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

JOURNAL OF CLEANER PRODUCTION

卷:297

Article

Piyapong, Chantima¹  Chamroensaksri, Nitcha²  Aroonsrimorakot, Sayam³  Eyosawat, Lawan⁴  Khankhum, Surasak⁵  Rattana, Sunirat⁶  Sunthamala, Nuchsupha⁵  Warapetcharayut, Panya⁷  Paradis, Emmanuel⁸ 

[1] Burapha Univ, Fac Sci, Dept Biol, Chon Buri 20131, Thailand

[2] Natl Sci & Technol Dev Agcy NSTDA, Natl Biobank Thailand NBT, 144 Thailand Sci Pk,Phahonyothin Rd, Khlong Luang Dist 12120, Pathum Thani, Thailand

[3] Mahidol Univ, Fac Environm & Resource Studies, Program Appropriate Technol & Innovat Environm Se, Salaya 73170, Nakhon Pathom, Thailand

[4] Minist Nat Resources & Environm, Reg Environm Off 13, Chon Buri 20000, Thailand

[5] Mahasarakham Univ, Fac Sci, Dept Biol, Maha Sarakham 44150, Thailand

[6] Minist Nat Resources & Environm, Reg Environm Off 7, Sara Buri 18120, Thailand

[7] Minist Nat Resources & Environm, Dept Environm Qual Promot, Environm Res & Training Ctr, Khlong Luang Dist 12120, Pathum Thani, Thailand

[8] Univ Montpellier, EPHE, CNRS, ISEM,IRD, Montpellier, France

关键词: Coliforms;    Microbiological water quality;    Mixed-effect models;    Rivers;    Thailand;   

DOI  :  10.1016/j.jclepro.2021.126704

来源: Elsevier

【 摘 要 】

Bacterial 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. (c) 2021 Elsevier Ltd. All rights reserved.

【 授权许可】

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