A new perspective on water quality: Exploring spatial and temporal patterns of impaired waters.

Managing surface water quality is a global challenge, and understanding spatial and temporal patterns of water quality is a key component to effective management. However, analysis of spatiotemporal patterns of impaired waters over broad areas is sparse due to disparate water quality data and variable water quality standards. Thus, here we leverage the Alabama 303(d) List of impaired waters to present a new perspective for investigating spatiotemporal water quality patterns. Every two years, each state in the United States is required to assess its surface water quality and compile a list of impaired waterbodies, meaning waters that do not meet water quality standards for their designated usage - referred to as the 303(d) List. The purpose of the 303(d) List is to identify impaired waters so that corrective action can be taken to reduce pollutant loads and, ultimately, improve water quality. Using GIS, a space time cube was created to analyze and visualize spatiotemporal patterns of the impaired rivers added to the Alabama 303(d) Lists from 1996 to 2022. For this analysis, the percentage of river length impaired out of the total river length, and number of times each impairment cause was listed, were summarized within Alabama sub-basins (Hydrologic Unit Code 8) (n = 51). Trend and hot spot analyses were conducted on the river impairment and causes. There was an up trend in river impairment for eight sub-basins across the state and a downtrend in one sub-basin. Over half of the sub-basins with an up trend in impairment also had an up trend in the number of times pathogens was listed as a cause of impairment. Additionally, coastal sub-basins were found to be a hot spot for river impairment. Interestingly, there was a down trend in the number of times nutrients, ammonia, and siltation were listed as a cause of impairment at the state and sub-basin scales of analysis. Altogether, these findings show the use of spatiotemporal pattern analysis of impaired waters and can indicate where, both spatially and by pollutant, management should prioritize water quality improvement efforts.

Development of the Groundwater Concept Inventory to Measure Groundwater Knowledge in a General Audience.

Groundwater is a critical resource globally, and understanding groundwater processes is vital to ensure sustainable management practices. However, there are many widely held misconceptions and inaccuracies about groundwater, and we currently lack tools to measure groundwater knowledge across large populations and measure how groundwater knowledge relates to management decisions or behaviors. Here, we present a survey instrument, the Groundwater Concept Inventory (GWCI), that has been designed for general audiences to measure groundwater knowledge comparable to that in an introductory geoscience curriculum. The GWCI was developed using ∼1200 responses using an online platform, Amazon Mechanical Turks, to represent a general population. Responses were evaluated using the Rasch model that configures a relationship between person-ability and item-difficulty. We found that the study population displayed similar misconceptions about groundwater compared with previous literature, and that age and education were not strong predictors of GWCI scores. The GWCI can be used by researchers to understand links between knowledge and behavior, and also by other stakeholders to quantify misconceptions about groundwater and target resources for a more informed public.

Investigating the Relationship between Surface Water Pollution and Onsite Wastewater Treatment Systems.

Onsite wastewater treatment systems (OWTSs) are important nonpoint sources (NPSs) of pollution to consider in watershed management. However, limited OWTS data availability makes it challenging to account for them as an NPS of water pollution. In this study, we succeeded in obtaining OWTS permits and integrated them with environmental data to model the pollution potential from OWTSs at the watershed scale using GIS-based multicriteria decision analysis. Then, in situ water quality parameters─Escherichia coli (E. coli), total nitrogen, total phosphorus, temperature, and pH─were measured along the main tributary at base-flow conditions. Three general linear models were developed to relate E. coli to water quality parameters and OWTS pollution indicators. It was found that the model with the OWTS pollution potential had the lowest corrected Akaike information criterion (AICc) value (35.01) compared to the models that included classified OWTS pollution potential input criteria (AICc = 36.76) and land cover (AICc = 36.74). These results demonstrate that OWTSs are a significant contributor to surface water pollution, and future efforts should be made to improve access to OWTS data (i.e., location and age) to account for these systems as an NPS of water pollution.

Honey bee colony loss linked to parasites, pesticides and extreme weather across the United States.

Honey bee (Apis mellifera) colony loss is a widespread phenomenon with important economic and biological implications, whose drivers are still an open matter of investigation. We contribute to this line of research through a large-scale, multi-variable study combining multiple publicly accessible data sources. Specifically, we analyzed quarterly data covering the contiguous United States for the years 2015-2021, and combined open data on honey bee colony status and stressors, weather data, and land use. The different spatio-temporal resolutions of these data are addressed through an up-scaling approach that generates additional statistical features which capture more complex distributional characteristics and significantly improve modeling performance. Treating this expanded feature set with state-of-the-art feature selection methods, we obtained findings that, nation-wide, are in line with the current knowledge on the aggravating roles of Varroa destructor and pesticides in colony loss. Moreover, we found that extreme temperature and precipitation events, even when controlling for other factors, significantly impact colony loss. Overall, our results reveal the complexity of biotic and abiotic factors affecting managed honey bee colonies across the United States.

Spatial distribution and mass transport of Perfluoroalkyl Substances (PFAS) in surface water: A statewide evaluation of PFAS occurrence and fate in Alabama.

Per- and polyfluoroalkyl substances (PFAS) have been previously detected near suspected sources in Alabama, but the overall extent of contamination across the state is unknown. This study evaluated the spatial distribution of 17 PFAS within the ten major river basins in Alabama and provided insights into their transport and fate through a mass flux analysis. Six PFAS were identified in 65 out of the 74 riverine samples, with mean ∑6PFAS levels of 35.2 ng L-1. The highest ∑6PFAS concentration of 237 ng L-1 was detected in the Coosa River, a transboundary river that receives discharges from multiple sources in Alabama and Georgia. PFAS distribution was not observed to be uniform across the state: while the Coosa, Alabama, and Chattahoochee rivers presented relatively high mean ∑6PFAS concentrations of 191, 100 and 28.8 ng L-1, respectively, PFAS were not detected in the Conecuh, Escatawpa, and Yellow rivers. Remaining river systems presented mean ∑6PFAS concentrations between 7.94 and 24.7 ng L-1. Although the short-chain perfluoropentanoic acid (PFPeA) was the most detected analyte (88%), perfluorobutanesulfonic acid (PFBS) was the substance with the highest individual concentration of 79.4 ng L-1. Consistent increases in the mass fluxes of PFAS were observed as the rivers flowed through Alabama, reaching up to 63.3 mg s-1, indicating the presence of numerous sources across the state. Most of the mass inputs would not have been captured if only aqueous concentrations were evaluated, since concentration is usually heavily impacted by environmental conditions. Results of this study demonstrate that mass flux is a simple and powerful complementary approach that can be used to broadly understand trends in the transport and fate of PFAS in large river systems.

GIS interpolation is key in assessing spatial and temporal bioremediation of groundwater arsenic contamination.

Arsenic (As) contamination in groundwater is a global crisis that is known to cause cancers of the skin, bladder, and lungs, among other health issues, and affects millions of people around the world. Due to the time and financial constraints associated with establishing in-depth monitoring programs, it is difficult to monitor and map arsenic concentrations over time and across large areas. The goal of this study was to determine the most accurate Geographic Information Systems (GIS) interpolation method for mapping the effects of bioremediation on groundwater arsenic sequestration across a local-scale study area in northwest Florida (~900 m2) over the duration of a nine-month period (pre-injection, one-month post-injection, and nine-months post-injection). We used groundwater data collected from 2018 to 2019 to visualize arsenic contamination over time. Measured arsenic concentrations from 23 wells were grouped into three categories: (1) decreasing, (2) fluctuating, or (3) largely unaffected by the bioremediation procedure. The accuracy of three interpolation methods was also investigated: Inverse Distance Weighted (IDW), Ordinary Kriging (OK), and Empirical Bayesian Kriging (EBK). Statistical results using the leave-one-out cross validation (LOOCV) process showed that OK consistently provided the most accurate predictions of arsenic concentrations across space and time ([Root Mean Square Error (RMSE) = 0.265] and accurately predicted regulatory arsenic concentrations below 0.05 mg/L in nine of 11 wells, while IDW and EBK only accurately predicted four and five wells, respectively. While it was shown that OK tends to underpredict arsenic maxima, this did not affect the overall accuracy of the interpolation compared to results from EBK (RMSE = 0.297) and IDW (RMSE = 0.272). Overall, these interpolations aided in the interpretation of the extent of bioremediation, revealing the need for repeated injections to continuously remove arsenic from the groundwater. The study will provide guidance and evaluation methods for international and governmental organizations, industrial companies, and local communities on how to understand spatial and temporal distributions of arsenic contamination and inform bioremediation efforts at various scales in the future.

Seno-destructive smooth muscle cells in the ascending aorta of patients with bicuspid aortic valve disease.

BACKGROUND: Ascending aortic aneurysms constitute an important hazard for individuals with a bicuspid aortic valve (BAV). However, the processes that degrade the aortic wall in BAV disease remain poorly understood. METHODS: We undertook in situ analysis of ascending aortas from 68 patients, seeking potentially damaging cellular senescence cascades. Aortas were assessed for senescence-associated-ß-galactosidase activity, p16Ink4a and p21 expression, and double-strand DNA breaks. The senescence-associated secretory phenotype (SASP) of cultured-aged BAV aortic smooth muscle cells (SMCs) was evaluated by transcript profiling and consequences probed by combined immunofluorescence and circular polarization microscopy. The contribution of p38 MAPK signaling was assessed by immunostaining and blocking strategies. FINDINGS: We uncovered SMCs at varying depths of cellular senescence within BAV- and tricuspid aortic valve (TAV)-associated aortic aneurysms. Senescent SMCs were also abundant in non-aneurysmal BAV aortas but not in non-aneurysmal TAV aortas. Multivariable analysis revealed that BAV disease independently associated with SMC senescence. Furthermore, SMC senescence was heightened at the convexity of aortas associated with right-left coronary cusp fusion. Aged BAV SMCs had a pronounced collagenolytic SASP. Moreover, senescent SMCs in the aortic wall were enriched with surface-localized MMP1 and surrounded by weakly birefringent collagen fibrils. The senescent-collagenolytic SMC phenotype depended on p38 MAPK signaling, which was chronically activated in BAV aortas. INTERPRETATION: We have identified a cellular senescence-collagen destruction axis in at-risk ascending aortas. This novel "seno-destructive" SMC phenotype could open new opportunities for managing BAV aortopathy. FUND: Canadian Institutes of Health Research, Lawson Health Research Institute, Heart and Stroke Foundation of Ontario/Barnett-Ivey Chair.

Ku70 Serine 155 mediates Aurora B inhibition and activation of the DNA damage response.

The Ku heterodimer (Ku70/Ku80) is the central DNA binding component of the classical non-homologous end joining (NHEJ) pathway that repairs DNA double-stranded breaks (DSBs), serving as the scaffold for the formation of the NHEJ complex. Here we show that Ku70 is phosphorylated on Serine 155 in response to DNA damage. Expression of Ku70 bearing a S155 phosphomimetic substitution (Ku70 S155D) in Ku70-deficient mouse embryonic fibroblasts (MEFs) triggered cell cycle arrest at multiple checkpoints and altered expression of several cell cycle regulators in absence of DNA damage. Cells expressing Ku70 S155D exhibited a constitutive DNA damage response, including ATM activation, H2AX phosphorylation and 53BP1 foci formation. Ku70 S155D was found to interact with Aurora B and to have an inhibitory effect on Aurora B kinase activity. Lastly, we demonstrate that Ku and Aurora B interact following ionizing radiation treatment and that Aurora B inhibition in response to DNA damage is dependent upon Ku70 S155 phosphorylation. This uncovers a new pathway where Ku may relay signaling to Aurora B to enforce cell cycle arrest in response to DNA damage.