Boiled or Bottled: Regional and Seasonal Exposures to Drinking Water Contamination and Household Air Pollution in Rural China

Background: Inadequate access to safe drinking water remains a global health problem, particularly in rural areas. Boiling is the most commonly used form of point-of-use household water treatment (HWT) globally, although the use of bottled water in low- and middle-income countries (LMICs) is increasing rapidly. Objectives: We assessed the regional and seasonal prevalence of HWT practices (including bottled water use) in low-income rural areas in two Chinese provinces, evaluated the microbiological safety of drinking water and associated health outcomes, and estimated the air pollution burden associated with the use of solid fuels for boiling. Methods: We conducted cross-sectional surveys and collected drinking water samples from 1,033 rural households in Guangxi and Henan provinces. Temperature sensors affixed to pots and electric kettles were used to corroborate self-reported boiling frequencies and durations, which were used to model household air pollution (HAP) in terms of estimated particulate matter ≤2.5μm in aerodynamic diameter (PM2.5) concentrations. Results: Based on summer data collection in both provinces, after controlling for covariates, boiling with electric kettles was associated with the largest log reduction in thermotolerant coliforms (TTCs) (−0.66 log10 TTC most probable number/100mL), followed by boiling with pots (−0.58), and bottled water use (−0.39); all were statistically significant (p<0.001). Boiling with electric kettles was associated with a reduced risk of TTC contamination [risk ratio (RR)=0.25, p<0.001] and reported diarrhea (RR=0.80, p=0.672). TTCs were detected in 51% (n=136) of bottled water samples. For households boiling with biomass, modeled PM2.5 concentrations averaged 79 μg/m3 (standard deviation=21). Discussion: Our findings suggest that where boiling is already common and electricity access is widespread, the promotion of electricity-based boiling may represent a pragmatic stop-gap means of expanding safe water access until centralized, or decentralized, treated drinking water is available; displacing biomass use for water boiling could also reduce HAP concentrations and exposures. Our results also highlight the risks of increasing bottled water use in rural areas, and its potential to displace other sources of safe drinking water, which could in turn hamper efforts in China and other LMICs toward universal and affordable safe water access. https://doi.org/10.1289/EHP7124

. Person/s in the household who usually boils drinking water (any method) by gender, age, and province. Table S2. Mean Thermotolerant Coliform concentrations (MPN) by household water treatment method : Summer data alone and with Guangxi winter data. Table S3. Log 10 Thermotolerant Coliform coefficients from the adjusted model for Guangxi & Henan (summer data) with bottled water cost data included. Table S4. Log 10 Thermotolerant Coliform coefficients from the adjusted model for Guangxi & Henan (summer data) using Maximum Likelihood Estimation. Table S5. Mean temperature and SUMS iButtons data for Guangxi summer and winter study villages. Table S6: Data used to calculate risk ratios for detected Thermotolerant Coliforms and reported diarrhea by household water treatment method: Summer data alone and with Guangxi winter data. Table S7. Modeled air pollution concentrations by province and season.     Table S15. Figure S4. Household water treatment use by household size in thirds. The source data (number of households) are reported in Table S16. Figure S5. Geometric mean of Log 10 concentrations for Total Bacteria (TB), Total Coliforms (TC), and Thermotolerant Coliforms (TTC) by HWT methodwith Guangxi winter data included. The summary data are reported in Table S17. Figure S6. Household water treatment rates during the summer and winter in four Guangxi villages. The source data are reported in Table S18. Figure S7. Bland-Altman (top) and Passing-Bablok (bottom) plots comparing observed (measured via the use of SUMS iButtons) and self-report (collected via survey) data on boiling durations (left) and frequencies (right) for Guangxi Province households during winter data collection. Figure S8. Gender and age of person/s who boiling drinking water by fuel type. The source data (number of households) are reported in Table S19. Figure S9. Histogram of 5,000 estimated concentrations output from the box model for one household. The solid black line is the mean estimate from the 5,000 draws (~125 µg/m 3 ). Table S9. Source data for Figure 1. Proportion of households boiling drinking water and using bottled water by study village.    Figure 7a. Associations between measured/observed and selfreport/survey data for average boiling durations. Table S14. Summary data for Figure 7b. Associations between measured/observed and selfreport/survey data for average daily frequencies of boiling. Table S15. Summary data for Figure S3. Geometric mean of Log10 concentrations as well as counts for Thermotolerant Coliforms (TTC) by JMP-defined a source water classifications. Table S16. Source data for Figure S4. Household water treatment use by household size in thirds. Table S17. Summary data for Figure S5. Geometric mean of Log10 concentrations for Total Bacteria (TB), Total Coliforms (TC), and Thermotolerant Coliforms (TTC) by HWT methodwith Guangxi winter data included. Table S18. Source data for Figure S6. Household water treatment rates during the summer and winter in four Guangxi villages. Table S19. Source data for Figure S8. Gender and age of person/s who boiling drinking water by fuel type.

N 555
Notes: HH = household | * p<0.05; ** p<0.01; *** p<0.001 | Values are Log 10 TTC β coefficients with standard errors (SE) in parentheses. √ψ and √θ are the between-cluster and within-cluster standard deviation, with SE in parentheses. As model fit improves, log-likelihood tends to decrease. The large bottled water price SE is because village means were used for all households in a village. "Improved" water source classifications were based on JMP definitions at the time of the study (WHO/UNICEF, 2014).

N 732
Notes: HH = household | * p<0.05; ** p<0.01; *** p<0.001 | Values are Log 10 TTC β coefficients with standard errors (SE) in parentheses. √ψ and √θ are the between-cluster and within-cluster standard deviation, with SE in parentheses. As model fit improves, log-likelihood tends to decrease. The large bottled water price SE is because village means were used for all households in a village. "Improved" water source classifications were based on JMP definitions at the time of the study (WHO/UNICEF, 2014).    Ventilation index results (1-9 scale) were binned into three categories to represent good (1-3), average (4-6), and poor (7-9) ventilation. Houses were then assigned to these three categories, with category 1 being the most ventilated and category 3 being the least ventilated.  Table S15.
a The Joint Monitoring Programme (JMP) defined "improved sources" as public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, rainwater collection, and piped household water connections (WHO/UNICEF, 2014).

Figure S4: Household water treatment use by household size in thirds.
The source data (number of households) are reported in Table S16.