Impact of Hurricane Maria on Drinking Water Quality in Puerto Rico.

This study performed a comprehensive assessment of the impact of Hurricane Maria (HM) on drinking water quality in Puerto Rico (PR) by integrating targeted chemical analysis of both inorganic (18 trace elements) and organic trace pollutants (200 micropollutants) with high-throughput quantitative toxicogenomics and in vitro biomarkers-based toxicity assays. Average concentrations of 14 detected trace elements and 20 organic micropollutants showed elevation after HM. Arsenic, sucralose, perfluorooctanoic acid (PFOA), atrazine-2-hydroxy, benzotriazole, acesulfame, and prometon were at significantly (p < 0.05) higher levels in the post-HM than in the pre-HM samples. Thirteen micropollutants, including four pesticides, were only detected in posthurricane samples. Spatial comparison showed higher pollutant and toxicity levels in the samples from northern PR (where eight Superfund sites are located) than in those from southern PR. Distinctive pathway-specific molecular toxicity fingerprints for water extracts before and after HM and at different locations revealed changes in toxicity nature that likely resulted from the impact of HM on drinking water composition. Correlation analysis and Maximum Cumulative Ratio assessment suggested that metals (i.e., arsenic) and PFOA were the top ranked pollutants that have the potential to cause increased risk after HM, providing a possible direction for future water resource management and epidemiological studies.

Fall Creek Monitoring Station: Using Environmental Covariates To Predict Micropollutant Dynamics and Peak Events in Surface Water Systems.

This research aimed to further our understanding of how environmental processes control micropollutant dynamics in surface water systems as a means to predict peak concentration events and inform intermittent sampling strategies. We characterized micropollutant concentrations in daily composite samples from the Fall Creek Monitoring Station over 18 months. These data were compiled alongside environmental covariates, including daily measurements of weather, hydrology, and water quality parameters, to generate a novel data set with high temporal resolution. We evaluated the temporal trends of several representative micropollutants, along with cumulative metrics of overall micropollutant contamination, by means of multivariable analyses to determine which combination of covariates best predicts micropollutant dynamics and peak events. Peak events of agriculture-derived micropollutants were best predicted by positive associations with turbidity and UV254 absorbance and negative associations with baseflow index. Peak events of wastewater-derived micropollutants were best predicted by positive associations with alkalinity and negative associations with streamflow rate. We demonstrate that these predictors can be used to inform intermittent sampling strategies aimed at capturing peak events, and we generalize the approach so that it could be applied in other watersheds. Finally, we demonstrate how our approach can be used to contextualize micropollutant data derived from infrequent grab samples.

Exploring co-occurrence patterns between organic micropollutants and bacterial community structure in a mixed-use watershed.

Complex mixtures of low concentrations of organic micropollutants are commonly found in rivers and streams, but their relationship to the structure of native bacterial communities that underlie critical ecological goods and services in these systems is poorly understood. To address this knowledge gap, we used correlation-based network analysis to explore co-occurrence patterns between measured micropollutant concentrations and the associated surface water and sediment bacterial communities in a restored riparian zone of the Des Plaines River (DPR) in Illinois that is impacted by both wastewater treatment plant (WWTP) effluent and agricultural runoff. Over a two year period, we collected 55 grab samples at 11 sites along the DPR and one of its tributaries (48 surface water samples) and from WWTP effluent (7 samples), and screened for 126 organic micropollutants. In parallel, we used high-throughput 16S rRNA gene amplicon sequencing to characterize the bacterial community in sediment and surface water. Our results revealed quantifiable levels of 102 micropollutants in at least one surface water or WWTP effluent sample, 85 of which were detected in at least one surface water sample. While micropollutants were temporally and spatially variable in terms of both presence and concentration, 21 micropollutants were measured in over 75% of the 48 surface water samples. 16S rRNA gene sequencing documented diverse bacterial communities along the DPR transect, with highly distinct community structures observed in sediment and water. Bacterial community structure in surface water, but not in sediment, was significantly associated with concentrations of micropollutants, based on a Mantel test. Correlation-based network analyses revealed diverse strong and significant co-occurrence and co-exclusion patterns between specific bacterial OTUs and both micropollutant groups (defined based on k-means clustering on chemical substructure) and individual micropollutants. Significantly more associations were documented between micropollutants and bacterial taxa in the water compared to the sediment microbiomes. Taken together, our results document a significant link between complex mixtures of micropollutants commonly found in aquatic systems and associated bacterial community structure. Furthermore, our results suggest that micropollutants may exert a more significant impact on water-associated than on sediment-associated bacterial taxa.

Fall Creek Monitoring Station: Highly Resolved Temporal Sampling to Prioritize the Identification of Nontarget Micropollutants in a Small Stream.

The goal of this research was to comprehensively characterize the occurrence and temporal dynamics of target and nontarget micropollutants in a small stream. We established the Fall Creek Monitoring Station in March 2017 and collected daily composite samples for one year. We measured water samples by means of high-resolution mass spectrometry and developed and optimized a postacquisition data processing workflow to screen for 162 target micropollutants and group all mass spectral (MS) features into temporal profiles. We used hierarchical clustering analysis to prioritize nontarget MS features based their similarity to target micropollutant profiles and developed a high-throughput pipeline to elucidate the structures of prioritized nontarget MS features. Our analyses resulted in the identification of 31 target micropollutants and 59 nontarget micropollutants with varying levels of confidence. Temporal profiles of the 90 identified micropollutants revealed unexpected concentration-discharge relationships that depended on the source of the micropollutant and hydrological features of the watershed. Several of the nontarget micropollutants have not been previously reported including pharmaceutical metabolites, rubber vulcanization accelerators, plasticizers, and flame retardants. Our data provide novel insights on the temporal dynamics of micropollutant occurrence in small streams. Further, our approach to nontarget analysis is general and not restricted to highly resolved temporal data acquisitions or samples collected from surface water systems.

Widespread Micropollutant Monitoring in the Hudson River Estuary Reveals Spatiotemporal Micropollutant Clusters and Their Sources.

The objective of this study was to identify sources of micropollutants in the Hudson River Estuary (HRE). We collected 127 grab samples at 17 sites along the HRE over 2 years and screened for up to 200 micropollutants. We quantified 168 of the micropollutants in at least one of the samples. Atrazine, gabapentin, metolachlor, and sucralose were measured in every sample. We used data-driven unsupervised methods to cluster the micropollutants on the basis of their spatiotemporal occurrence and normalized-concentration patterns. Three major clusters of micropollutants were identified: ubiquitous and mixed-use (core micropollutants), sourced from sewage treatment plant outfalls (STP micropollutants), and derived from diffuse upstream sources (diffuse micropollutants). Each of these clusters was further refined into subclusters that were linked to specific sources on the basis of relationships identified through geospatial analysis of watershed features. Evaluation of cumulative loadings of each subcluster revealed that the Mohawk River and Rondout Creek are major contributors of most core micropollutants and STP micropollutants and the upper HRE is a major contributor of diffuse micropollutants. These data provide the first comprehensive evaluation of micropollutants in the HRE and define distinct spatiotemporal micropollutant clusters that are linked to sources and conserved across surface water systems around the world.

Removal of micropollutants in biofilters: Hydrodynamic effects on biofilm assembly and functioning.

Global water resources contain a variety of micropollutants (MPs), including pharmaceuticals, personal care products, and pesticides. This study investigated the removal of MPs during drinking water production by means of biofiltration. The objective of this work was to investigate the influence of hydrodynamics on biofilm growth and development in a biofiltration process and the consequent effect on MP biotransformation rates. We operated three groups of biofiltration columns continuously for 381 days under three distinct hydrodynamic regimes (superficial velocity: 10, 20, 40 cm h-1) and fed them a mixture of 29 micropollutants at low concentrations. Total protein concentrations were used as a surrogate measurement for attached biomass and periodic tracer experiments were conducted to estimate dispersivity and assess changes in the depth of the biological zone in each biofilter. These data revealed significant differences in biofilm assembly among the biofilters; higher superficial velocities led to less concentrated surface biomass but a deeper biological zone and more total biomass. Eleven of the 29 MPs were biotransformed and nine of those could be evaluated to estimate biotransformation rates. The second-order rate constants for all nine MPs were not significantly different among the hydrodynamic regimes. However, a depth-based analysis of biotransformation rates revealed significantly greater second-order rate constants for 5 of the MPs at increasing biofilter depths, suggesting that sparse microbial communities found in deeper and more oligotrophic biofilters had a greater activity for the biotransformation of these MPs. The identification of several transformation products at similar relative distributions suggests that the greater activity was not the result of changing metabolic processes under more oligotrophic conditions. These results improve our fundamental understanding of biofilm assembly and functioning in biofiltration processes.

Water quantity and quality response of a green roof to storm events: Experimental and monitoring observations.

Syracuse, New York is working under a court-ordered agreement to limit combined sewer overflows (CSO) to local surface waters. Green infrastructure technologies, including green roofs, are being implemented as part of a CSO abatement strategy and to develop co-benefits of diminished stormwater runoff, including decreased loading of contaminants to the wastewater system and surface waters. The objective of this study was to examine the quantity and quality of discharge associated with precipitation events over an annual cycle from a green roof in Syracuse, NY and to compare measurements from this monitoring program with results from a roof irrigation experiment. Wet deposition, roof drainage, and water quality were measured for 87 storm events during an approximately 12 month period over 2011-2012. Water and nutrient (total phosphorus, total nitrogen, and dissolved organic carbon) mass balances were conducted on an event basis to evaluate retention annually and during the growing and non-growing seasons. These results are compared with a hydrological manipulation experiment, which comprised of artificially watering of the roof. Loadings of nutrients were calculated for experimental and actual storms using the concentration of nutrients and the flow data of water discharging the roof. The green roof was effective in retaining precipitation quantity from storm events (mean percent retention 96.8%, SD = 2.7%, n = 87), although the relative fraction of water retained decreased with increases in the size of the event. There was no difference in water retention of the green roof for the growing and non-growing seasons. Drainage waters exhibited high concentration of nutrients during the warm temperature growing season, particularly total nitrogen and dissolved organic carbon. Overall, nutrient losses were low because of the strong retention of water. However, there was marked variation in the retention of nutrients by season due to variations in concentrations in roof runoff.