Mathematical Model for Pollution Source Identification in Rivers

Mazaheri, Mehdi; Samani, Jamal Mohammad Vali; Samani, Hossein Mohammad Vali

doi:10.1080/15275922.2015.1059391

Cited by 34 publications

(11 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results of the backward model using real case of Severn River show that the release history of the injected tracer can be determined accurately. This results were given earlier by Mazaheri et al In contrast, they used a direct solution based on Tikhonov regulation (Mazaheri et al, 2015).…”

Section: Discussionmentioning

confidence: 59%

Location and release time identification of pollution point source in river networks based on the Backward Probability Method

Ghane

Mazaheri

Samani

2016

Journal of Environmental Management

Self Cite

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 59%

Location and release time identification of pollution point source in river networks based on the Backward Probability Method

Ghane

Mazaheri

Samani

2016

Journal of Environmental Management

Self Cite

View full text Add to dashboard Cite

“…Compared to numerous studies on pollutant source identification of in groundwater, only relatively few studies on solving an inverse source problem in surface waters can be found in literature (El Badia and Hamdi, 2007, Hamdi, 2009, Hamdi, 2016, Andrle and El Badia, 2012, Cheng and Jia, 2010, Mazaheri et al, 2015, Yang et al, 2016, Wang et al, 2018. While, the pollutant transport in rivers tends to be more advection-dominated than groundwater and, subsequently pollutant substance transported faster and further, which may lead to partial capturing of the pollution plume at the observation points.…”

Section: Introduction 44mentioning

confidence: 99%

An Innovative Framework for Real Time Monitoring of Pollutant Point Sources in River Networks

Mazaheri¹,

Jmv²,

Boano³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The simultaneous identification of location and source release history in complex river networks is a very complicated ill-posed problem, particularly in a case of multiple unknown pollutant sources with time-varying release pattern. This study presents an innovative method for simultaneous identification of the number, locations and release histories of multiple pollutant point sources in a river network using minimum observation data. Considering two different type of monitoring stations with an adaptive arrangement as well as real-time data collection at those stations and using a reliable numerical ﬂow and transport model, at first the number and suspected reach of presence of pollutant sources are determined. Then the source location and its intensity function is calculated by solving inverse source problem using a geostatistical approach. A case study with three different scenarios in terms of the number, release time and location of pollutant sources are discussed, concerning a river network with unsteady and non-uniform flow. Results showed the capability of the proposed method in identifying of sought source characteristics even in complicated cases with simultaneous activity of multiple pollutant sources.

show abstract

“…Singh and Beck () used the least squares method to optimize the results and combined the algorithm with an optimization method to determine pollution source parameters. An optimization method considers pollution source parameters as variables to be optimized (Mazaheri & Samani, ). This method can acquire the required parameters by minimizing the differences between the calculated and measured data.…”

Section: Introductionmentioning

confidence: 99%

“…Statistical methods consider source term parameters random variables and use distribution functions to predict these variables. Typically, stochastic differential equations are the main underlying equations in this approach (Mazaheri & Samani, ). Moreover, this feature is an important aspect of statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Inversion of multiple parameters for river pollution accidents using emergency monitoring data

Jing

Yang

Zhou

et al. 2019

Water Environment Research

View full text Add to dashboard Cite

This study presented a new inversion algorithm on the basis of least squares method for river point pollution sources. A series of numerical experiments was conducted to verify the accuracy of the proposed inversion algorithm. The general solution of the one‐dimensional (1D) pollutant transport equation is the governing equation of this method. Pollutant concentrations at various hypothetical monitoring points should be observed at two moments to obtain the point pollution source parameters, namely, velocity, longitudinal dispersion coefficient, emission moment of the pollution source, emission location, and emission intensity. Monitoring error was considered a random noise in the numerical experiments. The inversion result error was 3.69% when the monitoring error was 5%. Although the monitoring error reached 20%, the maximum error of inversion parameters was 8.58%. In addition, the effects of river flow velocity, contaminant decay rates, monitoring point setting, and time intervals between two sampling groups were analyzed in hypothetic cases. Practitioner points Present a new inversion algorithm for river point pollution sources. Multiple parameters can be obtained by inversion. The unknown river velocity does not affect the result of parameter inversion. Different levels of pollutant concentration monitoring errors are considered.

show abstract

Mathematical Model for Pollution Source Identification in Rivers

Cited by 34 publications

References 46 publications

Location and release time identification of pollution point source in river networks based on the Backward Probability Method

Location and release time identification of pollution point source in river networks based on the Backward Probability Method

An Innovative Framework for Real Time Monitoring of Pollutant Point Sources in River Networks

Inversion of multiple parameters for river pollution accidents using emergency monitoring data

Contact Info

Product

Resources

About