu, Fe, Ni, and Zn showed significant spatial variations, reflecting the influence of anthropogenic activities. The lowest total concentration of heavy metals was found at site 2 downstream of the Dicle Dam due to clean water from the dam. The concentrations of most metals were found lower when compared with results of previous studies due to reduction of the activity of the copper mine plant and the construction of two dams on the river. The lowest total concentrations were determined in February due to high precipitation and snow melts. Hierarchical agglomerative CA classified all the sampling sites into three main groups of spatial similarities. Clusters 1 (Maden and Bismil), 2 (Cizre), and 3 (Eğil, Diyarbakır, Batman, and Hasankeyf) corresponded to moderate polluted and relatively low polluted regions, respectively. PCA/FA, CA, and correlation analysis suggest that Cu, Ni, and Zn are controlled by anthropogenic sources.

Relationship between heavy metals concentrations in egret species, their environment and food chain differences from two Headworks of Pakistan

Concentration of ten metals (Cd, Cr, Co, Cu, Fe, Li, Mn, Ni, Pb and Zn) were analyzed in the egg contents, prey and soil samples of little egret (Egretta garzetta) and cattle egret (Bubulcus ibis) from two Headworks to determine habitat and species-specific differences; to assess the importance of prey and habitat contamination as an exposure source for heavy metals. Concentration of Cu, Mn, Cr and Pb in egg contents, Fe, Co, Cu, Mn, Zn in prey and Fe, Co, Cu, Ni, Li in surface soils were significantly different (P<0.05). Mean metal concentrations of Cr, Pb and Cd were relatively higher in little egret whereas Cu and Mn were higher in the egg contents of cattle egret. The mean concentrations of Cu, Mn and Zn were higher in prey samples of cattle egrets and Cr, Cd and Pb in prey samples of little egrets. In soil samples collected from little egret heronries metal concentrations were higher except Cu and Ni. Correlation Analysis and Hierarchical Agglomerative Cluster Analysis (HACA) identified relatively similar associations of metals and their source identification. Metals such as Fe, Cu, Mn, and Li were related with geochemical origin from parent rock material as well as anthropogenic input whereas Cr, Cd, Pb, Ni, Co and Zn were associated mostly with anthropogenic activities. The study suggested that eggs are useful bio-monitor of local heavy metal contamination.

Assessment of background levels of trace metals in water and soil from a remote region of Himalaya

Selected trace metals were estimated by atomic absorption spectrometry in the water and soil samples collected from the remote region of Himalaya. The soil samples were analysed for soluble and acid extractable fraction of trace metals. In water samples, Ca, Na, Mg and K emerged as dominant contributors, whereas, Ca, Na, K, Mg, Fe and Pb were estimated at comparatively higher levels in the water extract of the soil. In case of acid extract of the soil samples, Ca, K, Fe, Mg, Mn and Na were found at elevated concentrations. Based on mean levels of the metals, following decreasing concentration order was observed in water samples: Ca > Na > Mg > K > Pb > Co > Cu > Zn > Mn > Cr > Fe > Cd > Li, however, in the acid extract of the soil, following order was noted: Ca > K > Fe > Mg > Mn > Na > Pb > Zn > Cr > Li > Cu > Co > Cd. The correlation study revealed appreciably diverse mutual relationships of trace metals in the water and soil samples. The multivariate cluster analyses exhibited divergent apportionment of trace metals in water and soil samples. Among the trace metals, Cd, Pb, Li, Zn, Cr, Cu, Mn and Co exhibited extreme to significant anthropogenic enrichment in the soil samples, while the rest of the metals were mostly contributed by the natural processes.

Distribution, correlation and risk assessment of selected metals in urban soils from Islamabad, Pakistan

Urban soil samples were analyzed for Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Pb, Sr and Zn by atomic absorption spectrophotometric method. Multivariate statistical approach was used to study the apportionment of selected metals in the soil samples during summer and winter. The degree of contamination along with the geoaccumulation index, enrichment factor and contamination factor was also evaluated. In water-extract of the soil samples, relatively higher levels were noted for Na, Ca, K, Fe, Mg, and Pb with average concentrations of 56.38, 33.82, 12.53, 7.127, 5.994, and 1.045mg/kg during summer, while the mean metal levels during winter were 76.45, 38.05, 3.928, 0.627, 8.726, and 0.878mg/kg, respectively. In case of acid-extract of the soils, Ca, Fe, Mg, Na, K, Mn and Sr were found at 27,531, 12,784, 2769, 999.9, 737.9, 393.5, and 115.1mg/kg, during summer and 23,386, 3958, 3206, 254.6, 1511, 453.6, and 53.30mg/kg, during winter, respectively. Most of the metals showed random distribution with diverse correlations in both seasons. Principal component analysis and cluster analysis revealed significant anthropogenic intrusions of Cd, Pb, Co, Cr, Cu, Li, Zn and Na in the soils. Geoaccumulation indices and contamination factors indicated moderate to heavy contamination for Pb and Cd in the soils, while enrichment factor exhibited significant enrichment (EF>5) of Cd, Pb, Ca, Co, Li, Mn and Zn by anthropogenic activities. Overall, on the average basis, considerable degree of contamination (C(deg)>16) was observed in both seasons, although it was higher in winter. Present metal levels were also compared with those reported from other areas around the world.

Efficiencies of different microbial parameters as indicator to assess slight metal pollutions in a farm field near a gold mining area

In order to monitor changes in the concentrations of metals in the soil, different microbial indices such as BIOLOG, microbial carbon (C(mic)), basal respiration, and culturable microbe's most probable number were used. We compared these methods and wanted to discover which method was the best at measuring slight changes in the amounts of heavy metals. Factor analyses were applied to the BIOLOG data and metal concentrations so the combined effects of heavy metals on microbes could be analyzed via statistical data reduction and the distribution patterns of metal concentration could also be revealed. The results showed that the BIOLOG method could barely detect subtle characteristic changes in the soil samples, while the C(mic) method was more sensitive. Furthermore, different heavy metals did not have the same origin/source, and their effects on microbial indices should be analyzed separately. Significant positive correlations between C(mic) and metals were observed and suggested the limitation of using traditional microbial parameters as metal pollution indicators. Among all the soil characteristics in our study, pH seemed to be the most active abiotic factor that affected microorganisms.