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Yong YANG, Yan-ping WANG, Xiao-yan MING. Health risk in relation to microbial contamination of drinking water in rural areas of Yichang municipality: a GIS-based assessment[J]. Chinese Journal of Public Health, 2020, 36(7): 1101-1104. DOI: 10.11847/zgggws1120186
Citation: Yong YANG, Yan-ping WANG, Xiao-yan MING. Health risk in relation to microbial contamination of drinking water in rural areas of Yichang municipality: a GIS-based assessment[J]. Chinese Journal of Public Health, 2020, 36(7): 1101-1104. DOI: 10.11847/zgggws1120186

Health risk in relation to microbial contamination of drinking water in rural areas of Yichang municipality: a GIS-based assessment

  •   Objective  To explore the feasibility and applicability of utilization of geographic information system (GIS) technology in drinking water quality monitoring and early warning of drinking water related diseases in rural areas.
      Methods  We collected representative source water samples at 100 sampling points of 4 small centralized water plants in rural areas of Yichang municipality, Hubei province for detections of microbiological and other qualitative indicators between August and October 2017. The relevant data on previous drinking water quality monitoring, incidents of notifiable intestinal infectious diseases and infectious diarrhea were collected from Yichang Municipal Big Data Platform for Health Management simultaneously. We performed integration and visualization of the data gathered using GIS technology for early warning of drinking water related health risk and its map displays.
      Results  For the source water samples detected, the total bacterial count was significantly positively correlated with coliform bacteria (r = 0.850, P < 0.01) and Escherichia coli (E. coli) count (r = 0.566, P < 0.01); significantly positive correlations were also observed between coliform bacteria and E. coli count (r = 0.501, P < 0.01) and between E. coli and Salmonella (r = 0.632, P < 0.05). We successfully drew monitoring and early warming maps for drinking water related health risk by integrating the data on distribution of source water, water supply plants, regions and populations covered by the water supply, incidents and cluster of related diseases, and monitoring results using GIS technology.
      Conclusion  The study results suggest that E. coli could be used as an indicator of Salmonella contamination of source water from rivers and GIS technology-based microbial contamination monitoring and early warming for drinking water could play an important supportive role in disease prevention and public health emergency management in rural areas.
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