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Onsite wastewater treatment systems (OWTSs), such as septic systems, are widely used in the United States, with 16.4% of households relying on them. OWTSs process approximately 4 billion gallons of wastewater per day, yet only about half is safely treated. Identifying factors contributing to impaired functionality is crucial for developing effective management and monitoring strategies and protecting environmental and human health. This study uses a machine learning approach and a unique data set from Athens-Clarke County, Georgia, to predict OWTS failures based on environmental and system-specific variables. The three main predictors of impaired OWTS function were the number of bedrooms (25.4%), height above stream (18.6%), and system age (16.2%), with both older and younger systems prone to failure. Our findings suggest there is a need to reevaluate construction guidelines for effective tank and drainfield sizing, placement, and construction, and our findings indicate that additional training for permitters, installers, and homeowners may be beneficial. Our work demonstrates the power of using machine learning to assess OWTS function, which can better enable local governments and environment managers to identify areas in need of infrastructure and educational investment with limited data and highlights the data types that local jurisdictions should prioritize for collection. 

Abstract Studies of annual patterns of ecosystem metabolism in rivers have primarily been conducted in temperate ecosystems, and little is known about metabolic regimes of tropical rivers. We estimated ecosystem metabolism in four nonwadeable rivers in southern México that varied in size and the extent of human disturbance. The smaller rivers with limited human disturbance showed reduced gross primary production (GPP; 1.0 and 1.7 g O₂m⁻² d⁻¹), ecosystem respiration (ER; − 1.9 g O₂m⁻²d⁻¹), and net ecosystem production (NEP) approaching autotrophy (− 0. 8 and − 0.3 g O₂m⁻²d⁻¹) relative to rivers draining larger, more disturbed catchments (GPP, 1.2 and 2.7 g O₂m⁻²d⁻¹; ER, − 5.7 and − 6.9 g O₂m⁻²d⁻¹; NEP, − 3.8 and − 3.7 g O₂m⁻²d⁻¹). In all rivers, GPP and ER varied seasonally with discharge. The smaller rivers exhibited a distinct pattern of greater and sustained GPP during periods of low discharge, a seasonal metabolic regime we describe as “flow decline.” In general, process–discharge relationships exhibited thresholds, with an initial decline in GPP and ER, with increasing discharge and an increase in ER at higher flows. Relative to larger and more disturbed watersheds, smaller rivers showed a more constrained metabolic fingerprint. Annual NEP (− 1033 and − 641 g C m⁻² yr⁻¹) in the larger rivers was more negative than the global average, supporting evidence from other studies that tropical rivers are greater contributors to CO²emissions than temperate ecosystems. Our study indicates that hydrological seasonality is a major driver of metabolism in tropical rivers. 

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable, and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose-decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose-decomposition rates, when combined with genus-level litter-quality attributes, explain published leaf-litter-decomposition rates with impressive accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth, and reveals rapid decomposition across continental-scale areas dominated by human activities. v1.0 first data release includes all code for models, analyses, and figures. v1.1 addition of code for a new supplemental figure (Figure S1) v1.2 includes new color schemes for all figures, and new title 

This dataset contains tabular data at three scales (city, tract, and synoptic site) and related vector shapefiles (for watersheds or buffers around synoptic sites) for areas included in the Carbon in Urban River Biogeochemistry Project (CURB) to assess how social, built, and biophysical factors shape aquatic functions. The city scale included 486 urban areas in the continental United States with greater than 50,000 residents. Tabular data are provided for each urban area (CURB_CensusUrbanArea.csv) and all U.S. Census tracts within seven urban areas (Atlanta, GA, Boston, MA, Miami, FL, Phoenix, AZ, Portland, OR, Salt Lake City, UT, and San Francisco, CA; CURB_CensusTract.csv) to characterize a range of social, built, and biophysical factors. In six focal cities (Baltimore, MD, Boston, MA, Atlanta, GA, Miami, FL, Salt Lake City, UT, and Portland, OR) up to 100 sites were selected for synoptic water quality sampling. For each synoptic site tabular data (CURB_SynopticSite.csv) are provided to characterize a range of social, built, and biophysical factors within the watershed (Atlanta, Baltimore, Boston, Portland, Salt Lake City) or within a buffer of the site (Miami). Vector shapefiles are provided for the watershed boundaries (CURB_Synoptic_Watersheds.zip) for all synoptic sites in each city except Miami, FL where 400-m buffers (CURB_Miami_Synoptic_Buffers.zip) around the synoptic site were used.