Arsenic in residential soil and household dust in Cornwall, south west England: potential human exposure and the influence of historical mining

Identifying Sources of Lead Exposure for Children in the Republic of Georgia, with Lead Isotope Ratios

In the Republic of Georgia, a 2018 national survey estimated that more than 40% of children aged 2-7 years had a blood lead concentration (BLC) of more than 5 µg/dL. The objective of this study was to document the feasibility of employing lead isotope ratios (LIRs) to identify and rank the Pb (lead) exposure sources most relevant to children across Georgia. A cross-sectional survey between November 2019 and February 2020 of 36 children previously identified as having BLCs > 5 µg/dL from seven regions of Georgia involved the collection of blood and 528 environmental samples, a questionnaire on behaviours and potential exposures. The LIRs in blood and environmental samples were analysed in individual children and across the whole group to ascertain clustering. A fitted statistical mixed-effect model to LIR data first found that the blood samples clustered with spices, tea, and paint, then, further isotopically distinct from blood were sand, dust, and soil, and lastly, milk, toys, pens, flour, and water. Analysis of the LIRs provided an indication and ranking of the importance of Pb environmental sources as explanatory factors of BLCs across the group of children. The findings support the deployment of interventions aimed at managing the priority sources of exposure in this population.

A review on arsenic in the environment: contamination, mobility, sources, and exposure

Arsenic is one of the regulated hazard materials in the environment and a persistent pollutant creating environmental, agricultural and health issues and posing a serious risk to humans. In the present review, sources and mobility of As in various compartments of the environment (air, water, soil and sediment) around the World are comprehensively investigated, along with measures of health hazards. Multiple atomic spectrometric approaches have been applied for total and speciation analysis of As chemical species. The LoD values are basically under 1 μg L-1, which is sufficient for the analysis of As or its chemical species in environmental samples. Both natural and anthropogenic sources contributed to As in air, while fine particulate matter tends to have higher concentrations of arsenic and results in high concentrations of As up to a maximum of 1660 ng m-3 in urban areas. Sources for As in natural waters (as dissolved or in particulate form) can be attributed to natural deposits, agricultural and industrial effluents, for which the maximum concentration of 2000 μg L-1 was found in groundwater. Sources for As in soil can be the initial contents, fossil fuel burning products, industrial effluents, pesticides, and so on, with a maximum reported concentration up to 4600 mg kg-1. Sources for As in sediments can be attributed to their reservoirs, with a maximum reported concentration up to 2500 mg kg-1. It is notable that some reported concentrations of As in the environment are several times higher than permissible limits. However, many aspects of arsenic environmental chemistry including contamination of the environment, quantification, mobility, removal and health hazards are still unclear.

A hybrid framework for delineating the migration route of soil heavy metal pollution by heavy metal similarity calculation and machine learning method

Soil heavy metal contamination was a global environmental issue that posed adverse impacts on ecological and human health risks. The controlling of soil heavy metal is mainly focused on the emission source and pipe-end treatment, less is known about the intermediate controlling process. The migration route of heavy metals exhibited the spatial evolution of pollutants from the sources to the pipe-end, which provided the more reasonable location for the target-oriented treatment of soil heavy metal. Here, we proposed a new view of heavy metal similarity, which quantitatively expressed how closely of the contaminations between the study area and the test areas. We found that the similarity of different heavy metals was unequally distributed across locations that were related with five main sources, namely agricultural activities, natural sources, traffic emissions, industrial activities, and other sources. Based on the similarity, a state-of-the-art machine learning method was applied to delineate the migration route of soil heavy metals. Thereinto, As was concentrated around livestock farms, and its migration route was close to the water system. Cd migration route was over-dispersed in the areas where located mine fields and chemical plants. Migration routes of Hg and Pb were along rivers, which were related to agricultural activities and natural sources. Overall, the perspective on similarity and migration routes provided theoretical basis and method to alleviate soil heavy metal pollution at regional scale and can be extended across largescale regions.

Health Risk Assessment of Lead in Soils from an Historic Industrial Site in North-East England

The former St. Antony’s Lead Works site, now the central part of an urban recreational park (Walker Riverside Park) in the east end of Newcastle upon Tyne (England, UK), has been assessed based on the Pb concentration in topsoil according to recently derived Category 4 Screening Level (C4SL) for public open space–park (POSpark, 1300 mg/kg). The site was divided into eight sampling areas following its physical layout. In total 79 soil samples were collected, characterised for acidity and organic matter content, and analysed by energy dispersive X-ray fluorescence (ED-XRF). The Pb levels in most of the samples, particularly those from the fringes of the site, are below the generic guideline value (down to 70 mg/kg). More than 16% of the samples from nearly all sampling areas, especially those sampling points around the former horizontal condenser flue and main chimney, contain levels of Pb significantly exceeding the limit (up to 206,000 mg/kg). No correlation is found between the Pb concentration in soil samples and their acidity (mostly neutral, pH 7.0 ± 0.5) or organic matter content (15.5 ± 4.1%). Using the Contaminated Land Exposure Assessment (CLEA) model (version 1.071), the site-specific risk assessment criteria (SSAC) for Lead (C4SL child), 2862 mg/kg, is obtained based on adjusted exposure frequency and occupancy period. Nearly 9% of the individual sample Pb concentrations (n = 79) across sample locations B, C, D and H are still above the specific value. Further statistical evaluation based on 95% upper confidence limit confirms that the site still represents a potential human health risk. This is because Pb concentrations, from two areas in the centre of the site (sample locations B and C), are greatly over the SSAC specific threshold (sample mean at location B is 12,350 mg/kg and at location C is 11,745 mg/kg).

Characteristics of Potentially Toxic Elements, Risk Assessments, and Isotopic Compositions (Cu-Zn-Pb) in the PM10 Fraction of Road Dust in Busan, South Korea

The pollution status of ten potentially toxic elements (PTEs), isotopic compositions (Cu, Zn, Pb), and the potential ecological risk posed by them were investigated in the PM10 fraction of road dust in Busan Metropolitan city, South Korea. Enrichment factors revealed extremely to strongly polluted levels of Sb, Cd, Zn, Pb, and Cu in the PM10 fraction of road dust, with Sb levels being the highest. Statistical analyses showed that the major cause for contamination with PTEs was non-exhaust traffic emissions such as tire and brake wear. Cu and Zn isotopic compositions of road dust were related to traffic-related emission sources such as brake and tires. Pb isotopic compositions were close to that of road paint, indicating that Pb was a different source from Cu and Zn in this study. No significant health risk was posed by the PTEs. Taking into account the total length of road in Busan, a high quantity of PTEs in road dust (PM10) can have serious deleterious effects on the atmospheric environment and ecosystems. The results of metal concentrations and isotopic compositions in road dust will help identify and manage atmospheric fine particle and coastal metal contamination derived from fine road dust.

Risk Assessment of Contamination by Potentially Toxic Metals: A Case Study in the Vicinity of an Abandoned Pyrite Mine

Abandoned mining areas can display soil and water pollution and also a high incidence of endemic diseases. Here, based on preliminary results on mental retardation and high incidence rates of cancers, we investigate the contamination status and potential ecological risk at an abandoned pyrite mine located in Xianju village, Hubei province, central China. The study focused on the three potentially toxic metals As, Pb, and Zn and four other common elements, including Ca, Fe, S, and Se. Soil samples were collected from 12 sites and leachate seeping from five sites. Leachates were strongly acidic compared to the soil, with pH values <3.22. Soil As and Se concentrations exceeded the Chinese Grade II standards for soil environmental quality (GB 15618-2009) (30 and 3 mg·kg−1, respectively), ranging from 33.6 ± 0.7 to 78.2 ± 1.0 mg As·kg−1 dry matter (DM) and 5.3 ± 0.8 to 17.1 ± 1.4 mg Se·kg−1 DM. Arsenic, Fe, Se, and S in leachates all exceeded the Chinese Grade III standard for surface water environmental quality (GB 3838-2002) (0.1, 0.3, 0.03, and 1.0 mg·L−1). The potential ecological risks from Pb and Zn in soils were low, but As in soils and leachates represented a moderate or strong risk to children according to the Nemerow index and hazard quotient. Soil replacement combined with further remediation measures is required to remediate the contaminated area.

Incorporating bioaccessibility into health risk assessment of heavy metals in particulate matter originated from different sources of atmospheric pollution

Rapid industrialization and urbanization have resulted in widespread pollution of airborne particulate matter (PM) containing various heavy metals with adverse human-health effects. Health risk assessment of PM calls for accurate evaluation of the bioaccessibility, instead of the total content, of heavy metals in PM. Here, we demonstrated that the leachable fraction of particle-bound As, Pb, Cr, Mn, Cd, Cu, Ni and Zn in lung fluid within the typical retention duration of particles in human lungs varied drastically among particles originated from different air pollution sources, including coal combustion, biomass combustion, fugitive dust, road dust, construction dust, cement and soil. Moreover, bioaccessibility of heavy metals, particularly in biomass combustion, cement and soil particles, was strongly dependent on pollution sources, and the particulate Cu, Ni, Pb and Cd appeared to be the primary indicators of the source dependence of heavy metal bioaccessibility. Using total rather than bioaccessible concentrations of particle-bound heavy metals not only led to overestimation of the health risk of source particles, but more importantly, inaccurate identification of the high-risk pollution sources and the priority metal pollutants in the source particles. When considering bioaccessibility of particle-bound heavy metals examined in this study, coal combustion products exhibited the highest carcinogenic and noncarcinogenic risks among all source particles, whereas cement particles would be the source with highest risk based on total metal content. As and Mn appeared to be the main drivers for the noncarcinogenic risks of source particles, while As, Ni and Cr were the major contributors to the carcinogenic risks of source particles, significantly different from those based on total contents. This research underlines the importance of incorporating bioaccessibility into health risk indexes of frequently occurring particle-bound heavy metals from specific air pollution sources, which will facilitate risk-based assessment of source contribution and hence effective source regulation of airborne PM.

Hazard Ranking Method for Populations Exposed to Arsenic in Private Water Supplies: Relation to Bedrock Geology

Approximately one million people in the UK are served by private water supplies (PWS) where main municipal water supply system connection is not practical or where PWS is the preferred option. Chronic exposure to contaminants in PWS may have adverse effects on health. South West England is an area with elevated arsenic concentrations in groundwater and over 9000 domestic dwellings here are supplied by PWS. There remains uncertainty as to the extent of the population exposed to arsenic (As), and the factors predicting such exposure. We describe a hazard assessment model based on simplified geology with the potential to predict exposure to As in PWS. Households with a recorded PWS in Cornwall were recruited to take part in a water sampling programme from 2011 to 2013. Bedrock geologies were aggregated and classified into nine Simplified Bedrock Geological Categories (SBGC), plus a cross-cutting “mineralized” area. PWS were sampled by random selection within SBGCs and some 508 households volunteered for the study. Transformations of the data were explored to estimate the distribution of As concentrations for PWS by SBGC. Using the distribution per SBGC, we predict the proportion of dwellings that would be affected by high concentrations and rank the geologies according to hazard. Within most SBGCs, As concentrations were found to have log-normal distributions. Across these areas, the proportion of dwellings predicted to have drinking water over the prescribed concentration value (PCV) for As ranged from 0% to 20%. From these results, a pilot predictive model was developed calculating the proportion of PWS above the PCV for As and hazard ranking supports local decision making and prioritization. With further development and testing, this can help local authorities predict the number of dwellings that might fail the PCV for As, based on bedrock geology. The model presented here for Cornwall could be applied in areas with similar geologies. Application of the method requires independent validation and further groundwater-derived PWS sampling on other geological formations.