Simulation-based risk assessment of contaminated sites under remediation scenarios, planning periods, and land-use patterns—a Canadian case study

Risk assessment of contaminated sites is crucial for quantifying adverse impacts on human health and the environment. It also provides effective decision support for remediation and management of such sites. This study presents an integrated approach for environmental and health risk assessment of subsurface contamination through the incorporation of a multiphase multicomponent modeling system within a general risk assessment framework. The method is applied to a petroleum-contaminated site in western Canada. Three remediation scenarios with different efficiencies (0, 60, and 90%) and planning periods (10, 20, 40, 60, and 80 years later) are examined for each of the five potential land-use plans of the study site. Then three risky zones with different temporal and spatial distributions are identified based on the local environmental guidelines and the excess lifetime cancer risk criteria. The obtained results are useful for assessing potential human health effects when the groundwater is used for drinking water supply. They are also critical for evaluating environmental impacts when the groundwater is used for irrigation, stockbreeding, fish culture, or when the site remains the status quo. Moreover, the results indicate that the proposed method can effectively identify risky zones with different risk levels under various remediation actions, planning periods, and land-use patterns.     

ITOM: an interval-parameter two-stage optimization model for stochastic planning of water resources systems

Planning of water resources systems is often associated with many uncertain parameters and their interrelationships are complicated. Stochastic planning of water resources systems is vital under changing climate and increasing water scarcity. This study proposes an interval-parameter two-stage optimization model (ITOM) for water resources planning in an agricultural system under uncertainty. Compared with other optimization techniques, the proposed modeling approach offers two advantages: first, it provides a linkage to pre-defined water policies, and; second, it reflects uncertainties expressed as probability distributions and discrete intervals. The ITOM is applied to a case study of irrigation planning. Reasonable solutions are obtained, and a variety of decision alternatives are generated under different combinations of water shortages. It provides desired water-allocation patterns with respect to maximum system benefits and highest feasibility. Moreover, the modeling results indicate that an optimistic water policy corresponding to higher agricultural income may be subject to a higher risk of system-failure penalties; while, a too conservative policy may lead to wastage of irrigation supplies.     

Projected increases in near-surface air temperature over Ontario,Canada: a regional climate modeling approach

Climate Dynamics - As the biggest economy in Canada, the Province of Ontario is now suffering many consequences caused by or associated with global warming, such as frequent and intense heat waves,...     

Total consumption controlled water allocation management for multiple sources and users with inexact fuzzy chance-constrained programming: a case study of Tianjin,China

Environmental and ecological issues caused by water resources crisis have brought enormous challenges to the sustainable development of water-deficient area. Water resources allocation management balancing the relationship between the social-economic development and the ecological environment has become a hot topic in recent years. In this paper, an inexact fuzzy chance-constrained programming (IFCCP) approach is proposed for regional water resource allocation optimization with the aim of promoting the harmonious development of the social economic and the ecological environment, improving water utilization efficiency, and realizing water resources consumption control under uncertainties. The method is incorporated with interval parameter programming, fuzzy programming, and chance-constrained programming, for handling system uncertainties and balancing the optimal objectives with the risk of violating system constraints. Under this framework, an IFCCP model for water resources allocation management was successfully formulated and applied to a typical water-deficit area, Tianjin, China, for obtaining a better water resources plan among multiple users under resources and environmental limitation. Different total water consumption control policies are designed for assessing regional water allocation schemes. The results indicated that the gap of supply and demand will only be solved by foreign water, the transferred water from Luan River and Changjiang River would still be the main supplier in planning horizon. Moreover, the strict total water consumption control policy would guarantee the water requirement of ecological environment, lead to changes in the structure of water supply, actively guide on water conservation, and promote the large-scale utilization of desalted water and recycle water.     

A Comparison Study of Synkinematic Illite Isolation,Quantitative X-ray Powder Diffraction,and K-Ar Dating for Direct Fault Gouge Analyses

K-Ar dating of synkinematic illite is increasingly recognized as a central method to constrain the timing of shallow crustal faulting. Methods of efficient sample preparation and quantitative identification of illite polytypes are critical to acquiring K-Ar isotope data for authigenic clays. In this respect, we compared the commonly used clay size separation method through centrifugation with vacuum filtration technology, showing that the former is prone to extract fractions with finer particle ...     

板块差异漂移机制分析及其动力学模型

应用质点动力学原理,推导地出地幔流体在径向运移中受地球自旋惯性场作用而西移的速率及位移公式。根据地球自旋惯性系统特点设计地幔流体的２维实验模型,推导赤道面附近地幔流体的运动公式,并推广到３维。最后得出地壳块体被动西漂、地幔软流体环流及地球各层圈差速旋转等结论。     

Resources and environmental systems management under synchronic interval uncertainties

Resources and environmental systems management (RESM) is challenged by the synchronic effects of interval uncertainties in the related practices. The synchronic interval uncertainties are misrepresented as random variables, fuzzy sets, or interval numbers in conventional RESM programming techniques including stochastic programming. This may lead to ineffectiveness of resources allocation, high costs of recourse measures, increased risks of unreasonable decisions, and decreased optimality of system profits. To fill the gap of few corresponding studies, a synchronic interval linear programming (SILP) method is proposed in this study. The proposition of interval sets and interval functions and coupling them with linear programming models lead to development of an SILP model for RESM. This enables incorporation of interval uncertainties in resource constraints and synchronic interval uncertainties in the programming objective into the optimization process. An analysis of the distribution-independent geometric properties of the feasible regions of SILP models results in proposition of constraint violation likelihoods. The tradeoff between system optimality and constraint violation is analyzed. The overall optimality of SILP systems under synchronic intervalness is quantified through proposition of integrally optimal solutions. Integration of these efforts leads to a violation-constrained interval integral method for optimization of RESM systems under synchronic interval uncertainties. Comparisons with selected existing methods reveal the effectiveness of SILP at eliminating negativity of synchronic intervalness, enabling risk management of and achieving overall optimality of RESM systems, and enhancing the reliability of optimization techniques for RESM problems. The exploited framework for analyzing synchronic interval uncertainties in RESM systems is helpful for addressing synchronisms of other uncertainties such as randomness or fuzziness and avoiding the resultant decision mistakes and disasters due to neglecting them.     

Modeling of state of vegetation and soil erosion over large areas

A vegetation-erosion model was developed to assess the extent of soil erosion and development trend of vegetation in the context of existing and contemplated vegetation-based soil erosion controls under different climatic, topographical and soil conditions. The model recognizes four vegetation-mediated soil erosion states： （i） an expanding vegetation coverage coupled with reduced erosion （C）, （ii） a deteriorating vegetation coverage coupled with increased erosion （A）, （iii） two transitional states between A and C, one with increasing erosion and vegetation coverage （B） and the other with decreasing erosion and vegetation coverage （D）. With the model, the vegetation-erosion state of any particular area can be quantitatively described, by way of a vegetation-erosion chart, for varying climate, soil and topographic conditions, as demonstrated for the Xishan region, the East River basin, the Wangjiagou and Anjiagou watersheds （Loess Plateau）, and the Xiaojiang watersheds （hot and dry valleys in the upper Yangtze River basin） in China. This paper presents the principles and results of area-specific investigations that track the fractions of the areas covered by vegetation and experiencing soil erosion （with soil loss determined in t/km^2yr）. This is done within the context of local soil erosion control initiatives via re-vegetation efforts, or the lack thereof, over the course of 30 years. The effectiveness of reforestation and erosion-control measures vary under different climatic, topographical and soil conditions. The vegetation may be quickly restored in the hot and wet East River basin but is very difficult on the dry and cold Loess Plateau. In the hot and dry valleys the vegetation can be restored if erosion is controlled and intensive reforestations for small watersheds are performed.     

Discrete principal‐monotonicity inference for hydro‐system analysis under irregular nonlinearities,data uncertainties,and multivariate dependencies. Part I: methodology development

Hydrological system analyses are challenged by complexities of irregular nonlinearities, data uncertainties, and multivariate dependencies. Among them, the irregular nonlinearities mainly represent inexistence of regular functions for robustly simulating highly complicated relationships between variables. Few existing studies can enable reliable simulation of hydrological processes under these complexities. This may lead to decreased robustness of the constructed models, unfeasibility of suggestions for human activities, and damages to socio‐economy and eco‐environment. In the first of two companion papers, a discrete principal‐monotonicity inference (DPMI) method is proposed for hydrological systems analysis under these complexities. Normalization of non‐normally distributed samples and invertible restoration of modelling results are enabled through a discrete distribution transformation approach. To mitigate data uncertainties, statistical inference is employed to assess the significance of differences among samples. The irregular nonlinearity between the influencing factors (i.e. predictors) and the hydrological variable of interest (i.e. the predictand) is interpreted as piecewise monotonicity. Monotonicity is further represented as principal monotonicity under multivariate dependencies. Based on stepwise classification and cluster analyses, all paired samples representing the responsive relationship between the predictors and the predictand are discretized as a series of end nodes. A prediction approach is advanced for estimating the predictand value given any combination of predictors. The DPMI method can reveal evolvement rules of hydrological systems under these complexities. Reliance of existing hydro‐system analysis methods on predefined functional forms is removed, avoiding artificial disturbances, e.g. empiricism in selecting model functions under irregular nonlinearities, on the modelling process. Both local and global significances of predictors in driving the evolution of hydrological variables are identified. An analysis of interactions among these complexities is also achieved. The understanding obtained from the DPMI process and associated results can facilitate hydrological prediction, guide water resources management, improve hydro‐system analysis methods, or support hydrological systems analysis in other cases. The effectiveness and advantages of DPMI will be demonstrated through a case study of streamflow simulation in Xingshan Watershed, China, in another paper. Copyright © 2016 John Wiley & Sons, Ltd.