Environmental Integrated Assessment via Monte Carlo Simulation with a Case Study of the Mid-Atlantic Region,USA

Environmental integrated assessments are often carried out via the aggregation of a set of environmental indicators. Aggregated indices derived from the same data set can differ substantially depending upon how the indicators are weighted and aggregated, which is often a subjective matter. This article presents a method of generating aggregated environmental indices in an objective manner via Monte Carlo simulation. Rankings derived from the aggregated indices within and between three Monte Carlo simulations were used to evaluate the overall environmental condition of the study area. Other insights, such as the distribution of good or bad values of indicators at a watershed and/or a subregion, were observed in the study.     

城市景观娱乐水体微生物风险评价

随着城市居民亲水娱乐活动日益增加,开展景观娱乐水体微生物风险评价,并以此为基础开展有效的风险管理,对于人口密集的城市区域尤为重要.以我国南方某城市3个典型景观娱乐水体为案例,采用定量微生物风险评价的方法框架,综合运用水质监测、社会调查、文献调研等方法开展暴露评价,并由此开展基于粪大肠菌(FC)、大肠埃希氏菌(EC)和肠球菌(ENT)的多微生物指标人体健康风险评价.结果表明,基于FC、EC和ENT的多指标微生物风险评价结果具有一定的一致性和可比性;位于城市居住区的B河段不宜作为人体直接或非直接接触的娱乐水体,其对公众健康的风险主要来源于用作家庭杂用水;位于城市公共开放空间的C河段和风景旅游区的E湖可以满足现有的景观娱乐功能要求,其对公众健康的风险主要来源于各种休闲娱乐活动;应重视与城市景观娱乐水体功能不匹配的无序利用(如作为家庭杂用水)对公众健康的风险.     

BOD5容量计算的不确定性分析

应用蒙特卡罗方法对BODs容量计算的不确定性,作出定量估计。结合算例,讨论了主要参数的统计检验,随机变量生成等。给出了以容量——概率曲线估计不确定性的定量结果。     

石家庄市栾城区农田土壤重金属分布特征及作物风险评价

为探讨栾城区内农田土壤重金属的分布特征,对表层土壤As、Cd、Cr、Cu、Hg、Ni、Pb和Zn这8种重金属污染特征、空间分布和污染风险进行分析,并使用PMF模型进行污染源解析,探讨栾城区土壤管控方案.进一步检测作物中重金属含量,利用概率风险评价方法评价研究区农产品的非致癌健康风险,为栾城区内农田土壤重金属污染治理与防控提供理论基础.结果表明,表层土壤ω（Cd）、ω（Cr）、ω（Cu）、ω（Pb）和ω（Zn）分别为0.06~1.08、22.14~473.47、12.83~150.74、10.75~577.72和62.23~652.78 mg·kg^-1,点位超标率分别为1.83%、1.22%、0.61%、0.61%和1.22%.此外,部分区域土壤中的重金属有向作物中转移并积累的现象,小麦籽粒Cd和Pb含量超标率分别为2.13%和5.32%,玉米籽粒Cd含量超标率为1.20%.健康风险评价结果表明,栾城区玉米的复合非致癌健康风险（TTHQ）小于1,对人体没有明显的负面影响,食用研究区小麦的复合非致癌风险（TTHQ>1）,对人体产生负面影响的可能性较大.土壤中重金属的空间分布受工业区涉污企业分布的影响,8种重金属含量较高的区域均主要分布在工业企业较集中的中部、西部和南部.总体而言,研究区大部分表层耕地土壤未受到明显的重金属污染,存在中等强度（2级）的生态危害.Cd为无-中度污染（1级）,Cd和Hg存在中等强度潜在生态风险（2级）,其余重金属均为无污染,低潜在生态风险（1级）.根据PMF污染源解析结果和实地调查推测,土壤重金属主要来自土壤母质（52.05%）,历史污灌和工业生产的人为污染源（32.98%）和大气沉降（14.97%）.综上所述,研究区北部和东部土壤重金属含量较低,应划分为优先保护类,研究区西部、中部和南部部分点位重金属含量超标,其来源主要为化工、涂料、机械装备等企业,应划分为安全利用类,严控污染物的输入,采用农艺调控等措施,减少重金属向农作物的转移,降低食品安全健康风险.本研究将有助于栾城区土壤的分级治理和污染管控.     

Influence of indoor transport and mixing time scales on the performance of sensor systems for characterizing contaminant releases

Optimizing real-time sensor systems to detect and identify relevant characteristics of an indoor contaminant event is a challenging task. The interpretation of incoming sensor data is confounded by uncertainties in building operation, in the forces driving contaminant transport, and in the physical parameters governing transport. In addition, simulation tools used by the sensor interpretation algorithm introduce modeling uncertainties. This paper explores how the time scales inherent in contaminant transport influence the information that can be extracted from real-time sensor data. In particular, we identify three time scales (within room mixing, room-to-room transport, and removal from the building) and study how they affect the ability of a Bayesian Monte Carlo (BMC) sensor interpretation algorithm to identify the release location and release mass from a set of experimental data, recorded in a multi-floor building. The research shows that some limitations in the BMC approach do not depend on details of the models or the algorithmic implementation, but rather on the physics of contaminant transport. These inherent constraints have implications for the design of sensor systems.     

The effect of variability in industrial emissions on ozone formation in Houston, Texas

Ambient observations have indicated that high concentrations of ozone observed in the Houston/Galveston area are associated with plumes of highly reactive hydrocarbons, mixed with NO_x, from industrial facilities. Ambient observations and industrial process data, such as mass flow rates for industrial flares, indicate that the VOCs associated with these industrial emissions can have significant temporal variability. To characterize the effect of this variability in emissions on ozone formation in Houston, data were collected on the temporal variability of industrial emissions or emission surrogates (e.g., mass flow rates to flares). The observed emissions variability was then used to construct regionwide emission inventories with variable industrial emissions, and the impacts of the variability on ozone formation were examined for two types of meteorological conditions, both of which lead to high ozone concentrations in Houston. The air quality simulations indicate that variability in industrial emissions has the potential to cause increases and decreases of 10–52 ppb (13–316%), or more, in ozone concentration. The largest of these differences are restricted to regions of 10–20 km², but the variability also has the potential to increase regionwide maxima in ozone concentrations by up to 12 ppb.     

A Bayesian hierarchical model for monitoring harbor seal changes in Prince William Sound, Alaska

Bayesian hierarchical models were used to assess trends of harbor seals, Phoca vitulina richardsi, in Prince William Sound, Alaska, following the 1989 Exxon Valdez oil spill. Data consisted of 4–10 replicate observations per year at 25 sites over 10 years. We had multiple objectives, including estimating the effects of covariates on seal counts, and estimating trend and abundance, both per site and overall. We considered a Bayesian hierarchical model to meet our objectives. The model consists of a Poisson regression model for each site. For each observation the logarithm of the mean of the Poisson distribution was a linear model with the following factors: (1) intercept for each site and year, (2) time of year, (3) time of day, (4) time relative to low tide, and (5) tide height. The intercept for each site was then given a linear trend model for year. As part of the hierarchical model, parameters for each site were given a prior distribution to summarize overall effects. Results showed that at most sites, (1) trend is down; counts decreased yearly, (2) counts decrease throughout August, (3) counts decrease throughout the day, (4) counts are at a maximum very near to low tide, and (5) counts decrease as the height of the low tide increases; however, there was considerable variation among sites. To get overall trend we used a weighted average of the trend at each site, where the weights depended on the overall abundance of a site. Results indicate a 3.3% decrease per year over the time period.     

Modelling Replicated Weed Growth Data using Spatially-varying Growth Curves

Weed growth in agricultural fields constitutes a major deterrent to the growth of crops, often resulting in low productivity and huge losses for the farmers. Therefore, proper understanding of patterns in weed growth is vital to agricultural research. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data, which enable more sophisticated spatial analysis. Our current application concerns the development of statistical models for conducting spatial analysis of growth patterns in weeds. Our data comes from an experiment conducted in Waseca, Minnesota, that recorded growth of the weed Setariaspp. We capture the spatial variation in Setaria spp. growth using spatially-varying growth curves. An added challenge is that these designs are spatially replicated, with each plot being a lattice of sub-plots. Therefore, spatial variation may exist at different resolutions – a macro level variation between the plots and micro level variation between the sub-plots nested within each plot. We develop a Bayesian hierarchical framework for this setting. Flexible classes of models result which are fitted using simulation-based methods.     

Interpolation performance of a spatio-temporal model with spatially varying coefficients: application to PM<Subscript>10</Subscript> concentrations in Rio de Janeiro

In this work we present a Bayesian analysis in linear regression models with spatially varying coefficients for modeling and inference in spatio-temporal processes. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial spatial heterogeneity. We describe for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations. Using a simulation experiment we investigate how parameter inference and interpolation performance are affected by some features of the data and prior distribution that is used. The proposed methodology is used to model the dataset on PM₁₀ levels in the metropolitan region of Rio de Janeiro presented in Paez and Gamerman (2003).     

除草剂苯噻草胺土壤滞留因子的蒙特卡洛模拟

采用简单随机抽样和拉丁超立方抽样两种不同方法对除草剂苯噻草胺的土壤滞留因子模型进行蒙特卡洛模拟。结果表明，不同的土壤条件下，苯噻草胺的滞留因子（R）的数值呈Beta分布，最大极值为176．5，最小极值为3．3，最可能的取值在20－30的范围内。结果还显示了采用拉丁超立方抽样方法能够显著降低抽样的不均匀性，进而能降低Beta分布拟合的不确定性。本文还通过色轴在4维图上形象地表征了R值的变化。