Developing a SINTACS-based method to map groundwater multi-pollutant vulnerability using evolutionary algorithms

In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS m...

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Published inEnvironmental science and pollution research international Vol. 28; no. 7; pp. 7854 - 7869
Main Authors Jahromi, Maryam Naghdizadegan, Gomeh, Zinat, Busico, Gianluigi, Barzegar, Rahim, Samany, Najmeh Neysani, Aalami, Mohammad Taghi, Tedesco, Dario, Mastrocicco, Micol, Kazakis, Nerantzis
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021
Springer Nature B.V
Subjects
Algorithms
Anthropogenic factors
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Correlation analysis
Correlation coefficient
Correlation coefficients
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental Monitoring
Environmental Pollutants
Environmental science
Evolutionary algorithms
Gene mapping
Genetic algorithms
Groundwater
Italy
Land use
Nitrates
Nitrates - analysis
Parameter modification
Pollutants
pollution
Pollution sources
Ratings
Research Article
sulfates
Swarm intelligence
Waste Water Technology
Water Management
Water Pollutants, Chemical - analysis
Water Pollution Control
Italy
Groundwater vulnerability
Multi-pollutant
SINTACS
Anthropogenic activities
Campanian Plain
Evolutionary algorithm
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Abstract In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO 3 ) and sulfate (SO 4 ) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from − 0.35 to 0.43 for NO 3 and from − 0.28 to 0.33 for SO 4 after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO 3 and SO 4 pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO 3 and the Wilcoxon-GA-based SINTACS vulnerability map for SO 4 ). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.
AbstractList In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO3) and sulfate (SO4) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from - 0.35 to 0.43 for NO3 and from - 0.28 to 0.33 for SO4 after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO3 and SO4 pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO3 and the Wilcoxon-GA-based SINTACS vulnerability map for SO4). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO3) and sulfate (SO4) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from - 0.35 to 0.43 for NO3 and from - 0.28 to 0.33 for SO4 after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO3 and SO4 pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO3 and the Wilcoxon-GA-based SINTACS vulnerability map for SO4). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.
In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO ) and sulfate (SO ) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from - 0.35 to 0.43 for NO and from - 0.28 to 0.33 for SO after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO and SO pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO and the Wilcoxon-GA-based SINTACS vulnerability map for SO ). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.
In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO 3 ) and sulfate (SO 4 ) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from − 0.35 to 0.43 for NO 3 and from − 0.28 to 0.33 for SO 4 after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO 3 and SO 4 pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO 3 and the Wilcoxon-GA-based SINTACS vulnerability map for SO 4 ). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.
In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO₃) and sulfate (SO₄) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from − 0.35 to 0.43 for NO₃ and from − 0.28 to 0.33 for SO₄ after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO₃ and SO₄ pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO₃ and the Wilcoxon-GA-based SINTACS vulnerability map for SO₄). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.
In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO3) and sulfate (SO4) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from − 0.35 to 0.43 for NO3 and from − 0.28 to 0.33 for SO4 after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO3 and SO4 pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO3 and the Wilcoxon-GA-based SINTACS vulnerability map for SO4). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.
Author Kazakis, Nerantzis
Gomeh, Zinat
Busico, Gianluigi
Samany, Najmeh Neysani
Barzegar, Rahim
Mastrocicco, Micol
Jahromi, Maryam Naghdizadegan
Aalami, Mohammad Taghi
Tedesco, Dario
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  organization: Faculty of Geography, Department of Remote Sensing and GIS, University of Tehran
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  surname: Busico
  fullname: Busico, Gianluigi
  organization: Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”
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  givenname: Rahim
  surname: Barzegar
  fullname: Barzegar, Rahim
  organization: Department of Bioresource Engineering, McGill University, Faculty of Civil Engineering, University of Tabriz
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  givenname: Najmeh Neysani
  surname: Samany
  fullname: Samany, Najmeh Neysani
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  fullname: Aalami, Mohammad Taghi
  organization: Faculty of Civil Engineering, University of Tabriz
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  givenname: Dario
  surname: Tedesco
  fullname: Tedesco, Dario
  organization: Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”
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  givenname: Micol
  surname: Mastrocicco
  fullname: Mastrocicco, Micol
  organization: Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”
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  givenname: Nerantzis
  surname: Kazakis
  fullname: Kazakis, Nerantzis
  organization: Department of Geology, Lab. of Engineering Geology & Hydrogeology, Aristotle University of Thessaloniki
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33040292$$D View this record in MEDLINE/PubMed
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Thu Apr 24 22:51:49 EDT 2025
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Issue 7
Keywords Groundwater vulnerability
Multi-pollutant
SINTACS
Anthropogenic activities
Campanian Plain
Evolutionary algorithm
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PublicationTitle Environmental science and pollution research international
PublicationTitleAbbrev Environ Sci Pollut Res
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Publisher Springer Berlin Heidelberg
Springer Nature B.V
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Snippet In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to...
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SubjectTerms Algorithms
Anthropogenic factors
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Correlation analysis
Correlation coefficient
Correlation coefficients
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental Monitoring
Environmental Pollutants
Environmental science
Evolutionary algorithms
Gene mapping
Genetic algorithms
Groundwater
Italy
Land use
Nitrates
Nitrates - analysis
Parameter modification
Pollutants
pollution
Pollution sources
Ratings
Research Article
sulfates
Swarm intelligence
Waste Water Technology
Water Management
Water Pollutants, Chemical - analysis
Water Pollution Control
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Title Developing a SINTACS-based method to map groundwater multi-pollutant vulnerability using evolutionary algorithms
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