Informing water distribution line rehabilitation through quantitative microbial risk assessment

被引:7
|
作者
Jamal, Rubayat [1 ]
Mubarak, Shaista [2 ]
Sahulka, Sierra Q. [1 ]
Kori, Junaid A. [2 ]
Tajammul, Ayesha [2 ]
Ahmed, Jamil [2 ]
Mahar, Rasool B. [2 ]
Olsen, McKinley Snyder [3 ]
Goel, Ramesh [1 ]
Weidhaas, Jennifer [1 ]
机构
[1] Univ Utah, Civil & Environm Engn, 110 Cent Campus Dr Suite 2000, Salt Lake City, UT 84112 USA
[2] Mehran Univ Engn & Technol, US Pakistan Ctr Adv Studies Water, Jamshoro 76062, Sindh, Pakistan
[3] Geosyntec Consultants Inc, 1111 Broadway, Oakland, CA 94607 USA
基金
美国国家科学基金会;
关键词
Drinking water quality; Distribution system; Bayesian statistics; Quantitative microbial risk assessment; Waterborne pathogens; DOSE-RESPONSE RELATIONSHIP; DRINKING-WATER; HEALTH-RISK; MICROBIOLOGICAL CONTAMINATION; EXPOSURE; ADENOVIRUS; ILLNESS; INDICATORS; INFECTION; OUTBREAK;
D O I
10.1016/j.scitotenv.2020.140021
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Poor urban water quality has been linked to diminished source water quality, poorly functioning water treatment systems and infiltration into distribution lines after treatment resulting in microbiological contamination. With limited funding to rehabilitate distribution lines, developing nations need tools to identify the areas of greatest concern to human health so as to target cost effective remediation approaches. Herein, a case study of Hyderabad, Pakistan was used to demonstrate the efficacy of combining quantitative microbial risk assessment (QMRA) for multiple pathogens with spatial distribution system modeling to identify areas for pipe rehabilitation. Abundance of Escherichia coli, Enterococcus (enterococci), Salmonella spp., Shigella spp., Giardia intestinalis, Vibrio cholera, norovirus GI and adenovirus 40/41, were determined in 85 locations including the source water, treatment plant effluent and the city distribution lines. Bayesian statistics and Monte Carlo simulations were used in the QMRA to account for left-censored microbial abundance distributions. Bacterial and viral abundances in the distribution system samples decreased as follows: 9400 +/- 19,800 norovirus gene copies/100 mL (average +/- standard deviation, 100% of samples positive); 340 +/- 2200 enterococci CFU/100 mL (94%), 71 +/- 97 Shigella sp. CFU/100 mL (97%), 60 +/- 360 E. coli CFU/100 mL (89%), 35 +/- 79 adenovirus gene copies/100 mL (100%), and 21 +/- 46 Salmonella sp. CFU/100 mL (76%). The QMRA revealed unacceptable probabilities of illness (>1 in 10,000 illness level) from the four exposure routes considered (drinking water, or only showering, tooth brushing, and rinsing vegetables consumed raw). Disease severity indices based on the QMRA combined with mapping the distribution system revealed areas for targeted rehabilitation. The combined intensive sampling, risk assessment and mapping can be used in low- and middle-income countries to target distribution system rehabilitation efforts and improve health outcomes. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] RISK OF DISEASE DUE TO CONTAMINATION OF REFILL DRINKING WATER: USING QUANTITATIVE MICROBIAL RISK ASSESSMENT
    Alfian, Azyyati Ridha
    Firdani, Fea
    Sari, Putri Nilam
    SURANAREE JOURNAL OF SCIENCE AND TECHNOLOGY, 2023, 30 (03):
  • [22] Quantitative tools in microbial and chemical risk assessment
    Zabulione, Aelita
    Valdramidis, Vasilis P.
    EFSA JOURNAL, 2023, 21
  • [23] Quantitative tools in microbial and chemical risk assessment
    Zabulione, Aelita
    Valdramidis, Vasilis P.
    EFSA JOURNAL, 2023, 21
  • [24] Quantitative microbial risk assessment - An Australian perspective
    Vanderlinde, P
    FOOD AUSTRALIA, 1998, 50 (12): : 626 - 628
  • [25] Quantitative tools in microbial and chemical risk assessment
    Stratev, Deyan
    Valdramidis, Vasilis P.
    EFSA JOURNAL, 2023, 21
  • [26] 'Omics' technologies in quantitative microbial risk assessment
    Brul, S.
    Bassett, J.
    Cook, P.
    Kathariou, S.
    McClure, P.
    Jasti, P. R.
    Betts, R.
    TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2012, 27 (01) : 12 - 24
  • [27] Sensitivity analysis in quantitative microbial risk assessment
    Zwietering, MH
    van Gerwen, SJC
    INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY, 2000, 58 (03) : 213 - 221
  • [28] Quantitative microbial risk assessment and Australian Guidelines for Water Recycling: two case studies
    O'Toole, J.
    Sinclair, M.
    Leder, K.
    FOOD AUSTRALIA, 2010, 62 (09): : 408 - 412
  • [29] Water quality modelling and quantitative microbial risk assessment for uMsunduzi River in South Africa
    Ngubane, Zesizwe
    Bergion, Viktor
    Dzwairo, Bloodless
    Troell, Karin
    Amoah, Isaac Dennis
    Stenstrom, Thor Axel
    Sokolova, Ekaterina
    JOURNAL OF WATER AND HEALTH, 2022, 20 (04) : 641 - 656
  • [30] Improved quantitative microbial risk assessment (QMRA) for drinking water sources in developing countries
    Amatobi, D. A.
    Agunwamba, J. C.
    APPLIED WATER SCIENCE, 2022, 12 (03)