RBCA、CLEA及CalTOX模型在苯并[a]芘污染场地健康风险评估中的应用比较

为探讨不同模型对污染场地健康风险评估结果的影响,以苯并[a]芘为例,采用RBCA、CLEA和CalTOX模型对某工业污染场地表层土壤进行健康风险评估,分析了评估结果的差异和原因,同时对模型的主要暴露参数进行了敏感性分析,并推导出基于风险概率分布的土壤修复限值。结果表明,RBCA、CLEA和CalTOX模型计算的苯并[a]芘致癌总风险分别为2.40×10-4、6.32×10-4和7.04×10-6,且经口摄入和皮肤接触2个途径对人体健康造成的危害最大。降解作用是影响CalTOX模型风险评估结果不同于RBCA和CLEA模型的重要因素,3个模型间参数取值及方法学的差异也会导致风险评估结果不同。各模型暴露参数的敏感性排序也有差异。采用基于风险概率分布的方法推导土壤修复限值,RBCA、CLEA和CalTOX模型所得结果分别为0.18、0.08、0.13(不考虑降解作用CalTOX模型)和10.74(考虑降解作用CalTOX模型)mg·kg-1,为各模型直接推导值的1.5～2.6倍。基于风险概率分布的方法可有效降低风险评估过程中参数不确定性的影响,为工业污染场地土壤修复值的制定提供参考。     

Efficiency of composite sampling for estimating a lognormal distribution

In many environmental studies measuring the amount of a contaminant in a sampling unit is expensive. In such cases, composite sampling is often used to reduce data collection cost. However, composite sampling is known to be beneficial for estimating the mean of a population, but not necessarily for estimating the variance or other parameters. As some applications, for example, Monte Carlo risk assessment, require an estimate of the entire distribution, and as the lognormal model is commonly used in environmental risk assessment, in this paper we investigate efficiency of composite sampling for estimating a lognormal distribution. In particular, we examine the magnitude of savings in the number of measurements over simple random sampling, and the nature of its dependence on composite size and the parameters of the distribution utilizing simulation and asymptotic calculations.     

Difficulties with the lognormal model in mean estimation and testing

A frequent assumption in environmental risk assessment is that the underlying distribution of an analyte concentration is lognormal. However, the distribution of a random variable whose log has a t-distribution has infinite mean. Because of the proximity of the standard normal and t-distribution, this suggests that a distribution such as the gamma or truncated normal, with smaller right tail probabilities, might make a better statistical model for mean estimation than the lognormal. In order to assess the effect of departures from lognormality on lognormal-based statistics, we simulated complete lognormal, truncated normal, and gamma data for various sample sizes and coefficients of variation. In these cases, departures from lognormality were not easily detected with the Shapiro-Wilk test. Various lognormal-based estimates and tests were compared with alternate methods based on the ordinary sample mean and standard error. The examples were also considered in the presence of random left censoring with the mean and standard error of the product limit estimate replacing the ordinary sample mean and standard error. The results suggest that in the estimation of or tests about a mean, if the assumption of lognormality is at all suspect, then lognormal-based approaches may not be as good as the alternative methods.     

Current uses of the EPA lead model to assess health risk and action levels for soil

The EPA lead model predicts mean blood lead levels and risk of elevated blood lead levels in children based on lead uptake from multiple sources. In the latest model versions, environmental data from individual homes within a community can be used to predict the overall blood lead distribution and percent risk of exceeding a specific blood lead level (i.e. 10 g dl^–1). Recent criteria used by the EPA to evaluate this information include no more than 5% of houses with a greater than 5% lead risk, and a community weighted-average risk below 5%. Environmental (primarily soil) and blood lead data from a residential community near a smelter were used to illustrate recent uses of the model. Scheduled remediation in the community will remove soil for approximately 60% of the houses (i.e. those with lead levels > 1000 mg kg^–1). After remediation, the model results indicate a relatively low community risk (0.5–1.9%), although the percentage of houses with lead risks above 5% ranged from 3 to as high as 13%, depending on the variation in blood lead and assuming the model's 7 g dl^–1 increase in blood lead with each 1000 mg kg^–1 increase in soil lead level. A comparison of the limited blood lead data with soil lead levels below 1000 mg kg^–1, however, indicated no apparent relationship. Given these uncertainties, less invasive actions than additional soil removal (e.g. exposure intervention, monitoring conditions, and follow-up as necessary) may be appropriate under the new EPA guidance for lead in soil.     

Development of a probabilistic blood lead prediction model

A partially probabilistic blood lead prediction model has been developed, based on the US Environmental Protection Agency integrated exposure-uptake-biokinetic blood lead model (IEUBK model). This study translated the IEUBK model into a spreadsheet format. The uptake submodel incorporates uncertainty distributions for exposure and bioavailability parameters. The biokinetic submodel is duplicated with a table incorporating partitioning and decay of lead levels in the body. As a case study, the probabilistic model is applied to a lead exposure scenario involving a former smelter site in Sandy, Utah. The probabilistic model produces less biased estimates of means and standard deviations than the deterministic model. Parameter uncertainty is propagated in the model by the use of Monte Carlo simulation. Thus, sensitivity analysis is possible, and driving variables can be determined.     

Spatial relationships between lead sources and children’s blood lead levels in the urban center of Indianapolis (USA)

Urban children remain disproportionately at risk of having higher blood lead levels than their suburban counterparts. The Westside Cooperative Organization (WESCO), located in Marion County, Indianapolis, Indiana, has a history of children with high blood lead levels as well as high soil lead (Pb) values. This study aims at determining the spatial relationship between soil Pb sources and children’s blood lead levels. Soils have been identified as a source of chronic Pb exposure to children, but the spatial scale of the source–recipient relationship is not well characterized. Neighborhood-wide analysis of soil Pb distribution along with a furnace filter technique for sampling interior Pb accumulation for selected homes (n = 7) in the WESCO community was performed. Blood lead levels for children aged 0–5 years during the period 1999–2008 were collected. The study population’s mean blood lead levels were higher than national averages across all ages, race, and gender. Non-Hispanic blacks and those individuals in the Wishard advantage program had the highest proportion of elevated blood lead levels. The results show that while there is not a direct relationship between soil Pb and children’s blood lead levels at a spatial scale of ~100 m, resuspension of locally sourced soil is occurring based on the interior Pb accumulation. County-wide, the largest predictor of elevated blood lead levels is the location within the urban core. Variation in soil Pb and blood lead levels on the community level is high and not predicted by housing stock age or income. Race is a strong predictor for blood lead levels in the WESCO community.     

The use of Monte Carlo simulation techniques to predict population blood lead levels

Effective management of the risks posed by lead depends on an understanding of the relationship between exposure (the presence and accessibility of lead in the environment) and dose (blood lead levels). Our paper begins by outlining the type of information most valuable to a decision maker addressing the lead problem. A useful exposure-dose characterisation must address multiple contamination sources simultaneously, provide estimates of the number of people with blood lead levels exceeding critical thresholds, and assess the influence of modifying factors (e.g. the soil and dust ingestion rate) on population blood lead variability. We describe a pilot effort to develop an urban setting lead exposure-dose model, and use this model to compare three approaches for generating model input quantities: (1) worst-case estimates, (2) central estimates and (3) Monte Carlo simulation. Using the criteria outlined above, we find that the Monte Carlo technique provides the most useful model output. We describe the population blood lead level distribution generated by the model, as well as the relative influence of environmental and behavioural factors on the variability of the population distribution. Finally, we assess the impact of parameter uncertainty on the model output, and contend this type of information can help identify areas in which further empirical study would be most valuable.     

不同国家基于生态风险的土壤筛选值研究及启示

本文介绍了土壤生态筛选值的定义以及国外一些国家在污染土壤生态风险评估领域的相关土壤筛选值。由于地理生态、社会文化、行政法规以及标准制定的科学基础等差异使各国基于生态风险的土壤筛选值的制定方法各有特色,导致各国基于生态风险的土壤筛选值名称和筛选值之间存在较大差异。我国的生态风险评价研究起步较晚,目前还没有国家权威机构发布的诸如土壤生态筛选值、生态风险评价技术导则等文件。本文就目前国外一些权威机构推出的并得到广泛认可的相关标准、方法做简要介绍,就国际上不同国家的土壤生态筛选值的制定方法、理论体系、使用策略等进行详细描述,为我国制定相关筛选值的方法体系提供参考,旨在推动我国基于生态风险的污染土壤筛选值的建立和生态风险评估研究。     

Expositionsabschätzung: Human-Biomonitoring vs. Modellrechnungen

Exposure assessment represents an important and integral part of the assessment of health risks associated with the human exposure to toxic substances in soil and other environmental media. In recent times model calculations are widely used to assess the doses of toxic substances which have been incorporated into the human body via different routes of exposure. The present paper discusses the possibilities and limitations of such model calculations with regard to exposure and risk assessment. A critical point is that the results largely depend on the basic assumptions introduced into the model calculations. Moreover, the results refer to a more or less typical scenario of exposure, although they do not allow any conclusion with regard to individual exposure levels. In contrast, the use of biomarkers of exposure (human biological monitoring) represents a methodological approach to estimate individual exposure levels of subjects exposed to toxic substances in a given situation. In practice, the identification of persons at risk and the exclusion of individual health risks can only be based on human biological monitoring studies. Exposure assessment based on theoretical calculations and exposure assessment based on biomarkers of exposure and effects should be regarded as complementary approaches in exposure and risk assessment.     

On the use of log-transformation vs. nonlinear regression for analyzing biological power laws

Power-law relationships are among the most well-studied functional relationships in biology. Recently the common practice of fitting power laws using linear regression (LR) on log-transformed data has been criticized, calling into question the conclusions of hundreds of studies. It has been suggested that nonlinear regression (NLR) is preferable, but no rigorous comparison of these two methods has been conducted. Using Monte Carlo simulations, we demonstrate that the error distribution determines which method performs better, with NLR better characterizing data with additive, homoscedastic, normal error and LR better characterizing data with multiplicative, heteroscedastic, lognormal error. Analysis of 471 biological power laws shows that both forms of error occur in nature. While previous analyses based on log-transformation appear to be generally valid, future analyses should choose methods based on a combination of biological plausibility and analysis of the error distribution. We provide detailed guidelines and associated computer code for doing so, including a model averaging approach for cases where the error structure is uncertain.     

污染场地土壤多环芳烃（PAHs）生物可利用浓度的健康风险评价方法

随土壤不慎经口摄入是PAHs对人体健康造成危害的重要途径之一,目前该暴露途径下PAHs的健康风险计算主要基于土壤中总PAHs浓度进行计算。但是,这种计算方法并未考虑PAHs在土壤中的赋存状态及经口摄入后在人体不同器官中的毒理动力学过程,导致计算结果过于保守,修复目标过于严格,修复成本过高。针对这一问题国外相关研究人员已开展基于土壤中PAHs生物可利用性的健康风险评价研究并取得较大进展,但在国内就如何在风险评价过程中引入PAHs生物可利用性及其面临的障碍缺乏系统性报道。在对土壤中PAHs赋存形态及其随土壤经口腔摄入后在人体消化及循环系统中的动态分配最新研究成果进行综述的基础上,通过对该暴露途径下现有风险计算模型存在问题及原因进行分析,提出基于土壤中PAHs可利用浓度进行风险计算,并对相应计算模型进行推导以及模型参数的获取方法进行了简要概括,以解决目前模型计算结果过于保守的问题。同时,对于在现有分层次进行场地健康风险评价思路中如何科学地纳入基于土壤中PAHs可利用浓度进行风险计算的思路以及在实际风险评价中应用该思路还需进行的研究工作进行了简要讨论。     

Sources and pathways of environmental lead to children in a Derbyshire mining village

Garden soil and housedust samples, from households in a Derbyshire village closely associated with historic lead mining, have highly elevated lead levels. Handwipe samples from children also have relatively high lead concentrations suggesting that elevated levels of lead are transferred to the child by the soil-dust-hand-mouth pathway. However, this is not reflected in their blood lead concentrations which are within normal UK ranges and less than predicted by some lead exposure models. SEM analysis of soil grains has revealed that many are composed of pyromorphite [Pb₅(PO₄)₃Cl], a stable soil-lead mineral. This mineral is formed from the weathering of galena [PbS] but it is not clear to what extent weathering has occurred in the soil. Pyromorphite has an extremely low solubility which may contribute to a low human bioavailability of lead in these soils, resulting in the lower than expected blood lead concentrations.     

Probabilistische Modelle der Expositionsabschätzung

Probabilistic exposure and risk assessment can show and help to explicate the uncertainties of deterministic single-point estimates. Heterogeneity in population and exposure parameters (variability) is a part of probabilistic models which makes use of distributions for the main influential factors. The distributions of the target variables are calculated as a model-based combination of all influence factors by Monte Carlo-simulation methods. The differences between classical and probabilistic exposure assessment are demonstrated using a residential area with soil contamination as an example. The estimated arsenic exposure due to soil and dust is calculated by deterministic single-point estimates and probabilistic exposure assessment. The results are compared to the biomonitoring results of an epidemiological study. A criteria catalogue for exposure model validation is given and discussed for this example. In this scenario, the probabilistic exposure prognosis fits the empirical data better than that of a deterministic, single-point assessment. Both approaches seem to overestimate exposure with respect to empirical data.     

重金属污染土壤属性区间识别模型的赋权分析

土壤重金属污染评价为土壤重金属污染的及时防范和综合治理提供重要的理论依据。文章在属性区间识别理论的基础上,构建土壤重金属污染评价的属性区间识别模型：选取土壤重金属污染中普遍存在的Hg、Cd、As、Pb、Cu、Zn作为评价指标,采用均化系数将各评价指标的属性测度区间转化为综合属性测度;为避免主观因素,利用主成分分析法、熵权法、CRITIC法对各评价指标进行3次客观赋权;最后根据置信度准则和分级标准进行土壤重金属污染的综合评价。对3种赋权法得到的权重和评价结果进行比较,表明：属性识别模型在土壤重金属污染评价中适用且有利于评价结果准确性的提高;3种客观赋权法算得的权重合理,且其优异程度为主成分分析法〈熵权法〈CRITIC法。     

Lifting a veil on diversity: a Bayesian approach to fitting relative-abundance models.

Bayesian methods incorporate prior knowledge into a statistical analysis. This prior knowledge is usually restricted to assumptions regarding the form of probability distributions of the parameters of interest, leaving their values to be determined mainly through the data. Here we show how a Bayesian approach can be applied to the problem of drawing inference regarding species abundance distributions and comparing diversity indices between sites. The classic log series and the lognormal models of relative- abundance distribution are apparently quite different in form. The first is a sampling distribution while the other is a model of abundance of the underlying population. Bayesian methods help unite these two models in a common framework. Markov chain Monte Carlo simulation can be used to fit both distributions as small hierarchical models with shared common assumptions. Sampling error can be assumed to follow a Poisson distribution. Species not found in a sample, but suspected to be present in the region or community of interest, can be given zero abundance. This not only simplifies the process of model fitting, but also provides a convenient way of calculating confidence intervals for diversity indices. The method is especially useful when a comparison of species diversity between sites with different sample sizes is the key motivation behind the research. We illustrate the potential of the approach using data on fruit-feeding butterflies in southern Mexico. We conclude that, once all assumptions have been made transparent, a single data set may provide support for the belief that diversity is negatively affected by anthropogenic forest disturbance. Bayesian methods help to apply theory regarding the distribution of abundance in ecological communities to applied conservation.     

A concise review of ecological risk assessment for urban ecosystem application associated with rapid urbanization processes

ABSTRACT

Urban ecological risk (UER) caused by rapid urbanization means potential threat to urban ecosystem structure, pattern and services. The scales of ecological risk assessment (ERA) have been expanded from individual organisms to watersheds and regions. The types of stressor range from chemical to physical, biological and natural events. However, the application of ERA in urban ecosystems is relatively new. Here, we summarize the progress of urban ERA and propose an explicit framework to illumine future ERA based on UER identification, analysis, characterization, modeling, projection and early warning and management. The summary includes six urban ERA-relevant methods: weight-of-evidence (WoE), procedure for ecological tiered assessment of risks (PETAR), relative risk model (RRM), multimedia, multi-pathway, multi-receptor risk assessment (3MRA), landscape analysis and ecological models. Furthermore, we review critical cases of urban ERA in landscape ecology, soil, air, water and solid waste. Based on the Internet of Things (IoT) and cloud computing, an urban ERA management platform integrates various urban ERA methodologies that can be developed to provide better implementation strategies of UER for urban ecosystem managers and stakeholders. We develop a conceptual model of urban ERA based on the urban characteristics in China. The future applications of urban ERA include uncertainty analysis using Monte Carlo techniques on the basis of geospatial techniques and comprehensive urban ERA using nonlinear models or process models.     

Comparison of IEUBK model predictions and actual blood lead values at a former battery recycling site

This paper presents an evaluation of US EPA's integrated exposureuptake biokinetic model for lead (IEUBK model) using data obtained during emergency removal operations at a former lead-acid battery recycling plant and the surrounding community. Data employed in the study include soil lead and interior dust lead, air lead levels collected at the site perimeter, drinking water lead levels at the community water main and blood lead data collected from an annual blood lead monitoring programme conducted over a four year period during the remediation activities.Geometric mean soil and dust concentrations were found to be a better predictor of blood lead than arithmetic mean data. However, weight based dust lead data were believed to be an inappropriate measure of dust lead levels. Estimates of household dust lead concentrations based upon surface loading data (g m^–2) yielded blood lead predictions which were more consistent with data collected in the blood lead monitoring programme     

Identification of sources of environmental lead in South Africa from surface soil geochemical maps

The bioavailability of lead in soil is of considerable importance to human and animal health. Although selective extraction has been explored as a more appropriate technique than total heavy metal analysis in environmental pollution assessments, such studies remain scarce globally and are almost non-existent in developing countries. Results for a large-scale study of extractable lead levels in undisturbed soil samples in South Africa identify several geographic areas of concern. Lead levels are considerably elevated relative to background levels in the Johannesburg urban and industrial area. Areas of active lead mining also exhibit higher surface soil values. Interestingly, areas of active and intensive coal mining activity display relatively low soil Pb values, possibly attributable to the relatively low heavy metal content of South African coal. In all instances, distribution of cadmium, a carcinogenic element, correlates with that of lead. The results demonstrate the usefulness of the quick and easy Mehlich-3 single extractant technique, an established technique in micronutrient studies, to simultaneously provide valuable environmental data for toxic metals such as Pb and Cd.     

Rüstungsaltlasten

Initial surveys on the type and degree of contamination of soils at former military facilities requires in most cases the evaluation of several hundred soil samples. Of interest is not only the final product (i.e. the pure substance), but also by-products and decomposition products from the manufacturing process. All individual data have to be entered and used in the general assessment; interdependencies between individual parameters have to be identified. To achieve this goal, a computer program was developed which can correlate, calculate, and graphically represent a data matrix consisting of 10,000 individual data units. The initial survey should be followed by more detailed analyses. The goal is to develop a site-specific concept for safety and cleanup action.     

Garden soil and house dust as exposure media for lead uptake in the mining village of Stratoni,Greece

The relationships between two exposure media, garden soil and house dust, were studied for Pb uptake in Stratoni village in northern Greece, an industrial area of mining and processing of sulphide ore. Lead data for the two media were assessed in terms of total and bioaccessible content, measurement and geochemical variability, and mineralogical composition. It was found that total Pb was enriched in house dust samples by a factor of 2 on average. Total Pb concentration in soil samples had a maximum of 2,040 mg/kg and reached a maximum of 7,000 mg/kg in house dust samples. The estimated variability due to measurement uncertainty was dominated by the sampling process, and the proportion of sampling variance was greater for soil samples, indicating a higher degree of Pb heterogeneity in soil on the given spatial scale of sampling strata. Although the same general spatial trend was observed for both sampling media with decreasing Pb concentration by increasing distance from the ore-processing plant, Pb in dust samples displayed the highest concentrations within a 300–600-m zone from the ore-processing facility. The significant differences which were observed in Pb speciation between the studied media were explained by differences in mineralogical composition of outdoor soil and indoor dust. Lead-enriched Fe and Mn oxides predominated in soil samples while fine galena grains (<10–20 μm diameter) were the major Pb-bearing phase in dust samples. The integrated exposure uptake biokinetic model was used to predict the risk of elevated blood lead levels in children of Stratoni. Model prediction indicated an average probability of 61 % for blood-Pb to exceed 10 μg/dl. The results underline the importance of house dust in risk assessment and highlight the effect of outdoor and indoor conditions on the fate of Pb in the particular environment of Stratoni.