A REVIEW OF WATER QUALITY RESPONSES TO AIR TEMPERATURE AND PRECIPITATION CHANGES 2: NUTRIENTS, ALGAL BLOOMS, SEDIMENT, PATHOGENS

Elevation-associated pathways mediate aquatic biodiversity at multi-trophic levels along a plateau inland river

Aquatic biodiversity plays a significant role in maintaining the ecological balance and the overall health of riverine ecosystems. Elevation is an important factor influencing biodiversity patterns. However, it is still unclear through which pathway elevation influences riverine biodiversity at different trophic levels. In this study, the elevation-associated pathways affecting aquatic biodiversity at different trophic levels were explored using structural equation modeling (SEM) and taking the Bayin River, China as the case. The results showed that the elevational patterns were different among aquatic organisms at different trophic levels. For macroinvertebrates and bacteria, the pattern was hump-shaped; while for phytoplankton and zooplankton, it was U-shaped. Building upon these observed elevational patterns, our investigation delved into the direct and indirect pathways through which elevation influences aquatic biodiversity. We found that elevation exerts an impact on aquatic biodiversity via indirect pathways. For all aquatic organisms investigated, the major pathway through which elevation influences biodiversity is mediated by water temperature and water quality. For aquatic organisms at higher trophic levels, like macroinvertebrates and zooplankton, the crucial pathway is also mediated by the landscape. The results of this study contributed to understanding the effects of elevation on aquatic organisms at different trophic levels and provided an important basis for the assessment of riverine biodiversity at large scales.

Quantitative modelling reservoir microalgae proliferation in response to water-soluble anions and cations influx

Atmospheric precipitation deposits acid-forming substances into surface water. However, the effects of water-soluble components on microalgae proliferation are poorly understood. This study analysed the growth characteristics of three microalgae bioindicators of water quality: Scenedesmus quadricauda, Chlorella vulgaris, and Scenedesmus obliquus, adopting on-site monitoring, culture experiments simulating 96 types of water by supplementing anions and cations, and predictive modelling. The result quantified pH > 3.0 rain with dominant Ca2+, Mg2+, and K+ cations, together with anions of NO3- and SO42-. The presence of Ca2+ of up to 0.1 mM and Mg2+ concentrations (>0.5 mM) suppressed Scenedesmus quadricauda growth. Soluble ions, luminosity, and pH had significant impacts (p ≤ 0.01) on increased microalgae proliferation. A newly proposed microalgae growth model predicted a 10.7-fold increase in cell density six days post-incubation in the case of rainfall. The modelling supports algal outbreaks and delays prediction during regional water cycles.

Effects of wastewater treatment plant effluent on microbial risks of pathogens and their antibiotic resistance in the receiving river

The increase in effluent discharge from wastewater treatment plants (WWTPs) into urban rivers has raised concerns about the potential effects on pathogen risks. This study utilized metagenomic sequencing combined with flow cytometry to analyze pathogen concentrations and antibiotic resistance in a typical effluent-receiving river. Quantitative microbial risk assessment (QMRA) was employed to assess the microbial risks of pathogens. The results indicated obvious spatial-temporal differences (i.e., summer vs. winter and effluent vs. river) in microbial composition. Microcystis emerged as a crucial species contributing to these variations. Pathogen concentrations were found to be higher in the river than in the effluent, with the winter exhibiting higher concentrations compared to the summer. The effluent discharge slightly increased the pathogen concentrations in the river in summer but dramatically reduced them in winter. The combined effects of cyanobacterial bloom and high temperature were considered key factors suppressing pathogen concentrations in summer. Moreover, the prevalence of antibiotic resistance of pathogens in the river was inferior to that in the effluent, with higher levels in winter than in summer. Three high-concentration pathogens (Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) were selected for QMRA. The results showed that the risks of pathogens exceeded the recommended threshold value. Escherichia coli posed the highest risks. And the fishing scenario posed significantly higher risks than the walking scenario. Importantly, the effluent discharge helped reduce the microbial risks in the receiving river in winter. The study contributes to the management and decision-making regarding microbial risks in the effluent-receiving river.

Modeling benefits and tradeoffs of green infrastructure: Evaluating and extending parsimonious models for neighborhood stormwater planning

Green infrastructure is often proposed to complement conventional urban stormwater management systems that are stressed by extreme storms and expanding impervious surfaces. Established hydrological and hydraulic models inform stormwater engineering but are time- and data-intensive or aspatial, rendering them inadequate for rapid exploration of solutions. Simple spreadsheet models support quick site plan assessments but cannot adequately represent spatial interactions beyond a site. The present study builds on the Landscape Green Infrastructure Design (L-GrID) Model, a process-based spatial model that enables rapid development and exploration of green infrastructure scenarios to mitigate neighborhood flooding. We first explored how well L-GrID could replicate flooding reports in a neighborhood in Chicago, Illinois, USA, to evaluate its potential for green infrastructure planning. Although not meant for prediction, L-GrID was able to replicate the flooding reported and helped identify strategies for flood control. Once evaluated for this neighborhood, we extended the model to include water quality through the representation of dispersion and settling mechanisms for two pollutant surrogates-total nitrogen and total suspended solids. With the extended model, Landscape Green Infrastructure Design Model-Water Quality (L-GrID-WQ), we examined benefits, costs, and tradeoffs for different green infrastructure strategies. Bioswales were slightly more effective than other green infrastructure types in reducing flooding extent and downstream runoff and pollution, through increased infiltration and settling capacity. Permeable pavers followed in effectiveness and are suggested where spatial constraints may limit the installation of bioswales. Although green infrastructure supports both flooding and pollution control, small tradeoffs between these functions emerged across spatial layouts: strategies based on only curb-cuts better controlled pollution, while layouts that followed the path of water flow better controlled flooding. By illuminating such tradeoffs, L-GrID-WQ can support green infrastructure planning that prioritizes unique concerns in different areas of a landscape.

Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns

The escalation of global eutrophication has significantly increased due to the impact of climate change, particularly the increased frequency of extreme rainfall events. Predicting and managing eutrophication requires understanding the consequences of precipitation events on algal dynamics. Here, we assessed the influence of precipitation events throughout the year on nutrient and phytoplankton dynamics in a drinking water reservoir from January 2020 to January 2022. Four distinct precipitation patterns, namely early spring flood rain (THX), Plum rain (MY), Typhoon rain (TF), and Dry season (DS), were identified based on rainfall intensity, duration time, and cumulative rainfall. The study findings indicate that rainfall is the primary driver of algal dynamics by altering nutrient levels and TN:TP ratios during wet seasons, while water temperature becomes more critical during the Dry season. Combining precipitation characteristics with the lag periods between algal proliferation and rainfall occurrence is essential for accurately assessing the impact of rainfall on algal blooms. The highest algae proliferation occurred approximately 20 and 30 days after the peak rainfall during the MY and DS periods, respectively. This was influenced by the intensity and cumulative precipitation. The reservoir exhibited two distinct TN/TP ratio stages, with average values of 52 and 19, respectively. These stages were determined by various forms of nitrogen and phosphorus in rainfall-driven inflows and were associated with shifts from Bacillariophyta-dominated to Cyanophyta-dominated blooms during the MY and DS seasons. Our findings underscore the interconnected effects of nutrients, temperature, and hydrological conditions driven by diverse rainfall patterns in shaping algal dynamics. This study provides valuable insights into forecasting algal bloom risks in the context of climate change and developing sustainable strategies for lake or reservoir restoration.

A critical review of the mechanisms, factors, and performance of pervious concrete to remove contaminants from stormwater runoff

Stormwater can carry pollutants accumulated on impervious surfaces in urban areas into natural water bodies in absence of stormwater quality improvement devices. Pervious concrete (PC) pavement is one of the low-impact development practices introduced for urban flooding prevention and stormwater pollution reduction. PC removes various types of water contaminants. Mechanisms contributing to the water pollution removal capacity of PC can be categorized into three groups: physical, chemical, and biological. Properties of PC such as permeability, porosity, thickness, and adsorption capacity influence removal of all contaminants, although their impact might differ depending on the pollutant properties. Chemical mechanisms include precipitation, co-precipitation, ion and ligand exchange, complexation, diffusion, and sorption. Bulk organics and nutrients are removed primarily by biodegradation. Physical filtration is the primary mechanism to retain suspended solids, although biological activities may have a minor contribution. Release of calcium (Ca2+) and hydroxide (OH-) from hardened cement elevates the effluent pH, which is an environmental concern. However, the pH elevation is also the prime contributor to heavy metals and nutrients removal through precipitation. Specific cementitious materials (e.g., Pozzolans and nanoparticles) and carbonation curing approach are recommended to control effluent pH elevation. Complexation, diffusion, ion solubility, and stability constants are other mechanisms and parameters that influence heavy metal removal. Organic matter availability, electrostatic attraction, temperature, pH, contact time, specific surface area, and roughness of PC pores contribute to the pathogen removal process. Although PC has been found promising in removing various water pollutants, limited salinity removal can be achieved due to the inherent release of Ca2+and OH- from PC.

Pathogen profile of Baiyangdian Lake sediments using metagenomic analysis and their correlation with environmental factors

Increasing concerns about public health and safety after covid-19 have raised pathogen studies, especially in aquatic environments. However, the extent to how different location and human activities affect geographic occurrence and distribution of pathogens in response to agricultural pollution, boat tourism disturbances and municipal wastewater inflow in a degraded lake remains unclear. Since the surrounding residents depend on the lake for their livelihood, understanding the pathogens reserved in lake sediment and the regulation possibility by environmental factors are challenges with far-reaching significance. Results showed that 187 pathogens were concurrently shared by the nine sediment samples, with Salmonella enterica and Pseudomonas aeruginosa being the most abundant. The similar composition of the pathogens suggests that lake sediment may act as reservoirs of generalist pathogens which may pose infection risk to a wide range of host species. Of the four virulence factors (VFs) types analyzed, offensive VFs were dominant (>46 % on average) in all samples, with dominant subtypes including adherence, secretion systems and toxins. Notably, the lake sediments under the impact of agricultural use (g1) showed significantly higher diversity and abundance of pathogen species and VFs than those under the impact of boat tourism (g2) and/or municipal wastewater inflow with reed marshes filtration (g3). From the co-occurrence networks, pathogens and pesticides, aggregate fractions, EC, pH, phosphatase have strong correlations. Strong positive correlations between pathogens and diazinon in g1 and ppDDT in g2 and g3 suggest higher pesticide-pathogen co-exposure risk. These findings highlight the need to explore pathogen - environmental factor interaction mechanisms in the human-impacted water environments where the control of pathogen invasion by environmental factors may accessible.

Identification of potential microbial risk factors associated with fecal indicator exceedances at recreational beaches

BACKGROUND

Fecal bacterial densities are proxy indicators of beach water quality, and beach posting decisions are made based on Beach Action Value (BAV) exceedances for a beach. However, these traditional beach monitoring methods do not reflect the full extent of microbial water quality changes associated with BAV exceedances at recreational beaches (including harmful cyanobacteria). This proof of concept study evaluates the potential of metagenomics for comprehensively assessing bacterial community changes associated with BAV exceedances compared to non-exceedances for two urban beaches and their adjacent river water sources.

RESULTS

Compared to non-exceedance samples, BAV exceedance samples exhibited higher alpha diversity (diversity within the sample) that could be further differentiated into separate clusters (Beta-diversity). For Beach A, Cyanobacterial sequences (resolved as Microcystis and Pseudanabaena at genus level) were significantly more abundant in BAV non-exceedance samples. qPCR validation supported the Cyanobacterial abundance results from metagenomic analysis and also identified saxitoxin genes in 50% of the non-exceedance samples. Microcystis sp and saxitoxin gene sequences were more abundant on non-exceedance beach days (when fecal indicator data indicated the beach should be open for water recreational purposes). For BAV exceedance days, Fibrobacteres, Pseudomonas, Acinetobacter, and Clostridium sequences were significantly more abundant (and positively correlated with fecal indicator densities) for Beach A. For Beach B, Spirochaetes (resolved as Leptospira on genus level) Burkholderia and Vibrio sequences were significantly more abundant in BAV exceedance samples. Similar bacterial diversity and abundance trends were observed for river water sources compared to their associated beaches. Antibiotic Resistance Genes (ARGs) were also consistently detected at both beaches. However, we did not observe a significant difference or correlation in ARGs abundance between BAV exceedance and non-exceedance samples.

CONCLUSION

This study provides a more comprehensive analysis of bacterial community changes associated with BAV exceedances for recreational freshwater beaches. While there were increases in bacterial diversity and some taxa of potential human health concern associated with increased fecal indicator densities and BAV exceedances (e.g. Pseudomonas), metagenomics analyses also identified other taxa of potential human health concern (e.g. Microcystis) associated with lower fecal indicator densities and BAV non-exceedances days. This study can help develop more targeted beach monitoring strategies and beach-specific risk management approaches.

Sustainable bioremediation technologies for algal toxins and their ecological significance

The emergence of algal toxins in water ecosystems poses a significant ecological and human health concern. These toxins, produced by various algal species, can lead to harmful algal blooms, and have far-reaching consequences on biodiversity, food chains, and water quality. This review explores the types and sources of algal toxins, their ecological impacts, and the associated human health risks. Additionally, the review delves into the potential of bioremediation strategies to mitigate the effects of algal toxins. It discusses the role of microorganisms, enzymes, and algal-bacterial interactions in toxin removal, along with engineering approaches such as advanced oxidation processes and adsorbent utilization. Microbes and enzymes have been studied for their environmentally friendly and biocompatible properties, which make them useful for controlling or removing harmful algae and their toxins. The challenges and limitations of bioremediation are examined, along with case studies highlighting successful toxin control efforts. Finally, the review outlines future prospects, emerging technologies, and the need for continued research to effectively address the complex issue of algal toxins and their ecological significance.

Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs

This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L-1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.

Chlorophyll-a prediction in tropical reservoirs as a function of hydroclimatic variability and water quality

The study goal was to determine spatiotemporal variations in chlorophyll-a (Chl-a) concentration using models that combine hydroclimatic and nutrient variables in 150 tropical reservoirs in Brazil. The investigation of seasonal variability indicated that Chl-a varied in response to changes in total nitrogen (TN), total phosphorus (TP), volume (V), and daily precipitation (P). Therefore, an empirical model for Chl-a prediction based on the product of TN, TP, and normalized functions of V and P was proposed, but their individual exponents as well as a general multiplicative factor were adjusted by linear regression for each reservoir. The fitted relationships were capable of representing algal temporal dynamics and blooms, with an average coefficient of determination of R2 = 0.70. The results revealed that nutrients yielded better predictability of Chl-a than hydroclimatic variables. Chl-a blooms presented seasonal and interannual variability, being more frequent in periods of high precipitation and low volume. The equations demonstrate different Chl-a responses to the parameters. In general, Chl-a was positively related to TN and/or TP. However, in some cases (22%), high nutrient concentrations reduced Chl-a, which was attributed to limited phytoplankton growth driven by light deficiency due to increased turbidity. In 49% of the models, precipitation intensified Chl-a levels, which was related to increases in the nutrient concentration from external sources in rural watersheds. Contrastingly, 51% of the reservoirs faced a decrease in Chl-a with precipitation, which can be explained by the opposite effect of dilution of nutrient concentration at the reservoir inlet in urban watersheds. In terms of volume, in 67% of the reservoirs, water level reduction promoted an increase in Chl-a as a response to higher nutrient concentration. In the other cases, Chl-a decreased with lower water levels due to wind-induced destratification of the water column, which potentially decreased the internal nutrient release from bottom sediment. Finally, applying the model to the two largest studied reservoirs showed greater sensitivity of Chl-a to changes in water use classes regarding variations in TN, followed by TP, V, and P.

Phytoplankton diversity in wastewater impacted Indian Ramsar site: a study from East Kolkata Wetland

A seasonal study was undertaken to understand the influence of wastewater on phytoplankton distribution in a water body of East Kolkata Wetland (EKW), a designated Ramsar site in Kolkata, West Bengal, India. A total of 19 different genera of phytoplankton belonging to 5 phyla were recorded. Among all the groups, Chlorophyceae was found to be dominated by 8 genera followed by Bacillariophycaeae (4 genera), Cyanophyceae (4 genera), Euglenophyceae (2 genera), and Zygnematophyceae (1 genus). Seasonal variability showed maximum dominance of phytoplankton during post-monsoon and least during pre-monsoon months. Shannon-Wiener diversity (H') indices indicated Bacillariophyceae to be most species rich (1.059) while the most dominant group (D) was observed to be Chlorophyceae (0.507). Assessment of Palmer algal pollution index (PI) revealed the water body is impacted by high organic pollution during monsoon (22) compared to the pre-monsoon (19) and post-monsoon seasons (15). The results of canonical correspondence analysis (CCA) indicated water temperature, alkalinity, total dissolved solids, dissolved oxygen, and electrical conductivity as the major influencing parameters for growth and distribution of the phytoplankton in the water body. Therefore, it can be stated that hydrological alteration of a water body fed with wastewater plays a significant role in regulating the plankton density, richness, and diversity.

Harmful algal bloom aerosols and human health

Harmful algal blooms (HABs) are increasing across many locations globally. Toxins from HABs can be incorporated into aerosols and transported inland, where subsequent exposure and inhalation can induce adverse health effects. However, the relationship between HAB aerosols and health outcomes remains unclear despite the potential for population-level exposures. In this review, we synthesized the current state of knowledge and identified evidence gaps in the relationship between HAB aerosols and human health. Aerosols from Karenia brevis, Ostreopsis sp., and cyanobacteria were linked with respiratory outcomes. However, most works did not directly measure aerosol or toxin concentrations and instead relied on proxy metrics of exposure, such as cell concentrations in nearby waterbodies. Furthermore, the number of studies with epidemiological designs was limited. Significant uncertainties remain regarding the health effects of other HAB species; threshold dose and the dose-response relationship; effects of concurrent exposures to mixtures of toxins and other aerosol sources, such as microplastics and metals; the impact of long-term exposures; and disparities in exposures and associated health effects across potentially vulnerable subpopulations. Additional studies employing multifaceted exposure assessment methods and leveraging large health databases could address such gaps and improve our understanding of the public health burden of HABs.

Thermal power plants pollution assessment based on deep neural networks, remote sensing, and GIS: A real case study in Iran

To investigate the impact of the Bandar Abbas thermal power plant on the waters of the Persian Gulf coast, a combination of satellite images and ground data was utilized to determine the Sea Surface Temperature (SST) as a thermal index, Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) as biological indices. Additionally, measurements of SO₂, O₃, NO₂, CO₂, CO, and CH₄ values in the atmosphere were taken to determine the plant's impact on air pollution. Temperature values of the water for different months were predicted using Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Cascade neural networks. The results indicate that the waters near thermal power plants exhibit the highest temperatures in July and September, with temperatures reaching approximately 50 °C. Furthermore, the SST values were found to be strongly correlated with ecological indices. The Multiple Linear Regression (MLR) analysis revealed a strong correlation between the temperature and TOC, COD, and O₂ in water (R_TOC²=0.98), [Formula: see text] , R_COD²=0.87 and O₃, NO₃, CO₂, and CO in the air ( [Formula: see text] ). Finally, the results demonstrate that the LSTM method exhibited high accuracy in predicting the water temperature (R² = 0.98).

Air temperature spikes increase bacteria presence in drinking water wells downstream of hog lagoons

>44 million United States residents depend on private drinking water wells that are federally unregulated. Maintaining a clean groundwater supply for populations without access to public water systems is essential to supporting public health and falls to state regulators and private well owners. Yet, monitoring practices do not reflect the fact that groundwater pollution risk varies seasonally and with proximity to nearby surface-contaminated sites. Examination of nearly 50,000 well water samples across North Carolina, ranked second nationally in domestic well dependence and swine production, from 2013 to 2018 reveals a uniform sampling schedule but a variable risk of bacterial contamination within each calendar year. We document a threshold of 32.2 °C (90 °F) where total coliform bacteria and Escherichia coli (E. coli) detection in private well water spikes near swine lagoons but is absent from "upstream" wells and otherwise unexplained by a variety of other known contamination sites. Closing the gap between perceived and actual risks of drinking water contamination has potential to improve public health. State regulations and federal guidelines should consider coordinating domestic well sampling with seasonally and spatially fluctuating risks of groundwater contamination. Findings from this study are generalizable, having implications for other parts of the world with water sources that have the potential to get contaminated by nearby surface sources of human and animal waste, such as manure applications and leaching septic systems.

Long-term water quality monitoring in agricultural catchments in Sweden: Impact of climatic drivers on diffuse nutrient loads

Water quality related to non-point source pollution continues to pose challenges in agricultural landscapes, despite two completed cycles of Water Framework Directive actions by farmers and landowners. Future climate projections will cause new challenges in landscape hydrology and subsequently, the potential responses in water quality. Investigating the nutrient trends in surface waters and studying the efficiency of mitigation measures revealed that loads and measures are highly variable both spatially and temporally in catchments with different agro-climatic and environmental conditions. In Sweden, nitrogen and phosphorus loads in eight agricultural catchments (470-3300 ha) have been intensively monitored for >20 years. This study investigated the relationship between precipitation, air temperature, and discharge patterns in relation to nitrogen (N) and phosphorus (P) loads at catchment outlets. The time series data analysis was carried out by integrating Mann-Kendall test, Pettitt break-points, and Generalized Additive Model. The results showed that the nutrient loads highly depend on water discharge, which had large variation in annual average (158-441 mm yr^-1). The annual average loads were also considerably different among the catchments with total N (TN) loads ranging from 6.76 to 35.73 kg ha^-1, and total P (TP) loads ranging from 0.11 to 1.04 kg ha^-1. The climatic drivers were highly significant indicators of nutrient loads but with varying degree of significance. Precipitation (28-962 mm yr^-1) was a significant indicator of TN loads in five catchments (loamy sand/sandy loam) while annual average temperature (6.5-8.7 °C yr^-1) was a significant driver of TN loads in six out of eight catchments. TP loads were associated with precipitation in two catchments and significantly correlated to water discharge in six catchments. Considering the more frequent occurrence of extreme weather events, it is necessary to tailor N and P mitigation measures to future climate-change features of precipitation, temperature, and discharge.

Relationships between benthic infauna and groundwater eutrophication on a sandy beach in southern Brazil

Urban expansion in Brazilian coastal zones has caused various anthropic impacts on coastal marine ecosystems that have resulted from unorganized use and the lack of infrastructure projects. The inadequate disposal of domestic and industrial effluents in coastal waterbodies is notable, which can cause severe environmental problems. For sandy beaches, the relationships between the contamination of groundwater with domestic sewage and the possible effects on spatial and temporal variations in the density and composition of benthic infauna are still poorly understood. This work aimed to relate variations in benthic infaunal associations with the concentrations of groundwater nutrients in summer and winter on Enseada Beach. The greater concentrations of nutrients in water percolating through the sediment in the summer, increasing of domestic effluents, and periods of intense precipitation increased the contamination of the surface and groundwater. This contributes to an increase in the population density of Thoracophelia furcifera, demonstrating its use as an indicator of eutrophication of the groundwater, allowing monitoring and contribution to actions aimed at improving the environmental quality of sandy beaches.

Long-term spatial variation of algal blooms extracted using the U-net model from 10 years of GOCI imagery in the East China Sea

Long-term satellite missions could help to provide insights into spatial and temporal variations in algal blooms. However, the traditional reflectance-based method has limitations in regards to determining the available threshold for algal bloom detection among the time-varying observation conditions. In terms of extracting useful information from long-term data series precisely and efficiently, the deep learning method has shown its superiority over traditional algorithms in batch data processing. In this study, a U-net model for algal bloom extraction along the coast of the East China Sea was developed using GOCI images. The U-net model was trained with two different datasets that were constructed with six-band channels (all visible bands from GOCI imagery) and RGB-band channels (bands of 443, 555, and 680 nm from GOCI imagery). The quantitative assessment from the U-net models suggests that the U-net model trained with the six-band channel datasets outperformed the RGB-band channel datasets, with increases of 23.6%, 18.1%, and 12.5% in terms of accuracy, precision, and F-score, respectively. The validation map derived from the U-net model trained with six-band channel datasets also showed considerable matching with the ground-truth maps. By using the U-net model, the occurrence of algal blooms was automatically extracted from GOCI images. A 10-year time series of GOCI data collected between 2011 and 2020 was derived using an output-trained U-net model to explore spatial variation along the coast of the ECS. It was found that the most affected areas of the algal blooms varied by year, but were mainly located in the Zhoushan and Zhejiang coasts. Additionally, by performing principal component analysis on the daily meteorological data during April and August 2011-2020, factors related to algal bloom occurrence were discussed.

Increased chlorophyll-a concentration in Barra Bonita reservoir during extreme drought periods

Climate projections models indicate that longer periods of droughts are expected within the next 100 years in various parts of South America. To understand the effects of longer periods of droughts on aquatic environments, we investigated the response of chlorophyll-a (Chl-a) concentration to recent severe drought events in the Barra Bonita Hydroelectric Reservoir (BBHR) in São Paulo State, Brazil. We used satellite imagery to estimate the Chl-a concentration from 2014 to 2020 using the Slope Index (NRMSE of 18.92% and bias of -0.20 mg m^-3). Ancillary data such as precipitation, water level and air temperature from the same period were also used. Drought events were identified using the standardized precipitation index (SPI). In addition, we computed the probability of future drought events. Two periods showed extremely dry conditions: 1) January-February (2014) and 2) April-May (2020). Both periods were characterized by a recurrence probability of 1in every 50 years. The highest correlation was observed between Chl-a concentration and SPI (-0.97) in 2014, while Chl-a had had the highest correlation with water level (-0.59) in 2020. These results provide new insights into the influence of extreme drought events on the Chl-a concentration in the BBHR and their relationship with other climate variables and reservoir water levels. Drought events imply less rainfall, higher temperatures, and atmospheric dryness, and these factors affect evaporation and the water levels in the reservoir.

A review of climate change effects on practices for mitigating water quality impacts

Water quality practices are commonly implemented to reduce human impacts on land and water resources. In series or parallel in a landscape, systems of practices can reduce local and downstream pollution delivery. Many practices function via physical, chemical, and biological processes that are dependent on weather and climate. Climate change will alter the function of many such systems, though effects will vary in different hydroclimatic and watershed settings. Reducing the risk of impacts will require risk-based, adaptive planning. Here, we review the literature addressing climate change effects on practices commonly used to mitigate the water quality impacts of urban stormwater, agriculture, and forestry. Information from the general literature review is used to make qualitative inferences about the resilience of different types of practices. We discuss resilience in the context of two factors: the sensitivity of practice function to changes in climatic drivers, and the adaptability, or relative ease with which a practice can be modified as change occurs. While only a first step in addressing a complex topic, our aim is to help communities incorporate consideration of resilience to climate change as an additional factor in decisions about water quality practices to meet long-term goals.

Nitrate Water Contamination from Industrial Activities and Complete Denitrification as a Remediation Option

Freshwater is a scarce resource that continues to be at high risk of pollution from anthropogenic activities, requiring remediation in such cases for its continuous use. The agricultural and mining industries extensively use water and nitrogen (N)-dependent products, mainly in fertilizers and explosives, respectively, with their excess accumulating in different water bodies. Although removal of NO3 from water and soil through the application of chemical, physical, and biological methods has been studied globally, these methods seldom yield N2 gas as a desired byproduct for nitrogen cycling. These methods predominantly cause secondary contamination with deposits of chemical waste such as slurry brine, nitrite (NO2), ammonia (NH3), and nitrous oxide (N2O), which are also harmful and fastidious to remove. This review focuses on complete denitrification facilitated by bacteria as a remedial option aimed at producing nitrogen gas as a terminal byproduct. Synergistic interaction of different nitrogen metabolisms from different bacteria is highlighted, with detailed attention to the optimization of their enzymatic activities. A biotechnological approach to mitigating industrial NO3 contamination using indigenous bacteria from wastewater is proposed, holding the prospect of optimizing to the point of complete denitrification. The approach was reviewed and found to be durable, sustainable, cost effective, and environmentally friendly, as opposed to current chemical and physical water remediation technologies.

Removal of Fecal Indicator Bacteria by River Networks

Fecal contamination is a significant source of water quality impairment globally. Aquatic ecosystems can provide an important ecosystem service of fecal contamination removal. Understanding the processes that regulate the removal of fecal contamination among river networks across flow conditions is critical. We applied a river network model, the Framework for Aquatic Modeling in the Earth System (FrAMES-Ecoli), to quantify removal of fecal indicator bacteria by river networks across flow conditions during summers in a series of New England watersheds of different characteristics. FrAMES-Ecoli simulates sources, transport, and riverine removal of Escherichia coli (E. coli). Aquatic E. coli removal was simulated in both the water column and the hyporheic zone, and is a function of hydraulic conditions, flow exchange rates with the hyporheic zone, and die-off in each compartment. We found that, at the river network scale during summers, removal by river networks can be high (19–99%) with variability controlled by hydrologic conditions, watershed size, and distribution of sources in the watershed. Hydrology controls much of the variability, with 68–99% of network scale inputs removed under base flow conditions and 19–85% removed during storm events. Removal by the water column alone could not explain the observed pattern in E. coli, suggesting that processes such as hyporheic removal must be considered. These results suggest that river network removal of fecal indicator bacteria should be taken into consideration in managing fecal contamination at critical downstream receiving waters.

Warming world, changing ocean: mitigation and adaptation to support resilient marine systems

Proactive and coordinated action to mitigate and adapt to climate change will be essential for achieving the healthy, resilient, safe, sustainably harvested and biodiverse ocean that the UN Decade of Ocean Science and sustainable development goals (SDGs) seek. Ocean-based mitigation actions could contribute 12% of the emissions reductions required by 2030 to keep warming to less than 1.5 ºC but, because substantial warming is already locked in, extensive adaptation action is also needed. Here, as part of the Future Seas project, we use a "foresighting/hindcasting" technique to describe two scenarios for 2030 in the context of climate change mitigation and adaptation for ocean systems. The "business-as-usual" future is expected if current trends continue, while an alternative future could be realised if society were to effectively use available data and knowledge to push as far as possible towards achieving the UN SDGs. We identify three drivers that differentiate between these alternative futures: (i) appetite for climate action, (ii) handling extreme events, and (iii) climate interventions. Actions that could navigate towards the optimistic, sustainable and technically achievable future include:(i)proactive creation and enhancement of economic incentives for mitigation and adaptation;(ii)supporting the proliferation of local initiatives to spur a global transformation;(iii)enhancing proactive coastal adaptation management;(iv)investing in research to support adaptation to emerging risks;(v)deploying marine-based renewable energy;(vi)deploying marine-based negative emissions technologies;(vii)developing and assessing solar radiation management approaches; and(viii)deploying appropriate solar radiation management approaches to help safeguard critical ecosystems.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11160-021-09678-4.

Bioavailable Nutrients (N and P) and Precipitation Patterns Drive Cyanobacterial Blooms in Missisquoi Bay, Lake Champlain

Anthropogenic activities release large amounts of nitrogen (N) and phosphorus (P) nutrients into the environment. Sources of nutrients include surface and sub-surface runoffs from agricultural practices with the application of chemical fertilizers and manure as well as combined sewer overflows (CSOs). Nutrient runoffs contribute to the eutrophication of aquatic ecosystems and enhance the growth of cyanobacteria. Precipitation is an important driving force behind the runoff of nutrients from agricultural fields into surrounding water bodies. To understand the dynamics between nutrient input, precipitation and cyanobacterial growth in Missisquoi Bay, Lake Champlain (Quebec), one location in Pike River (a major tributary into the bay) and four locations in Missisquoi Bay were monitored from April to November in 2017 and 2018. Biweekly water samples were analyzed using chemical methods and high-throughput sequencing of 16S rRNA gene amplicons. High concentrations of N and P were typically measured in April and May. Three major spikes in nutrient concentrations were observed in early and mid-summer as well as early fall, all of which were associated with intense cumulative precipitation events of 40 to 100 mm within 7 days prior to sampling. Despite the high concentrations of nutrients in the spring and early summer, the cyanobacterial blooms appeared in mid to late summer as the water temperature increased. Dolichospermum sp. was the major bloom-forming cyanobacterium during both summers. A second intense bloom event of Microcystis was also observed in the fall (October and November) for both years. Variation in the cyanobacteria population was strongly associated with inorganic and readily available fractions of N and P such as nitrites and nitrates (NOx), ammonia (NH₃) and dissolved organic phosphorus (DOP). During blooms, total Kjeldahl nitrogen (TKN) and total particulate phosphorus (TPP) fractions had a substantial influence on total nitrogen (TN) and total phosphorus (TP) concentrations, respectively. The abundance of bacteria involved in the metabolism of nitrogen compared to that of phosphorus revealed the importance of nitrogen on overall microbial dynamics as well as CB formation in the bay. Our findings emphasize the combined influence of precipitation events, temperature and several bioavailable fractions of nitrogen and phosphorus on cyanobacterial bloom episodes.

Synergistic effect of drought and rainfall events of different patterns on watershed systems

The increase in extreme climate events such as flooding and droughts predicted by the general circulation models (GCMs) is expected to significantly affect hydrological processes, erosive dynamics, and their associated nonpoint source (NPS) pollution, resulting in a major challenge to water availability for human life and ecosystems. Using the Hydrological Simulation Program–Fortran model, we evaluated the synergistic effects of droughts and rainfall events on hydrology and water quality in an upstream catchment of the Miyun Reservoir based on the outputs of five GCMs. It showed substantial increases in air temperature, precipitation intensity, frequency of heavy rains and rainstorms, and drought duration, as well as sediment and nutrient loads in the RCP 8.5 scenario. Sustained droughts followed by intense precipitation could cause complex interactions and mobilize accumulated sediment, nutrients and other pollutants into surface water that pose substantial risks to the drinking water security, with the comprehensive effects of soil water content, antecedent drought duration, precipitation amount and intensity, and other climate characteristics, although the effects varied greatly under different rainfall patterns. The Methods and findings of this study evidence the synergistic impacts of droughts and heavy rainfall on watershed system and the significant effects of initial soil moisture conditions on water quantity and quality, and help to guide a robust adaptive management system for future drinking water supply.

Warming world, changing ocean: mitigation and adaptation to support resilient marine systems

Proactive and coordinated action to mitigate and adapt to climate change will be essential for achieving the healthy, resilient, safe, sustainably harvested and biodiverse ocean that the UN Decade of Ocean Science and sustainable development goals (SDGs) seek. Ocean-based mitigation actions could contribute 12% of the emissions reductions required by 2030 to keep warming to less than 1.5 ºC but, because substantial warming is already locked in, extensive adaptation action is also needed. Here, as part of the Future Seas project, we use a "foresighting/hindcasting" technique to describe two scenarios for 2030 in the context of climate change mitigation and adaptation for ocean systems. The "business-as-usual" future is expected if current trends continue, while an alternative future could be realised if society were to effectively use available data and knowledge to push as far as possible towards achieving the UN SDGs. We identify three drivers that differentiate between these alternative futures: (i) appetite for climate action, (ii) handling extreme events, and (iii) climate interventions. Actions that could navigate towards the optimistic, sustainable and technically achievable future include:(i)proactive creation and enhancement of economic incentives for mitigation and adaptation;(ii)supporting the proliferation of local initiatives to spur a global transformation;(iii)enhancing proactive coastal adaptation management;(iv)investing in research to support adaptation to emerging risks;(v)deploying marine-based renewable energy;(vi)deploying marine-based negative emissions technologies;(vii)developing and assessing solar radiation management approaches; and(viii)deploying appropriate solar radiation management approaches to help safeguard critical ecosystems.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11160-021-09678-4.

The direct and interactive impacts of hydrological factors on bacillary dysentery across different geographical regions in central China

Previous studies found non-linear mutual interactions among hydrometeorological factors on diarrheal disease. However, the complex interactions of the hydrometeorological, topographical and human activity factors need to be further explored. This study aimed to reveal how hydrological and other factors jointly influence bacillary dysentery in different geographical regions. Using Anhui Province in China, consisted of Huaibei plain, Jianghuai hilly and Wannan mountainous regions, we integrated multi-source data (6 meteorological, 3 hydrological, 2 topographic, and 9 socioeconomic variables) to explore the direct and interactive relationship between hydrological factors (quick flow, baseflow and local recharge) and other factors by combining the ecosystem model InVEST with spatial statistical analysis. The results showed hydrological factors had significant impact powers (q = 0.444 (Huaibei plain) for local recharge, 0.412 (Jianghuai hilly region) and 0.891 (Wannan mountainous region) for quick flow, respectively) on bacillary dysentery in different regions, but lost powers at provincial level. Land use and soil properties have created significant interactions with hydrological factors across Anhui province. Particularly, percentage of farmland in Anhui province can influence quick flow across Jianghuai, Wannan regions and the whole province, and it also has significant interactions with the baseflow and local recharge across the plain as well as the whole province. Percentage of urban areas had interactions with baseflow and local recharge in Jianghuai and Wannan regions. Additionally, baseflow and local recharge could be interacted with meteorological factors (e.g. temperature and wind speed), while these interactions varied in different regions. In conclusion, it was evident that hydrological factors had significant impacts on bacillary dysentery, and also interacted significantly with meteorological and socioeconomic factors. This study applying ecosystem model and spatial analysis help reveal the complex and nonlinear transmission of bacillary dysentery in different geographical regions, supporting the development of precise public health interventions with consideration of hydrological factors.

Using Multiobjective Optimization to Inform Green Infrastructure Decisions as Part of Robust Integrated Water Resources Management Plans

Uncertainty in the impacts of climate change and development on freshwater resources pose significant challenges for water resources management. Integrated and adaptive approaches to water resources management are a promising means of addressing uncertainty that afford flexibility in balancing multiple stakeholder objectives. However, guidance on designing such plans is lacking. In this study, we use multi-objective optimization to strategically incorporate green infrastructure (GI) into water resources management plans that maximize reductions in nutrient loads, minimize stormwater runoff, and minimize costs. Robust decision-making methods are applied to the resulting plan options to evaluate how optimized GI implementation varies under different possible future climates and to determine which solutions would be robust under a range of plausible future conditions. We demonstrate these coupled methods using a case study in southern Massachusetts, to address water quality issues related to point and nonpoint source nutrients in a rapidly developing watershed.

Synergies between local and climate-driven impacts on coral reefs in the Tropical Pacific: A review of issues and adaptation opportunities

Coral reefs in the tropical Pacific region are exposed to a range of anthropogenic local pressures. Climate change is exacerbating local impacts, causing unprecedented declines in coral reef habitats and bringing negative socio-economic consequences to Pacific communities who depend heavily on coral reefs for food, income and livelihoods. Continued increases in greenhouse gas emissions will drive future climate change, which will accelerate coral reef degradation. Traditional systems of resource governance in Pacific island nations provide a foundation to address local pressures and build reef resilience to climate change. Management and adaptation options should build on the regional diversity of governance systems and traditional knowledge to support community-based initiatives and cross-sectoral cooperation to address local pressures and minimize climate change impacts. Such an inclusive approach will offer enhanced opportunities to develop and implement transformative adaptation solutions, particularly in remote and regional areas where centralized management does not extend.

Multi-Year Monitoring of Ecosystem Metabolism in Two Branches of a Cold-Water Stream

Climate change is likely to have large impacts on freshwater biodiversity and ecosystem function, especially in cold-water streams. Ecosystem metabolism is affected by water temperature and discharge, both of which are expected to be affected by climate change and, thus, require long-term monitoring to assess alterations in stream function. This study examined ecosystem metabolism in two branches of a trout stream in Minnesota, USA over 3 years. One branch was warmer, allowing the examination of elevated temperature on metabolism. Dissolved oxygen levels were assessed every 10 min from spring through fall in 2017–2019. Gross primary production (GPP) was higher in the colder branch in all years. GPP in both branches was highest before leaf-out in the spring. Ecosystem respiration (ER) was greater in the warmer stream in two of three years. Both streams were heterotrophic in all years (net ecosystem production—NEP < 0). There were significant effects of temperature and light on GPP, ER, and NEP. Stream discharge had a significant impact on all GPP, ER, and NEP in the colder stream, but only on ER and NEP in the warmer stream. This study indicated that the impacts of temperature, light, and discharge differ among years, and, at least at the local scale, may not follow expected patterns.

Modeling the Effects of Future Hydroclimatic Conditions on Microbial Water Quality and Management Practices in Two Agricultural Watersheds

Anticipated future hydroclimatic changes are expected to alter the transport and survival of fecally-sourced waterborne pathogens, presenting an increased risk of recreational water quality impairments. Managing future risk requires an understanding of interactions between fecal sources, hydroclimatic conditions and best management practices (BMPs) at spatial scales relevant to decision makers. In this study we used the Hydrologic Simulation Program FORTRAN to quantify potential fecal coliform (FC - an indicator of the potential presence of pathogens) responses to a range of mid-century climate scenarios and assess different BMP scenarios (based on reduction factors) for reducing the risk of water quality impairment in two, small agricultural watersheds - the Chippewa watershed in Minnesota, and the Tye watershed in Virginia. In each watershed, simulations show a wide range of FC responses, driven largely by variability in projected future precipitation. Wetter future conditions, which drive more transport from non-point sources (e.g. manure application, livestock grazing), show increases in FC loads. Loads typically decrease under drier futures; however, higher mean FC concentrations and more recreational water quality criteria exceedances occur, likely caused by reduced flow during low-flow periods. Median changes across the ensemble generally show increases in FC load. BMPs that focus on key fecal sources (e.g., runoff from pasture, livestock defecation in streams) within a watershed can mitigate the effects of hydroclimatic change on FC loads. However, more extensive BMP implementation or improved BMP efficiency (i.e., higher FC reductions) may be needed to fully offset increases in FC load and meet water quality goals, such as total maximum daily loads and recreational water quality standards. Strategies for managing climate risk should be flexible and to the extent possible include resilient BMPs that function as designed under a range of future conditions.

A REVIEW OF WATER QUALITY RESPONSES TO AIR TEMPERATURE AND PRECIPITATION CHANGES 1: FLOW, WATER TEMPERATURE, SALTWATER INTRUSION

Anticipated future increases in air temperature and regionally variable changes in precipitation will have direct and cascading effects on U.S. water quality. In this paper, and a companion paper by Coffey et al. (2019), we review technical literature addressing the responses of different water quality attributes to historical and potential future changes in air temperature and precipitation. The goal is to document how different attributes of water quality are sensitive to these drivers, to characterize future risk to inform management responses and to identify research needs to fill gaps in our understanding. Here we focus on potential changes in streamflow, water temperature, and salt water intrusion (SWI). Projected changes in the volume and timing of streamflow vary regionally, with general increases in northern and eastern regions of the U.S., and decreases in the southern Plains, interior Southwest and parts of the Southeast. Water temperatures have increased throughout the U.S. and are expected to continue to increase in the future, with the greatest changes in locations where high summer air temperatures occur together with low streamflow volumes. In coastal areas, especially the mid-Atlantic and Gulf coasts, SWI to rivers and aquifers could be exacerbated by sea level rise, storm surges, and altered freshwater runoff. Management responses for reducing risks to water quality should consider strategies and practices robust to a range of potential future conditions.