A Robust Statistical Analysis Approach for Pollutant Loadings in Urban Rivers

Chen, B.; Jiang, Ping; Chen, Y.; Howboldt, K.

doi:10.3808/jei.201000176

Cited by 21 publications

(15 citation statements)

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“…The obtained information can be used for Monte Carlo simulation to generate a set of data required for risk assessment. Some other parameters in Equation (1) are usually assumed as constants, such as the chemical-specific dermal permeability constant as 0.6 cm/h for dermal contact, absorption into the bloodstream as 6% and the conversion factor as 10 −6 kg/mg [18,25]. According to a survey, children mainly play in the water at Site 4.…”

Section: Other Parametersmentioning

confidence: 99%

“…Due to this reason, in the summer of 2005, the Northeast Avalon Atlantic Coastal Action Program (NAACAP) initiated a monitoring project on the Nut Brook stream system. In that report, the collection and analysis of water and sediment samples had been done for the purpose of compiling baseline information, helping NAACAP to determine and analyze the impact extent of environmental damage coming from heavy metal loadings and extensive sedimentation [19,25].…”

Section: Case Studymentioning

confidence: 99%

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Using Statistical and Probabilistic Methods to Evaluate Health Risk Assessment: A Case Study

Chen

2014

Toxics

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Abstract:The toxic chemical and heavy metals within wastewater can cause serious adverse impacts on human health. Health risk assessment (HRA) is an effective tool for supporting decision-making and corrective actions in water quality management. HRA can also help people understand the water quality and quantify the adverse effects of pollutants on human health. Due to the imprecision of data, measurement error and limited available information, uncertainty is inevitable in the HRA process. The purpose of this study is to integrate statistical and probabilistic methods to deal with censored and limited numbers of input data to improve the reliability of the non-cancer HRA of dermal contact exposure to contaminated river water by considering uncertainty. A case study in the Kelligrews River in St. John's, Canada, was conducted to demonstrate the feasibility and capacity of the proposed approach. Five heavy metals were selected to evaluate the risk level, including arsenic, molybdenum, zinc, uranium and manganese. The results showed that the probability of the total hazard index of dermal exposure exceeding 1 is very low, and there is no obvious evidence of risk in the study area.

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Section: Other Parametersmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Using Statistical and Probabilistic Methods to Evaluate Health Risk Assessment: A Case Study

Chen

2014

Toxics

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“…Uncertainty is one of the major hinders in improving the efficiency of MSW management, which may arise from a variety of possible sources. Such sources include but not limited to incomplete information, errors in sampling, subjective judgment, random variations of and dynamic interactions among operating factors, approximations and assumptions in measurement, and changes of environmental conditions (Huang et al, 1993;Chen et al, 2008b;Ping et al, 2010). The uncertainties lead to difficulties in developing optimization models for supporting decision making in MSW management and impair the confidence of decisions.…”

Section: Introductionmentioning

confidence: 96%

FSILP: Fuzzy-stochastic-interval linear programming for supporting municipal solid waste management

Chen

2011

Journal of Environmental Management

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“…In response to the above concerns, a number of optimization methods were developed for allocating and managing water in more efficient ways under uncertainty (Luo et al 2003(Luo et al , 2007Jung et al 2006;Wu et al 2008;He et al 2010;Ping et al 2010;Jing and Chen 2011). Among these methods, the two-stage stochastic programming (TSP), as a kind of stochastic optimization method, was widely used for dealing with randomness in water resources management systems (Loucks et al 1981;Edirisinghe and Ziemba 1994;Wagner et al 1994;Huang and Loucks 2000;Li et al 2007;Lu et al 2009).…”

Section: Introductionmentioning

confidence: 98%

Inexact two-stage stochastic partial programming: application to water resources management under uncertainty

Fan

Huang

Guo

et al. 2011

Stoch Environ Res Risk Assess

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In this study, an inexact two-stage stochastic partial programming (ITSPP) method is developed for tackling uncertainties presented as intervals and partial probability distributions. A scenario-based interactive algorithm is proposed to solve the ITSPP model. This algorithm is implemented through: (i) obtaining extreme points of the linear partial information (LPI); (ii) generating an inexact two-stage stochastic programming (ITSP) model under each extreme point; (iii) solving ITSP models through interactive algorithm proposed by Huang and Loucks (Civil Eng Environ Syst 17: 2000); (iv) acquiring the interval solutions under each extreme point and the final optimal interval for the objective function. The developed method is applied to a case study for waterresources planning. The modelling results can generate a series of decision alternatives under various system conditions, and thus help decision makers identify the desired water-resources management policies under uncertainty.

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A Robust Statistical Analysis Approach for Pollutant Loadings in Urban Rivers

Cited by 21 publications

References 0 publications

Using Statistical and Probabilistic Methods to Evaluate Health Risk Assessment: A Case Study

Using Statistical and Probabilistic Methods to Evaluate Health Risk Assessment: A Case Study

FSILP: Fuzzy-stochastic-interval linear programming for supporting municipal solid waste management

Inexact two-stage stochastic partial programming: application to water resources management under uncertainty

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