Contamination Source Apportionment Research Articles

Heavy metal geochemistry and toxicity assessment of water environment from Ib valley coalfield, India: Implications to contaminant source apportionment and human health risks

The present study aims to investigate the hydrogeochemical evolution of heavy metals and assesses impacts of mining activities on the groundwater resources and potential human health risks in the coal mining areas of Ib valley coalfield. In this perspective, a total of one hundred and two mine water and groundwater samples were collected from different locations. The water samples were analysed for some selected heavy metals i.e. Mn, Cu, Pb, Zn, Ni, Co, As, Se, Al, Sr, Ba, Cd, Cr, V and Fe using ICP-MS. In addition, pH and SO42− concentration were also measured following APHA procedure. The water pH in the Ib valley coalfields ranged from 3.26 to 8.18 for mine water and 5.23 to 8.52 for groundwater, indicating acidic to alkaline nature of water. Mn in mine water and Zn in groundwater environment were observed as the most dominant metals. The water hazard index (WHI) reflects that around 80% of mine water are non-toxic (WHI<5), 5% slightly toxic (5<WHI>10) and 15% extremely toxic (WHI>15). Relatively high pH and low concentration of dissolved metals and SO42− in groundwater as compared to mine water indicate lesser impact of mining activities. The calculated drinking water quality index (DWQI) suggests that Mn, Al, Ni and Fe in mine water and Mn, Fe, Ni and Pb in groundwater were the major objectionable metals which caused the water quality deterioration for drinking uses. Further, the non-carcinogenic health risk assessment for adult male, female and child populations identifies Co, Mn, Ni as the key elements making the water hazardous for human health. Comparatively higher ratio of ingestion rate and body weight in child population might be causing higher health risks in child population as compared to adult male and adult female population.

Assessment of Groundwater Quality Using APCS-MLR Model: A Case Study in the Pilot Promoter Region of Yangtze River Delta Integration Demonstration Zone, China

Groundwater contaminant source identification is an endeavor task in highly developed areas that have been impacted by diverse natural processes and anthropogenic activities. In this study, groundwater samples from 84 wells in the pilot promoter region of the Yangtze River Delta integration demonstration zone in eastern China were collected and then analyzed for 17 groundwater quality parameters. The principal component analysis (PCA) method was utilized to recognize the natural and anthropogenic aspects impacting the groundwater quality; furthermore, the absolute principal component score-multiple linear regression (APCS-MLR) model was employed to quantify the contribution of potential sources to each groundwater quality parameter. The results demonstrated that natural hydro-chemical evolution, agricultural activities, domestic sewage, textile industrial effluent and other industrial activities were responsible for the status of groundwater quality in the study area. Meanwhile, the contribution of these five sources obtained by the APCS-MLR model were ranked as natural hydro-chemical evolution (18.89%) &gt; textile industrial effluent (18.18%) &gt; non-point source pollution from agricultural activities (17.08%) &gt; other industrial activities (15.09%) &gt; domestic sewage (4.19%). It is believed that this contaminant source apportionment result could provide a reliable basis to the local authorities for groundwater pollution management.

Water quality assessment of alpine glacial blue water lakes and glacial-fed rivers

The study mainly focuses on heavy metals’ quantification, investigation of physicochemical characteristics, source apportionments, human health risk assessment, and connectivity between the water of alpine glacial lakes and rivers. In this regard, twenty-six water samples are taken from sixteen lakes and two lakes-fed rivers. The physiochemical parameters, particularly heavy metals are quantified using atomic absorption spectrophotometer. The Pearson correlations are used to find out the relationship between various heavy metals and physicochemical parameters. The multivariate methods, Principal component analysis (PCA), and Hierarchal cluster analysis (HCA) are conducted for contamination source apportionment. In the findings of the current analysis, the mean concentration of Lead (Pb 0.51), Cadmium (Cd 0.04), Nickel (Ni 0.06), Chromium (Cr 0.08), Iron (Fe 0.06), and Manganese (Mn 0.09) is the most common heavy metal contaminants found in both lakes and river water. Copper (Cu 0.00) and Arsenic (As 0.001) concentrations are lower in both lakes and rivers. Other tested physicochemical characteristics, such as pH, Hardness, Chemical oxygen demands (COD), total dissolved solids (TDS), dissolved oxygen (DO) chloride (Cl-), sulfate (SO4), nitrate (NO3), and bicarbonates (HCO3) are within the maximum permissible limits of World Health Organization’s (WHO).

Spatial distribution of contaminants and water quality assessment using an indexical approach, Astore River basin, Western Himalayas, Northern Pakistan

Astore River is an important tributary located in the Upper Indus River Basin, Western Himalayas, Northern Pakistan. This study aimed at assessing water quality of the river for domestic, drinking, and irrigation purposes. Astore river (n = 40) samples were collected and analyzed for a set of 17 physicochemical parameters. Water quality index (WQI), corrosion, scaling tendencies, and irrigation water quality indices were used for its grading and statistical analyses for the source apportionment of contaminants. Results indicated that turbidity, fluoride, and nitrate (NO3) values surpassed the safe limits in 60%, 2.5%, and 2.5% of samples. WQI revealed that only 19.5% of samples were classified in poor and very poor water quality classes due to turbidity and NO3. Irrigation and corrosion indices of the river showed its suitability for agriculture and corrosive tendency. The dominant hydro-chemical facies were Ca-Mg/Cl-SO4 and results suggested that natural sources play a role in shaping hydrochemistry.

Geospatial and statistical approaches to nitrate health risk and groundwater quality assessment of an alluvial aquifer in SE Nigeria for drinking and irrigation purposes

In agricultural districts, like Ogbaru in southeastern Nigeria, water supplies are under obvious threats from human activities. Therefore, continuous monitoring and assessment of water quality suitability for drinking, domestic, and irrigation purposes are encouraged to keep check of the effects of water contaminants. In this paper, several quality water assessment tools were integrated to determine the suitability of the groundwater in Ogbaru for drinking and irrigation uses and to assess the ingestion and dermal health risks of nitrate occurrence. The majority of the physicochemical parameters analyzed in this study were within their respective standard limits. However, groundwater quality index classified 52.63% of the groundwaters as excellent water and 47.37% as good water. Overall index of pollution, on the other hand, classified 84.21% as excellent water and 15.79% as acceptable water. The pH ranged from 5.4 to 6.8, indicating that the groundwater is acidic. Most irrigation water quality assessment indices (SAR, Na%, PS, KR, RCO, CAI-1 and 2) revealed that the groundwater samples are suitable for irrigation. However, magnesium hazard and permeability index grouped the majority of the water samples as unsuitable resources for irrigation uses. The nitrate health risk assessment (mean score = 1.371) indicated that children are exposed to higher non-carcinogenic health risk due to ingestion of contaminated groundwater than the other populations. It was observed that 21.05% of the groundwater samples had the highest concentrations of the analyzed chemical species. Multivariate statistical methods (hierarchical cluster analysis and factor analysis) efficiently aided the contaminant source apportionment. Based on the findings of this paper, it is advised that adequate regulatory strategies and water treatment techniques be adopted in the area to protect and sustain the groundwater quality and public health.

A new inverse distance model to calculate the percentage contribution of various Pb sources

Lead (Pb) isotopic composition analysis is a useful tool to accurately identify the origin of Pb in environmental media. The existing calculation method of the contribution of Pb sources from Pb isotope ratios greatly restricted the development of Pb contamination source apportionment. In the present study, a new distance model for calculating the mass proportion of Pb sources, which is based on the distance between the samples and the possible Pb sources in the Pb isotope ratios plot, was presented. The inverse distance model was applied to calculate the contribution proportion of two Pb sources in three previous studies. The average absolute differences between the proportions calculated by the conventional binary mixing equation and the inverse distance model were 0.21%, 1% and 1.9%, respectively, indicating that the new model agreeably calculated the contribution of two Pb sources. The anthropogenic sources proportion (52%) calculated by the inverse distance model of three Pb sources to park soil Pb in Shanghai was comparable to the result that was calculated by the conventional ternary mixing equation (53%), which showed the validity of the new model in calculating the contribution proportion of three Pb sources. Rational results were obtained by the inverse distance model in calculating the contribution of four Pb sources, illustrating that the new model has potential use in calculating the apportionment of four or more Pb sources. These results suggest that the inverse distance model is a simple and efficient approach for calculating the contribution proportion of various Pb contamination sources, and provides a prospective in the study of this field.

Snow buntings (Plectrophenax nivealis) as bio-indicators for exposure differences to legacy and emerging persistent organic pollutants from the Arctic terrestrial environment on Svalbard

Eggs of snow buntings (Plectrophenax nivealis) were applied as a bio-indicator to examine differences in exposure to legacy persistent organic pollutants (POPs) and perflouroalkyl subtances (PFAS) from the terrestrial environment surrounding the settlements of Longyearbyen, Barentsburg and Pyramiden on Svalbard, Norway. Significantly higher concentrations of summed polychlorinated biphenyls (sumPCB7) in eggs collected from Barentsburg (2980 ng/g lipid weight (lw)) and Pyramiden (3860 ng/g lw) compared to Longyearbyen (96 ng/g lw) are attributed to local sources of PCBs within these settlements. Similar findings were observed for p,p′-dichlorodiphenyldichloroethylene (p,p′-DDE) where higher median concentrations observed in Pyramiden (173 ng/g lw) and Barentsburg (75 ng/g lw) compared to Longyearbyen (48 ng/g lw) may be influenced by guano inputs from breeding seabird populations, although other point sources cannot be ruled out. Concentrations of perfluorooctane sulphonate (PFOS) and several perfluorinated carboxylic acids (PFCAs) in snow bunting eggs were found to be statistically higher in the populated settlements of Longyearbyen and Barentsburg compared to the abandoned Pyramiden. Narrow foraging ranges of snow buntings during breeding season was useful in assessing point sources of exposure for PCBs and PFAS at particular sites with extreme differences observed between nest locations. SumPCB7 concentrations ranged from 2 μg/g ww to below detection limits between nest sites located less than a kilometer from each other in Pyramiden. Similar findings were observed in Longyearbyen, where several PFCAs ranged from 2 to 55 times higher between nest sites with similar spatial distances. These findings indicate that snow buntings can be a useful bio-indicator offering high spatial resolution for contaminant source apportionment in terrestrial environments on Svalbard.

Contamination source apportionment and health risk assessment of heavy metals in soil around municipal solid waste incinerator: A case study in North China

Few studies have comprehensively taken into account the source apportionment and human health risk of soil heavy metals in the vicinity of municipal solid waste incinerator (MSWI) in high population density area. In this study, 8 elements (Cr, Pb, Cu, Ni, Zn, Cd, Hg, and As) in fly ash, soil samples from different functional areas and vegetables collected surrounding the MSWI in North China were determined. The single pollution index, integrated Nemerow pollution index, principal component analysis (PCA), absolute principle component score-multiple linear regression (APCS-MLR) model and dose-response model were used in this study. The results showed that the soils around the MSWI were moderately polluted by Cu, Pb, Zn, and Hg, and heavily polluted by As and Cd. MSWI had a significant influence on the distribution of soil heavy metals in different distances from MSWI. The source apportionment results showed that MSWI, natural source, industrial discharges and coal combustion were the four major potential sources for heavy metals in the soils, with the contributions of 36.08%, 29.57%, 10.07%, and 4.55%, respectively. MSWI had a major impact on Zn, Cu, Pb, Cd, and Hg contamination in soil. The non-carcinogenic risk and carcinogenic risk posed by soil heavy metals surrounding the MSWI were unacceptable. The soil heavy metals concentrations and health risks in different functional areas were distinct. MSWI was the predominate source of non-carcinogenic risk with the average contribution rate of 36.99% and carcinogenic risk to adult male, adult female and children with 4.23×10−4, 4.57×10−4, and 1.41×10−4 respectively, implying that the impact of MSWI on human health was apparent. This study provided a new insight for the source apportionment and health risk assessment of soil heavy metals in the vicinity of MSWI.

Characterisation of the dynamics of organic contaminants (n-alkanes, PAHs and PCBs) in a coastal area

The dynamics of three classes of organic contaminants, namely n-alkanes, polycyclic aromatic hydrocarbons and polychlorinated biphenyls, were evaluated from the settled suspended particulate matter (SPM) of two wastewater treatment plant outlets (O1 and O2) at the Marseille coast. We used a 1-m-high Plexiglas settling column filled with 7L of seawater to determine the particles' settling rates, size distribution, and the extent of organic contaminants. Six classes of SPM (50–200μm particles size) were obtained from 15 fractions of 500-mL successive filtering samples ranging from 30s to 5days, including those in the tube wall. The results of the experiment indicated that >68% of the particles settled within 15min, which highly correlated with the distribution of organic carbon and contaminant contents. Furthermore, we demonstrated that 9%–13% of the SPM, which contained 5%–11% of the organic contaminants, are non-settable even after 5days. Extrapolating such behaviour in situ using molecular diagnostic indices for organic contaminant source apportionment indicated that these non-settable contaminant particles are exported to the sea.

Sediment PAH source apportionment in the Liaohe River using the ME2 approach: A comparison to the PMF model

Environmental contaminant source apportionment is essential for pollution management and control. This study analysed surface sediment samples for 16 priority polycyclic aromatic hydrocarbons (PAHs). PAH sources were identified by two receptor models, which included positive matrix factorization (PMF) and multilinear engine 2 (ME2). Three PAH sources in the Liaohe River sediments were identified by PMF, including traffic, coke oven and coal combustion. The ME2 model apportioned one additional source. The two models yielded excellent correlation coefficients between the measured and predicted PAH concentrations. Traffic emission was the primary PAH source associated with the Liaohe River sediments, with estimated PMF contributions of 58% in May and 63% in September. Coke oven (19%–25%) and coal combustion (13%–18%) were the other two major PAH sources. For ME2, gasoline and diesel were separated: accounted for 14% in May and 16% in September; and 53% in May and 48% in September. This study marks the first application of the ME2 model to study sediment contaminant source apportionment. The methodology can potentially be applied to other aquatic environment contaminants.

Surface water quality contamination source apportionment and physicochemical characterization at the upper section of the Jakara Basin, Nigeria

The present study investigates the surface water quality of three important tributaries of Jakara Basin, northwestern Nigeria to provide an overview of the relationship and sources of physicochemical and biological parameters. A total of 405 water samples were collected from 27 sampling points and analyzed for 13 parameters: dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), suspended solids (SS), pH, ammonia-nitrogen (NH3NL), dissolved solids (DS), total solids (TS), nitrates (NO3), chloride (Cl), phosphates (PO4), Escherichia coli (E. coli) and fecal coliform bacteria (FCB). Pearson’s product–moment correlation matrix and principal component analysis (PCA) were used to distinguish the main pollution sources in the basin. Four varimax components were extracted from PCA, which explained 84.86, 83.60, and 78.69 % of the variation in the surface water quality for Jakara, Tsakama, and Gama-Kwari Rivers, respectively. Strong positive loading included BOD5, COD, NH3NL, E. coli, and FCB with negative loading on DO attribute to a domestic waste water pollution source. One-way ANOVA revealed that there was no significant difference in the mean of the three water bodies (p > 0.05). It is therefore recommended that the government should be more effective in controlling the point source of pollution in the area.

Bayesian Chemical Mass Balance Method for Surface Water Contaminant Source Apportionment

AbstractA Bayesian chemical mass balance (CMB) source apportionment method is developed using the Markov Chain Monte Carlo (MCMC) approach. Compared with deterministic approaches, the Bayesian method is capable of accounting for the measurement errors and the impact of variability of the source elemental compositions resulting from the heterogeneities and estimate the uncertainties associated with the estimated source contributions. The method estimates the joint probability densities and consequently, the credible intervals and correlation matrices of source contributions of various sources into a receiving water using observed elemental profiles of samples from both potential sources and the receiving surface waters. The model is applied to samples collected from possible sources and runoff and stream flow from two stream crossing sites along Highway 89 in the Lake Tahoe Basin. The contributing sources of total dissolved nitrogen, total dissolved phosphorus concentrations, and microparticles (<20 μm) f...

Origin and Fate of Organic Compounds in Water: Characterization by Compound-Specific Stable Isotope Analysis

Within the past 15 years, compound-specific stable isotope analysis has continued to increase in popularity in the area of contaminant hydrology of organic molecules. In particular, in cases where concentration data alone are insufficient to elucidate environmental processes unequivocally, the isotope signature can provide additional unique information. Specifically, it can help answer questions about contaminant source apportionment, quantification of biotic and abiotic processes, and identification of transformation reactions on a mechanistic level. We review advances in laboratory and field investigations and exemplary applications in contaminant hydrology via stable isotope analysis. We also highlight future directions in the field.

Stable lead isotope ratios and metals in freshwater mussels from a uranium mining environment in Australia’s wet-dry tropics

Abstract Concentrations of Fe, Mn, Cu, Zn, U and Pb, and stable Pb isotopes 206Pb, 207Pb and 208Pb were measured via inductively coupled plasma mass spectrometry in sediments, water and freshwater mussels (Velesunio angasi) from two catchments in the Alligator Rivers Region, Australia. Sediment U and Pb concentrations were higher in Magela Creek downstream than upstream of the Ranger U mine due to the mineralised nature of the catchment and potential local input of sediment from the mine site. Water metal concentrations were highest in Georgetown Creek, which is a tributary of Magela Creek and part drains the Ranger mine site, but there was little difference in concentrations between the Magela Creek upstream and downstream sites. Metal concentrations in mussels collected immediately upstream and downstream of the mine site also showed little difference, whereas Pb isotope ratios displayed a very distinct pattern. The 206Pb/207Pb and 208Pb/207Pb isotope ratios were more uranogenic downstream than upstream of the site and also more uranogenic than ratios measured in Sandy Billabong, a reference billabong in a catchment not influenced by U mineralisation. Isotope ratios were also more uranogenic in younger mussels, potentially due to the increasing footprint of the mine site over the past decade. The most uranogenic ratios were found in mussels from Georgetown Creek and at a site approximately 2 km downstream. At Mudginberri Billabong, approximately 12 km downstream of the Ranger mine, the relative contribution of uranogenic Pb to the total Pb concentration in mussels was small and overwhelmed by the input of industrial Pb with a Broken Hill type Pb signature. Whereas metal uptake by and thus concentrations in mussel flesh are influenced by water chemistry, mussel condition and metabolic rates, Pb isotope ratios are independent of these factors and provide a powerful means of source apportionment of contaminants in mussels and waterways, in particular in an U mining environment.

Trace element fingerprinting as a means of contaminant source apportionment in road surface deposits

This investigation seeks to quantify the potential of multi-element fingerprinting as a method for contaminant source apportionment in road surface deposits. The concentrations of metals in roadside deposits are usually a result of anthropogenic activity, elemental-fingerprinting may provide a method by which pollutant sources may be differentiated. Samples from both test-track environments, with negligible traffic and real road systems were taken and digested for analysis by ICP-MS (trace elements) and ICP-OES (major elements). Significant differences were observed in the fingerprint patterns for samples taken from high traffic roads and roads with no traffic. The results provide an indication of the potential for source identification using elemental fingerprinting.

Contaminant source apportionment by PIMMS lead isotope analysis and SEM-image analysis.

By combining scanning electron microscopy (SEM) image analysis and laser ablation plasma ionisation multi-collector mass spectrometry (LA-PIMMS), high precision lead isotope analyses can be obtained from individual metal-rich particles. Soils from Wolverhampton and Nottingham were sampled on the basis of high Pb concentrations or brownfield location. Pressed powder pellets of each were rastered by LA-PIMMS to obtain a bulk Pb-isotope signature. The results plot along an apparent mixing line between the major sources of lead contamination in the UK, that is UK ore deposits and alkyl-lead from petrol additives (Australian ore). Two particularly lead-rich soils were chosen to investigate the lead distribution and isotope variability between size and density fractions. The fine-grained and low-density fractions contained most of the lead and have Pb-isotope ratios comparable with the bulk soils. By contrast, the small, lead-enriched denser fractions contained only a minor proportion of the total lead but Pb-isotope signatures indicating relative enrichment in one or other of the end-members from the mixing line. Further characterisation of individual Pb-rich grains is in progress.