{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:36:09Z","timestamp":1787020569589,"version":"build-2736575974"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"S1","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Proc"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>While the influence of meteorology on carbon monoxide (CO) poisoning has been reported, few data are available on the association between air pollutants and the prediction of CO poisoning. Our objective is to explore meteorological and pollutant patterns associated with CO poisoning and to establish a predictive model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      CO poisoning was found to be significantly associated with meteorological and pollutant patterns: low temperatures, low wind speeds, low air concentrations of sulfur dioxide (SO\n                      <jats:sub>2<\/jats:sub>\n                      ) and ozone (O\n                      <jats:sub>3<\/jats:sub>\n                      8h), and high daily temperature changes and ambient CO (r absolute value range: 0.079 to 0.232, all\n                      <jats:italic>P<\/jats:italic>\n                      values &lt;\u20090.01). Based on the above factors, a predictive model was established: \u201clogitPj = aj - 0.193 * temperature - 0.228 * wind speed + 0.221 * 24 h temperature change + 1.25 * CO - 0.0176 * SO\n                      <jats:sub>2<\/jats:sub>\n                      + 0.0008 *O\n                      <jats:sub>3<\/jats:sub>\n                      8h; j = 1, 2, 3, 4; a1 = -4.12, a2 = -2.93, a3 = -1.98, a4 = -0.92.\u201d The proposed prediction model based on combined factors showed better predictive capacity than a model using only meteorological factors as a predictor.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Low temperatures, wind speed, and SO\n                      <jats:sub>2<\/jats:sub>\n                      and high daily temperature changes, O\n                      <jats:sub>3<\/jats:sub>\n                      8h, and CO are related to CO poisoning. Using both meteorological and pollutant factors as predictors could help facilitate the prevention of CO poisoning.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12919-021-00206-7","type":"journal-article","created":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T19:04:09Z","timestamp":1614625449000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Carbon monoxide poisoning: a prediction model using meteorological factors and air pollutant"],"prefix":"10.1186","volume":"15","author":[{"given":"Hai-Lin","family":"Ruan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang-Shen","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian-Bing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Liang","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan-Shan","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuo-Jun","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,2]]},"reference":[{"key":"206_CR1","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1056\/NEJMoa013121","volume":"347","author":"LK Weaver","year":"2002","unstructured":"Weaver LK, Hopkins RO, Chan KJ, Churchill S, Elliott CG, Clemmer TP, Orme JF Jr, Thomas FO, Morris AH. 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