Investigation of land use changes in rural areas using MCDM and CA-Markov chain and their effects on water quality and soil fertility in south of Iran

The purpose of the study is to predict drought changes in Dariun, Fars Province, and their impact on water and soil quality. To prepare drought, water, and soil quality zoning maps, Landsat satellite images and the kriging method were used. The fuzzy maps and weights for each parameter were then determined using fuzzy and analytic hierarchy process (AHP) methods. Additionally, cellular automata (CA)-Markov chains were used in order to predict the impact of drought changes on water and soil quality. Using the fuzzy-AHP method, water quality and soil fertility in 2020 were lower compared to previous years, mainly because of land use changes that increased pollution. Based on results of the Markov and CA-Markov chains, approximately 31% of the region will have very poor levels of soil fertility and water quality in 2050. Further, based on remote sensing indicators, it is determined that about 25% of the region will be at high risk of drought in 2050. Thus, if adequate management of the region is not done, the possibility of living in these areas may diminish in the coming years due to drought and deteriorated water and soil quality.

     

Using a probabilistic approach in an ecological risk assessment simulation tool: test case for depleted uranium (DU)

A probabilistic approach was applied in an ecological risk assessment (ERA) to characterize risk and address uncertainty employing Monte Carlo simulations for assessing parameter and risk probabilistic distributions. This simulation tool (ERA) includes a Window's based interface, an interactive and modifiable database management system (DBMS) that addresses a food web at trophic levels, and a comprehensive evaluation of exposure pathways. To illustrate this model, ecological risks from depleted uranium (DU) exposure at the US Army Yuma Proving Ground (YPG) and Aberdeen Proving Ground (APG) were assessed and characterized. Probabilistic distributions showed that at YPG, a reduction in plant root weight is considered likely to occur (98% likelihood) from exposure to DU; for most terrestrial animals, likelihood for adverse reproduction effects ranges from 0.1% to 44%. However, for the lesser long-nosed bat, the effects are expected to occur (>99% likelihood) through the reduction in size and weight of offspring. Based on available DU data for the firing range at APG, DU uptake will not likely affect survival of aquatic plants and animals (<0.1% likelihood). Based on field and laboratory studies conducted at APG and YPG on pocket mice, kangaroo rat, white-throated woodrat, deer, and milfoil, body burden concentrations observed fall into the distributions simulated at both sites.     

Bayesian inference for source determination with applications to a complex urban environment

The problem of determining the source of an emission from the limited information provided by a finite and noisy set of concentration measurements obtained from real-time sensors is an ill-posed inverse problem. In general, this problem cannot be solved uniquely without additional information. A Bayesian probabilistic inferential framework, which provides a natural means for incorporating both errors (model and observational) and prior (additional) information about the source, is presented. Here, Bayesian inference is applied to find the posterior probability density function of the source parameters (location and strength) given a set of concentration measurements. It is shown how the source–receptor relationship required in the determination of the likelihood function can be efficiently calculated using the adjoint of the transport equation for the scalar concentration. The posterior distribution of the source parameters is sampled using a Markov chain Monte Carlo method. The inverse source determination method is validated against real data sets acquired in a highly disturbed flow field in an urban environment. The data sets used to validate the proposed methodology include a water-channel simulation of the near-field dispersion of contaminant plumes in a large array of building-like obstacles (Mock Urban Setting Trial) and a full-scale field experiment (Joint Urban 2003) in Oklahoma City. These two examples demonstrate the utility of the proposed approach for inverse source determination.     

Eulerian—Lagrangian relationships in monte carlo simulations of turbulent diffusion

A Monte Carlo technique for simulating turbulent diffusion is developed based on the Eulerian space-time velocity autocorrelation function. This method is an improvement over the usual Lagrangian approach since the Eulerian statistical properties of the turbulence are more easily measured. The analysis leads to some simple analytic relations between Eulerian and Lagrangian integral scales.     

Multiple batch extraction test to estimate contaminant release parameters using a Bayesian approach

Industrial activities produce vast amounts of weakly contaminated materials which are commonly reused as filling materials on natural ground. There is a strong demand to define guidelines for the application of these materials, to estimate the leaching potential of contaminants from the materials, and to assess the potential hazard for groundwater pollution. We present a multiple batch experiment, where measurements of liquid-phase concentrations at varying liquid/solid ratios are used to estimate the total mass of contaminant that can be extracted from a contaminated material with a mild extractant like water. Furthermore, the experiment yields estimates of the isotherm describing the partitioning of the contaminant between the solid and liquid phases, and a concentration that might be expected under soil hydraulic conditions representative for the field situation. Model parameters are estimated from liquid-phase concentrations within a Bayesian framework by applying the Shuffled Complex Evolution Metropolis Algorithm (SCEM-UA), an efficient Markov Chain Monte Carlo sampler. A sensitivity analysis and inversions of synthetically generated data corrupted with noise show the general suitability of the proposed method. An uncertainty analysis for model parameters and model predictions shows the expected accuracy of the estimates. An application to concentration measurements obtained from a multiple batch extraction test illustrates the applicability of the approach for a real situation.     

适于环境容量初始配置的两步削减模型及应用

在由目标总量控制向容量总量控制的过渡时期,现有排污量往往超过区域的环境容量.因此,确定企业排污削减的比例,成为该时期环境容量初始配置的核心任务.以公平与效率为基本原则,构建了由法定削减与公平削减组成的两步削减模型,并联合应用模糊聚类分析法和层次分析法对模型参数进行求解,在太仓经济开发区环境容量的初始分配中进行了运用.模型的建构思路与求解方法可为相关的环境管理者提供参考.     

基于灰色层次分析法的渗滤液浓缩液回灌填埋体评价

为科学合理地选择渗滤液浓缩液回灌的最优填埋体,采用灰色层次分析法对回灌填埋体进行了综合评价。首先构建灰色层次分析法的层次结构模型及评价指标,并根据层次分析法计算各评价指标权重,然后基于灰色关联理论建立评价指标关联系数矩阵,最后根据各评价指标权重及其关联系数矩阵建立综合评价矩阵。采用建立的综合评价方法对垃圾年份分别为1 a、5 a和15 a的回灌填埋体进行了综合评价。结果表明,垃圾年份为15 a的回灌填埋体与理想方案的关联度最大,为最优的回灌填埋体,其次为5 a、1 a。     

Migration of a water pulse through fractured porous media

Contaminant transport from waste-disposal sites is strongly affected by the presence of fractures and the degree of fracture matrix interaction. Characterization of potential contaminant plumes at such sites is difficult, both experimentally and numerically. Simulations of water flow through fractured rock were performed to examine the penetration depth of a large pulse of water entering such a system. Construction water traced with lithium bromide was released during the excavation of a tunnel at Yucca Mountain, Nevada, which is located in an unsaturated fractured tuff formation. Modeling of construction-water migration is qualitatively compared with bromide-to-chloride ratio (Br/Cl) data for pore-water salts extracted from drillcores. The influences of local heterogeneities in the fracture network and variations in hydrogeologic parameters were examined by sensitivity analyses and Monte Carlo simulations. The simulation results are qualitatively consistent with the observed Br/Cl signals, although these data may only indicate a minimum penetration depth, and water may have migrated farther through the fracture network.     

Deterministic and stochastic modelling of water, heat and nitrogen dynamics on different scales with WHNSIM

With respect to groundwater quality objectives, it is difficult to understand the nitrogen balance and nitrogen dynamics of agricultural ecosystems without the support of deterministic and stochastic simulation tools. This paper focuses on the deterministic and stochastic modelling of water, heat and nitrogen dynamics in the soil compartments of agricultural ecosystems. The deterministic nitrogen simulation model for agricultural soils presented in this paper, WHNSIM, was originally designed for site conditions typical of southern Germany but can easily be applied to other regions due to its physically based design. Model results are compared with data originating from different experimental sites in Germany. Special consideration is given to the effects of spatial variability. Two different techniques of Monte Carlo simulation are used to study the impact of parameter variability on target variables of nitrogen dynamics on the field scale. Moreover, a procedure is presented that allows modelling nitrogen dynamics on the scale of small catchment areas, combining the geographical information system Arc Info with agricultural management data and model calculations.     

Database uncertainty as a limiting factor in reactive transport prognosis

The effect of uncertainties in thermodynamic databases on prediction performances of reactive transport modeling of uranium (VI) is investigated with a Monte Carlo approach using the transport code TReaC. TReaC couples the transport model to the speciation code PHREEQC by a particle tracking method. A speciation example is given to illustrate the effect of uncertainty in thermodynamic data on the predicted solution composition. The transport calculations consequently show the prediction uncertainty resulting from uncertainty in thermodynamic data. A conceptually simple scenario of elution of uranium from a sand column is used as an illustrating example. Two different cases are investigated: a carbonate-enriched drinking water and an acid mine water associated with uranium mine remediation problems. Due to the uncertainty in the relative amount of positively charged and neutral solution species, the uncertainty in the thermodynamic data also infers uncertainty in the retardation behavior. The carbonated water system shows the largest uncertainties in speciation calculation. Therefore, the model predictions of total uranium solubility have a broad range. The effect of data uncertainty in transport prediction is further illustrated by a prediction of the time when eluted uranium from the column exceeds a threshold value. All of these Monte Carlo transport calculations consume large amounts of computing time.     

Health risk assessment of exposure to organic matter from the use of reclaimed water in toilets

To estimate the carcinogenic and non-carcinogenic risks of six volatile organic compounds (VOCs) found in reclaimed water used for flushing toilets, a probabilistic health risk assessment based on Monte Carlo simulations was used. Before Monte Carlo simulations, the contaminant volatilization model was applied to estimate the concentration of the pollutants in air. Moreover, a questionnaire was used to acquire daily exposure time and the Batch Fit tool of Crystal Ball software was applied to find the best-fitting distribution of a part of the input parameters. The risk analysis indicated that the health risks from six VOCs were lower than the negligible risk level (1?×?10^?8) in all cases, and the health risk for females was slightly higher than that for males. Overall, exposure to organic pollutants in reclaimed water during toilet flushing does not currently pose a significant carcinogenic risk to humans. In this study, we want to provide some information on the health risk from reclaimed water used for toilet flushing in China and hope that it will be useful to promote the application of reclaimed water in water-deficient areas.     

Joint propagation of variability and imprecision in assessing the risk of groundwater contamination

Estimating risks of groundwater contamination often require schemes for representing and propagating uncertainties relative to model input parameters. The most popular method is the Monte Carlo method whereby cumulative probability distributions are randomly sampled in an iterative fashion. The shortcoming of the approach, however, arises when probability distributions are arbitrarily selected in situations where available information is incomplete or imprecise. In such situations, alternative modes of information representation can be used, for example the nested intervals known as “possibility distributions”. In practical situations of groundwater risk assessment, it is common that certain model parameters may be represented by single probability distributions (representing variability) because there are data to justify these distributions, while others are more faithfully represented by possibility distributions (representing imprecision) due to the partial nature of available information. This paper applies two recent methods, designed for the joint-propagation of variability and imprecision, to a groundwater contamination risk assessment. Results of the joint-propagation methods are compared to those obtained using both interval analysis and the Monte Carlo method with a hypothesis of stochastic independence between model parameters. The two joint-propagation methods provide results in the form of families of cumulative distributions of the probability of exceeding a certain value of groundwater concentration. These families are delimited by an upper cumulative distribution and a lower distribution respectively called Plausibility and Belief after evidence theory. Slight differences between the results of the two joint-propagation methods are explained by the different assumptions regarding parameter dependencies. Results highlight the point that non-conservative results may be obtained if single cumulative probability distributions are arbitrarily selected for model parameters in the face of imprecise information and the Monte Carlo method is used under the assumption of stochastic independence. The proposed joint-propagation methods provide upper and lower bounds for the probability of exceeding a tolerance threshold. As this may seem impractical in a risk-management context, it is proposed to introduce “a-posteriori subjectivity” (as opposed to the “a-priori subjectivity” introduced by the arbitrary selection of single probability distributions) by defining a single indicator of evidence as a weighted average of Plausibility and Belief, with weights to be defined according to the specific context.     

Assessing the inhalation cancer risk of particulate matter bound polycyclic aromatic hydrocarbons (PAHs) for the elderly in a retirement community of a mega city in North China

Assessment of the health risks resulting from exposure to ambient polycyclic aromatic hydrocarbons (PAHs) is limited by the lack of environmental exposure data among different subpopulations. To assess the exposure cancer risk of particulate carcinogenic polycyclic aromatic hydrocarbon pollution for the elderly, this study conducted a personal exposure measurement campaign for particulate PAHs in a community of Tianjin, a city in northern China. Personal exposure samples were collected from the elderly in non-heating (August–September, 2009) and heating periods (November–December, 2009), and 12 PAHs individuals were analyzed for risk estimation. Questionnaire and time-activity log were also recorded for each person. The probabilistic risk assessment model was integrated with Toxic Equivalent Factors (TEFs). Considering that the estimation of the applied dose for a given air pollutant is dependent on the inhalation rate, the inhalation rate from both EPA exposure factor book was applied to calculate the carcinogenic risk in this study. Monte Carlo simulation was used as a probabilistic risk assessment model, and risk simulation results indicated that the inhalation-ILCR values for both male and female subjects followed a lognormal distribution with a mean of 4.81?×?10^?6 and 4.57?×?10^?6, respectively. Furthermore, the 95 % probability lung cancer risks were greater than the USEPA acceptable level of 10^?6 for both men and women through the inhalation route, revealing that exposure to PAHs posed an unacceptable potential cancer risk for the elderly in this study. As a result, some measures should be taken to reduce PAHs pollution and the exposure level to decrease the cancer risk for the general population, especially for the elderly.     

A stochastic stormwater quality volume-sizing method with first flush emphasis

A Monte Carlo simulation technique was applied to assess the effect of stormwater quality volume captured by best management practices (BMPs) on the frequency of discharging concentrations of constituents above certain designated threshold limits. The method used an assumption of a power law relationship between the cumulative load and flow to incorporate the first flush effect. The exponent of this relationship was considered a random variable and its frequency distribution was obtained from 78 measured pollutographs from three urban highway sites in West Los Angeles, California. Although the effect of rain depth captured by BMPs is site-specific, the method offered here provides a systematic approach to evaluate the effect of selecting various regulatory guidelines for controlling urban stormwater pollution on the overall discharge of pollutants into waterways. This allows selecting the requirements for capturing runoff volume by BMPs based on the tradeoff between the probability of concentration criteria violation and economic factors.     

A risk index for characterising flow pattern in soils using dye tracer distributions

A novel risk index for the vulnerability of groundwater by pollutants is defined as the form parameter of the Pareto distribution and estimated from dye tracer experiments. The Pareto distribution appears as the limit distribution of the extreme value theory, which has been applied to an idealized model of drops that run along a path. The properties of the risk index are investigated by a Monte Carlo study, where the paths are modelled by means of Gaussian random fields. The method is applied to three profiles obtained from Brilliant Blue tracer experiments of the soil physics group at ETH Zurich. It is shown that a single profile can be rather well characterised by the risk index. However, due to the high variability of the dye tracer profiles, an estimated number of at least 15 profile pictures are necessary to characterise a soil.     

层次分析法与地理信息系统在农田土壤重金属污染评价中的应用

以《土壤环境质量标准》(GB 15618—1995)二级标准作为评价标准,采用以改进层次分析法确定权重的加权平均法对南京市郊典型蔬菜地的土壤重金属污染状况进行了评价。结果表明,研究区土壤中Cu、Pb、Cr的单因子污染指数平均值均小于1,含量超标率均不超过10.32%,而Cd的单因子污染指数平均值达2.15,含量超标率为84.13%,急需对该区土壤和受体做进一步的Cd污染深入调查或风险评估;研究区土壤重金属综合污染指数平均值为1.28,属玷污水平;利用ArcGIS 8.3软件进行土壤重金属污染的空间分布特征分析结果表明,研究区各元素的污染程度为Cd>Pb>Cu>Cr,各元素的污染格局较为相似,城市化活动对土壤污染贡献较大;研究区有81.16%的土壤重金属污染级别为玷污,3.78%处于本底级,15.06%处于安全级。     

Contaminant transport in a municipal drinking water supply: a steady-state approach to estimate rate and uncertainty

Contaminant transport is generally considered to be a key factor when assessing and classifying the environmental risk of polluted areas. In the study presented here, a steady-state approach was applied to obtain estimates of the transit time and concentration of the pesticide metabolite BAM (2,6-dichlorobenzoamide) at a site where it is contaminating a municipal drinking water supply. A Monte Carlo simulation technique was used to quantify the uncertainty of the results and to evaluate the sensitivity of the used parameters. The adopted approach yielded an estimated median transit time of 10 y for the BAM transport from the polluted site to the water supply. Soil organic carbon content in the unsaturated zone and the hydraulic conductivity in the saturated zone explained 44% and 23% of the uncertainty in the transit time estimate, respectively. The sensitivity analysis showed that the dilution factor due to regional groundwater flow and the soil organic carbon content at the polluted site explained 53% and 31% of the uncertainty of concentration estimates, respectively. In conclusion, the adopted steady-state approach can be used to obtain reliable first estimates of transit time and concentration, but to improve concentration predictions of degrading contaminants, a dynamic model is probably required.     

基于CA-MAS的水域总有机碳扩散模拟研究

当前水环境污染扩散研究一般基于普通数值模型模拟,忽略了水污染扩散微观驱动力的影响。为能更真实地反映其动态扩散过程,针对水域总有机碳(TOC)扩散机理,基于CA和MAS技术,将影响TOC扩散的自然和社会经济因素抽象为微观的水流Agent、风速Agent、径流量Agent、污水排放口Agent、人工管理Agent以及农业生产地Agent,将研究水域抽象为CA元胞空间,建立了CA-MAS水域总有机碳扩散模型,对水域总有机碳的动态演化过程进行模拟,并以武汉理工大学鉴湖水域作为实验区域,借助NetLogo仿真平台完成了模型的实现与验证。模拟结果表明,该模型基本能够反映水体总有机碳的扩散规律,可以为水环境污染控制提供参考。     

Evaluation of air quality zone classification methods based on ambient air concentration exposure

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO₂ and 8-hr O₃ within a zone that meets the compliance requirements of each method. The first method, the “3 Strike” method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method.

Implications: A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.     

Hierarchical space-time modelling of PM10 pollution

In this paper, we propose a hierarchical spatio-temporal model for daily mean concentrations of PM₁₀ pollution. The main aims of the proposed model are the identification of the sources of variability characterising the PM₁₀ process and the estimation of pollution levels at unmonitored spatial locations. We adopt a fully Bayesian approach, using Monte Carlo Markov Chain algorithms. We apply the model on PM₁₀ data measured at 11 monitoring sites located in the major towns and cities of Italy's Emilia-Romagna Region. The model is designed for areas with PM₁₀ measurements available; the case of PM₁₀ level estimation from emissions data is not handled. The model has been carefully checked using Bayesian p-values and graphical posterior predictive checks. Results show that the temporal random effect is the most important when explaining PM₁₀ levels.