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Abstract
The type of research used is descriptive research with a Microbial Risk Assessment (MRA) on refilled drinking water (Ryan et al., 2022). The design of this study uses a Geographic Information System (GIS) approach. Retrieval of data using the exploratory method, namely sampling is done directly. The research was carried out in the Working Area of the Moncongloe Health Center, Maros Regency, for approximately 1 (one) month in October 2020. The population in this study were all Refill Drinking Water Depots in the working area of the Moncongloe Health Center, Maros Regency, namely 17 depots. The sampling technique in this study was total sampling, namely 17 Refill Drinking Water Depots in the working area of the Moncongloe Health Center, Maros Regency. The bacteria that became the research were Escherichia coli bacteria. The exposure assessment was carried out on 170 respondents, namely 10 respondents who consumed drinking water from each depot. The type of data used in this study is quantitative data in the form of the results of laboratory examinations for Escherichia coli content in refilled drinking water.
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References
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References
Amoah, I. D., Kumari, S., & Bux, F. (2022). A probabilistic assessment of microbial infection risks due to occupational exposure to wastewater in a conventional activated sludge wastewater treatment plant. Science of The Total Environment, 843, 156849. https://doi.org/https://doi.org/10.1016/j.scitotenv.2022.156849
Debnath, P., Mamun, M. M. A. Al, Karmakar, S., Uddin, M. S., & Nath, T. K. (2022). Drinking water quality of Chattogram city in Bangladesh: An analytical and residents’ perception study. Heliyon, 8(12), e12247. https://doi.org/https://doi.org/10.1016/j.heliyon.2022.e12247
Mohamed, N. A., Wachemo, A. C., Karuppannan, S., & Duraisamy, K. (2022). Spatio-temporal variation of groundwater hydrochemistry and suitability for drinking and irrigation in Arba Minch Town, Ethiopia: An integrated approach using water quality index, multivariate statistics, and GIS. Urban Climate, 46, 101338. https://doi.org/https://doi.org/10.1016/j.uclim.2022.101338
Narita, K., Matsui, Y., Matsushita, T., & Shirasaki, N. (2023). Screening priority pesticides for drinking water quality regulation and monitoring by machine learning: Analysis of factors affecting detectability. Journal of Environmental Management, 326, 116738. https://doi.org/https://doi.org/10.1016/j.jenvman.2022.116738
Perez-Mercado, L. F., Lalander, C., Joel, A., Ottoson, J., Iriarte, M., & Vinnerås, B. (2022). Managing microbial risks in informal wastewater-irrigated agriculture through irrigation water substitution. Agricultural Water Management, 269, 107733. https://doi.org/https://doi.org/10.1016/j.agwat.2022.107733
Ryan, U., Hill, K., & Deere, D. (2022). Review of generic screening level assumptions for quantitative microbial risk assessment (QMRA) for estimating public health risks from Australian drinking water sources contaminated with Cryptosporidium by recreational activities. Water Research, 220, 118659. https://doi.org/https://doi.org/10.1016/j.watres.2022.118659
Basic Health Research (Riskesdas). 2013. Health Research and Development Agency, Ministry of Health of the Republic of Indonesia. 2013 Dec 1:50-5.
WHO. 2016. Quantitative microbial risk assessment: application for water safety management. World Health Organization.
WHO. 2019. Definition, diagnosis, and classification of water clean: report of a WHO consultation. Part 1, No. WHO/NCD/NCS/99.2. Geneva: World health organization.Zhu, F., Wang, X., Wang, L., & Yu, M. (2021). Project manager’s emotional intelligence and project performance: The mediating role of project commitment. International Journal of Project Management, 39(7), 788–798. https://doi.org/https://doi.org/10.1016/j.ijproman.2021.08.002