Joel Podgorski | Geology Reports

USGS2024

Arsenic and other geogenic contaminants in global groundwater

Geogenic groundwater contaminants (GGCs) affect drinking-water availability and safety, with up to 60% of groundwater sources in some regions contaminated by more than recommended concentrations. As a result, an estimated 300–500 million people are at risk of severe health impacts and premature mortality. In this Review, we discuss the sources, occurrences and cycling of arsenic, fluoride, selenium and uranium, which are GGCs with widespread distribution and/or high toxicity. The global distribution of GGCs is controlled by basin geology and tectonics, with GGC enrichment in both orogenic systems and cratonic basement rocks. This regional distribution is broadly influenced by climate, geomorphology and hydrogeochemical evolution along groundwater flow paths. GGC distribution is locally heterogeneous and affected by in situ lithology, groundwater flow and water–rock interactions. Local biogeochemical cycling also determines GGC concentrations, as arsenic, selenium and uranium mobilizations are strongly redox-dependent. Increasing groundwater extraction and land-use changes are likely to modify GGC distribution and extent, potentially exacerbating human exposure to GGCs, but the net impact of these activities is unknown. Integration of science, policy, community involvement programmes and technological interventions is needed to manage GGC-enriched groundwater and ensure equitable access to clean water.

Nature Reviews Earth & Environment↗

USGS2024

Deep learning for water quality

Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.

Nature Water↗