Atmospheric mold spore counts in relation to meteorological parameters View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1997-08

AUTHORS

R. K. Katial, Yiming Zhang, Richard H. Jones, Philip D. Dyer

ABSTRACT

Fungal spore counts of Cladosporium, Alternaria, and Epicoccum were studied during 8 years in Denver, Colorado. Fungal spore counts were obtained daily during the pollinating season by a Rotorod sampler. Weather data were obtained from the National Climatic Data Center. Daily averages of temperature, relative humidity, daily precipitation, barometric pressure, and wind speed were studied. A time series analysis was performed on the data to mathematically model the spore counts in relation to weather parameters. Using SAS PROC ARIMA software, a regression analysis was performed, regressing the spore counts on the weather variables assuming an autoregressive moving average (ARMA) error structure. Cladosporium was found to be positively correlated (P < 0.02) with average daily temperature, relative humidity, and negatively correlated with precipitation. Alternaria and Epicoccum did not show increased predictability with weather variables. A mathematical model was derived for Cladosporium spore counts using the annual seasonal cycle and significant weather variables. The model for Alternaria and Epicoccum incorporated the annual seasonal cycle. Fungal spore counts can be modeled by time series analysis and related to meteorological parameters controlling for seasonallity; this modeling can provide estimates of exposure to fungal aeroallergens. More... »

PAGES

17-22

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s004840050048

DOI

http://dx.doi.org/10.1007/s004840050048

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007165743

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/9334570


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/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "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": "Air Microbiology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Alternaria", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cladosporium", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Colony Count, Microbial", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Meteorological Concepts", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mitosporic Fungi", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Biological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Seasons", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Spores, Fungal", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Fitzsimons Army Medical Center", 
          "id": "https://www.grid.ac/institutes/grid.414922.8", 
          "name": [
            "Department of Allergy and Immunology, Fitzsimons Army Medical Center, Aurora, Co, USA, 80045, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Katial", 
        "givenName": "R. K.", 
        "id": "sg:person.0751463565.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751463565.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Colorado Denver", 
          "id": "https://www.grid.ac/institutes/grid.241116.1", 
          "name": [
            "Department of Preventive Medicine and Biometric-University of Colorado School of Medicine, Denver, Co, USA, 80262, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Yiming", 
        "id": "sg:person.01336150522.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01336150522.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Colorado Denver", 
          "id": "https://www.grid.ac/institutes/grid.241116.1", 
          "name": [
            "Department of Preventive Medicine and Biometric-University of Colorado School of Medicine, Denver, Co, USA, 80262, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jones", 
        "givenName": "Richard H.", 
        "id": "sg:person.0731350632.70", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0731350632.70"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Fitzsimons Army Medical Center", 
          "id": "https://www.grid.ac/institutes/grid.414922.8", 
          "name": [
            "Department of Allergy and Immunology, Fitzsimons Army Medical Center, Aurora, Co, USA, 80045, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dyer", 
        "givenName": "Philip D.", 
        "id": "sg:person.0660317202.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660317202.50"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1997-08", 
    "datePublishedReg": "1997-08-01", 
    "description": "Fungal spore counts of Cladosporium, Alternaria, and Epicoccum were studied during 8 years in Denver, Colorado. Fungal spore counts were obtained daily during the pollinating season by a Rotorod sampler. Weather data were obtained from the National Climatic Data Center. Daily averages of temperature, relative humidity, daily precipitation, barometric pressure, and wind speed were studied. A time series analysis was performed on the data to mathematically model the spore counts in relation to weather parameters. Using SAS PROC ARIMA software, a regression analysis was performed, regressing the spore counts on the weather variables assuming an autoregressive moving average (ARMA) error structure. Cladosporium was found to be positively correlated (P < 0.02) with average daily temperature, relative humidity, and negatively correlated with precipitation. Alternaria and Epicoccum did not show increased predictability with weather variables. A mathematical model was derived for Cladosporium spore counts using the annual seasonal cycle and significant weather variables. The model for Alternaria and Epicoccum incorporated the annual seasonal cycle. Fungal spore counts can be modeled by time series analysis and related to meteorological parameters controlling for seasonallity; this modeling can provide estimates of exposure to fungal aeroallergens.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s004840050048", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2512149", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1017657", 
        "issn": [
          "0020-7128", 
          "1432-1254"
        ], 
        "name": "International Journal of Biometeorology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "41"
      }
    ], 
    "name": "Atmospheric mold spore counts in relation to meteorological parameters", 
    "pagination": "17-22", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2221d5ad7986cee3e305f71a9751978b1f8a41e24dcb6cd3d66b969a51aff0d4"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "9334570"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "0374716"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s004840050048"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1007165743"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s004840050048", 
      "https://app.dimensions.ai/details/publication/pub.1007165743"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T15:51", 
    "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_8664_00000510.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs004840050048"
  }
]
 

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/s004840050048'

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/s004840050048'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s004840050048'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s004840050048'


 

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

135 TRIPLES      20 PREDICATES      39 URIs      31 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s004840050048 schema:about N005887fdf151426eb47adc8dfd510fd3
2 N1d2b733c0bd947ffaab1c4bab036fefd
3 N2beb7b8ce3314e7f946aa6f8f83c3695
4 N6e135d0f579b427795e8f18c22a66afa
5 N7880f2a8e33547d5b52e9cd6538dd270
6 N7c5e3ab502bb479fb08b87ea19e715d1
7 N9f54c7dfd053490896f28c0f871f16b0
8 Nc0803053b99541618fe467e866880361
9 Nc7717408865844df8cb9b590c870446c
10 Ncd2d124c33df4a30a0645f60a375eaee
11 anzsrc-for:04
12 anzsrc-for:0401
13 schema:author Nb8b256522d514f8aa4f28935cdc4fc96
14 schema:datePublished 1997-08
15 schema:datePublishedReg 1997-08-01
16 schema:description Fungal spore counts of Cladosporium, Alternaria, and Epicoccum were studied during 8 years in Denver, Colorado. Fungal spore counts were obtained daily during the pollinating season by a Rotorod sampler. Weather data were obtained from the National Climatic Data Center. Daily averages of temperature, relative humidity, daily precipitation, barometric pressure, and wind speed were studied. A time series analysis was performed on the data to mathematically model the spore counts in relation to weather parameters. Using SAS PROC ARIMA software, a regression analysis was performed, regressing the spore counts on the weather variables assuming an autoregressive moving average (ARMA) error structure. Cladosporium was found to be positively correlated (P < 0.02) with average daily temperature, relative humidity, and negatively correlated with precipitation. Alternaria and Epicoccum did not show increased predictability with weather variables. A mathematical model was derived for Cladosporium spore counts using the annual seasonal cycle and significant weather variables. The model for Alternaria and Epicoccum incorporated the annual seasonal cycle. Fungal spore counts can be modeled by time series analysis and related to meteorological parameters controlling for seasonallity; this modeling can provide estimates of exposure to fungal aeroallergens.
17 schema:genre research_article
18 schema:inLanguage en
19 schema:isAccessibleForFree false
20 schema:isPartOf N2b55c26a2ec74a269ed211848c6689c4
21 N3d5ae418535b44d1a8078c4848570d79
22 sg:journal.1017657
23 schema:name Atmospheric mold spore counts in relation to meteorological parameters
24 schema:pagination 17-22
25 schema:productId N298a06239f884b9eb1ae782de0f2713c
26 N403f8695e74f4b7ba78b2667860d4d63
27 Nd2537117d0404d178e0d81ce7d56e1a2
28 Ne638c81137e74842a9f514a296c25e7f
29 Ne949bbce1d294fc5b10f5e6b54dde2e1
30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007165743
31 https://doi.org/10.1007/s004840050048
32 schema:sdDatePublished 2019-04-10T15:51
33 schema:sdLicense https://scigraph.springernature.com/explorer/license/
34 schema:sdPublisher N2088614323ec44199d487d3d6a4d4a8f
35 schema:url http://link.springer.com/10.1007%2Fs004840050048
36 sgo:license sg:explorer/license/
37 sgo:sdDataset articles
38 rdf:type schema:ScholarlyArticle
39 N005887fdf151426eb47adc8dfd510fd3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
40 schema:name Spores, Fungal
41 rdf:type schema:DefinedTerm
42 N1476f3fc6a6d4a3c97366d8a24863240 rdf:first sg:person.01336150522.42
43 rdf:rest N88ce2898ee8547b4b2c4ed088ca4edc2
44 N1d2b733c0bd947ffaab1c4bab036fefd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
45 schema:name Humans
46 rdf:type schema:DefinedTerm
47 N2088614323ec44199d487d3d6a4d4a8f schema:name Springer Nature - SN SciGraph project
48 rdf:type schema:Organization
49 N298a06239f884b9eb1ae782de0f2713c schema:name nlm_unique_id
50 schema:value 0374716
51 rdf:type schema:PropertyValue
52 N2b55c26a2ec74a269ed211848c6689c4 schema:volumeNumber 41
53 rdf:type schema:PublicationVolume
54 N2beb7b8ce3314e7f946aa6f8f83c3695 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
55 schema:name Air Microbiology
56 rdf:type schema:DefinedTerm
57 N3d5ae418535b44d1a8078c4848570d79 schema:issueNumber 1
58 rdf:type schema:PublicationIssue
59 N403f8695e74f4b7ba78b2667860d4d63 schema:name dimensions_id
60 schema:value pub.1007165743
61 rdf:type schema:PropertyValue
62 N6e135d0f579b427795e8f18c22a66afa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
63 schema:name Models, Biological
64 rdf:type schema:DefinedTerm
65 N7880f2a8e33547d5b52e9cd6538dd270 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
66 schema:name Seasons
67 rdf:type schema:DefinedTerm
68 N7c5e3ab502bb479fb08b87ea19e715d1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
69 schema:name Cladosporium
70 rdf:type schema:DefinedTerm
71 N88ce2898ee8547b4b2c4ed088ca4edc2 rdf:first sg:person.0731350632.70
72 rdf:rest N9703cd5254fb4cd8968f098174d8d34f
73 N9703cd5254fb4cd8968f098174d8d34f rdf:first sg:person.0660317202.50
74 rdf:rest rdf:nil
75 N9f54c7dfd053490896f28c0f871f16b0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
76 schema:name Alternaria
77 rdf:type schema:DefinedTerm
78 Nb8b256522d514f8aa4f28935cdc4fc96 rdf:first sg:person.0751463565.79
79 rdf:rest N1476f3fc6a6d4a3c97366d8a24863240
80 Nc0803053b99541618fe467e866880361 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
81 schema:name Meteorological Concepts
82 rdf:type schema:DefinedTerm
83 Nc7717408865844df8cb9b590c870446c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Mitosporic Fungi
85 rdf:type schema:DefinedTerm
86 Ncd2d124c33df4a30a0645f60a375eaee schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Colony Count, Microbial
88 rdf:type schema:DefinedTerm
89 Nd2537117d0404d178e0d81ce7d56e1a2 schema:name pubmed_id
90 schema:value 9334570
91 rdf:type schema:PropertyValue
92 Ne638c81137e74842a9f514a296c25e7f schema:name readcube_id
93 schema:value 2221d5ad7986cee3e305f71a9751978b1f8a41e24dcb6cd3d66b969a51aff0d4
94 rdf:type schema:PropertyValue
95 Ne949bbce1d294fc5b10f5e6b54dde2e1 schema:name doi
96 schema:value 10.1007/s004840050048
97 rdf:type schema:PropertyValue
98 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
99 schema:name Earth Sciences
100 rdf:type schema:DefinedTerm
101 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
102 schema:name Atmospheric Sciences
103 rdf:type schema:DefinedTerm
104 sg:grant.2512149 http://pending.schema.org/fundedItem sg:pub.10.1007/s004840050048
105 rdf:type schema:MonetaryGrant
106 sg:journal.1017657 schema:issn 0020-7128
107 1432-1254
108 schema:name International Journal of Biometeorology
109 rdf:type schema:Periodical
110 sg:person.01336150522.42 schema:affiliation https://www.grid.ac/institutes/grid.241116.1
111 schema:familyName Zhang
112 schema:givenName Yiming
113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01336150522.42
114 rdf:type schema:Person
115 sg:person.0660317202.50 schema:affiliation https://www.grid.ac/institutes/grid.414922.8
116 schema:familyName Dyer
117 schema:givenName Philip D.
118 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660317202.50
119 rdf:type schema:Person
120 sg:person.0731350632.70 schema:affiliation https://www.grid.ac/institutes/grid.241116.1
121 schema:familyName Jones
122 schema:givenName Richard H.
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0731350632.70
124 rdf:type schema:Person
125 sg:person.0751463565.79 schema:affiliation https://www.grid.ac/institutes/grid.414922.8
126 schema:familyName Katial
127 schema:givenName R. K.
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751463565.79
129 rdf:type schema:Person
130 https://www.grid.ac/institutes/grid.241116.1 schema:alternateName University of Colorado Denver
131 schema:name Department of Preventive Medicine and Biometric-University of Colorado School of Medicine, Denver, Co, USA, 80262, US
132 rdf:type schema:Organization
133 https://www.grid.ac/institutes/grid.414922.8 schema:alternateName Fitzsimons Army Medical Center
134 schema:name Department of Allergy and Immunology, Fitzsimons Army Medical Center, Aurora, Co, USA, 80045, US
135 rdf:type schema:Organization
 




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


...