Know Before You Go: Data-Driven Beach Water Quality Forecasting

Forecasting environmental hazards is critical in preventing or building resilience to their impacts on human communities and ecosystems. Environmental data science is an emerging field that can be harnessed for forecasting, yet more work is needed to develop methodologies that can leverage increasingly large and complex data sets for decision support. Here, we design a data-driven framework that can, for the first time, forecast bacterial standard exceedances at marine beaches with 3 days lead time. Using historical data sets collected at two California sites, we train nearly 400 forecast models using statistical and machine learning techniques and test forecasts against predictions from both a naive “persistence” model and a baseline nowcast model. Overall, forecast models are found to have similar sensitivities and specificities to the persistence model, but significantly higher areas under the ROC curve (a metric distinguishing a model’s ability to effectively parse classes across decision thresholds), suggesting that forecasts can provide enhanced information beyond past observations alone. Forecast model performance at all lead times was similar to that of nowcast models. Together, results suggest that integrating the forecasting framework developed in this study into beach management programs can enable better public notification and aid in proactive pollution and health risk management

<i>Escherichia coli</i> Removal in Biochar-Augmented Biofilter: Effect of Infiltration Rate, Initial Bacterial Concentration, Biochar Particle Size, and Presence of Compost

Bioretention systems and biofilters are used in low impact development to passively treat urban stormwater. However, these engineered natural systems are not efficient at removing fecal indicator bacteria, the contaminants responsible for a majority of surface water impairments. The present study investigates the efficacy of biochar-augmented model sand biofilters for <i>Escherichia coli</i> removal under a variety of stormwater bacterial concentrations and infiltration rates. Additionally, we test the role of biochar particle size and “presence of compost on model” biofilter performance. Our results show that <i>E. coli</i> removal in a biochar-augmented sand biofilter is ∼96% and is not greatly affected by increases in stormwater infiltration rates and influent bacterial concentrations, particularly within the ranges expected in field. Removal of fine (<125 μm) biochar particles from the biochar-sand biofilter decreased the removal capacity from 95% to 62%, indicating biochar size is important. Addition of compost to biochar–sand biofilters not only lowered <i>E. coli</i> removal capacity but also increased the mobilization of deposited bacteria during intermittent infiltration. This result is attributed to exhaustion of attachment sites on biochar by the dissolved organic carbon leached from compost. Overall, our study indicates that biochar has potential to remove bacteria from stormwater under a wide range of field conditions, but for biochar to be effective, the size should be small and biochar should be applied without compost. Although the results aid in the optimization of biofilter design, further studies are needed to examine biochar potential in the field over an entire rainy season

DataSheet1.PDF

<p>The transcriptional response of Staphylococcus aureus strain Newman to sunlight exposure was investigated under both oxic and anoxic conditions using RNA sequencing to gain insight into potential mechanisms of inactivation. S. aureus is a pathogenic bacterium detected at recreational beaches which can cause gastrointestinal illness and skin infections, and is of increasing public health concern. To investigate the S. aureus photostress response in oligotrophic seawater, S. aureus cultures were suspended in seawater and exposed to full spectrum simulated sunlight. Experiments were performed under oxic or anoxic conditions to gain insight into the effects of oxygen-mediated and non-oxygen-mediated inactivation mechanisms. Transcript abundance was measured after 6 h of sunlight exposure using RNA sequencing and was compared to transcript abundance in paired dark control experiments. Culturable S. aureus decayed following biphasic inactivation kinetics with initial decay rate constants of 0.1 and 0.03 m² kJ⁻¹ in oxic and anoxic conditions, respectively. RNA sequencing revealed that 71 genes had different transcript abundance in the oxic sunlit experiments compared to dark controls, and 18 genes had different transcript abundance in the anoxic sunlit experiments compared to dark controls. The majority of genes showed reduced transcript abundance in the sunlit experiments under both conditions. Three genes (ebpS, NWMN_0867, and NWMN_1608) were found to have the same transcriptional response to sunlight between both oxic and anoxic conditions. In the oxic condition, transcripts associated with porphyrin metabolism, nitrate metabolism, and membrane transport functions were increased in abundance during sunlight exposure. Results suggest that S. aureus responds differently to oxygen-dependent and oxygen-independent photostress, and that endogenous photosensitizers play an important role during oxygen-dependent indirect photoinactivation.</p

Transport of Fecal Indicators from Beach Sand to the Surf Zone by Recirculating Seawater: Laboratory Experiments and Numerical Modeling

Recirculating seawater is an important component of submarine groundwater discharge, yet its role in transporting microbial contaminants from beach sand to coastal water is unknown. This study investigated the extent to which recirculating seawater carries fecal indicators, <i>Enterococcus</i> and bird-associated <i>Catellicoccus</i>, through the beach subsurface. Laboratory experiments and numerical modeling were performed to characterize the transport of fecal indicators suspended in seawater through medium-grained beach sand under transient and saturated flow conditions. <i>Enterococcus</i> was measured both by culture (cENT) and DNA assay (tENT), and <i>Catellicoccus</i> (CAT) by DNA assay. There were differences between transport of tENT and CAT compared to cENT through laboratory columns containing beach sands. Under transient flow conditions, first-order attachment rate coefficients (<i>k</i>_att) of DNA markers were greater (∼10 h^–1) than <i>k</i>_att of cENT (∼1 h^–1), although under saturated conditions <i>k</i>_att values were similar (∼1 h^–1). First-order detachment rate coefficients, <i>k</i>_det, of DNA markers were greater (∼1 h^–1) than <i>k</i>_det of cENT (∼0.1h^–1) under both types of flow conditions. Incorporating the rate coefficients into field-scale subsurface transport simulations showed that, in this sand type, the contribution of recirculating seawater to surf zone contamination is likely to be minimal unless bird feces are deposited close to the land–sea interface

Details of the filtration columns tested.

<p>Details of the filtration columns tested.</p

Physicochemical properties of the collectors.

<p>Physicochemical properties of the collectors.</p

Removal of <i>E</i>. <i>coli</i> in sand-biochar (70% sand, 30% biochar: by volume) packed 10 cm laboratory columns.

<p>Removal are shown as a) breakthrough curves; b) log removal. Log removal was calculated by numerical integration of the breakthrough curve normalized by total pore volume of <i>E</i>. <i>coli</i> laden synthetic stormwater injected. All experiments were conducted at room temperature. Error bars represent standard deviation between replicate measurements (n = 3). Y- and N- prefix indicate presence and absence (of NOM or biofilm), respectively. For example, N-NOM N-Biofilm means without biofilm cases in the absence of NOM.</p

Removal of <i>E</i>. <i>coli</i> in sand packed 10 cm laboratory columns.

<p>Removal is presented as a) breakthrough curves; b) log removal. Log removal was calculated by numerical integration of the breakthrough curve normalized by total pore volume of injected <i>E</i>. <i>coli</i> laden synthetic stormwater. All experiments were conducted at room temperature. Error bars represent standard deviation between replicate experiments (n = 2). Y- and N- prefix indicate presence and absence (of NOM or biofilm), respectively. For example, N-NOM Y-Biofilm means with biofilm cases in the absence of NOM.</p

<i>Escherichia coli</i> Removal in Biochar-Modified Biofilters: Effects of Biofilm

<div><p>The presence of microbial contaminants in urban stormwater is a significant concern for public health; however, their removal by traditional stormwater biofilters has been reported as inconsistent and inadequate. Recent work has explored the use of biochar to improve performance of stormwater biofilters under simplified conditions that do not consider potential effects of biofilm development on filter media. The present study investigates the role of biofilm on microbial contaminant removal performance of stormwater biofilters. <i>Pseudomonas aeruginosa</i> biofilms were formed in laboratory-scale sand and biochar-modified sand packed columns, which were then challenged with <i>Escherichia coli</i> laden synthetic stormwater containing natural organic matter. Results suggests that the presence of biofilm influences the removal of <i>E</i>. <i>coli</i>. However, the nature of the influence depends on the specific surface area and the relative hydrophobicity of filter media. The distribution of attached bacteria within the columns indicates that removal by filter media varies along the length of the column: the inlet was the primary removal zone regardless of experimental conditions. Findings from this research inform the design of field-scale biofilters for better and consistent performance in removing microbial contaminants from urban stormwater.</p></div

Distribution of retained E. coli bacteria in biochar augmented sand columns.

<p>a) <i>E</i>. <i>coli</i> count per gram of collectors; b) total number influent <i>E</i>. <i>coli</i> (T_c) normalized count of bacteria per gram of collectors. Error bars represent one standard deviation between replicate measurements (n = 3). Y- and N- prefix indicate presence and absence (of NOM or biofilm), respectively. For example, N-NOM N-Biofilm means without biofilm cases in the absence of NOM.</p