山东某农村间歇式供水系统微生物健康风险评价
Quantitative microbiological risk assessment of a rural intermittent water supply in Shandong
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摘要:
背景 我国农村间歇式供水系统应用普遍，但研究表明间歇式供水会增加水质微生物污染，增加供水覆盖人群健康风险。
目的 了解我国某农村间歇式供水系统微生物污染现状，定量评估该村间歇式供水微生物健康风险。
方法 以山东省淄博市某农村地区一典型间歇式供水系统作为研究对象，选择水源类型和水处理工艺相似的连续供水系统作为对照，分别采集出厂水（各1份）、末梢水（各20个采样点，间歇式供水系统分别在供水瞬间、恢复供水5 min后和恢复供水30 min后采集1份水样，连续供水系统分别在打开水龙头瞬间和放水5 min后采集1份水样）及间歇式供水家庭储水（10个采样点，各采样1份）水样进行微生物指标检测，参照GB 5749—2006《生活饮用水卫生标准》分析水样中微生物超标情况，并对两种不同供水系统的不同类型水样中微生物含量的差异性进行比较。以总大肠菌群为致病微生物的指示菌，采用微生物定量风险评价方法评价个体不同暴露情况下的饮水致病微生物年感染风险概率，并采用蒙特卡罗模拟法进行不确定性分析。
结果 两种供水系统出厂水水样中微生物指标均合格。间歇式供水末梢水水样中菌落总数超标率为48.3%（29/60），总大肠菌群超标率为23.3%（14/60），家庭储水水样中两种微生物超标率分别为80.0%（8/10）和50.0%（5/10）；连续供水末梢水水样中两种微生物的超标率分别为40.0%（16/40）和20.0%（8/40）。间歇式供水末梢水恢复供水瞬间采集的水样中总大肠菌群含量（对数中位数为2.11 lg CFU·100 mL-1）高于连续供水末梢水打开水龙头瞬间采集的水样（对数中位数为0.30 lg CFU·100 mL-1）（
P < 0.05）；间歇式供水末梢水恢复供水30 min后采集的水样中菌落总数（对数均数为2.04 lg CFU·mL-1）高于连续供水末梢水放水5 min后水样（对数均数为1.62 lg CFU·mL-1）（P < 0.05）。家庭储水水样中菌落总数（对数均数为3.20 lg CFU·mL-1）高于两种供水系统的末梢水水样（P < 0.05）；储水水样中总大肠菌群含量（对数中位数为1.52 lg CFU·100 mL-1）高于间歇式供水末梢水恢复供水30 min后水样（P < 0.05）。经蒙特卡罗模拟，间歇式供水系统覆盖人群中个体感染致病微生物的年风险概率的M （P 5~P 95）为47.67×10-4（0~1 392.46×10-4），连续供水系统为4.85×10-4（0~182.37×10-4），差异具有统计学意义（P < 0.05）。结论 本研究结果显示该地间歇式供水系统较连续供水系统增加了人群微生物暴露与感染的风险。
Abstract:Background Intermittent water supply (IWS) is widely used in rural China, and related studies have shown that IWS increases microbial contamination of tap water and increases health risks for the population in coverage.
Objective This study aims to understand the current situation of microbial pollution of a typical IWS in rural China, and to quantitatively evaluate its health risks.
Methods A typical rural IWS in Zibo, Shandong was selected as a study subject, and a continuous water supply (CWS) with similar water source type and water treatment was selected as a control. Finished water samples (1 water sample from each water plant), tap water samples (20 sampling points for each water plant; for IWS, 1 tap water sample was collected at the moment of supplying water, 5 min after water supply restart, and 30 min after water supply restart, respectively; for CWS, 1 tap water sample was collected at the moment of supplying water and 5 min after water supply, respectively), and household stored water samples (only for IWS, 10 sampling pionts, 1 sample from each point) were collected and tested for microbiological parameters, and the results were evaluated according to the
Standards for drinking water quality (GB 5749—2006) and compared among the three types of water samples and between the two water supply systems. Total coliform (TC) was used as an indicator of pathogenic microorganisms, and quantitative microbial risk assessment (QMRA) method was adopted to evaluate the annual infection probability from piped water for the IWS and CWS serviced populations. Monte-Carlo simulations were used for uncertainty analysis.Results For the finished water samples from the two water supply systems, the testing results of TC and total bacteria (TB) met the national standard. The unqualified rates of TB and TC for IWS tap water samples were 48.3% (29/60) and 23.3% (14/60), the rates for household stored water samples were 80.0% (8/10) and 50.0% (5/10), and the rates for CWS tap water samples were 40.0% (16/40) and 20.0% (8/40), respectively. The TC count of IWS tap water samples at the moment of supplying water (logarithm median, 2.11 lg CFU·100 mL-1) was higher than that of corresponding CWS tap water samples (logarithm median, 0.30 lg CFU·100 mL-1) (
P < 0.05); the TB count of IWS tap water samples at 30 min after water supply restart (logarithm mean, 2.04 lg CFU·mL-1) was higher than that of CWS water samples at 5 min after water supply restart (logarithm mean, 1.62 lg CFU·mL-1) (P < 0.05). The TB count of IWS household stored water samples (logarithm mean, 3.20 lg CFU·mL-1) was higher than that of tap water samples from IWS and CWS (P < 0.05); the TC count of IWS household stored water samples (logarithm median, 1.52 lg CFU·100 mL-1) was higher than that of tap water samples at 30 min after water supply restart (P < 0.05). The Monte-Carlo simulation results showed that the annual infection probabilityM (P 5-P 95) for IWS was 47.67×10-4 (0-1392.46×10-4), and that for CWS was 4.85×10-4 (0-182.37×10-4) (P < 0.05).Conclusion Compared with CWS, IWS increases the risk of microbial exposure and infection for the populations in service in the selected area.
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