Temperature-Electrical Conductivity Relation of Water for Environmental Monitoring and Geophysical Data Inversion View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-08

AUTHORS

Masaki Hayashi

ABSTRACT

Electrical conductivity (EC) is widely used for monitoring the mixing of fresh water and saline water, separating stream hydrographs, and geophysical mapping of contaminated groundwater. The measured EC values at various temperatures need to be reported as corresponding to a standard temperature because EC is dependent on temperature. An arbitrary constant is commonly used for temperature compensation assuming that EC-temperature relation is linear (for example 2% increase of EC per 1 degrees C). This paper examines the EC-temperature relation of natural waters having vastly different compositions and salinities. EC-temperature relation was slightly nonlinear in a temperature range 0-30 degrees C, but the linear equation approximated the relation reasonably well. The temperature compensation factor corresponding to 25 degrees C ranged between 0.0175 and 0.0198. When the mean value 0.0187 was used, the error of estimating EC at 25 degrees C from EC at 10 degrees C was less than about 2% for all samples tested. Temperature compensation factors vary substantially depending on the choice of standard temperature. Therefore, a care must be taken when standard temperatures different from 25 degrees C are used. More... »

PAGES

119-128

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/b:emas.0000031719.83065.68

DOI

http://dx.doi.org/10.1023/b:emas.0000031719.83065.68

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002323842

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15327152


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/2008/0406", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Geography and Environmental Geoscience", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Calibration", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Electric Conductivity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Environmental Monitoring", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Temperature", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Water", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Calgary", 
          "id": "https://www.grid.ac/institutes/grid.22072.35", 
          "name": [
            "Department of Geology and Geophysics, University of Calgary, Calgary, Alberta, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hayashi", 
        "givenName": "Masaki", 
        "id": "sg:person.01010002376.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010002376.12"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s100400100146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004994296", 
          "https://doi.org/10.1007/s100400100146"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-1694(95)02895-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006078109"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-1694(89)90109-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010805810"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-1694(89)90109-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010805810"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0016-7037(77)90186-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014702593"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0016-7037(77)90186-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014702593"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02442136", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021753040", 
          "https://doi.org/10.1007/bf02442136"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02442136", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021753040", 
          "https://doi.org/10.1007/bf02442136"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-1694(86)90049-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035748791"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4141/cjss83-008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037797568"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0926-9851(02)00146-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042163116"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac00140a003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054975301"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/j100721a006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055677266"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004-08", 
    "datePublishedReg": "2004-08-01", 
    "description": "Electrical conductivity (EC) is widely used for monitoring the mixing of fresh water and saline water, separating stream hydrographs, and geophysical mapping of contaminated groundwater. The measured EC values at various temperatures need to be reported as corresponding to a standard temperature because EC is dependent on temperature. An arbitrary constant is commonly used for temperature compensation assuming that EC-temperature relation is linear (for example 2% increase of EC per 1 degrees C). This paper examines the EC-temperature relation of natural waters having vastly different compositions and salinities. EC-temperature relation was slightly nonlinear in a temperature range 0-30 degrees C, but the linear equation approximated the relation reasonably well. The temperature compensation factor corresponding to 25 degrees C ranged between 0.0175 and 0.0198. When the mean value 0.0187 was used, the error of estimating EC at 25 degrees C from EC at 10 degrees C was less than about 2% for all samples tested. Temperature compensation factors vary substantially depending on the choice of standard temperature. Therefore, a care must be taken when standard temperatures different from 25 degrees C are used.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1023/b:emas.0000031719.83065.68", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1095684", 
        "issn": [
          "0167-6369", 
          "1573-2959"
        ], 
        "name": "Environmental Monitoring and Assessment", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1-3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "96"
      }
    ], 
    "name": "Temperature-Electrical Conductivity Relation of Water for Environmental Monitoring and Geophysical Data Inversion", 
    "pagination": "119-128", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "0b1084b875ff9ee6efc34ed8b0ed99666cf6d4d9c8515b9ada0b412c77092e68"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "15327152"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "8508350"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1023/b:emas.0000031719.83065.68"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1002323842"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1023/b:emas.0000031719.83065.68", 
      "https://app.dimensions.ai/details/publication/pub.1002323842"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T23:22", 
    "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_8693_00000503.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1023%2FB%3AEMAS.0000031719.83065.68"
  }
]
 

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.10.1023/b:emas.0000031719.83065.68'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1023/b:emas.0000031719.83065.68'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/b:emas.0000031719.83065.68'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/b:emas.0000031719.83065.68'


 

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

121 TRIPLES      21 PREDICATES      44 URIs      26 LITERALS      14 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1023/b:emas.0000031719.83065.68 schema:about N215af12ecdd44446b1610354085d6a05
2 N54d752abdeb14d14937bbb35666d0249
3 N7a2dfb22af2c48908d44804f5dcaffc3
4 N8f10e6cc81204747ab0774561f842147
5 N9648b7cc7f764957839e7b61ee594ae3
6 anzsrc-for:04
7 anzsrc-for:0406
8 schema:author N8c5f3db72b564c59bac6cd87c8697adf
9 schema:citation sg:pub.10.1007/bf02442136
10 sg:pub.10.1007/s100400100146
11 https://doi.org/10.1016/0016-7037(77)90186-7
12 https://doi.org/10.1016/0022-1694(86)90049-1
13 https://doi.org/10.1016/0022-1694(89)90109-1
14 https://doi.org/10.1016/0022-1694(95)02895-1
15 https://doi.org/10.1016/s0926-9851(02)00146-5
16 https://doi.org/10.1021/ac00140a003
17 https://doi.org/10.1021/j100721a006
18 https://doi.org/10.4141/cjss83-008
19 schema:datePublished 2004-08
20 schema:datePublishedReg 2004-08-01
21 schema:description Electrical conductivity (EC) is widely used for monitoring the mixing of fresh water and saline water, separating stream hydrographs, and geophysical mapping of contaminated groundwater. The measured EC values at various temperatures need to be reported as corresponding to a standard temperature because EC is dependent on temperature. An arbitrary constant is commonly used for temperature compensation assuming that EC-temperature relation is linear (for example 2% increase of EC per 1 degrees C). This paper examines the EC-temperature relation of natural waters having vastly different compositions and salinities. EC-temperature relation was slightly nonlinear in a temperature range 0-30 degrees C, but the linear equation approximated the relation reasonably well. The temperature compensation factor corresponding to 25 degrees C ranged between 0.0175 and 0.0198. When the mean value 0.0187 was used, the error of estimating EC at 25 degrees C from EC at 10 degrees C was less than about 2% for all samples tested. Temperature compensation factors vary substantially depending on the choice of standard temperature. Therefore, a care must be taken when standard temperatures different from 25 degrees C are used.
22 schema:genre research_article
23 schema:inLanguage en
24 schema:isAccessibleForFree false
25 schema:isPartOf N74ebd565d8e14ae695618c814d02bf05
26 N8d0b117679b34632964858a9962519e9
27 sg:journal.1095684
28 schema:name Temperature-Electrical Conductivity Relation of Water for Environmental Monitoring and Geophysical Data Inversion
29 schema:pagination 119-128
30 schema:productId N49a0f73fd6e544c699958dc8c3f2c8ec
31 N5792a5ea2f474ca18193bdbd7d01b44e
32 N6911b6b4f5bd46f9b7aaf3480621ce29
33 N6ac3e297c1924eaa94a54431942c1366
34 Ne756e05b88a94062be09d6216a14e748
35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002323842
36 https://doi.org/10.1023/b:emas.0000031719.83065.68
37 schema:sdDatePublished 2019-04-10T23:22
38 schema:sdLicense https://scigraph.springernature.com/explorer/license/
39 schema:sdPublisher N1089a31ccb504719b7c28ceb4f88ba13
40 schema:url http://link.springer.com/10.1023%2FB%3AEMAS.0000031719.83065.68
41 sgo:license sg:explorer/license/
42 sgo:sdDataset articles
43 rdf:type schema:ScholarlyArticle
44 N1089a31ccb504719b7c28ceb4f88ba13 schema:name Springer Nature - SN SciGraph project
45 rdf:type schema:Organization
46 N215af12ecdd44446b1610354085d6a05 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
47 schema:name Electric Conductivity
48 rdf:type schema:DefinedTerm
49 N49a0f73fd6e544c699958dc8c3f2c8ec schema:name pubmed_id
50 schema:value 15327152
51 rdf:type schema:PropertyValue
52 N54d752abdeb14d14937bbb35666d0249 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
53 schema:name Temperature
54 rdf:type schema:DefinedTerm
55 N5792a5ea2f474ca18193bdbd7d01b44e schema:name dimensions_id
56 schema:value pub.1002323842
57 rdf:type schema:PropertyValue
58 N6911b6b4f5bd46f9b7aaf3480621ce29 schema:name readcube_id
59 schema:value 0b1084b875ff9ee6efc34ed8b0ed99666cf6d4d9c8515b9ada0b412c77092e68
60 rdf:type schema:PropertyValue
61 N6ac3e297c1924eaa94a54431942c1366 schema:name nlm_unique_id
62 schema:value 8508350
63 rdf:type schema:PropertyValue
64 N74ebd565d8e14ae695618c814d02bf05 schema:issueNumber 1-3
65 rdf:type schema:PublicationIssue
66 N7a2dfb22af2c48908d44804f5dcaffc3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
67 schema:name Environmental Monitoring
68 rdf:type schema:DefinedTerm
69 N8c5f3db72b564c59bac6cd87c8697adf rdf:first sg:person.01010002376.12
70 rdf:rest rdf:nil
71 N8d0b117679b34632964858a9962519e9 schema:volumeNumber 96
72 rdf:type schema:PublicationVolume
73 N8f10e6cc81204747ab0774561f842147 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
74 schema:name Water
75 rdf:type schema:DefinedTerm
76 N9648b7cc7f764957839e7b61ee594ae3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
77 schema:name Calibration
78 rdf:type schema:DefinedTerm
79 Ne756e05b88a94062be09d6216a14e748 schema:name doi
80 schema:value 10.1023/b:emas.0000031719.83065.68
81 rdf:type schema:PropertyValue
82 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
83 schema:name Earth Sciences
84 rdf:type schema:DefinedTerm
85 anzsrc-for:0406 schema:inDefinedTermSet anzsrc-for:
86 schema:name Physical Geography and Environmental Geoscience
87 rdf:type schema:DefinedTerm
88 sg:journal.1095684 schema:issn 0167-6369
89 1573-2959
90 schema:name Environmental Monitoring and Assessment
91 rdf:type schema:Periodical
92 sg:person.01010002376.12 schema:affiliation https://www.grid.ac/institutes/grid.22072.35
93 schema:familyName Hayashi
94 schema:givenName Masaki
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010002376.12
96 rdf:type schema:Person
97 sg:pub.10.1007/bf02442136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021753040
98 https://doi.org/10.1007/bf02442136
99 rdf:type schema:CreativeWork
100 sg:pub.10.1007/s100400100146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004994296
101 https://doi.org/10.1007/s100400100146
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1016/0016-7037(77)90186-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014702593
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1016/0022-1694(86)90049-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035748791
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1016/0022-1694(89)90109-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010805810
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/0022-1694(95)02895-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006078109
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/s0926-9851(02)00146-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042163116
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1021/ac00140a003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054975301
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1021/j100721a006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055677266
116 rdf:type schema:CreativeWork
117 https://doi.org/10.4141/cjss83-008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037797568
118 rdf:type schema:CreativeWork
119 https://www.grid.ac/institutes/grid.22072.35 schema:alternateName University of Calgary
120 schema:name Department of Geology and Geophysics, University of Calgary, Calgary, Alberta, Canada
121 rdf:type schema:Organization
 




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


...