Providing access to safe drinking water is a global challenge, for which groundwater is increasingly being used throughout the world. However, geogenic contaminants limit the suitability of groundwater for domestic purposes over large geographic areas across most continents. Geogenic contaminants in groundwater are often evaluated individually, but here we demonstrate the need to evaluate multiple contaminants to ensure that groundwater is safe for human consumption and agricultural usage. We compiled groundwater chemical data from three aquifer regions across the world that have been reported to have widespread As and Mn contamination including the Glacial Aquifer in the U.S., the Ganges-Brahmaputra-Mehta Basin within Bangladesh, and the Mekong Delta in Cambodia, along with newly sampled wells in the Yangtze River Basin of China. The proportion of contaminated wells increase by up to 40% in some cases when both As and Mn contaminants are considered. Wilcoxon rank-sum analysis indicates that Mn contamination consistently occurs at significantly shallower depths than As contaminated wells in all regions. Arsenic concentrations in groundwater are well predicted by redox indicators (Eh and dissolved oxygen) whereas Mn shows no significant relationship with either parameter. These findings illustrate that the number of safe wells may be drastically overestimated in some regions when Mn contamination is not taken into account and that depth may be used as a distinguishing variable in efforts to predict the presence of groundwater contaminants regionally.

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Abstract. We compared dry and rainy season water sources and their quality in the urban region of Port Harcourt, Nigeria. Representative sampling indicated that municipal water supplies represent < 1% of the water sources. Residents rely on privately constructed and maintained boreholes that are supplemented by commercially packaged bottled and sachet drinking water. Contamination by thermotolerant coliforms increased from 21% of drinking water sources in the dry season to 42% of drinking water sources in the rainy season (N = 356 and N = 397). The most significant increase was in sachet water, which showed the lowest frequencies of contamination in the dry season compared with other sources (15%, N = 186) but the highest frequencies during the rainy season (59%, N = 76). Only half as many respondents reported drinking sachet water in the rainy season as in the dry season. Respondents primarily used flush or pour-flush toilets connected to septic tanks (85%, N = 399). The remainder relied on pit latrines and hanging (pier) latrines that drained into surface waters. We found significant associations between fecal contamination in boreholes and the nearby presence of hanging latrines. Sanitary surveys of boreholes showed that more than half were wellconstructed, and we did not identify associations between structural or site deficiencies and microbial water quality. The deterioration of drinking water quality during the rainy season is a serious public health risk for both untreated groundwater and commercially packaged water, highlighting a need to address gaps in monitoring and quality control.

The relationships among land use patterns, geology, soil, and major solute concentrations in stream water for eight tributaries of the Kayaderosseras Creek watershed in Saratoga County, NY, were investigated using Pearson correlation coefficients and multivariate regression analysis. Sub-watersheds corresponding to each sampling site were delineated, and land use patterns were determined for each of the eight sub-watersheds using GIS. Four land use categories (urban development, agriculture, forests, and wetlands) constituted more than 99 % of the land in the sub-watersheds. Eleven water chemistry parameters were highly and positively correlated with each other and urban development. Multivariate regression models indicated urban development was the most powerful predictor for the same eleven parameters (conductivity, TN, TP, NO[Formula: see text], Cl(-), HCO(-)3, SO9(2-)4, Na(+), K(+), Ca(2+), and Mg(2+)). Adjusted R(2) values, ranging from 19 to 91 %, indicated that these models explained an average of 64 % of the variance in these 11 parameters across the samples and 70 % when Mg(2+) was omitted. The more common R (2), ranging from 29 to 92 %, averaged 68 % for these 11 parameters and 72 % when Mg(2+) was omitted. Water quality improved most with forest coverage in stream watersheds. The strong associations between water quality variables and urban development indicated an urban source for these 11 water quality parameters at all eight sampling sites was likely, suggesting that urban stream syndrome can be detected even on a relatively small scale in a lightly developed area. Possible urban sources of Ca(2+) and HCO(-)3 are suggested.

Water quality monitoring is important for identifying public health risks and ensuring water safety. However, even when water sources are tested, many institutions struggle to access data for immediate action or long-term decision-making. We analyzed water testing structures among 26 regulated water suppliers and public health surveillance agencies across six African countries and identified four water quality data management typologies. Within each typology, we then analyzed the potential for information and communication technology (ICT) tools to facilitate water quality information flows. A consistent feature of all four typologies was that testing activities occurred in laboratories or offices, not at water sources; therefore, mobile phone-based data management may be most beneficial for institutions that collect data from multiple remote laboratories. We implemented a mobile phone application to facilitate water quality data collection within the national public health agency in Senegal, Service National de l’Hygiène. Our results indicate that using the phones to transmit more than just water quality data will likely improve the effectiveness and sustainability of this type of intervention. We conclude that an assessment of program structure, particularly its data flows, provides a sound starting point for understanding the extent to which ICTs might strengthen water quality monitoring efforts.