Publication:
A predictive model of the impact of urbanization on bacterial loads in watersheds

dc.contributor.authorChantima Piyapongen_US
dc.contributor.authorNitcha Chamroensaksrien_US
dc.contributor.authorSayam Aroonsrimorakoten_US
dc.contributor.authorLawan Eyosawaten_US
dc.contributor.authorSurasak Khankhumen_US
dc.contributor.authorSunirat Rattanaen_US
dc.contributor.authorNuchsupha Sunthamalaen_US
dc.contributor.authorPanya Warapetcharayuten_US
dc.contributor.authorEmmanuel Paradisen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherBurapha Universityen_US
dc.contributor.otherCNRS Centre National de la Recherche Scientifiqueen_US
dc.contributor.otherThailand National Science and Technology Development Agencyen_US
dc.contributor.otherMahasarakham Universityen_US
dc.contributor.otherMinistry of Natural Resources and Environmenten_US
dc.contributor.otherMinistry of Natural Resources and Environmenten_US
dc.contributor.otherMinistry of Natural Resources and Environmenten_US
dc.date.accessioned2022-08-04T08:16:36Z
dc.date.available2022-08-04T08:16:36Z
dc.date.issued2021-05-15en_US
dc.description.abstractBacterial concentration is one of the most important aspects of water quality. Many regions in the world are affected by increasing urbanization and a potential increase in bacterial concentrations in waters. We used long-term data from 68 stations in eight watersheds in Eastern Thailand to quantify the temporal and geographical variation in total and fecal coliform bacteria. Descriptive statistics showed considerable seasonal, inter-annual, and geographical variation. In order to quantify this multi-level variation, we built a predictive model of bacterial loads. Using fixed- and mixed-effects regression models, we built a model including the effects of urbanization and other significant variables. The best model, fitted by restricted maximum likelihood, included the effects of season, year, urbanization as fixed effects, and of watershed and station as nested, random effects. Temporal variation was related to seasonal and annual variations. Spatial variation had a very significant impact on the bacterial concentrations. Urbanization was an important factor controlling concentrations of bacteria in rivers: we found that the proportion of urban area around a station had a statistically significant effect on log-transformed total coliform bacterial concentration with a slope equal to 1.3 (SE = 0.3), and on log-transformed fecal coliform bacterial concentration with a slope equal to 1.4 (SE = 0.3). Our model predicts that bacterial concentrations would be multiplied by 20 if land is transformed from non-urban to fully urban.en_US
dc.identifier.citationJournal of Cleaner Production. Vol.297, (2021)en_US
dc.identifier.doi10.1016/j.jclepro.2021.126704en_US
dc.identifier.issn09596526en_US
dc.identifier.other2-s2.0-85102976673en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/76445
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102976673&origin=inwarden_US
dc.subjectBusiness, Management and Accountingen_US
dc.subjectEnergyen_US
dc.subjectEngineeringen_US
dc.subjectEnvironmental Scienceen_US
dc.titleA predictive model of the impact of urbanization on bacterial loads in watershedsen_US
dc.typeArticleen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102976673&origin=inwarden_US

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