南京城郊不同土地利用类型农业土壤多环芳烃污染特征及风险评价

以南京市城郊不同土地利用类型的农业土壤(水田、菜地和林地)为研究对象,测定了16种PAHs的含量.结果表明,苊烯(Acy)在所有土壤样本中均未被检出,南京城郊农业土壤15种ω(PAHs)的范围在24.49～925.54μg·kg^-1之间,平均值为259.88μg·kg^-1.PAHs含量由高到低依次为：林地>水田>菜地,总体上以高环PAHs(HMW)含量为主.不同土壤理化性质对PAHs的影响表明：土壤有机碳(TOC)和黏粒(clay)含量与PAHs存在一定的相关性,pH和全氮(TN)与PAHs无明显相关性.毒性当量法和CSI指数法表明,南京城郊农业土壤中PAHs生态风险较小,但是林地中应当给予一定的重视.增量终身癌症风险(ILCR)进行健康风险评价表明,儿童健康的威胁风险略大于成人,林地的总的致癌风险(CR)明显高于菜地和水田,仍处于可接受的范围内.对成人进行了蒙特卡洛模拟表明,确定性健康风险的风险分析低估了PAHs的健康风险.敏感性分析结果表明,对CR总方差影响最大的输入参数是暴露频率EF(占50.7%).     

Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results

The chemical mass balance (CMB) model was applied for source apportionment of PM_2.5 in Atlanta in order to explore levels and causes of uncertainties in source contributions. Monte Carlo analysis with Latin hypercube sampling (MC-LHS) was performed to evaluate the source impact uncertainties and quantify how uncertainties in ambient measurement and source profile data affect results. In general, uncertainties in the source profile data contribute more to the final uncertainties in source apportionment results than do those in ambient measurement data. Uncertainty contribution estimates suggest that non-linear interactions among source profiles also affect the final uncertainties although their influence is typically less than uncertainties in source profile data.     

Using stochastic risk assessment in setting information priorities for managing dioxin impact from a municipal waste incinerator

The objectives of this study were to assess site-specific carcinogenic risk of incinerator-emitted dioxins in a manner reflecting pollutant transfer across multimedia and multi-pathways. The study used site-specific environmental and exposure information and combined the Monte Carlo method with multimedia modeling to produce probability distributions of risk estimates. The risk estimates were further categorized by contaminated environmental media and exposure pathways that are experienced by human receptors in order to pinpoint significant sources of risk. Rank correlation coefficients were also calculated along with the Monte Carlo sampling to identify key factors that influenced estimation of risk. The results showed that ingestion accounted for more than 90% of the total risk and that risk control on ingestion of eggs, aboveground vegetables, and poultry should receive priority. It was also found that variation of parameters with variability accounted for around 35% of the total risk variance, while uncertainty contributed to the remaining 65%. Intake rates of aboveground vegetables, eggs, and poultry were the key parameters with the largest contribution to variance. In addition, sufficient sampling and analysis of dioxin contents in eggs, aboveground vegetables, poultry, soil, and fruit should be performed to improve risk estimation because the variation in concentrations in these media accounted for the largest overall risk variance. Finally, focus should be placed on reduction of uncertainty associated with the risk estimation through ingestion of aboveground vegetables, eggs, poultry, fruit, and soil because the risk estimates associated with these exposure pathways had the largest variance.     

Space-time modeling for the Missouri Turkey Hunting Survey

The Missouri Turkey Hunting Survey (MTHS) is a postseason mail survey conducted by the Missouri Department of Conservation. The 1996 MTHS provides information concerning the number of turkeys harvested by hunters on each day and the total number of trips made to the counties by these hunters on each day of the hunting season. The success rates are then found from this information. Small sample sizes produce large standard errors for the estimates at the county level. We use a Bayesian hierarchical generalized linear model to estimate daily hunting success rates at the county level. The model includes an autoregressive process for the days of the hunting season and spatially correlated random geographic effects. The computations are performed using Gibbs sampling and adaptive rejection sampling techniques. Results show that there are significant spatial corelations between counties and correlations between days of the hunting season. The estimates are close to the frequency estimates at the state level and much more stable at the county level.     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.     

Probabilistic prediction of exposures to arsenic contaminated residential soil

Probabilistic modelling using Monte Carlo simulation has been proposed as a more scientifically valid method of estimating soil contaminant exposures than conservative deterministic methods currently used by regulatory agencies. A retrospective application of probabilistic modelling to an exposure scenario involving arsenic-contaminated residential soil near the former ASARCO smelter near Tacoma, Washington is presented. The population of interest is children, aged 2–6 years, living within one-half mile (0.3 km) of the smelter site. Models that predict urinary arsenic levels based on unintentional soil ingestion and inhalation exposure pathways are used. Distributions of exposure variables are based on site-specific data and previous exposure studies. Simulated urinary arsenic levels are compared with data from two biomonitoring studies performed during the late 1980s. Arsenic distributions produced by simulation and biomonitoring are significantly different, and likely contributors to this difference are discussed. However the probabilistic model provides closer estimations of urinary arsenic levels than conservative deterministic models similar to those used by regulatory agencies, and provides useful information regarding parameter uncertainty. Soil ingestion rate was a driving variable in the probabilistic models. Further quantification of soil ingestion rates is warranted.     

地下水DNAPLs污染多相流的随机模拟及其不确定性分析

水文地质参数本身存在不确定性,为分析水文地质参数不确定性对地下水DNAPLs污染多相流数值模拟模型输出结果的影响,本文针对假想算例展开研究,首先建立了研究区地下水DNAPLs污染多相流数值模拟模型;然后,运用灵敏度分析法筛选对模型输出结果影响较大的参数作为随机变量;为减少反复调用多相流模拟模型产生的计算负荷,运用克里格方法建立多相流模拟模型的替代模型,利用替代模型完成蒙特卡洛随机模拟;最后,对随机模拟的结果进行统计分析并完成地下水污染风险评价.结果表明,利用污染物浓度分布函数可以估算单井遭受污染的风险;利用地下水污染风险图可以对全区地下水遭受不同程度污染的风险大小进行分区,为地下水污染防治提供更加科学、丰富的参考依据.     

基于蒙特卡罗方法的铅酸蓄电池厂土壤健康风险评价

以土壤中的风险指标(Pb,Cd,As)为研究对象,将蒙特卡罗模拟方法应用到某铅酸蓄电池厂土壤重金属的健康风险评价中,解决了土壤重金属影响人体健康风险的不确定性问题。结果表明,土壤中Pb对成人不存在非致癌风险,对儿童存在非致癌风险;土壤中Cd,As对成人、儿童都不存在致癌风险;影响Pb,Cd和As进入人体单位体重的致癌、非致癌风险水平大小的主要因素为土壤重金属浓度。研究结果在一定程度上可为铅酸蓄电池厂土壤不确定性健康风险评价提供参考依据。     

Comprehensive framework for underground pipeline management with reliability and cost factors using Monte Carlo simulation

A reliability model for underground pipeline management that can quantify the trade-off between risk reduction and increased maintenance costs in various underground piping management scenarios can be useful for many pipeline-maintenance decision-makers. In this paper, we propose a comprehensive framework for analyzing underground pipeline management options. Pipeline reliability is calculated using time-dependent and independent limit state functions with a probabilistic model and a deterministic model about the frequency of a failure occurrence event. The proposed framework includes the target reliability, consequences, and cost model, and has the advantage that it can be intuitively utilized for piping management decision-making. We conducted several case studies using a Monte Carlo simulation on pipelines in industrial complexes in Korea.