Abstract
Diffuse pollution caused by rainfall events potentially affects water quality in rivers and, therefore, must be investigated in order to improve water quality planning and management recovery strategies. For these, a quali-quantitative approach was used to monitor the water quality parameters in a river located in a semi-urban watershed area based upon automatic sampling. Thirteen water quality parameters were measured during five rainfall events. Events ranged from 2.3 to 56.8 mm and water peak flows from 3.3 to 4.5 m3/s. The pollutographs measured showed a standard pattern for total suspended solids (TSS). However, for the other chemical parameters, as total phosphorous (TP) and dissolved organic carbon (DOC), the dilution effects were more evident. It was possible to observe the rainfall influence mainly for physical parameters indicating a mass transport pattern for diffuse pollutants, which increased, for example, the amount of TSS in the river. Furthermore, hydrological characteristics were relevant considering the pollutant behavior. Antecedent dry periods, ranging from 1.3 days to 21.4 days, and critical time, from 2.0 to 10.4 h, are determinants to evaluate non-traditional water quality impacts in the river. In general, each rainfall episode has its own characteristics, which produces distinct mass contribution and temporal behavior, being challenging in making generalization. Therefore, the results indicate that diffuse pollution has to be considered to establish future decision-making strategies to water resources management.
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The authors wish to thank the Coordenação de Aperfeiçoamento Pessoal de Nível Superior (CAPES) and Brazilian Council for Research (CNPq) for the financial support of this research and the Water Resources and Environmental Engineering Graduate Program (PPGERHA) at the Federal University of Paraná (UFPR) for all the technical support.
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Kozak, C., Fernandes, C.V.S., Braga, S.M. et al. Water quality dynamic during rainfall episodes: integrated approach to assess diffuse pollution using automatic sampling. Environ Monit Assess 191, 402 (2019). https://doi.org/10.1007/s10661-019-7537-6
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DOI: https://doi.org/10.1007/s10661-019-7537-6