Monte Carlo method applied to modeling copper transport in river sediments View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-12

AUTHORS

Marcela Z. Corazza, Taufik Abrão, Fábio Grandis Lepri, Sonia M. N. Gimenez, Elisabeth Oliveira, Maria Josefa Santos

ABSTRACT

Monte Carlo simulation (MCS) methodology has been applied to explain the variability of parameters for pollutant transport and fate modeling. In this study, the MCS method was used to evaluate the transport and fate of copper in the sediment of the Tibagi River sub-basin tributaries, Southern Brazil. The statistical distribution of the variables was described by a dataset obtained for copper concentration using sequential extraction, organic matter (OM) amount, and pH. The proposed stochastic spatial model for the copper transport in the river sediment was discussed and implemented by the MCS technique using the MatLab 7.3™ mathematical software tool. In order to test some hypotheses, the sediment and the water column in the river ecosystem were considered as compartments. The proposed stochastic spatial model makes it possible to predict copper mobility and associated risks as a function of the organic matter input into aquatic systems. The metal mobility can increase with the OM posing a rising environmental risk. More... »

PAGES

1063-1079

References to SciGraph publications

  • 2011-05. Effects of climate change on thermal properties of lakes and reservoirs, and possible implications in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2009-07. Risk-based assessment of arsenic-affected aquacultural water in blackfoot disease hyperendemic areas in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2011-08. Stochastic analysis of flux and head moments in a heterogeneous aquifer system in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2013-01. Water quantity and quality simulation by improved SWAT in highly regulated Huai River Basin of China in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00477-012-0564-2

    DOI

    http://dx.doi.org/10.1007/s00477-012-0564-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1021014768


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Statistics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Mathematical Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Londrina State University", 
              "id": "https://www.grid.ac/institutes/grid.411400.0", 
              "name": [
                "Chemistry Department, State University of Londrina (DQ-UEL), 86051-990, Londrina, PR, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Corazza", 
            "givenName": "Marcela Z.", 
            "id": "sg:person.01014317732.25", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01014317732.25"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Londrina State University", 
              "id": "https://www.grid.ac/institutes/grid.411400.0", 
              "name": [
                "Electrical Engineering Department, State University of Londrina (UEL), Londrina, PR, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Abr\u00e3o", 
            "givenName": "Taufik", 
            "id": "sg:person.015065544457.52", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015065544457.52"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Fluminense Federal University", 
              "id": "https://www.grid.ac/institutes/grid.411173.1", 
              "name": [
                "Analytical Chemistry Department, Fluminense Federal University, 24020-141, Niter\u00f3i, RJ, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lepri", 
            "givenName": "F\u00e1bio Grandis", 
            "id": "sg:person.01103337300.65", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01103337300.65"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Londrina State University", 
              "id": "https://www.grid.ac/institutes/grid.411400.0", 
              "name": [
                "Chemistry Department, State University of Londrina (DQ-UEL), 86051-990, Londrina, PR, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gimenez", 
            "givenName": "Sonia M. N.", 
            "id": "sg:person.07543667545.60", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07543667545.60"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Sao Paulo", 
              "id": "https://www.grid.ac/institutes/grid.11899.38", 
              "name": [
                "Chemistry Institute, University of S\u00e3o Paulo (IQ-USP), S\u00e3o Paulo, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Oliveira", 
            "givenName": "Elisabeth", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Londrina State University", 
              "id": "https://www.grid.ac/institutes/grid.411400.0", 
              "name": [
                "Chemistry Department, State University of Londrina (DQ-UEL), 86051-990, Londrina, PR, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Santos", 
            "givenName": "Maria Josefa", 
            "id": "sg:person.01203233026.47", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01203233026.47"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.jhydrol.2005.01.021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005128015"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00477-011-0459-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005265656", 
              "https://doi.org/10.1007/s00477-011-0459-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0883-2927(01)00086-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009293756"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ecocom.2006.05.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010239048"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0022-1694(95)02759-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013354350"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0003-2670(94)85088-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014154235"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00477-008-0245-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014959215", 
              "https://doi.org/10.1007/s00477-008-0245-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00477-008-0245-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014959215", 
              "https://doi.org/10.1007/s00477-008-0245-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0048-9697(00)00733-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015647491"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0016-7037(89)90234-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021564593"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0016-7037(89)90234-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021564593"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0304-3800(97)00180-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022683635"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.scitotenv.2007.08.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026414173"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jconhyd.2009.11.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026799864"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.envint.2004.03.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030465577"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.marpolbul.2004.08.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032139177"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0043-1354(02)00240-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033368444"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0043-1354(02)00240-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033368444"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.gca.2004.01.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038439982"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ecolmodel.2007.01.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040018133"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.envpol.2005.11.016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042043624"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00477-010-0414-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043075933", 
              "https://doi.org/10.1007/s00477-010-0414-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00477-011-0546-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046518719", 
              "https://doi.org/10.1007/s00477-011-0546-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0043-1354(00)00379-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050381029"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.chemosphere.2003.11.066", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051497815"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/ac50043a017", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055010914"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/es970481v", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055519214"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/es970481v", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055519214"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1061/(asce)0733-9496(2003)129:4(345)", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1057605997"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/cbo9780511755347", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1098713118"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2012-12", 
        "datePublishedReg": "2012-12-01", 
        "description": "Monte Carlo simulation (MCS) methodology has been applied to explain the variability of parameters for pollutant transport and fate modeling. In this study, the MCS method was used to evaluate the transport and fate of copper in the sediment of the Tibagi River sub-basin tributaries, Southern Brazil. The statistical distribution of the variables was described by a dataset obtained for copper concentration using sequential extraction, organic matter (OM) amount, and pH. The proposed stochastic spatial model for the copper transport in the river sediment was discussed and implemented by the MCS technique using the MatLab 7.3\u2122 mathematical software tool. In order to test some hypotheses, the sediment and the water column in the river ecosystem were considered as compartments. The proposed stochastic spatial model makes it possible to predict copper mobility and associated risks as a function of the organic matter input into aquatic systems. The metal mobility can increase with the OM posing a rising environmental risk.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00477-012-0564-2", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1039987", 
            "issn": [
              "1436-3240", 
              "1436-3259"
            ], 
            "name": "Stochastic Environmental Research and Risk Assessment", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "8", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "26"
          }
        ], 
        "name": "Monte Carlo method applied to modeling copper transport in river sediments", 
        "pagination": "1063-1079", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "642066008206e547fe82425d94bf3d48c0045db8bcde80a0f39f4964f6bfeb95"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00477-012-0564-2"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1021014768"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00477-012-0564-2", 
          "https://app.dimensions.ai/details/publication/pub.1021014768"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T19:57", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8681_00000512.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs00477-012-0564-2"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00477-012-0564-2'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00477-012-0564-2'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00477-012-0564-2'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00477-012-0564-2'


     

    This table displays all metadata directly associated to this object as RDF triples.

    184 TRIPLES      21 PREDICATES      53 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00477-012-0564-2 schema:about anzsrc-for:01
    2 anzsrc-for:0104
    3 schema:author N704be0a95c6742e9b7217e42b07442c6
    4 schema:citation sg:pub.10.1007/s00477-008-0245-3
    5 sg:pub.10.1007/s00477-010-0414-z
    6 sg:pub.10.1007/s00477-011-0459-7
    7 sg:pub.10.1007/s00477-011-0546-9
    8 https://doi.org/10.1016/0003-2670(94)85088-7
    9 https://doi.org/10.1016/0016-7037(89)90234-2
    10 https://doi.org/10.1016/0022-1694(95)02759-9
    11 https://doi.org/10.1016/j.chemosphere.2003.11.066
    12 https://doi.org/10.1016/j.ecocom.2006.05.002
    13 https://doi.org/10.1016/j.ecolmodel.2007.01.004
    14 https://doi.org/10.1016/j.envint.2004.03.001
    15 https://doi.org/10.1016/j.envpol.2005.11.016
    16 https://doi.org/10.1016/j.gca.2004.01.014
    17 https://doi.org/10.1016/j.jconhyd.2009.11.001
    18 https://doi.org/10.1016/j.jhydrol.2005.01.021
    19 https://doi.org/10.1016/j.marpolbul.2004.08.010
    20 https://doi.org/10.1016/j.scitotenv.2007.08.011
    21 https://doi.org/10.1016/s0043-1354(00)00379-1
    22 https://doi.org/10.1016/s0043-1354(02)00240-3
    23 https://doi.org/10.1016/s0048-9697(00)00733-6
    24 https://doi.org/10.1016/s0304-3800(97)00180-4
    25 https://doi.org/10.1016/s0883-2927(01)00086-5
    26 https://doi.org/10.1017/cbo9780511755347
    27 https://doi.org/10.1021/ac50043a017
    28 https://doi.org/10.1021/es970481v
    29 https://doi.org/10.1061/(asce)0733-9496(2003)129:4(345)
    30 schema:datePublished 2012-12
    31 schema:datePublishedReg 2012-12-01
    32 schema:description Monte Carlo simulation (MCS) methodology has been applied to explain the variability of parameters for pollutant transport and fate modeling. In this study, the MCS method was used to evaluate the transport and fate of copper in the sediment of the Tibagi River sub-basin tributaries, Southern Brazil. The statistical distribution of the variables was described by a dataset obtained for copper concentration using sequential extraction, organic matter (OM) amount, and pH. The proposed stochastic spatial model for the copper transport in the river sediment was discussed and implemented by the MCS technique using the MatLab 7.3™ mathematical software tool. In order to test some hypotheses, the sediment and the water column in the river ecosystem were considered as compartments. The proposed stochastic spatial model makes it possible to predict copper mobility and associated risks as a function of the organic matter input into aquatic systems. The metal mobility can increase with the OM posing a rising environmental risk.
    33 schema:genre research_article
    34 schema:inLanguage en
    35 schema:isAccessibleForFree false
    36 schema:isPartOf Ncf83e215c51342d897b99063c92427c0
    37 Nd3567f95fa9542eba328f807c9095227
    38 sg:journal.1039987
    39 schema:name Monte Carlo method applied to modeling copper transport in river sediments
    40 schema:pagination 1063-1079
    41 schema:productId N8446f708a1eb4813abc683422427a361
    42 N91d859987008464bab64788cdfdcca81
    43 Ne6f037bf3eb644f38a28237d2ca98bc3
    44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021014768
    45 https://doi.org/10.1007/s00477-012-0564-2
    46 schema:sdDatePublished 2019-04-10T19:57
    47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    48 schema:sdPublisher Nca738eab71ca49e3a6b51d2da6b13fb2
    49 schema:url http://link.springer.com/10.1007%2Fs00477-012-0564-2
    50 sgo:license sg:explorer/license/
    51 sgo:sdDataset articles
    52 rdf:type schema:ScholarlyArticle
    53 N442d0c98cc5e45609849dff6ffd366f8 schema:affiliation https://www.grid.ac/institutes/grid.11899.38
    54 schema:familyName Oliveira
    55 schema:givenName Elisabeth
    56 rdf:type schema:Person
    57 N630f041507c144d184635e3b61b0e88a rdf:first sg:person.015065544457.52
    58 rdf:rest Ne660633798014d01b6f9c6d601e9e92f
    59 N6b849f3f68ed44fe80f7d8bd0a07ee8c rdf:first sg:person.07543667545.60
    60 rdf:rest N7e3b7d30b46b4bf09864e84c5ad4fcfc
    61 N704be0a95c6742e9b7217e42b07442c6 rdf:first sg:person.01014317732.25
    62 rdf:rest N630f041507c144d184635e3b61b0e88a
    63 N7e3b7d30b46b4bf09864e84c5ad4fcfc rdf:first N442d0c98cc5e45609849dff6ffd366f8
    64 rdf:rest N85391b7701564f80adc4e63bf5fe8aa8
    65 N8446f708a1eb4813abc683422427a361 schema:name readcube_id
    66 schema:value 642066008206e547fe82425d94bf3d48c0045db8bcde80a0f39f4964f6bfeb95
    67 rdf:type schema:PropertyValue
    68 N85391b7701564f80adc4e63bf5fe8aa8 rdf:first sg:person.01203233026.47
    69 rdf:rest rdf:nil
    70 N91d859987008464bab64788cdfdcca81 schema:name dimensions_id
    71 schema:value pub.1021014768
    72 rdf:type schema:PropertyValue
    73 Nca738eab71ca49e3a6b51d2da6b13fb2 schema:name Springer Nature - SN SciGraph project
    74 rdf:type schema:Organization
    75 Ncf83e215c51342d897b99063c92427c0 schema:volumeNumber 26
    76 rdf:type schema:PublicationVolume
    77 Nd3567f95fa9542eba328f807c9095227 schema:issueNumber 8
    78 rdf:type schema:PublicationIssue
    79 Ne660633798014d01b6f9c6d601e9e92f rdf:first sg:person.01103337300.65
    80 rdf:rest N6b849f3f68ed44fe80f7d8bd0a07ee8c
    81 Ne6f037bf3eb644f38a28237d2ca98bc3 schema:name doi
    82 schema:value 10.1007/s00477-012-0564-2
    83 rdf:type schema:PropertyValue
    84 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    85 schema:name Mathematical Sciences
    86 rdf:type schema:DefinedTerm
    87 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
    88 schema:name Statistics
    89 rdf:type schema:DefinedTerm
    90 sg:journal.1039987 schema:issn 1436-3240
    91 1436-3259
    92 schema:name Stochastic Environmental Research and Risk Assessment
    93 rdf:type schema:Periodical
    94 sg:person.01014317732.25 schema:affiliation https://www.grid.ac/institutes/grid.411400.0
    95 schema:familyName Corazza
    96 schema:givenName Marcela Z.
    97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01014317732.25
    98 rdf:type schema:Person
    99 sg:person.01103337300.65 schema:affiliation https://www.grid.ac/institutes/grid.411173.1
    100 schema:familyName Lepri
    101 schema:givenName Fábio Grandis
    102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01103337300.65
    103 rdf:type schema:Person
    104 sg:person.01203233026.47 schema:affiliation https://www.grid.ac/institutes/grid.411400.0
    105 schema:familyName Santos
    106 schema:givenName Maria Josefa
    107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01203233026.47
    108 rdf:type schema:Person
    109 sg:person.015065544457.52 schema:affiliation https://www.grid.ac/institutes/grid.411400.0
    110 schema:familyName Abrão
    111 schema:givenName Taufik
    112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015065544457.52
    113 rdf:type schema:Person
    114 sg:person.07543667545.60 schema:affiliation https://www.grid.ac/institutes/grid.411400.0
    115 schema:familyName Gimenez
    116 schema:givenName Sonia M. N.
    117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07543667545.60
    118 rdf:type schema:Person
    119 sg:pub.10.1007/s00477-008-0245-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014959215
    120 https://doi.org/10.1007/s00477-008-0245-3
    121 rdf:type schema:CreativeWork
    122 sg:pub.10.1007/s00477-010-0414-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1043075933
    123 https://doi.org/10.1007/s00477-010-0414-z
    124 rdf:type schema:CreativeWork
    125 sg:pub.10.1007/s00477-011-0459-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005265656
    126 https://doi.org/10.1007/s00477-011-0459-7
    127 rdf:type schema:CreativeWork
    128 sg:pub.10.1007/s00477-011-0546-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046518719
    129 https://doi.org/10.1007/s00477-011-0546-9
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1016/0003-2670(94)85088-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014154235
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1016/0016-7037(89)90234-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021564593
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1016/0022-1694(95)02759-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013354350
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1016/j.chemosphere.2003.11.066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051497815
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1016/j.ecocom.2006.05.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010239048
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1016/j.ecolmodel.2007.01.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040018133
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1016/j.envint.2004.03.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030465577
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1016/j.envpol.2005.11.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042043624
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1016/j.gca.2004.01.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038439982
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1016/j.jconhyd.2009.11.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026799864
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1016/j.jhydrol.2005.01.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005128015
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1016/j.marpolbul.2004.08.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032139177
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1016/j.scitotenv.2007.08.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026414173
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1016/s0043-1354(00)00379-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050381029
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1016/s0043-1354(02)00240-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033368444
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1016/s0048-9697(00)00733-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015647491
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1016/s0304-3800(97)00180-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022683635
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1016/s0883-2927(01)00086-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009293756
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1017/cbo9780511755347 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098713118
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1021/ac50043a017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055010914
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1021/es970481v schema:sameAs https://app.dimensions.ai/details/publication/pub.1055519214
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1061/(asce)0733-9496(2003)129:4(345) schema:sameAs https://app.dimensions.ai/details/publication/pub.1057605997
    174 rdf:type schema:CreativeWork
    175 https://www.grid.ac/institutes/grid.11899.38 schema:alternateName University of Sao Paulo
    176 schema:name Chemistry Institute, University of São Paulo (IQ-USP), São Paulo, Brazil
    177 rdf:type schema:Organization
    178 https://www.grid.ac/institutes/grid.411173.1 schema:alternateName Fluminense Federal University
    179 schema:name Analytical Chemistry Department, Fluminense Federal University, 24020-141, Niterói, RJ, Brazil
    180 rdf:type schema:Organization
    181 https://www.grid.ac/institutes/grid.411400.0 schema:alternateName Londrina State University
    182 schema:name Chemistry Department, State University of Londrina (DQ-UEL), 86051-990, Londrina, PR, Brazil
    183 Electrical Engineering Department, State University of Londrina (UEL), Londrina, PR, Brazil
    184 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...