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.     

区域水土流失遥感与核素示踪联合评价技术研究

水土流失遥感调查具有宏观、快速、效率高的优点,可以实现水土流失时空动态监测;侵蚀137Cs核素示踪技术能够提供独立的土壤侵蚀与堆积数据以及空间分布的信息,与其它方法相比,不仅具有无时空限制、操作简便快捷的优点,而且可提供上世纪60年代以来的多空间尺度(测点到流域)年均净侵蚀量。核素示踪与遥感解译的结合,可实现水土流失评价的宏观与微观、点与面、估算与实测的结合,大大地提高水土流失评价时空分辨率和精准度。文章以锦屏二级水电站工程建设区域为例,将137Cs侵蚀示踪技术和水土流失遥感调查方法相结合,基于GIS技术,对区域内水土流失现状进行了评价。     

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.     

阴极射线管中低熔点玻璃的溶解规律研究

阴极射线管是电子显示系统的主要部件,含有大量铅,其废弃物的浸出毒性超过我国危险废物鉴别标准.废阴极射线管资源化的关键是锥-屏的分离,分离后的锥-屏玻璃可用于显像管的再制造、铅的提炼和玻璃的再生产等.采用超声波辅助酸-热冲击法对低熔点玻璃的溶解规律进行研究的结果表明,同乙酸相比硝酸溶解低熔点玻璃的速率更大,硝酸浓度对低熔点玻璃的溶解速率有影响;低熔点玻璃中存在的金属离子与硝酸反应后生成的物质,如硝酸铅、硝酸钡、硝酸锌及硼酸等的存在会使低熔点玻璃的溶解速率降低.     

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.     

喀斯特峰丛洼地泥沙堆积的137Cs示踪研究——以丫吉试验场为例

西南喀斯特地区是我国生态环境最脆弱的地区之一,土壤侵蚀及其造成的石漠化已成为制约该区可持续发展最严重的生态环境问题。但是该区的侵蚀泥沙研究基础薄弱,利用核素示踪法研究侵蚀泥沙的报道较少。本文以桂林丫吉试验场的峰丛洼地小流域为研究对象,运用137Cs示踪技术定量研究了洼地泥沙堆积速率,确定了该洼地小流域1963年以来的泥沙堆积速率。初步研究结果表明,1963~2008年的45年间,丫吉1号洼地的泥沙堆积速率和堆积模数分别为0.104cm·a-1和13.68 t·km-2·a-1。讨论了研究小流域泥沙堆积与地面土壤流失的关系,认为研究区域近几十年以来的地面水土流失相当轻微,地面土壤流失速率仅为10余t·km-2·a-1。