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杨勇, 汪艳平, 明小燕. 基于GIS技术农村饮用水微生物污染健康风险分析[J]. 中国公共卫生, 2020, 36(7): 1101-1104. DOI: 10.11847/zgggws1120186
引用本文: 杨勇, 汪艳平, 明小燕. 基于GIS技术农村饮用水微生物污染健康风险分析[J]. 中国公共卫生, 2020, 36(7): 1101-1104. DOI: 10.11847/zgggws1120186
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

基于GIS技术农村饮用水微生物污染健康风险分析

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

  • 摘要:
      目的  探索地理信息系统(GIS)技术应用于农村饮用水水质监测分析及疾病监测预警的可行性和适用性。
      方法  监测100个有代表性的农村水厂水源水样微生物污染状况,利用健康管理大数据分析平台获取水质监测、法定肠道传染病和感染性腹泻病例信息,应用地理信息系统对数据进行整合与可视化处理,实现农村饮水安全的监测预警和地图展示。
      结果  河水(水源水)菌落总数与大肠菌群、大肠埃希菌检出值呈显著正相关(r = 0.850、0.566,P < 0.01),大肠菌群与大肠埃希菌检出值呈显著正相关(r = 0.501,P < 0.01),大肠埃希菌与沙门菌检出值呈明显相关性(r = 0.632,P < 0.05)。应用GIS技术将农村水源、水厂、供水区域范围、覆盖人群、疾病、水质监测等数据整合与可视化处理,绘制出农村饮水安全风险监测与预警展示地图,地理信息系统对水质监测和病例聚集情况可直观展示和智能预警。
      结论  河水可采用大肠埃希菌作为沙门菌的指示菌,基于GIS技术的农村饮用水微生物污染风险监测预警系统可作为疾病防控和卫生应急处置的重要技术支撑。

     

    Abstract:
      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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