Estimation of river pollution source using the space-time radial basis collocation method

Abstract

a b s t r a c tRiver contaminant source identification problems can be formulated as an inverse model to estimate the missing source release history from the observed contaminant plume. In this study, the identification of pollution sources in rivers, where strong advection is dominant, is solved by the global space-time radial basis collocation method (RBCM). To search for the optimal shape parameter and scaling factor which strongly determine the accuracy of the RBCM method, a new cost function based on the residual errors of not only the observed data but also the specified governing equation, the initial and boundary conditions, was constructed for the k-fold cross-validation technique. The performance of three global radial basis functions, Hardy's multiquadric, inverse multiquadric and Gaussian, were also compared in the test cases. The numerical results illustrate that the new cost function is a good indicator to search for near-optimal solutions. Application to a real polluted river shows that the source release history is reasonably recovered, demonstrating that the RBCM with the k-fold cross-validation is a powerful tool for source identification problems in advectiondominated rivers.

Health Risk Assessment of Heavy Metals in Surface Water near a Uranium Tailing Pond in Jiangxi Province, South China

“…Rivers are of both ecological and economic significances to society [1]. However, river waters are quite vulnerable to pollution because they are naturally open, easily accessible, and substantially used in agricultural, industrial, and municipal processes [2]. China's per capita arable land area and per capita water resources are less than half and about one quarter of their world average, respectively [3].…”

“…Radiotoxicity is through radioactive substances that enter the human body continue to emit multiple rays in the body to cause internal radiation. Recently, although researchers have gradually paid more attention on heavy metal risk assessment over China, limited studies have been conducted to evaluate the human health risk of riverine heavy metals and radionuclides in Jiangxi Province [2,25].…”

Estimativa da Forma de Lançamento de Poluentes em Cursos Hídricos Empregando Expansões em Autofunções e Inferência Bayesiana

“…Existem diversos trabalhos na literatura que analisam a dispersão de poluentes em rios, encontrando-se diferentes abordagens, tanto na resolução do problema direto quanto na análise inversa (Hamdi (2009), Wang (2012, Verdière (2013), Parolin (2015), Li (2016), Kulbay (2016) e Lugão(2017). Estas pesquisas envolvem a recuperação de fontes de poluição em bacias hidrográficas, considerando domínios finitos e infinitos, a estimativa de parâmetros como localização, intensidade e duração do despejo de efluentes cuja forma funcional é considerada conhecida a priori, além de estimativas de funções que representam a maneira com que um determinado poluente é inserido no meio hídrico.…”

Utilization of a Bayesian probabilistic inferential framework for contamination source identification in river environment

“…Among them, one of the most serious threats is the contamination injection of toxic chemical compounds into river. For instance, in November 2005, approximately 100 tons of benzene were spilled into Songhuajiang River, China and caused severe social and ecological problems [2]. Once occurring, quick and accurate identification of the contamination sources is essential to manage the emergency response and mitigate environmental consequences in the river system [3].…”

“…In the past two decades, several deterministic and stochastic methods have been developed successively to facilitate the solution to the source identification problem for identifying the release locations of unknown sources, and estimate the release time and emission loads [2][3][4][5][6][7][8][9][10][11]. Among them, Bayesian approach has been applied widely for source identification in recent years as it has a number of distinctive attributes [10,11].…”