Climate change and urbanization can increase pressures on groundwater resources, but little is known about how groundwater quality will change. Here, we use a global synthesis (n = 9,404) to reveal the drivers of dissolved organic carbon (DOC), which is an important component of water chemistry and substrate for microorganisms that control biogeochemical reactions. Dissolved inorganic chemistry, local climate and land use explained~31% of observed variability in groundwater DOC, whilst aquifer age explained an additional 16%. We identify a 19% increase in DOC associated with urban land cover. We predict major groundwater DOC increases following changes in precipitation and temperature in key areas relying on groundwater. Climate change and conversion of natural or agricultural areas to urban areas will decrease groundwater quality and increase water treatment costs, compounding existing constraints on groundwater resources.
We assess the use of fluorescent dissolved organic matter at excitation-emission wavelengths of 280nm and 360nm, termed tryptophan-like fluorescence (TLF), as an indicator of faecally contaminated drinking water. A significant logistic regression model was developed using TLF as a predictor of thermotolerant coliforms (TTCs) using data from groundwater- and surface water-derived drinking water sources in India, Malawi, South Africa and Zambia. A TLF threshold of 1.3ppb dissolved tryptophan was selected to classify TTC contamination. Validation of the TLF threshold indicated a false-negative error rate of 15% and a false-positive error rate of 18%. The threshold was unsuccessful at classifying contaminated sources containing <10 TTC cfu per 100mL, which we consider the current limit of detection. If only sources above this limit were classified, the false-negative error rate was very low at 4%. TLF intensity was very strongly correlated with TTC concentration (ρ=0.80). A higher threshold of 6.9ppb dissolved tryptophan is proposed to indicate heavily contaminated sources (≥100 TTC cfu per 100mL). Current commercially available fluorimeters are easy-to-use, suitable for use online and in remote environments, require neither reagents nor consumables, and crucially provide an instantaneous reading. TLF measurements are not appreciably impaired by common intereferents, such as pH, turbidity and temperature, within typical natural ranges. The technology is a viable option for the real-time screening of faecally contaminated drinking water globally.
Microbial water quality is frequently assessed with a risk indicator approach that relies on Escherichia coli. Relying exclusively on E. coli is limiting, particularly in low-resource settings, and we argue that risk assessments could be improved by a complementary parameter, tryptophan-like fluorescence (TLF). Over two campaigns (June 2016 and March 2017) we sampled 37 water points in rural Kwale County, Kenya for TLF, E. coli and thermotolerant coliforms (total n = 1082). Using three World Health Organization defined classes (very high, high, and low/intermediate), risk indicated by TLF was not significantly different from risk indicated by E. coli (p = 0.85). However, the TLF and E. coli risk classifications did show disagreement, with TLF indicating higher risk for 14% of samples and lower risk for 13% of samples. Comparisons of duplicate/replicate results demonstrated that precision is higher for TLF (average relative percent difference of duplicates = 14%) compared to culture-based methods (average RPD of duplicates ≥ 26%). Additionally, TLF sampling is more practical because it requires less time and resources. Precision and practicality make TLF well-suited to high-frequency sampling in low resource contexts. Interpretation and interference challenges are minimised when TLF is measured in groundwaters, which typically have low dissolved organic carbon, relatively consistent temperature, negligible turbidity and pH between 5 and 8. TLF cannot be used as a proxy for E. coli on an individual sample basis, but it can add value to groundwater risk assessments by improving prioritization of sampling and by increasing understanding of spatiotemporal variability.
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