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Childhood lead exposure through drinking water has long-term effects on cognition and development, and is a significant public health concern. The comprehensive lead testing of public schools entails high expense and time. In prior work, random forest modeling was used successfully to predict the likelihood of lead contamination in the drinking water from schools in the states of California and Massachusetts. In those studies, data from 70% of the schools was used to predict the probability of unsafe water lead levels (WLLs) in the remaining 30%. This study explores how the model predictions degrade, as the training dataset forms a progressively smaller proportion of schools. The size of the training set was varied from 80% to 10% of the total samples in four US states: California, Massachusetts, New York, and New Hampshire. The models were evaluated using the precision-recall area under curve (PR AUC) and area under the receiver operating characteristic curve (ROC AUC). While some states required as few as 10% of the schools to be included in the training set for an acceptable ROC AUC, all four states performed within an acceptable ROC AUC range when at least 50% of the schools were included. The results in New York and New Hampshire were consistent with the prior work that found the most significant predictor in the modeling to be the Euclidean distance to the closest school in the training set demonstrating unsafe WLLs. This study further supports the efficacy of predictive modeling in identifying the schools at a high risk of lead contamination in their drinking water supply, even when the survey data is incomplete on WLLs in all schools.

Accurately predicting the effect of temperature on soil C storage remains a crucial objective in the effort to understand, mitigate, and adapt to climate change. Associations between soil organic C and short‐range order minerals like the iron hydroxide ferrihydrite contribute substantially to C sequestration, but little is known about how these associations respond to changes in temperature. We investigated the effects of temperature (7, 25, and 45 °C) and dissolved organic matter (DOM) source and fraction on DOM sorption to ferrihydrite‐coated sand in continuous flow and batch systems at circumneutral pH. Our findings demonstrate a positive relationship between temperature and adsorption, especially between 25 and 45 °C. For aquatic DOM, desorption also increased appreciably with temperature, whereas desorption of peat and soil DOM was less sensitive. More aquatic DOM adsorbed compared with soil DOM at all temperatures, but flow studies revealed that aquatic DOM also desorbed at higher rates. Modeling of DOM breakthrough curves using the advection‐diffusion equation with a modified Freundlich isotherm suggested the partition coefficient increased over time. Specific ultraviolet absorbance values of initial column effluents were low, suggesting that aromatic‐rich compounds preferentially adsorbed to the mineral surface. Our results indicate that some circumstances may favor the accumulation of DOM on iron mineral surfaces as temperature increases, potentially removing organic substrates from the accessible dissolved C pool. However, the stability of new associations likely depends on the chemical characteristics of DOM and the conditions under which it adsorbed. Core Ideas: DOM sorption to ferrihydrite generally increased with temperature, especially from 25 to 45 °C.More aquatic DOM adsorbed than soil DOM at 7, 25, and 45 °C.Modeling of breakthrough curves suggests that sorption occurred in multiple phases.Low specific ultraviolet absorbance values during initial sorption may indicate preferential sorption of aromatic groups. [ABSTRACT FROM AUTHOR]

The combined use of water erosion models and geographic information systems has facilitated soil loss estimation at the watershed scale. Tools such as the Geo-spatial interface for the Water Erosion Prediction Project (GeoWEPP) model provide a convenient spatially distributed soil loss estimate but require discretization to identify hillslopes and channels. In GeoWEPP, the TOpographic PArameteriZation (TOPAZ) model is used as an automated procedure to extract a watershed boundary, hillslopes and channels from a digital elevation model (DEM). Previous studies in small watersheds have shown that the size of the hillslopes and the channel distribution affect the model estimates, but in large watersheds, the effects on the soil loss estimates have yet to be tested. Therefore, the objective of this study was to evaluate the effect of discretization on the hillslope sediment yield estimates using GeoWEPP in two large watersheds (>10 km2). The watersheds were selected and discretized varying the TOPAZ parameters [critical source area (CSA) and minimum source channel length (MSCL)] in a 30-m resolution digital elevation model. The drainage networks built with TOPAZ were compared with each other using the drainage density index. The results showed that the discretization affected hillslope sediment yield estimates and their spatial distribution more than the total runoff. The drainage density index and the hillslope sediment yield were proportional but inversely related; thus, soil loss estimates were highly affected by the spatial discretization. As a result of this analysis, a method to choose the CSA and MSCL values that generates the greatest fraction of hillslopes having profile lengths less than 200m was developed. This slope length condition is particularly crucial when using the WEPP and GeoWEPP models, in order for them to produce realistic estimates of sheet and rill erosion. Finally, and as a result of this analysis, a more reliable method was developed for selecting the TOPAZ channel network parameters (CSA and MSCL). [ABSTRACT FROM AUTHOR]