Uncertainty in environmental health impact assessment: quantitative methods and perspectives

Designing Retirement Strategies for Coal-Fired Power Plants To Mitigate Air Pollution and Health Impacts

Retiring coal power plants can reduce air pollution and health damages. However, the spatial distribution of those impacts remains unclear due to complex power system operations and pollution chemistry and transport. Focusing on coal retirements in Pennsylvania (PA), we analyze six counterfactual scenarios for 2019 that differ in retirement targets (e.g., reducing 50% of coal-based installed capacity vs generation) and priorities (e.g., closing plants with higher cost, closer to Environmental Justice Areas, or with higher CO2 emissions). Using a power system model of the PJM Interconnection, we find that coal retirements in PA shift power generation across PA and Rest of PJM, leading to scenario-varying changes in the plant-level release of air pollutants. Considering pollution transport and the size of the exposed population, these emissions changes, in turn, give rise to a reduction of 6-136 PM2.5-attributable deaths in PJM across the six scenarios, with most reductions occurring in PA. Among our designed scenarios, those that reduce more coal power generation yield greater aggregate health benefits due to air quality improvements in PA and adjacent downwind regions. In addition, comparing across the six scenarios evaluated in this study, vulnerable populations─in both PA and Rest of PJM─benefit most in scenarios that prioritize plant closures near Environmental Justice Areas in PA. These results demonstrate the importance of considering cross-regional linkages and sociodemographics in designing equitable retirement strategies.

Elemental profile of wheat in the las vegas market: Geographic origin discrimination and probabilistic health risk assessment

This study investigates concentrations of toxic and potentially toxic elements (PTEs) in organic and conventional wheat flour and grains marketed in Las Vegas. Geographic origins of the samples were evaluated using Linear Discriminant Analysis (LDA). Monte Carlo Simulation technique was also employed to evaluate non-carcinogenic risk in four life stages. Concentrations of Al, As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Se, Sr, and Zn were determined using inductively coupled plasma mass spectrometry (ICP-MS) following hot block-assisted digestion. Obtained results showed non-significant differences in contents of toxic and PTEs between conventional and organic wheat grains/flour. Using LDA, metal (loid)s were found to be indicative of geographical origin. The LDA produced a total correct classification rate of 95.8% and 100% for US and West Pacific Region samples, respectively. The results of the present study indicate that the estimated non-carcinogenic risk associated with toxic element intakes across the four life stages were far lower than the threshold value (Target Hazard Quotient (THQ) > 1). However, the probability of exceeding the threshold value for Mn is approximately 32% in children aged between 5 and 8 years. The findings of this study can aid in understanding dietary Mn exposure in children in Las Vegas.

Origin, spatial distribution, sediment contamination, ecological and health risk evaluation of trace metals in sediments of ship breaking area of Bangladesh

Eleven trace metals (Cd, Cr, Fe, Mn, Cu, Ni, Co, Zn, As, Pb, and Ag) in sediments of Bangladesh's ship breaking area were measured by an atomic absorption spectrometer to determine origin, contamination extent, spatial distributions, and associated ecological and human health hazards. This study found considerable quantities of Pb, Cd, Mn, Zn, and Cu when compared with standards and high levels of Pb, Cd, Zn, Cu, As, and Ag contamination according to pollution evaluation indices. Different indices indicate most of the sampling sites were highly polluted. However, spatial distribution maps indicate that trace metals were predominantly deposited in the northern and southern region. The ecological risk index revealed that Cd has the highest while Pb and As had moderate risk. Based on the health index values, Zn for both adults and children were higher than the safe limit while Mn, Pb, Cr, As, Fe, Cu, Ni, and Co for children were close to the threshold. The mean total carcinogenic risk values of Cr, As, and Ni for children and Ni for adults exceeded the permissible threshold. The cancer risk possibilities were further assessed using Monte Carlo simulation. Most trace metals have anthropogenic origins, which were attributed to ship breaking activities.

Spatial distribution, contamination characteristics and ecological-health risk assessment of toxic heavy metals in soils near a smelting area

Soil heavy metals (HMs) contamination stemming from smelting and mining activities is becoming a global concern due to its devastating impacts on the environment and human health. In this study, 128 soil samples were investigated to assess the spatial distribution, contamination characteristics, ecological and human health risk of HMs in soils near a smelting area by using BP artificial neural network (BP-ANN) and Monte Carlo simulation. The results showed that the concentrations of all five HMs in the soil greatly exceeded the background value of study area with a basic trend: Pb > As > Cr > Cd > Hg, indicating a high pollution level. Arsenic and lead were the major pollutants in the study area with an exceedance rate of 78.95% and 28.95%, respectively. The toxic fume and dust emitted during the smelting process were identified as the major sources of HMs pollution in soil, while Cd pollution was mainly caused by agricultural activities near the study area. The probabilistic risk assessment suggested that the average HQ values of five HMs for children and adults exceeded the acceptable threshold with a trend: As > Pb > Cr > Cd > Hg. The average CR values of As, Cr and Pb for all population were greatly larger than the acceptable threshold (CR ≥ 1), indicating a high cancer risk. However, the CR values of Cd for adults and children were within the acceptable threshold (CR < 1), implying no cancer risk. The results of the present study can provide some insight into the contamination characteristics, ecological and human health risk of HMs in contaminated soils by mining and smelting activities, which can help prevent and control soil pollution and environmental risk.

Spatial variability and risk assessment of heavy metals in the soil surrounding solid waste from coking plants in Shanxi, China

Heavy metal pollution in the soil surrounding solid wastes from coking plants poses potential threats to human health and has attracted widespread attention. This study is the first to assess the spatial variability and risks of heavy metals in the soil surrounding solid waste from coking plants. The results showed that the concentrations of Cu, Ni, Pb, and Cd in the soil were much higher than the background value of the soil. Solid waste had a clear influence on the contents of Ni, Cd, Mn, Pb, and Cr in the soil. The ecological risk assessment of heavy metal pollution demonstrated that the pollution degree of Cu, Pb, and Cd was more serious than others, and the ecological risk of heavy metals was mainly caused by Cd in the soil. The human health risk assessment showed that adults and children near coking plants might face carcinogenic risk from exposure to Cr. This study can provide a theoretical basis for the prevention and management of soil heavy metal pollution surrounding solid waste in coking plants.

A Sustainable Approach towards Disposable Face Mask Production Amidst Pandemic Outbreaks

SARS-CoV-2 has become a global pandemic, causing many disruptions in multiple sectors. The World Health Organization has urged the public to wear face masks as part of the countermeasure. As the demand for face masks increased, research on the environmental sustainability of face masks production started to emerge. However, the scope of the prior studies is limited to environmental impacts during the manufacturing process. Broadening the research scope is critical to acquire a comprehensive environmental impact analysis. Therefore, this study investigates the life cycle impact assessment of disposable face mask production, from raw material extraction to the point of sale, by adopting the life cycle assessment method. Disposable face masks are assessed for a single person, over one functional unit (FU) of 30 12-h days. The ReCiPe approach was used with a Hierarchist perspective. The results reveal that disposable face mask manufacture contributes significantly to enormous environmental impact categories. As a solution, this study proposes a reconfiguration of the manufacturing process, by altering the design and material proportion of the earloop to minimise the environmental impact. The investigation indicates that the proposed design might decrease the global warming contribution, from 1.82593 kg CO2 eq. to 1.69948 kg CO2 eq.

Potential driving forces and probabilistic health risks of heavy metal accumulation in the soils from an e-waste area, southeast China

The integrated analysis of the distribution characteristics, health risks, and source identification of heavy metals is crucial for formulating prevention and control strategies for soil contamination. In this study, the area around an abandoned electronic waste dismantling center in China was selected as the research area. The probabilistic health risks caused by heavy metals were evaluated by the Monte Carlo simulation. Random forest, partial least squares regression, and generalized linear models were utilized to predict heavy metal distributions and identify the potential driving factors affecting heavy metal accumulation in soil. The relationships of spatial variation between the heavy metal contents and environmental variables were further visualized. The results revealed that cadmium (Cd) and copper (Cu) were the primary soil pollutants in the study area and caused high ecological risks. The probabilistic health risk assessment indicated that the non-carcinogenic and carcinogenic risks for all populations were acceptable. However, children are more susceptible to heavy metal soil contamination than adults. The sensitivity analyses indicated that the total contents of soil heavy metals and soil ingestion rate were the dominant factors affecting human health. The random forest model, with R² values of 0.41, 0.65, 0.57, 0.71, and 0.58 for Cd, Cu, Ni, Zn, and Pb, respectively, predicted the heavy metal concentrations better than the other two models. The distance to the nearest industrial enterprise, industrial output, and agricultural chemical input were the main factors affecting Cd, Cu, Zn, and Pb accumulations in the soil, and soil pH and soil parent material were the primary factors influencing Ni accumulation in the soil. The visualization results of the geographically weighted regression model showed a significant relationship between soil heavy metal contents and industrial activity level. This study could be utilized as a reference for policymakers to formulate prevention and control strategies for heavy metal pollution in agricultural areas.

Heavy metals accumulation and risk assessment in a soil-maize (Zea mays L.) system around a zinc-smelting area in southwest China

Smelting of nonferrous metals causes significant concerns because of its emissions of heavy metals (HMs) into surface soil, and its potential threat to human health through the food chain. To investigate the HMs concentrations in a soil-maize system, a total of 41 paired soil-maize samples were collected from a typical indigenous zinc-smelting area of northwestern Guizhou Province, China. Results showed that the concentrations of the targeted HMs in the soil were significantly higher than their corresponding background values of Guizhou Province. Results obtained of the geo-accumulation index (I_geo) and the potential ecological risk factor of an individual metal (E _r ⁱ ) revealed that Cd and Pb were identified as the top-priority control HMs in the study area. The mean concentrations in maize grain decreased in the order of Zn > Pb > Cu > Cd. Bio-accumulation factor (BAF) indicated a strong ability for Cd to be accumulated in the maize root. Translocation factor (TF) and Fourier transform infrared (FTIR) spectroscopy revealed that the maize root played an important role in reducing the mobilization of HMs to stem, except for Zn. Kriging interpolation results illustrated that the spatial distribution patterns of HMs in the maize grain were generally similar to those in sampled soil, and the higher concentrations for the investigated HMs were partially overlapping between soil and maize grain. The average hazard quotient (HQ) of the investigated HMs for both children and adults were all lower than the threshold value (HQ = 1). The total hazard index (HI) was 5.51E-01 and 4.24E-01 for the two population groups, respectively, implying no potential non-carcinogenic risk for local maize-consumers. Sensitivity analysis demonstrated that the oral ingestion rate (IngR) of grain was the predominated contribution to the output of the risk assessment.

Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components

Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM_2.5) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical transport model. In cross-validation experiments, BART demonstrated good out-of-sample prediction performance at monitoring locations (R² from 0.62 to 0.73). More importantly, prediction intervals associated with concentration estimates from BART showed good coverage probability at locations with and without monitoring data. In our case study, major PM_2.5 components could be estimated with good accuracy, especially when collocated PM_2.5 total mass observations were available. In conclusion, BART is an attractive approach for modeling ambient air pollution levels, especially for its ability to provide uncertainty in estimates that may be useful for subsequent health impact and health effect analyses.

Monte Carlo simulation-based probabilistic health risk assessment of metals in groundwater via ingestion pathway in the mining areas of Singhbhum copper belt, India

Probabilistic health risk assessment was conducted for metal exposure through groundwater in mining areas of Singhbhum Copper Belt, India. The concentrations of metals showed notable spatial variation exceeding drinking water standards at some of the locations. Hazard Quotient revealed that chronic risks to the local population were largely contributed by Mn, Co and As. The 95^th percentiles of Hazard Index (HI) calculated using Monte Carlo simulations showed that the HI for male, female and child populations was 2.87, 2.54 and 4.57 for pre-monsoon, 2.16, 1.88 and 3.49 for monsoon and 2.28, 2.02 and 3.75 for post-monsoon seasons, respectively. The Hazard Indices indicated that amongst the populations, risk was greater for child population and considering the seasons the risk was higher during the pre-monsoon season. The sensitivity analysis suggested that concentration of metals in groundwater and exposure duration were 2 most influential input variables that contributed to the total risk.

Status of mercury accumulation in agricultural soils across China (1976-2016)

Soil contamination with mercury (Hg) is a serious and widespread issue in China, with particularly severe effects on the quality of agricultural soils. To analyse long-term, nation-wide trends in Hg contamination of agriculture soil, we conducted a review of Hg concentrations in agricultural soils over four decades, based on 791 studies comprising 1411 sites, published between 1976 and 2016. We assessed spatiotemporal variations in Hg concentration, along with ecological and health risks. While Hg concentrations in agricultural soils showed an increasing trend between 1979 and 2010, they declined thereafter. Moreover, Hg concentrations in agricultural soils were generally high in western (e.g. Guizhou), southern (e.g. Hunan) and north-eastern provinces (e.g. Liaoning), where mining activities were concentrated. Using the geoaccumulation index (I_geo) and other ecological and health risk indices, we found most sampling sites to be uncontaminated, or to have a low level of contamination, although some mining sites showed moderate to extreme Hg contamination. The noncarcinogenic risk to exposure groups followed the order of children (4.42) > adult females (2.71) > adult males (2.45). Therefore, children were identified as the priority risk group. Noncarcinogenic risk values exceeded 100 in some areas in Guizhou and Hunan provinces; these areas should be prioritised for Hg control measures. This review examined Hg pollution in Chinese agricultural soils to provide insight to policymakers for the development of targeted contamination prevention measures.

Status of copper accumulation in agricultural soils across China (1985-2016)

The first national-scale assessment of Cu contamination of agricultural soils covering 1731 sites in China was performed based on 1837 published papers. The temporal and spatial variations from 1985 to 2016 in the Cu concentrations and the ecological and health risks associated with Cu exposure were analyzed. Approximately, 21.02% of the sampling sites revealed Cu concentrations that exceeded the screening value (50.00 mg/kg; GB15618-2018). The Cu concentrations differed among five geographical regions in the decreasing order of South China > West China > East China > Northeast China > North China. Notably, concentration of Cu in agricultural soils have begun to diminish since 2011 due to reduced heavy metal inputs. Cu mainly originated from anthropogenic activities such as mining and agricultural activities. Linear correlations were observed between the amounts of fungicides and fertilizers applied and the Cu concentrations in the soils, which suggested that the application of fungicides and fertilizers is an important contributor to the accumulation of Cu in soils. Additionally, the geoaccumulation index (I_geo) and ecological risk index (E_fⁱ) values implied that pollution and ecological risk resulting from soil Cu concentration were in low levels. The hazard index (HI) values were higher for children than for adults. Therefore, children should be prioritized for protection from heavy metal pollution. Overall, this study details the status of Cu contamination of agricultural soils in China, and thus provides insights for policymakers regarding the preventive measures.

Assessment of health risks associated with potentially toxic element contamination of soil by end-of-life ship dismantling in Bangladesh

Ship breaking and recycling industry (SBRI) loops back scarce ferrous and non-ferrous materials from dismantled ships and also renews the global shipping fleet by treating the end-of-life (EoL) ships. Currently, SBRIs in Bangladesh, India, and Pakistan are dismantling the majority of the EoL ships by open beaching method. Accordingly, ship dismantling carries the blame of releasing potentially toxic elements (PTEs) to the coastal and marine environment risking the food chain through potential bioaccumulation and biomagnification. Health risk assessment associated with PTEs from open beach ship dismantling is scarce. This study aimed at assessing concentrations and seasonal variations of PTEs in soils exposed to the activities of SBRIs for their source apportionment by using contamination factor (CF) and multivariate statistical analysis, while carcinogenic and non-carcinogenic health risks due to the PTEs have also been determined. Soil samples were collected twice-during pre-monsoon and post-monsoon seasons-from three working zones of each of the 15 different ship breaking yards spanning the entire SBRI zone in Bangladesh. Soil contamination was assessed by using the CF, and inverse distance weighting interpolation mapping showed the spatial distribution of metals at SBRI zone in Bangladesh. Multivariate statistical analysis, principal component analysis, and correlation matrix yielded the source apportionment of PTEs. Subsequently, carcinogenic and non-carcinogenic health risks were assessed following the approach recommended by the United States Environmental Protection Agency (USEPA) with uncertainty estimation through Monte Carlo simulation. Contamination levels of PTEs followed Cd > Zn > Cr > Cu > Pb > Ni > Mn > As. Concentrations of Cd, Cr, Mn, and Zn were higher than the maximum allowable regulatory limits at storage zone and also higher as compared with the beaching and cutting zones in general. The contamination index indicated extreme Cd contamination in the area with elevated levels in pre-monsoon. Two principal components (PC) were identified-PC1 (Cd, Cu, Mn, Pb, Zn) and PC2 (As, Cr, Ni) inferring their source segmentation. Indirect soil ingestion is the major possible exposure path to PTEs. The health index indicated the absence of any obvious health effects on the people active at SBRI yards in Bangladesh. The carcinogenic risk was for 6 to 7 persons per 100,000 people which was within the USEPA acceptable range.

Health risk assessment of potentially harmful elements in subsidence water bodies using a Monte Carlo approach: An example from the Huainan coal mining area, China

Enrichment of potentially harmful elements in surface water results in ecological risk to the surrounding environment. Assessing the environmental risk of these elements is of great importance. In this study, surface water samples from 6 different subsidence water bodies in the Huainan coal mining area were collected. The concentrations of Cu, Ni, Pb, Cd, Co, Cr, V, Fe, Mn and Zn were measured by atomic absorption spectrophotometry, and those of As and Hg were analyzed by atomic fluorescence spectrometry. Then, human health risks through the ingestion and dermal contact pathways were assessed and analyzed on the basis of a Monte Carlo simulation. The mean and 95th percentile risks were reported. The results showed that the total carcinogenic risk values in every subsidence water body summed for Cr, Ni and As via two exposure pathways were greater than the maximum acceptable level (1 × 10^-4), and Xinji'er water body had the highest carcinogenic risk. Among three elements, Ni was the highest contributor to carcinogenic risk. All non-carcinogenic health risk (hazard quotients) values except for one water area of Co (Xinji'er) were less than 1; however, the total non-carcinogenic health risks of two water bodies (Xinji'er, Xinjiyi) summed for all the elements based on mean concentrations were higher than 1. Xinji'er had the highest hazard index. The extent of the impacts of the total hazard quotients followed the order of Co > As > Cd > Hg > Pb > V >Fe > Ni > Mn > Zn > Cr. Furthermore, the total hazard quotients of Co and As via ingestion pathway summed for the six subsidence water areas were greater than 1, which should be a concern.

Air pollutant strategies to reduce adverse health impacts and health inequalities: a quantitative assessment for Detroit, Michigan

The development of air quality management (AQM) strategies provides opportunities to improve public health and reduce health inequalities. This study evaluates health and inequality impacts of alternate SO2 control strategies in Detroit, MI, a designated non-attainment area. Control alternatives include uniform reductions across sources, ranking approaches based on total emissions and health impacts per ton of pollutant emitted, and optimizations that meet concentration and health goals. Using dispersion modeling and quantitative health impact assessment (HIA), these strategies are evaluated in terms of ambient concentrations, health impacts, and the inequality in health risks. The health burden attributable to SO2 emissions in Detroit falls primarily among children and includes 70 hospitalizations and 6,000 asthma-related respiratory symptom-days annually, equivalent to 7 disability-adjusted life years (DALYs). The health burden disproportionately falls on Hispanic/Latino residents, residents with less than a high school diploma, and foreign-born residents. Control strategies that target smaller facilities near exposed populations provide the greatest benefit in terms of the overall health burden reductions and the inequality of attributable health risk; conventional strategies that target the largest emission sources can increase inequality and provide only modest health benefits. The assessment is novel in using spatial analyses that account for urban scale gradients in exposure, demographics, vulnerability, and population health. We show that quantitative HIA methods can be used to develop AQM strategies that simultaneously meet environmental, public health, and environmental justice goals, advancing AQM beyond its current compliance-oriented focus.

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

Participatory quantitative health impact assessment of urban and transport planning in cities: A review and research needs

INTRODUCTION

Urban and transport planning have large impacts on public health, but these are generally not explicitly considered and/or quantified, partly because there are no comprehensive models, methods and tools readily available. Air pollution, noise, temperature, green space, motor vehicle crashes and physical activity are important pathways linking urban and transport planning and public health. For policy decision-making, it is important to understand and be able to quantify the full-chain from source through pathways to health effects and impacts to substantiate and effectively target actions. In this paper, we aim to provide an overview of recent studies on the health impacts related to urban and transport planning in cities, describe the need for novel participatory quantitative health impact assessments (HIA) and provide recommendations.

METHOD

To devise our searches and narrative, we were guided by a recent conceptual framework linking urban and transport planning, environmental exposures, behaviour and health. We searched PubMed, Web of Science, Science Direct, and references from relevant articles in English language from January 1, 1980, to November 1, 2016, using pre-defined search terms.

RESULTS

The number of HIA studies is increasing rapidly, but there is lack of participatory integrated and full-chain HIA models, methods and tools. These should be based on the use of a systemic multidisciplinary/multisectorial approach and state-of-the-art methods to address questions such as what are the best, most feasible and needed urban and transport planning policy measures to improve public health in cities? Active citizen support and new forms of communication between experts and citizens and the involvement of all major stakeholders are crucial to find and successfully implement health promoting policy measures.

CONCLUSION

We provided an overview of the current state-of-the art of HIA in cities and made recommendations for further work. The process on how to get there is as important and will provide answers to many crucial questions on e.g. how different disciplines can effectively work together, how to incorporate citizen and stakeholder opinion into quantitative HIA modelling for urban and transport planning, how different modelling and measurement methods can be effectively integrated, and whether a public health approach can bring about positive changes in urban and transport planning.

Source apportionment and health risk assessment of trace metals in surface soils of Beijing metropolitan, China

Understanding the exposure risks of trace metals in contamination soils and apportioning their sources are the basic preconditions for soil pollution prevention and control. In this study, a detailed investigation was conducted to assess the health risks of trace metals in surface soils of Beijing which is one of the most populated cities in the world and to apportion their potential sources. The data set of metals for 12 elements in 240 soil samples was collected. Pollution index and enrichment factor were used to identify the general contamination characteristic of soil metals. The probabilistic risk model was employed for health risk assessment, and a chemometrics technique, multivariate curve resolution-weighted alternating least squares (MCR-WALS), was applied to apportion sources. Results suggested that the soils in Beijing metropolitan region were contaminated by Hg, Cd, Cu, As, and Pb in varying degree, lying in the moderate pollution level. As a whole, the health risks posed by soil metals were acceptable or close to tolerable. Comparatively speaking, children and adult females were the relatively vulnerable populations for the non-carcinogenic and carcinogenic risks, respectively. Atmospheric deposition, fertilizers and agrochemicals, and natural source were apportioned as the potential sources determining the contents of trace metals in soils of Beijing area with contributions of 15.5%-16.4%, 5.9%-7.7% and 76.0%-78.6%, respectively.

Health impact metrics for air pollution management strategies

Health impact assessments (HIAs) inform policy and decision making by providing information regarding future health concerns, and quantitative HIAs now are being used for local and urban-scale projects. HIA results can be expressed using a variety of metrics that differ in meaningful ways, and guidance is lacking with respect to best practices for the development and use of HIA metrics. This study reviews HIA metrics pertaining to air quality management and presents evaluative criteria for their selection and use. These are illustrated in a case study where PM2.5 concentrations are lowered from 10 to 8μg/m(3) in an urban area of 1.8 million people. Health impact functions are used to estimate the number of premature deaths, unscheduled hospitalizations and other morbidity outcomes. The most common metric in recent quantitative HIAs has been the number of cases of adverse outcomes avoided. Other metrics include time-based measures, e.g., disability-adjusted life years (DALYs), monetized impacts, functional-unit based measures, e.g., benefits per ton of emissions reduced, and other economic indicators, e.g., cost-benefit ratios. These metrics are evaluated by considering their comprehensiveness, the spatial and temporal resolution of the analysis, how equity considerations are facilitated, and the analysis and presentation of uncertainty. In the case study, the greatest number of avoided cases occurs for low severity morbidity outcomes, e.g., asthma exacerbations (n=28,000) and minor-restricted activity days (n=37,000); while DALYs and monetized impacts are driven by the severity, duration and value assigned to a relatively low number of premature deaths (n=190 to 230 per year). The selection of appropriate metrics depends on the problem context and boundaries, the severity of impacts, and community values regarding health. The number of avoided cases provides an estimate of the number of people affected, and monetized impacts facilitate additional economic analyses useful to policy analysis. DALYs are commonly used as an aggregate measure of health impacts and can be used to compare impacts across studies. Benefits per ton metrics may be appropriate when changes in emissions rates can be estimated. To address community concerns and HIA objectives, a combination of metrics is suggested.

Human health risk assessment of heavy metals in the irrigated area of Jinghui, Shaanxi, China, in terms of wheat flour consumption

Contamination of heavy metals (HMs) in agricultural soil has become a serious environmental problem because it poses a serious threat to human health by entering into food chains. Wheat is a staple food of the majority of the world's population; therefore, understanding the relationship between HM concentration in soils and its accumulation in wheat grain is imperative. This study assessed the concentrations of HMs (i.e., Hg, As, Cd, Cr, Pb, Cu, Zn, and Ni) in agricultural soils (a loess soil, eum-orthic anthrosol) and wheat flour in the historical irrigated area of Jinghui, Northwest China. The potential human health risks of HMs among local residents were also determined by evaluating the consumption of wheat flour. Results showed that the mean soil concentrations of HMs exceeded the corresponding natural background values of agricultural surface soil in Shaanxi: 0.07 mg kg(-1) for Hg, 15.4 mg kg(-1) for As, 0.25 mg kg(-1) for Cd, 75.5 mg kg(-1) for Cr, 27.2 mg kg(-1) for Pb, 28.1 mg kg(-1) for Cu, 81.1 mg kg(-1) for Zn, and 36.6 mg kg(-1) for Ni, respectively. However, all of the mean concentrations of HMs in soil were within the safety limits set by the Chinese regulation (HJ332-2006). The total HM concentrations in wheat flour were 0.0017 mg kg(-1) for Hg, 0.028 mg kg(-1) for As, 0.020 mg kg(-1) for Cd, 0.109 mg kg(-1) for Cr, 0.128 mg kg(-1) for Pb, 2.66 mg kg(-1) for Cu, 24.20 mg kg(-1) for Zn, and 0.20 mg kg(-1) for Ni, and they were significantly lower than the tolerance limits of Chinese standards. However, 15% of the wheat flour samples exceeded the Chinese standard (GB2762-2012) for Pb. This study highlighted the human health risks in the relationship of wheat flour consumption for both adults and children with HMs accumulated area. HMs did not cause noncarcinogenic risks in the area (HI < 1) except for children in Jingyang county; Cd generated the greatest carcinogenic risk, which poses a potential health risk to consumers. The results obtained in this study showed that the government and other institutions should implement measures to prevent and control HM contamination in agricultural soil and crops to mitigate the associated health risks.

Heavy Metals Speciation and Human Health Risk Assessment at an Illegal Gold Mining Site in Igun, Osun State, Nigeria

BACKGROUND

There is increasing global concern over the health effects of heavy metals arising from various anthropogenic activities, especially mining. Mining activities in developing countries are often carried out at an artisanal level using a variety of extraction methods with human health and environmental consequences.

OBJECTIVES

The broad objective of this study is to assess the chemical forms, distribution pattern, and health risks due to mining and processing techniques at a gold mining site in Igun, Osun State, Nigeria.

METHODS

Samples were collected from 28 active mine pits and sequentially extracted using standard methods. Extracts were analyzed for metals using inductively coupled plasma optical emission spectrometer (ICP/OES), while health risk was assessed using United States Environmental Protection Agency (USEPA) and Dutch methods. Chemical speciation of heavy metals and health risk assessment was calculated using mobile phase fraction summation.

RESULTS

Metals were exclusively present in the residual fractions, indicating that these metals are strongly bound to the resistant components of the soil matrix. The percentage in the residual fraction ranged from 9.41% (tin) to 99.42% (aluminium). The heavy metals geoaccumulation index for the site ranged from 0 (no contamination) to 6 (extremely contaminated). The cancer risk ranged from 6.17E-13 to 7.77E-05 and 2.73E-12 to 4.64E-04 for adults and children, respectively.

DISCUSSION

Cancer risk and non-cancer risk (hazard index) assessment showed that arsenic poses a higher risk in adults and children compared to other metals through the dermal exposure route.

Contamination features and health risk of soil heavy metals in China

China faces a big challenge of environmental deterioration amid its rapid economic development. To comprehensively identify the contamination characteristics of heavy metals in Chinese soils on a national scale, data set of the first national soil pollution survey was employed to evaluate the pollution levels using several pollution indicators (pollution index, geoaccumulation index and enrichment factor) and to quantify their exposure risks posed to human health with the risk assessment model recommended by the US Environmental Protection Agency. The results showed that, due to the drastically increased industrial operations and fast urban expansion, Chinese soils were contaminated by heavy metals in varying degrees. As a whole, the exposure risk levels of soil metals in China were tolerable or close to acceptable. Comparatively speaking, children and adult females were the relatively vulnerable populations for the non-carcinogenic and carcinogenic risks, respectively. Cadmium and mercury have been identified as the priority control metals due to their higher concentrations in soils or higher health risks posed to the public, as well as, arsenic, lead, chromium and nickel. Spatial distribution pattern analysis implied that the soil metal pollutions in southern provinces of China were relatively higher than that in other provinces, which would be related to the higher geochemical background in southwest regions and the increasing human activities in southeast areas. Meanwhile, it should be noticed that Beijing, the capital of China, also has been labeled as the priority control province for its higher mercury concentration. These results will provide basic information for the improvement of soil environment management and heavy metal pollution prevention and control in China.

Commuting-adjusted short-term health impact assessment of airborne fine particles with uncertainty quantification via Monte Carlo simulation

BACKGROUND

Exposure to air pollution is associated with a short-term increase in mortality, and this field has begun to focus on health impact assessment.

OBJECTIVES

Our aim was to estimate the impact of PM10 on mortality within 2 days from the exposure in the Italian region of Lombardy for the year 2007, at the municipality level, examining exposure entailed by daily intermunicipality commuting and accounting for uncertainty propagation.

METHODS

We combined data from different sources to derive probabilistic distributions for all input quantities used to calculate attributable deaths (mortality rates, PM10 concentrations, estimated PM10 effects, and commuting flows) and applied a Monte Carlo procedure to propagate uncertainty and sample the distribution of attributable deaths for each municipality.

RESULTS

We estimated that annual average PM10 concentrations above the World Health Organization-recommended threshold of 20 μg/m3 were responsible for 865 short-term deaths (80% credibility interval: 475, 1,401), 26% of which were attributable to PM10 above the European Union limit of 40 μg/m3. Reducing annual average PM10 concentrations > 20 μg/m3 by 20% would have reduced the number of attributable deaths by 36%. The largest estimated impacts were along the basin of the Po River and in the largest cities. Commuting contributed to the spatial distribution of the estimated impact.

CONCLUSIONS

Our estimates, which incorporated uncertainty quantification, indicate that the short-term impact of PM10 on mortality in Lombardy in 2007 was notable, and that reduction in air pollution would have had a substantial beneficial effect on population health. Using commuting data helped to identify critical areas for prioritizing intervention.

A systematic review of methods of uncertainty analysis and their applications in the assessment of chemical exposures, effects, and risks

Methods of uncertainty analysis are being included increasingly in regulatory chemical risk assessment. Although best practices have been established by several safety agencies in Europe and the United States, they exist only in the grey literature - there has been no comprehensive analysis of the scientific, peer-reviewed literature on these methods. We therefore conducted a systematic review of the recent peer-reviewed literature (2007-2013) on uncertainty analysis relevant to chemical risks. The main objective was to determine whether current methods are robust enough for regulatory use, because the methods used to protect public health must meet the most stringent scientific standards. Based on 297 papers, we concluded that the peer-reviewed literature is much more critical about the disadvantages of those methods, compared to the grey literature. Furthermore, uncertainty analyses can be influenced significantly by subjective expert judgment. As a suggested improvement, we developed guidelines for transparent reporting of uncertainty assessment results.

Impact of input data uncertainty on environmental exposure assessment models: A case study for electromagnetic field modelling from mobile phone base stations

BACKGROUND

With the increased availability of spatial data and computing power, spatial prediction approaches have become a standard tool for exposure assessment in environmental epidemiology. However, such models are largely dependent on accurate input data. Uncertainties in the input data can therefore have a large effect on model predictions, but are rarely quantified.

METHODS

With Monte Carlo simulation we assessed the effect of input uncertainty on the prediction of radio-frequency electromagnetic fields (RF-EMF) from mobile phone base stations at 252 receptor sites in Amsterdam, The Netherlands. The impact on ranking and classification was determined by computing the Spearman correlations and weighted Cohen's Kappas (based on tertiles of the RF-EMF exposure distribution) between modelled values and RF-EMF measurements performed at the receptor sites.

RESULTS

The uncertainty in modelled RF-EMF levels was large with a median coefficient of variation of 1.5. Uncertainty in receptor site height, building damping and building height contributed most to model output uncertainty. For exposure ranking and classification, the heights of buildings and receptor sites were the most important sources of uncertainty, followed by building damping, antenna- and site location. Uncertainty in antenna power, tilt, height and direction had a smaller impact on model performance.

CONCLUSIONS

We quantified the effect of input data uncertainty on the prediction accuracy of an RF-EMF environmental exposure model, thereby identifying the most important sources of uncertainty and estimating the total uncertainty stemming from potential errors in the input data. This approach can be used to optimize the model and better interpret model output.

Landscape structure and diseases profile: associating land use type composition with disease distribution

Human health and well-being presuppose environmental quality. Several studies have documented the indicative role of land use types in environmental quality. However, the exact role of land use composition on disease distribution has remained scientifically vague. We assessed the congruence of diseases' distribution with land use composition, focusing on high environmental quality areas, defined as tranquil areas with view to indicating places offering well-being. Landscape composition is linked to the presence of diseases across 51 prefectures of Greece. Agricultural and natural land use types proved to be the main drivers of disease distribution. Tranquility demonstrated a strong negative correlation with population density, thus could be considered as a quantitative spatial index of life-quality. We concluded that the landscape context affects the dominance of diseases' patterns. Special emphasis should be put on the role of tranquil areas in human health and the relative environmental health policies.

A review of soil heavy metal pollution from mines in China: pollution and health risk assessment

Heavy metal pollution has pervaded many parts of the world, especially developing countries such as China. This review summarizes available data in the literature (2005-2012) on heavy metal polluted soils originating from mining areas in China. Based on these obtained data, this paper then evaluates the soil pollution levels of these collected mines and quantifies the risks these pollutants pose to human health. To assess these potential threat levels, the geoaccumulation index was applied, along with the US Environmental Protection Agency (USEPA) recommended method for health risk assessment. The results demonstrate not only the severity of heavy metal pollution from the examined mines, but also the high carcinogenic and non-carcinogenic risks that soil heavy metal pollution poses to the public, especially to children and those living in the vicinity of heavily polluted mining areas. In order to provide key management targets for relevant government agencies, based on the results of the pollution and health risk assessments, Cd, Pb, Cu, Zn, Hg, As, and Ni are selected as the priority control heavy metals; tungsten, manganese, lead-zinc, and antimony mines are selected as the priority control mine categories; and southern provinces and Liaoning province are selected as the priority control provinces. This review, therefore, provides a comprehensive assessment of soil heavy metal pollution derived from mines in China, while identifying policy recommendations for pollution mitigation and environmental management of these mines.

Quantifying uncertainty in health impact assessment: a case-study example on indoor housing ventilation

Quantitative health impact assessment (HIA) is increasingly being used to assess the health impacts attributable to an environmental policy or intervention. As a consequence, there is a need to assess uncertainties in the assessments because of the uncertainty in the HIA models. In this paper, a framework is developed to quantify the uncertainty in the health impacts of environmental interventions and is applied to evaluate the impacts of poor housing ventilation. The paper describes the development of the framework through three steps: (i) selecting the relevant exposure metric and quantifying the evidence of potential health effects of the exposure; (ii) estimating the size of the population affected by the exposure and selecting the associated outcome measure; (iii) quantifying the health impact and its uncertainty. The framework introduces a novel application for the propagation of uncertainty in HIA, based on fuzzy set theory. Fuzzy sets are used to propagate parametric uncertainty in a non-probabilistic space and are applied to calculate the uncertainty in the morbidity burdens associated with three indoor ventilation exposure scenarios: poor, fair and adequate. The case-study example demonstrates how the framework can be used in practice, to quantify the uncertainty in health impact assessment where there is insufficient information to carry out a probabilistic uncertainty analysis.

Assessing framing assumptions in quantitative health impact assessments: a housing intervention example

Health impact assessment (HIA) is often used to determine ex ante the health impact of an environmental policy or an environmental intervention. Underpinning any HIA is the framing assumption, which defines the causal pathways mapping environmental exposures to health outcomes. The sensitivity of the HIA to the framing assumptions is often ignored. A novel method based on fuzzy cognitive map (FCM) is developed to quantify the framing assumptions in the assessment stage of a HIA, and is then applied to a housing intervention (tightening insulation) as a case-study. Framing assumptions of the case-study were identified through a literature search of Ovid Medline (1948-2011). The FCM approach was used to identify the key variables that have the most influence in a HIA. Changes in air-tightness, ventilation, indoor air quality and mould/humidity have been identified as having the most influence on health. The FCM approach is widely applicable and can be used to inform the formulation of the framing assumptions in any quantitative HIA of environmental interventions. We argue that it is necessary to explore and quantify framing assumptions prior to conducting a detailed quantitative HIA during the assessment stage.