Uncertain numbers and uncertainty in the selection of input distributions--consequences for a probabilistic risk assessment of contaminated land

Probabilistic risk assessment of heavy metals in urban farmland soils of a typical oasis city in northwest China

Previous studies lacked quantitative evaluation studies of the probability of ecology and human health risks from soil heavy metals. This study assessed heavy metal risk level by collecting topsoil samples from a typical oasis city (Wuwei) in northwest China and then quantitatively evaluating the ecological risk from heavy metals by incorporating the uncertainty of health risk model parameters into the risk assessment. This study found that anthropogenic activities have influenced the accumulation of heavy metals in the study area and that the risk of contamination of soil heavy metals was characterized as light to moderate contamination and low ecological risk. On this basis, the species sensitivity distribution curves of heavy metals were constructed using species acute toxicity data, the predicted no effect concentrations of heavy metals were derived, and a probabilistic ecological risk evaluation was conducted. The results show that the current soil environmental quality standards in China are not effective in protecting species diversity. In addition, the probability of ecological risk for Cr, Ni and As in the study area was 63.3%, 23.8% and 7.1%, however, traditional pollution assessment methods underestimate the hazard of Cr. Monte Carlo simulations have shown that the probability of the carcinogenic risk of Cr (adults: 79.4%; children: 94.5%) and As (adults: 78.9%; children: 94.0%) is high, the probability of the total carcinogenic risk exceeding 1E-06 is 99.0%, the probability of the non-carcinogenic risk is low, and the slope factor and reference dose can significantly affect the evaluation of human health risks.

Carcinogenic and non-carcinogenic risk assessment induced by pesticide residues in commercially available ready-to-eat raisins of Iran based on Monte Carlo Simulation

Pesticide residues in fruits lead to serious public health and environmental problems. This study was undertaken to analyze 57 pesticides residues in 60 commercially available ready-to-eat packed raisins samples from Iran's markets using Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) approach along with acetonitrile for the extraction, surface adsorbents for clean-up procedure, following with an ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Moreover, the probabilistic health risk assessment includes non-carcinogenic and carcinogenic risk were estimated by hazard quotient (HQ), Hazard Index (HI) and cancer risk (CR) using the Monte Carlo Simulation (MCS) method. The respective numbers attributed to LOD and LOQ were 0.001-0.03 mg kg^-1 and 0.005-0.05 mg kg^-1. Results showed that 23% of samples were contaminated by at least one pesticide according to national MRLs. The rank order of pesticides based on HQ was carbendazim > acetamiprid > thiodicarb > iprodione for raisins with percentile 95% benchmark. The HI amount in the adult's age group was 0.001, and in the children's age group was obtained to be 0.006. According to the risk assessment results (HQ and HI < 1 in both age groups), it can be acknowledged that consumption of raisins may not pose remarkable health hazards in short term exposures. The rank order of pesticides based on CR was thiodicarb > iprodion. But total CR (TCR) was lower than 1E-6 value (7.6E-8), so consumers were not at considerable carcinogenic risk in this commodity. Acetamiprid and carbendazim does not pose a cancer risk to humans. Therefore, a dietary exposure assessment for the purpose of assessing cancer risk is unnecessary.

Comprehensive Probabilistic Health Risk Assessment for Exposure to Arsenic and Cadmium in Groundwater

Probabilistic health risk assessment has widely been used for more realistic risk analysis of contaminants. However, the existing probabilistic modeling process may be unable to reflect the actual health risks comprehensively. In the present study, the Monte Carlo simulation was employed to assess the probabilistic health risks of exposing to arsenic (As) and cadmium (Cd) in groundwater through ingestion and dermal contact pathways. To systematically evaluate the actual health risks of residents, two scenarios of the probabilistic health risk assessment were proposed: (1) fixed exposure parameters, whereas uncertain metal concentrations, and (2) uncertain exposure parameters and metal concentrations. The results indicated that the mean hazard index (HI) for local residents was mostly accepted (HI < 1), while the non-cancer risk of infants at the 95th percentile under scenario 2 (HI = 1.42) exceeded the safe level of 1, signifying the potential non-cancer risk on infants. Meanwhile, the average total cancer risk (TCR) values were several times greater than the acceptable limit of 1E-06 for all the age groups under both scenarios 1 and 2, reflecting the unacceptable cancer risk. Moreover, sensitivity analysis identified the exposure duration (ED) and concentration factor (C_W) were the most relevant parameters that affect the health risk. Overall, the results of this study will be useful for the policy makers in comprehensively understanding the actual health risks of the heavy metal(loids) contamination in groundwater on receptors, as well as setting up suitable groundwater management strategies to guarantee safe water supply and to maintain health for local residents.

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.

Quantitative health risk assessment of inhalation exposure to automobile foundry dust

With a growing awareness of environmental protection, the dust pollution caused by automobile foundry work has become a serious and urgent problem. This study aimed to explore contamination levels and health effects of automobile foundry dust. A total of 276 dust samples from six types of work in an automobile foundry factory were collected and analysed using the filter membrane method. Probabilistic risk assessment model was developed for evaluating the health risk of foundry dust on workers. The health risk and its influencing factors among workers were then assessed by applying the Monte Carlo method to identify the most significant parameters. Health damage assessment was conducted to translate health risk into disability-adjusted life year (DALY). The results revealed that the mean concentration of dust on six types of work ranged from 1.67 to 5.40 mg/m³. The highest health risks to be come from melting, cast shakeout and finishing, followed by pouring, sand preparation, moulding and core-making. The probability of the risk exceeding 10^-6 was approximately 85%, 90%, 90%, 75%, 70% and 45%, respectively. The sensitivity analysis indicated that average time, exposure duration, inhalation rate and dust concentration (C) made great contribution to dust health risk. Workers exposed to cast shakeout and finishing had the largest DALY of 48.64a. These results can further help managers to fully understand the dust risks on various types of work in the automobile foundry factories and provide scientific basis for the management and decision-making related to health damage assessment.

Probabilistic Human Health Risk Assessment of Heavy Metal Intake via Vegetable Consumption around Pb/Zn Smelters in Southwest China

Vegetable contamination in mining and smelting areas has resulted in high dietary intakes of heavy metals, which pose potential health risks to local residents. In this study, paired soil-vegetable samples were collected around Pb/Zn smelters in Southwest China. Probabilistic risks to local residents via vegetable consumption were evaluated with a Monte Carlo simulation. The mean concentrations of As, Cd, Cu, Pb, and Zn in the soils were 116.76, 3.59, 158.56, 196.96, and 236.74 mg/kg, respectively. About 38.18%, 58.49%, and 52.83% of the vegetable samples exceeded the maximum allowable concentrations for As, Cd, and Pb, respectively. The daily dietary intake of As, Cd, and Pb exceeded the provisional tolerable daily intakes for local residents, with children showing the highest intake via vegetable consumption. The percentages of the target hazard quotients of As, Cd, and Pb for local residents exceeding the safe value of one were about 95%, 50%, and 25%, respectively. The 95th percentiles of the hazard index for children, adolescents, and adults were 15.71, 11.15, and 9.34, respectively, indicating significant risks to local residents, especially children. These results highlight a need to develop effective strategies to reduce heavy metal contamination and exposure to protect human health.

Levels, sources and probabilistic health risks of polycyclic aromatic hydrocarbons in the agricultural soils from sites neighboring suburban industries in Shanghai

The levels, sources and quantitative probabilistic health risks for polycyclic aromatic hydrocarbons (PAHs) in agricultural soils in the vicinity of power, steel and petrochemical plants in the suburbs of Shanghai are discussed. The total concentration of 16 PAHs in the soils ranges from 223 to 8214ng g^-1. The sources of PAHs were analyzed by both isomeric ratios and a principal component analysis-multiple linear regression method. The results indicate that PAHs mainly originated from the incomplete combustion of coal and oil. The probabilistic risk assessments for both carcinogenic and non-carcinogenic risks posed by PAHs in soils with adult farmers as concerned receptors were quantitatively calculated by Monte Carlo simulation. The estimated total carcinogenic risks (TCR) for the agricultural soils has a 45% possibility of exceeding the acceptable threshold value (10^-6), indicating potential adverse health effects. However, all non-carcinogenic risks are below the threshold value. Oral intake is the dominant exposure pathway, accounting for 77.7% of TCR, while inhalation intake is negligible. The three PAHs with the highest contribution for TCR are BaP (64.35%), DBA (17.56%) and InP (9.06%). Sensitivity analyses indicate that exposure frequency has the greatest impact on the total risk uncertainty, followed by the exposure dose through oral intake and exposure duration. These results indicate that it is essential to manage the health risks of PAH-contaminated agricultural soils in the vicinity of typical industries in megacities.

A CONSISTENT DATA FUSION APPROACH FOR UNCERTAINTY QUANTIFICATION IN RIGID MUSCULOSKELETAL SIMULATION

Uncertainty quantification in rigid musculoskeletal modeling is essential to analyze the risks related to the simulation outcomes. Data fusion from multiple sources is a potential solution to reduce data uncertainties. This present study aimed at proposing a new data fusion rule leading to a more consistent and coherent data for uncertainty quantification. Moreover, a new uncertainty representation was developed using imprecise probability approach. A biggest maximal coherent subsets (BMCS) operator was defined to fuse interval-valued data ranges from multiple sources. Fusion-based probability-box structure was developed to represent the data uncertainty. Case studies were performed for uncertainty propagation through inverse dynamics and static optimization algorithms. Hip joint moment and muscle force estimation were computed under effect of the uncertainties of thigh mass and muscle properties. Respective p-boxes of these properties were generated. Regarding the uncertainty propagation analysis, correlation coefficients showed a very good value ([Formula: see text]) for the proposed fusion operator according to classical operators. Muscle force variation of the rectus femoris was computed. Peak-to-peak (i.e., difference between maximal values) rectus femoris forces showed deviations of 55[Formula: see text]N and 40[Formula: see text]N for the first and second peaks, respectively. The development of the new fusion operator and fusion-based probability-box leads to a more consistent uncertainty quantification. This allows the estimation of risks associated with the simulation outcomes under input data uncertainties for rigid musculoskeletal modeling and simulation.

Regional probabilistic risk assessment of heavy metals in different environmental media and land uses: An urbanization-affected drinking water supply area

In this study, we proposed a Regional Probabilistic Risk Assessment (RPRA) to estimate the health risks of exposing residents to heavy metals in different environmental media and land uses. The mean and ranges of heavy metal concentrations were measured in water, sediments, soil profiles and surface soils under four land uses along the Shunde Waterway, a drinking water supply area in China. Hazard quotients (HQs) were estimated for various exposure routes and heavy metal species. Riverbank vegetable plots and private vegetable plots had 95th percentiles of total HQs greater than 3 and 1, respectively, indicating high risks of cultivation on the flooded riverbank. Vegetable uptake and leaching to groundwater were the two transfer routes of soil metals causing high health risks. Exposure risks during outdoor recreation, farming and swimming along the Shunde Waterway are theoretically safe. Arsenic and cadmium were identified as the priority pollutants that contribute the most risk among the heavy metals. Sensitivity analysis showed that the exposure route, variations in exposure parameters, mobility of heavy metals in soil, and metal concentrations all influenced the risk estimates.

The risk of overestimating the risk-metal leaching to groundwater near contaminated glass waste deposits and exposure via drinking water

This study investigates metal contamination patterns and exposure to Sb, As, Ba, Cd and Pb via intake of drinking water in a region in southeastern Sweden where the production of artistic glass has resulted in a large number of contaminated sites. Despite high total concentrations of metals in soil and groundwater at the glassworks sites properties, all drinking water samples from households with private wells, located at a 30-640m distance from a glassworks site, were below drinking water criteria from the WHO for Sb, As, Ba and Cd. A few drinking water samples showed concentrations of Pb above the WHO guideline, but As was the only element found in concentrations that could result in human exposure near toxicological reference values. An efficient retention of metals in the natural soil close to the source areas, which results in a moderate impact on local drinking water, is implied. Firstly, by the lack of significant difference in metal concentrations when comparing households located upstream and downstream of the main waste deposits, and secondly, by the lack of correlation between the metal concentration in drinking water and distance to the nearest glassworks site. However, elevated Pb and Cd concentrations in drinking water around glassworks sites when compared to regional groundwater indicate that diffuse contamination of the soils found outside the glassworks properties, and not only the glass waste landfills, may have a significant impact on groundwater quality. We further demonstrate that different mobilization patterns apply to different metals. Regarding the need to use reliable data to assess drinking water contamination and human exposure, we finally show that the conservative modelling approaches that are frequently used in routine risk assessments may result in exposure estimates many times higher than those based on measured concentrations in the drinking water that is actually being used for consumption.

Assessing the human health impacts of exposure to disinfection by-products--a critical review of concepts and methods

Understanding the public health implications of chemical contamination of drinking water is important for societies and their decision-makers. The possible population health impacts associated with exposure to disinfection by-products (DBPs) are of particular interest due to their potential carcinogenicity and their widespread occurrence as a result of treatments employed to control waterborne infectious disease. We searched the literature for studies that have attempted quantitatively to assess population health impacts and health risks associated with exposure to DBPs in drinking water. We summarised and evaluated these assessments in terms of their objectives, methods, treatment of uncertainties, and interpretation and communication of results. In total we identified 40 studies matching our search criteria. The vast majority of studies presented estimates of generic cancer and non-cancer risks based on toxicological data and methods that were designed with regulatory, health-protective purposes in mind, and therefore presented imprecise and biased estimates of health impacts. Many studies insufficiently addressed the numerous challenges to DBP risk assessment, failing to evaluate the evidence for a causal relationship, not appropriately addressing the complex nature of DBP occurrence as a mixture of chemicals, not adequately characterising exposure in space and time, not defining specific health outcomes, not accounting for characteristics of target populations, and not balancing potential risks of DBPs against the health benefits related with drinking water disinfection. Uncertainties were often poorly explained or insufficiently accounted for, and important limitations of data and methods frequently not discussed. Grave conceptual and methodological limitations in study design, as well as erroneous use of available dose-response data, seriously impede the extent to which many of these assessments contribute to understanding the public health implications of exposure to DBPs. In some cases, assessment results may cause unwarranted alarm among the public and potentially lead to poor decisions being made in sourcing, treatment, and provision of drinking water. We recommend that the assessment of public health impacts of DBPs should be viewed as a means of answering real world policy questions relating to drinking water quality, including microbial contaminants; that they should be conducted using the most appropriate and up-to-date data and methods, and that associated uncertainties and limitations should be accounted for using quantitative methods where appropriate.

Assessing the risk of an excess fluoride intake among Swedish children in households with private wells--expanding static single-source methods to a probabilistic multi-exposure-pathway approach

It is often assumed that water consumption is the major route of exposure for fluoride and analysis of water fluoride content is the most common approach for ensuring that the daily intake is not too high. In the present study, the risk of excess intake was characterized for children in households with private wells in Kalmar County, Sweden, where the natural geology shows local enrichments in fluorine. By comparing water concentrations with the WHO drinking water guideline (1.5 mg/L), it was found that 24% of the ca. 4800 sampled wells had a concentration above this limit, hence providing a figure for the number of children in the households concerned assessed to be at risk using this straightforward approach. The risk of an excess intake could, alternatively, also be characterized based on a tolerable daily intake (in this case the US EPA RfD of 0.06 mg/kg-day). The exposure to be evaluated was calculated using a probabilistic approach, where the variability in all exposure factors was considered, again for the same study population. The proportion of children assessed to be at risk after exposure from drinking water now increased to 48%, and when the probabilistic model was adjusted to also include other possible exposure pathways; beverages and food, ingestion of toothpaste, oral soil intake and dust inhalation, the number increased to 77%. Firstly, these results show how the risk characterization is affected by the basis of comparison. In this example, both of the reference values used are widely acknowledged. Secondly, it illustrates how much of the total exposure may be overlooked when only focusing on one exposure pathway, and thirdly, it shows the importance of considering the variability in all relevant pathways.

Public health risk assessment associated with heavy metal and arsenic exposure near an abandoned mine (Kirki, Greece)

The 'Agios Philippos' lead-zinc mine in the Kirki region (NE Greece) is now closed, but its legacy of heavy metal contamination remains at the site. At present, management of the contaminated land is of major concern. The area is in a reclamation process and requires immediate remediation action, whereas human risks need to be carefully evaluated. In order to assess these risks, samples from around the mine were collected and analyzed and a scenario involving the oral, dermal, and inhaled doses of arsenic and heavy metals was formulated. A Monte Carlo approach was undertaken, in order to model the average daily dose and quantify the corresponding hazard index and cancer risk. A toxicological risk was associated with samples collected in the vicinity of the mine (floatation, mine tailings) and a pronounced carcinogenic risk for arsenic was evident at the broader occupational/environmental setting. These findings urge for immediate rehabilitation actions that will mitigate population exposures and promote long-term environmental safety in the area.

Description and propagation of uncertainty in input parameters in environmental fate models

Today, chemical risk and safety assessments rely heavily on the estimation of environmental fate by models. The key compound-related properties in such models describe partitioning and reactivity. Uncertainty in determining these properties can be separated into random and systematic (incompleteness) components, requiring different types of representation. Here, we evaluate two approaches that are suitable to treat also systematic errors, fuzzy arithmetic, and probability bounds analysis. When a best estimate (mode) and a range can be computed for an input parameter, then it is possible to characterize the uncertainty with a triangular fuzzy number (possibility distribution) or a corresponding probability box bound by two uniform distributions. We use a five-compartment Level I fugacity model and reported empirical data from the literature for three well-known environmental pollutants (benzene, pyrene, and DDT) as illustrative cases for this evaluation. Propagation of uncertainty by discrete probability calculus or interval arithmetic can be done at a low computational cost and gives maximum flexibility in applying different approaches. Our evaluation suggests that the difference between fuzzy arithmetic and probability bounds analysis is small, at least for this specific case. The fuzzy arithmetic approach can, however, be regarded as less conservative than probability bounds analysis if the assumption of independence is removed. Both approaches are sensitive to repeated parameters that may inflate the uncertainty estimate. Uncertainty described by probability boxes was therefore also propagated through the model by Monte Carlo simulation to show how this problem can be avoided.

Doing our best: optimization and the management of risk

Tools and concepts of optimization are widespread in decision-making, design, and planning. There is a moral imperative to "do our best." Optimization underlies theories in physics and biology, and economic theories often presume that economic agents are optimizers. We argue that in decisions under uncertainty, what should be optimized is robustness rather than performance. We discuss the equity premium puzzle from financial economics, and explain that the puzzle can be resolved by using the strategy of satisficing rather than optimizing. We discuss design of critical technological infrastructure, showing that satisficing of performance requirements--rather than optimizing them--is a preferable design concept. We explore the need for disaster recovery capability and its methodological dilemma. The disparate domains--economics and engineering--illuminate different aspects of the challenge of uncertainty and of the significance of robust-satisficing.

Water pollution risk associated with natural gas extraction from the Marcellus Shale

In recent years, shale gas formations have become economically viable through the use of horizontal drilling and hydraulic fracturing. These techniques carry potential environmental risk due to their high water use and substantial risk for water pollution. Using probability bounds analysis, we assessed the likelihood of water contamination from natural gas extraction in the Marcellus Shale. Probability bounds analysis is well suited when data are sparse and parameters highly uncertain. The study model identified five pathways of water contamination: transportation spills, well casing leaks, leaks through fractured rock, drilling site discharge, and wastewater disposal. Probability boxes were generated for each pathway. The potential contamination risk and epistemic uncertainty associated with hydraulic fracturing wastewater disposal was several orders of magnitude larger than the other pathways. Even in a best-case scenario, it was very likely that an individual well would release at least 200 m³ of contaminated fluids. Because the total number of wells in the Marcellus Shale region could range into the tens of thousands, this substantial potential risk suggested that additional steps be taken to reduce the potential for contaminated fluid leaks. To reduce the considerable epistemic uncertainty, more data should be collected on the ability of industrial and municipal wastewater treatment facilities to remove contaminants from used hydraulic fracturing fluid.

Climate change - An uncertainty factor in risk analysis of contaminated land

Metals frequently occur at contaminated sites, where their potential toxicity and persistence require risk assessments that consider possible long-term changes. Changes in climate are likely to affect the speciation, mobility, and risks associated with metals. This paper provides an example of how the climate effect can be inserted in a commonly used exposure model, and how the exposure then changes compared to present conditions. The comparison was made for cadmium (Cd) exposure to 4-year-old children at a highly contaminated iron and steel works site in southeastern Sweden. Both deterministic and probabilistic approaches (through probability bounds analysis, PBA) were used in the exposure assessment. Potential climate-sensitive variables were determined by a literature review. Although only six of the total 39 model variables were assumed to be sensitive to a change in climate (groundwater infiltration, hydraulic conductivity, soil moisture, soil:water distribution, and two bioconcentration factors), the total exposure was clearly affected. For example, by altering the climate-sensitive variables in the order of 15% to 20%, the deterministic estimate of exposure increased by 27%. Similarly, the PBA estimate of the reasonable maximum exposure (RME, defined as the upper bound of the 95th percentile) increased by almost 20%. This means that sites where the exposure in present conditions is determined to be slightly below guideline values may in the future exceed these guidelines, and risk management decisions could thus be affected. The PBA, however, showed that there is also a possibility of lower exposure levels, which means that the changes assumed for the climate-sensitive variables increase the total uncertainty in the probabilistic calculations. This highlights the importance of considering climate as a factor in the characterization of input data to exposure assessments at contaminated sites. The variable with the strongest influence on the result was the soil:water distribution coefficient (Kd).

Variability and uncertainty in Swedish exposure factors for use in quantitative exposure assessments

Information of exposure factors used in quantitative risk assessments has previously been compiled and reported for U.S. and European populations. However, due to the advancement of science and knowledge, these reports are in continuous need of updating with new data. Equally important is the change over time of many exposure factors related to both physiological characteristics and human behavior. Body weight, skin surface, time use, and dietary habits are some of the most obvious examples covered here. A wealth of data is available from literature not primarily gathered for the purpose of risk assessment. Here we review a number of key exposure factors and compare these factors between northern Europe--here represented by Sweden--and the United States. Many previous compilations of exposure factor data focus on interindividual variability and variability between sexes and age groups, while uncertainty is mainly dealt with in a qualitative way. In this article variability is assessed along with uncertainty. As estimates of central tendency and interindividual variability, mean, standard deviation, skewness, kurtosis, and multiple percentiles were calculated, while uncertainty was characterized using 95% confidence intervals for these parameters. The presented statistics are appropriate for use in deterministic analyses using point estimates for each input parameter as well as in probabilistic assessments.

Sensitivity analysis of the pressure-based direct integrity test for membranes used in drinking water treatment

We conducted a sensitivity analysis of the commonly employed pressure-based direct integrity test (DIT), the most sensitive test for defects in low-pressure hollow fiber (LPHF) microfiltration and ultrafiltration systems used in drinking water treatment. Incorporating uncertainty to assess the practice of DIT, we find the resolution in some tests may be insufficient to verify the presence of a barrier to oocysts of Cryptosporidium. Applying distributions and boundaries derived from literature and practice, we solved for the defect size resolution (DSR) using Monte Carlo and Probability Bounds Analysis for five commercial membrane designs. Surface tension was modeled using annual temperature profiles from three rivers. Contact angle measurement error and variability were derived from literature, respectively, as a standard deviation of 5.7 degrees and +/- 9.6 degrees median change due to natural organic matter (NOM) fouling. These measures of contact angle uncertainty and variability were combined in a normal distribution with the discrete values currently applied. Additionally we considered model uncertainty, applying the maximum bubble pressure method, an established method of surface tension measurement in liquids in which the maximum air pressure in a submerged capillary is developed after the contact angle becomes zero prior to bubble formation. Where the DSR exceeds 3 microm the test design is not compliant with applicable drinking water regulations. Implications include uncertain and variable log-removal values (LRV) as determined by DIT due to the possible emergence of defects large enough to allow oocysts to pass without detection by the DIT. Specifically, we found the DSR may exceed 3 microm and may be as large as 8 microm. With the variable contact angle model, all lower bound possibilities are compliant, whereas the upper bound is over 80% noncompliant for three of five commercial designs. Using the Maximum Bubble Pressure Method, the lower bounds in three designs start to exceed 3 microm for between 50 and 100% of the produced water, whereas the upper bounds of the DSR completely exceed 3 microm for four of five commercial designs examined.

Exposure to contaminated sediments during recreational activities at a public bathing place

More and more time is spent on recreational activities, but few risk assessments focus specifically on these situations and exposure factor data are often scarce. To assess exposure to contaminants at a public bathing place in an urban environment, we have compiled literature data, conducted observation studies, and analyzed water and sediment samples. The levels of anthropogenic contaminants are high in urban environments and traffic frequently plays an important role. In this study, to characterize variability and uncertainty, the deterministic exposure calculations for metal pollutants were supplemented by a probability bounds analysis for the polycyclic aromatic hydrocarbons (PAH). The results from these calculations show that oral intake is the major exposure route for metals, while skin absorption, with present assumptions, is more important for the PAH. The presently measured levels of contaminants, at this public bathing place, cannot be anticipated to cause any significant adverse influence on public health. This assessment methodology is easy to adapt and can be used routinely in other situations with more heavily contaminated surface sediments and lake water.

Applicability of a neuroprobabilistic integral risk index for the environmental management of polluted areas: a case study

Recently, we developed a GIS-Integrated Integral Risk Index (IRI) to assess human health risks in areas with presence of environmental pollutants. Contaminants were previously ranked by applying a self-organizing map (SOM) to their characteristics of persistence, bioaccumulation, and toxicity in order to obtain the Hazard Index (HI). In the present study, the original IRI was substantially improved by allowing the entrance of probabilistic data. A neuroprobabilistic HI was developed by combining SOM and Monte Carlo analysis. In general terms, the deterministic and probabilistic HIs followed a similar pattern: polychlorinated biphenyls (PCBs) and light polycyclic aromatic hydrocarbons (PAHs) were the pollutants showing the highest and lowest values of HI, respectively. However, the bioaccumulation value of heavy metals notably increased after considering a probability density function to explain the bioaccumulation factor. To check its applicability, a case study was investigated. The probabilistic integral risk was calculated in the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain), where an environmental program has been carried out since 2002. The risk change between 2002 and 2005 was evaluated on the basis of probabilistic data of the levels of various pollutants in soils. The results indicated that the risk of the chemicals under study did not follow a homogeneous tendency. However, the current levels of pollution do not mean a relevant source of health risks for the local population. Moreover, the neuroprobabilistic HI seems to be an adequate tool to be taken into account in risk assessment processes.

Probabilistic reliability modeling for oil exploration & production (E&P) facilities in the tallgrass prairie preserve

The aging domestic oil production infrastructure represents a high risk to the environment because of the type of fluids being handled (oil and brine) and the potential for accidental release of these fluids into sensitive ecosystems. Currently, there is not a quantitative risk model directly applicable to onshore oil exploration and production (E&P) facilities. We report on a probabilistic reliability model created for onshore exploration and production (E&P) facilities. Reliability theory, failure modes and effects analysis (FMEA), and event trees were used to develop the model estimates of the failure probability of typical oil production equipment. Monte Carlo simulation was used to translate uncertainty in input parameter values to uncertainty in the model output. The predicted failure rates were calibrated to available failure rate information by adjusting probability density function parameters used as random variates in the Monte Carlo simulations. The mean and standard deviation of normal variate distributions from which the Weibull distribution characteristic life was chosen were used as adjustable parameters in the model calibration. The model was applied to oil production leases in the Tallgrass Prairie Preserve, Oklahoma. We present the estimated failure probability due to the combination of the most significant failure modes associated with each type of equipment (pumps, tanks, and pipes). The results show that the estimated probability of failure for tanks is about the same as that for pipes, but that pumps have much lower failure probability. The model can provide necessary equipment reliability information for proactive risk management at the lease level by providing quantitative information to base allocation of maintenance resources to high-risk equipment that will minimize both lost production and ecosystem damage.