Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications.

Anthropogenic nutrient inputs have led to nutrient enrichment in many waterbodies worldwide, including Chesapeake Bay (USA). River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. We demonstrate with the Chesapeake Bay Non-Tidal Monitoring Network that machine learning approaches - i.e., hierarchical clustering and random forest (RF) classification - can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks, resulting in information useful for watershed management. Cluster analysis revealed regional patterns of short-term TN trends (2007-2018) and categorized the stations into three distinct trend clusters, namely, V-shape (n = 23), monotonic decline (n = 35), and monotonic increase (n = 26). RF models identified regional drivers of TN trend clusters by quantifying the effects of watershed characteristics (land use, geology, physiography) and major N sources on the trend clusters. Results provide encouraging evidence that improved agricultural nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water-quality improvement in our period of analysis. Moreover, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. By contrast, water-quality improvements are less likely in Coastal Plain watersheds, reflecting the effect of legacy N in groundwater. Notably, results show degrading trends in forested watersheds, suggesting new and/or remobilized sources that may compromise management efforts. Finally, the developed RF models were used to predict TN trend clusters for the entire Chesapeake Bay watershed at the fine scale of river segments (n = 979), providing fine spatial information that can facilitate targeted watershed management, including unmonitored areas. More broadly, this combined use of clustering and classification approaches can be applied to other regional monitoring networks to address similar water-quality questions.

Unprocessed Atmospheric Nitrate in Waters of the Northern Forest Region in the U.S. and Canada.

Little is known about the regional extent and variability of nitrate from atmospheric deposition that is transported to streams without biological processing in forests. We measured water chemistry and isotopic tracers (δ18O and δ15N) of nitrate sources across the Northern Forest Region of the U.S. and Canada and reanalyzed data from other studies to determine when, where, and how unprocessed atmospheric nitrate was transported in catchments. These inputs were more widespread and numerous than commonly recognized, but with high spatial and temporal variability. Only 6 of 32 streams had high fractions (>20%) of unprocessed atmospheric nitrate during baseflow. Seventeen had high fractions during stormflow or snowmelt, which corresponded to large fractions in near-surface soil waters or groundwaters, but not deep groundwater. The remaining 10 streams occasionally had some (<20%) unprocessed atmospheric nitrate during stormflow or baseflow. Large, sporadic events may continue to be cryptic due to atmospheric deposition variation among storms and a near complete lack of monitoring for these events. A general lack of observance may bias perceptions of occurrence; sustained monitoring of chronic nitrogen pollution effects on forests with nitrate source apportionments may offer insights needed to advance the science as well as assess regulatory and management schemes.

Ongoing biodegradation of Deepwater Horizon oil in beach sands: Insights from tracing petroleum carbon into microbial biomass.

Heavily weathered petroleum residues from the Deepwater Horizon (DwH) disaster continue to be found on beaches along the Gulf of Mexico as oiled-sand patties. Here, we demonstrate the ongoing biodegradation of weathered Macondo Well (MW) oil residues by tracing oil-derived carbon into active microbial biomass using natural abundance radiocarbon (14C). Oiled-sand patties and non-oiled sand were collected from previously studied beaches in Mississippi, Alabama, and Florida. Phospholipid fatty acid (PLFA) analyses illustrated that microbial communities present in oiled-sand patties were distinct from non-oiled sand. Depleted 14C measurements of PLFA revealed that microbes on oiled-sand patties were assimilating MW oil residues five years post-spill. In contrast, microbes in non-oiled sand assimilated recently photosynthesized carbon. These results demonstrate ongoing biodegradation of weathered oil in sand patties and the utility of 14C PLFA analysis to track the biodegradation of MW oil residues long after other indicators of biodegradation are no longer detectable.