Effect of Artificial Solar Radiation on the Die-Off of Pathogen Indicator Organisms in Urban Floods

Abstract

In the last decade, flooding has caused the death of over 60,000 people and affected over 900 million people globally. This is expected to increase as a result of climate change, increased populations and urbanisation. Floods can cause infections due to the release of water-borne pathogenic microorganisms from surcharged combined sewers and other sources of fecal contamination. This research contributes to a better understanding of how the occurrence of water-borne pathogens in contaminated shallow water bodies is affected by different environmental conditions. The inactivation of fecal indicator bacteria Escherichia coli was studied in an open stirred reactor, under controlled exposure to simulated sunlight, mimicking the effect of different latitudes and seasons, and different concentrations of total suspended solids (TSS) corresponding to different levels of dilution and runoff. While attachment of bacteria on the solid particles did not take place, the decay rate coefficient, k (d −1 ), was found to depend on light intensity, I (W m −2 ), and duration of exposure to sunlight, T (h d −1 ), in a linear way ( k = k D + 0.03· I and k = k D + 0.65· T , respectively) and on the concentration of TSS (mg L −1 ), in an inversely proportional exponential way ( k = k D + 14.57·e −0.02· [TSS] ). The first-order inactivation rate coefficient in dark conditions, k D = 0.37 d −1 , represents the effect of stresses other than light. This study suggests that given the sunlight conditions during an urban flood, and the concentration of indicator organisms and TSS, the above equations can give an estimate of the fate of selected pathogens, allowing rapid implementation of appropriate measures to mitigate public health risks. Electronic supplementary material The online version of this article (10.1007/s41742-018-0160-5) contains supplementary material, which is available to authorized users.

Microbial Contamination of Drinking Water Supplied by Private Wells after Hurricane Harvey

“…These reductions could be likely associated with remediation efforts (e.g., well disinfection, flushing, or regular water use), natural attenuation of microbes within groundwater supplies or well water, and/or the decline in samples collected. 7,17,24,25 Flood Impacts Were Not Isolated to Private Wells in Rural Counties. Private wells are often thought of as rural drinking water systems, likely attributed to the high percentage of residents in rural counties reliant on well water; however, urban counties often have substantially larger numbers of well users but well users are a smaller percentage of the total population.…”

“…20 Moreover, there are concerns that users implementing recovery strategies (e.g., well disinfection) may not be effective. 24,25 After Hurricane Harvey, an unprecedented amount of well water testing was conducted by local health departments and university researchers, as 8822 private wells in 44 counties were sampled starting within days of floodwaters receding and continuing for 10 months. In this study, we combined these testing results with publicly available geospatial data to explore whether environmental factors, well system characteristics, time of sampling, and user behaviors influenced testing and contamination rates in the aftermath of Hurricane Harvey.…”

“…However, the occurrence of coliform bacteria in private wells varies substantially even under nonflooded conditionsroutine state and regional surveys report TC- and EC-positive rates ranging from 15–69 to 2–15%, respectively. − The variability in contamination rates has been attributed to factors such as geologic formations, well construction practices, and inherent limitations of grab sampling techniques. ,− Although it is assumed that there is an increased incidence of microbial contamination in private wells after flooding, efforts exploring contamination mechanisms and pathways have usually occurred weeks to months after. ,− Environmental factors (e.g., proximity to floodways and flood fringe) have not been observed to be strongly associated with contamination, leading researchers to suggest that individual well factors (e.g., construction practices) may be more predictive of contamination . Moreover, there are concerns that users implementing recovery strategies (e.g., well disinfection) may not be effective. , …”

Groundwater pollution assessment in a coastal aquifer in Cape Coast, Ghana

“…For 0.8 R 2 0.9, the fit was considered good, and when R 2 < 0.8, the fit was considered weak. The Chick-Watson model [33] in equation [1] below was used to estimate and extrapolate the bacteria die-off coefficient k d (per day) and the time (t) in days needed for complete bacterial inactivation (no E. coli cells in water samples), respectively.…”

“…0.97 in all cases even though exponential and linear distributions also fitted well with R 2 values of at least 0.8 with the exception of exponential fit for KS02 and linear for AS8 which were respectively, 0.56 and 0.75. The estimation of inactivation rates using the Chick-Watson model [33] revealed values from 0.01/day (AS9) to 0.049/day (AS8) (see Table 4). The duration of time extrapolated for complete inactivation in the absence of predators ranged from 32.6 days (AS9) to 1450.9 days for AS8.…”