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Abstract Oil and gas wells pose long-term risks to water resources, yet their cumulative impact remains poorly constrained, particularly for marginal (low productivity) and orphaned sites. Using approximately three million water-quality records across New York, Pennsylvania, Colorado, and Texas, we quantify how well proximity and density are associated with concentrations of oil-and-gas-relevant analytes, including barium, chloride, sodium, sulfate, strontium, and specific conductivity, in surface and groundwater. Unlike prior large-sample studies focused primarily on unconventional development, this analysis jointly evaluates marginal, orphaned, and unconventional wells using a uniform statistical framework. Although these analytes can also arise from natural and non-oil-and-gas sources, statistically significant relationships were widespread, with negative correlations most directly consistent with elevated concentrations near wells. Signals were stronger and more consistent in surface water than in groundwater, and subregional analyses in Texas show that statewide assessments can mask localized patterns controlled by hydrogeology and aquifer connectivity. Together, these results indicate that oil and gas infrastructure leaves a measurable geochemical imprint on water systems and highlight the need for hydrogeology-informed monitoring to detect, predict, and mitigate contamination from legacy and active oil and gas wells. 

Abstract Multi-year El Niño-Southern Oscillation (ENSO) events, where the warming (El Niño) or cooling (La Niña) extends beyond a single year, have become increasingly prominent in recent decades. Using observations and climate model simulations, we show that the South Pacific Oscillation (SPO) plays a crucial, previously unrecognized role in determining whether ENSO evolves into a multi-year event. Specifically, when an El Niño (La Niña) triggers a positive (negative) SPO in the extratropical Southern Hemisphere during its decaying phase, the SPO feedbacks onto the tropical Pacific through the wind-evaporation-sea surface temperature mechanism, helping sustain ENSO into a multi-year event. This SPO–ENSO interaction is absent in single-year ENSO events. Furthermore, whether ENSO can trigger the SPO depends systematically on the central SST anomaly location for El Niños and the anomaly intensity for La Niñas, with interference from atmospheric internal variability. These findings highlight the importance of including off-equatorial processes from the Southern Hemisphere in studies of ENSO complexity dynamics. 

End‐member mixing analysis (EMMA) is widely used to analyze geoscience data for their end‐members and mixing proportions. Many traditional EMMA methods depend on known end‐members, which are sometimes uncertain or unknown. Unsupervised EMMA methods infer end‐members from data, but many existing ones don't strictly follow necessary constraints and lack full mathematical interpretability. Here, we introduce a novel unsupervised machine learning method, simplex projected gradient descent‐archetypal analysis (SPGD‐AA), which uses the ML model archetypal analysis to infer end‐members intuitively and interpretably without prior knowledge. SPGD‐AA uses extreme corners in data as end‐members or “archetypes,” and represents data as mixtures of end‐members. This method is most suitable for linear (conservative) mixing problems when samples with similar characteristics to end‐members are present in data. Validation on synthetic and real data sets, including river chemistry, deep‐sea sediment elemental composition, and hyperspectral imaging, shows that SPGD‐AA effectively recovers end‐members consistent with domain expertise and outperforms conventional approaches. SPGD‐AA is applicable to a wide range of geoscience data sets and beyond. 

Geochemical comparison of groundwater samples in treatment and control zones, and typical anthropogenic and natural sources.