Long-term trends, current status, and transitions of water quality in Chesapeake Bay

Response of hypoxia to future climate change is sensitive to methodological assumptions

Climate-induced changes in hypoxia are among the most serious threats facing estuaries, which are among the most productive ecosystems on Earth. Future projections of estuarine hypoxia typically involve long-term multi-decadal continuous simulations or more computationally efficient time slice and delta methods that are restricted to short historical and future periods. We make a first comparison of these three methods by applying a linked terrestrial-estuarine model to the Chesapeake Bay, a large coastal-plain estuary in the eastern United States. Results show that the time slice approach accurately captures the behavior of the continuous approach, indicating a minimal impact of model memory. However, increases in mean annual hypoxic volume by the mid-twenty-first century simulated by the delta approach (+ 19%) are approximately twice as large as the time slice and continuous experiments (+ 9% and + 11%, respectively), indicating an important impact of changes in climate variability. Our findings suggest that system memory and projected changes in climate variability, as well as simulation length and natural variability of system hypoxia, should be considered when deciding to apply the more computationally efficient delta and time slice methods.

Assessing the utility of shellfish sanitation monitoring data for long-term estuarine water quality analysis

Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to investigate management-driven bias in fecal coliform observations collected in a particular area.

Application of unsupervised clustering model based on graph embedding in water environment

Surface water monitoring data has spatiotemporal characteristics, and water quality will change with time and space in different seasons and climates. Data of this nature brings challenges to clustering, especially in terms of obtaining the temporal and spatial characteristics of the data. Therefore, this paper proposes an improved TADW algorithm and names it RTADW to obtain the spatiotemporal characteristics of surface water monitoring points. We improve the feature matrix in TADW and input the original time series data and spatial information into the improved model to obtain the spatiotemporal feature vector. When the improved TADW model captures watershed information for clustering, it can simultaneously extract the temporal and spatial characteristics of surface water compared with other clustering algorithms such as the DTW algorithm. We applied the proposed method to multiple different monitoring sites in the Liaohe River Basin, analyzed the spatiotemporal regional distribution of surface water monitoring points. The results show that the improved feature extraction method can better capture the spatiotemporal feature information between surface water monitoring points. Therefore, this method can provide more potential information for cluster analysis of water environment monitoring, thereby providing a scientific basis for watershed zoning management.

Chromosome-level genome and high nitrogen stress response of the widespread and ecologically important wetland plant Typha angustifolia

Typha angustifolia L., known as narrowleaf cattail, is widely distributed in Eurasia but has been introduced to North America. Typha angustifolia is a semi-aquatic, wetland obligate plant that is widely distributed in Eurasia and North America. It is ecologically important for nutrient cycling in wetlands where it occurs and is used in phytoremediation and traditional medicine. In order to construct a high-quality genome for Typha angustifolia and investigate genes in response to high nitrogen stress, we carried out complete genome sequencing and high-nitrogen-stress experiments. We generated a chromosomal-level genome of T. angustifolia, which had 15 pseudochromosomes, a size of 207 Mb, and a contig N50 length of 13.57 Mb. Genome duplication analyses detected no recent whole-genome duplication (WGD) event for T. angustifolia. An analysis of gene family expansion and contraction showed that T. angustifolia gained 1,310 genes and lost 1,426 genes. High-nitrogen-stress experiments showed that a high nitrogen level had a significant inhibitory effect on root growth and differential gene expression analyses using 24 samples found 128 differentially expressed genes (DEGs) between the nitrogen-treated and control groups. DEGs in the roots and leaves were enriched in alanines, aspartate, and glutamate metabolism, nitrogen metabolism, photosynthesis, phenylpropanoid biosynthesis, plant-pathogen interaction, and mitogen-activated protein kinase pathways, among others. This study provides genomic data for a medicinal and ecologically important herb and lays a theoretical foundation for plant-assisted water pollution remediation.

Spatially distributed water quality responses to freshwater discharge in a tropical estuary, Hilo Bay, Hawai'i

Hilo Bay, Hawai'i, is an estuary of great importance to its neighboring coastal community, but it is threatened by impaired water quality indicated by excessive turbidity and chlorophyll a associated with river discharges of sediments and nutrients. The Wailuku River in the western half of the bay is the primary source of freshwater discharge, hypothesized here to form a surface water-dominant half of the bay with different water quality traits than the groundwater-dominant, eastern half of the bay where the spring-fed Wailoa River discharges. The water quality of both halves of the bay over different flow conditions of the Wailuku River is examined in this study using spatially distributed water quality sampling which collects hundreds of samples in either half of the bay at a distance of about every 40 m. The dense sample shows significant differences between the two halves of the bay, with greater salinity dilution and turbidity in the surface water-dominant area. Both salinity and turbidity have a predictable relation to discharge, with salinity decreasing and turbidity increasing in higher flow conditions. Chlorophyll a, however, has a more complex relation to discharge, as chlorophyll a concentrations are greatest in high-flow conditions, but this may be because the water quality samples were collected in different seasons. Furthermore, significantly greater chlorophyll a concentrations in the groundwater-dominant half of the bay in low-flow conditions show that discharge may be spuriously correlated to chlorophyll a, and further studies of the effects of surface water discharge on chlorophyll a concentrations are warranted.

What drove the nonlinear hypoxia response to nutrient loading in Chesapeake Bay during the 20th century?

Previous data analysis showed that the large expansion of hypoxia in Chesapeake Bay between 1950s and 1980s was correlated to the increased riverine nutrient loading, but the physical and biogeochemical processes driving this hypoxia response need to be better understood. Using a validated coupled hydrodynamic-biogeochemical model, we conducted a hindcast simulation of dissolved oxygen during the 40-year period (1950-1989) when the nutrient loading doubled. The model reproduced the observed decline in O₂ concentration at monitoring stations and the expansion of the hypoxic volume. The peak summer hypoxic volume expanded from ∼5 km³ during 1950-1969 to ∼10 km³ during 1970-1989. To discern how different physical and biochemical processes regulated dissolved O₂, we examined O₂ budget in a fixed control volume of the bottom water most susceptible to hypoxia. The increased water column respiration was found to be the dominant driver of the hypoxia expansion. Further analysis showed a nonlinear response to the nutrient loading. The accumulative hypoxia volume days per unit of nitrate load showed an abrupt (∼50 %) jump around 1968. The summer mean hypoxic volume increased with the winter-spring nutrient load, but it was 1.3 km³ (about 30 %) higher in 1968-1989 than in 1950-1967 at the same nutrient load. This upward shift in hypoxia was caused by the upward shift in the relationship between the water column respiration and winter-spring nutrient load. Hypoxia suppressed nitrification and denitrification processes in the sediment, amplifying nutrient recycling by 15 % and water column respiration by 12 %. Our modeling analysis demonstrated a feedback mechanism for driving the nonlinear hypoxia response to nutrient loading.

Trajectories of nutrients concentrations and ratios in the French coastal ecosystems: 20 years of changes in relation with large-scale and local drivers

Along with their important diversity, coastal ecosystems receive various amounts of nutrients, principally arising from the continent and from the related human activities (mainly industrial and agricultural activities). During the 20th century, nutrients loads have increased following the increase of both the global population and need of services. Alongside, climate change including temperature increase or atmospheric circulation change has occurred. These processes, Ecosystem state changes are hard to monitor and predict. To study the long-term changes of nutrients concentrations in coastal ecosystems, eleven French coastal ecosystems were studied over 20 years as they encompass large climatic and land pressures, representative of temperate ecosystems, over a rather small geographical area. Both univariate (time series decomposition) and multivariate (relationships between ecosystems and drivers) statistical analyses were used to determine ecosystem trajectories as well as typologies of ecosystem trajectories. It appeared that most of the French coastal ecosystems exhibited trajectories towards a decrease in nutrients concentrations. Differences in trajectories mainly depended on continental and human influences, as well as on climatic regimes. One single ecosystem exhibited very different trajectories, the Arcachon Bay with an increase in nutrients concentrations. Ecosystem trajectories based on ordination techniques were proven to be useful tools to monitor ecosystem changes. This study highlighted the importance of local environments and the need to couple uni- and multi-ecosystem studies. Although the studied ecosystems were influenced by both local and large-scale climate, by anthropogenic activities loads, and that their trajectories were mostly similar based on their continental influence, non-negligible variations resulted from their internal functioning.

Evaluation of seasonal dynamics of the surface water hydrochemistry using multivariate statistical techniques and aquatic macrophyte productivity in a mountainous lake, Northeast India

The present work elucidates the effective application of multivariate statistics in understanding the probable relations between surface water hydrochemistry and aquatic macrophyte productivity and their underlying seasonal dynamics in a remote mountainous lake of northeast India. The result of hierarchical cluster analysis revealed three distinct clusters corresponding to the pre-monsoon (35.42%), post-monsoon (52.08%), and monsoon (12.50%) seasons. The factor analysis yielded three principal components suggesting the sediment flux, farming discharge, domestic waste, bacterial oxidation of sulfur compounds, and dissolution of plant matters associated with dissolved feldspar minerals as the influential factors. The lake hydrochemistry also varied significantly, both spatially and temporally implying geogenic weathering processes from rock-soil-water interactions. Overall, sixteen aquatic macrophytes were identified, and their monthly and daily net primary productivity varied considerably in different seasons. Regression analysis highlighted the effect of temperature, total dissolved solids, electrical conductivity, and turbidity on the seasonal fluctuations in macrophyte productivity. Overall, the study provides insights into seasonal variation in the lake water chemistry and highlights the role of statistical tools in understanding the fragile aquatic ecosystems over cost-, labor-, and time-intensive inventory studies.

Satellite observations estimating the effects of river discharge and wind-driven upwelling on phytoplankton dynamics in the Chesapeake Bay

Phytoplankton growth in estuaries is regulated by a complex combination of physical factors with freshwater discharge usually playing a dominating role controlling nutrient and light availability. The role of other factors, including upwelling-generating winds, is still unclear because most estuaries are too small for upwelling to emerge. In this study, we used remotely sensed proxies of phytoplankton biomass and concentration of suspended mineral particles to compare the effect of river discharge with the effect of upwelling events associated with persistent along-channel southerly winds in the Chesapeake Bay, a large estuary where upwelling and its effects on biogeochemical dynamics have been previously reported. The surface chlorophyll-a concentrations (Chl-a) were estimated from Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data using the Generalized Stacked-Constraints Model (GSCM) corrected for seasonal effects by comparing remotely sensed and field-measured data. Light limitation of phytoplankton growth was assessed from the concentration of suspended mineral particles estimated from the remotely sensed backscattering at blue (443 nm) wavelength b_bp (443). The nine-year time series (2012-2020) of Chl-a and b_bp (443) confirmed that a primary factor regulating phytoplankton growth in this nearshore eutrophic area is discharge from the Susquehanna River, and presumably the nutrients it delivers, with a time lag up to four months. Persistent southerly wind events (2-3 days with wind speed >4 m/s) affected the water column stratification in the central part of the bay but did not result in significant increases in remotely sensed Chl-a. Analysis of model simulations of selected upwelling-favorable wind events revealed that strong southerly winds resulted in well-defined lateral (East-West) responses but were insufficient to deliver high-nutrient water to the surface layer to support phytoplankton bloom. We conclude that, in the Chesapeake Bay, which is a large, eutrophic estuary, wind-driven upwelling of deep water plays a limited role in driving phytoplankton growth under most conditions compared with river discharge. Integr Environ Assess Manag 2022;18:921-938. © 2022 SETAC.

Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters

Satellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leaving radiance are affected by interactions with the intervening atmosphere, the efficacy of atmospheric correction algorithms is essential to derive accurate water-leaving radiometry. Modern ocean color satellite sensors such as the Ocean and Land Colour Instrument (OLCI) onboard the Copernicus Sentinel-3A and -3B satellites are providing unprecedented operational data at the higher spatial, spectral, and temporal resolution that is necessary to resolve optically complex coastal water quality. Validating these satellite-based radiance measurements with vicarious in situ radiometry, especially in optically complex coastal waters, is a critical step in not only evaluating atmospheric correction algorithm performance but ultimately providing accurate water quality metrics for stakeholders. In this study, a regional in situ dataset from the Chesapeake Bay was used to evaluate the performance of four atmospheric correction algorithms applied to OLCI Level-1 data. Images of the Chesapeake Bay are processed through a neural-net based algorithm (C2RCC), a spectral optimization-based algorithm (POLYMER), an iterative two-band bio-optical-based algorithm (L2gen), and compared to the standard Level-2 OLCI data (BAC). Performance was evaluated through a matchup analysis to in situ remote sensing reflectance data. Statistical metrics demonstrated that C2RCC had the best performance, particularly in the longer wavelengths (>560 nm) and POLYMER contained the most clear day coverage (fewest flagged data). This study provides a framework with associated uncertainties and recommendations to utilize OLCI ocean color data to monitor the water quality and biogeochemical dynamics in Chesapeake Bay.

Increases in Picocyanobacteria Abundance in Agriculturally Eutrophic Pampean Lakes Inferred from Historical Records of Secchi Depth and Chlorophyll-a

Phytoplankton size structure has profound consequences on food-web organization and energy transfer. Presently, picocyanobacteria (size < 2 µm) represent a major fraction of the autotrophic plankton of Pampean lakes. Glyphosate is known to stimulate the development of picocyanobacteria capable of degrading the herbicide. Due to the worldwide adoption of glyphosate-resistant crops, herbicide usage has increased sharply since the mid-1990s. Unfortunately, there are very few studies (none for the Pampa region) reporting picocyanobacteria abundance before 2000. The proliferation of µm sized particles should decrease Secchi disc depth (ZSD). Therefore ZSD, conditional to chlorophyll-a, may serve as an indicator of picocyanobacteria abundance. We use generalized additive models (GAMs) to analyze a “validation” dataset consisting of 82 records of ZSD, chlorophyll-a, and picocyanobacteria abundance from two Pampean lakes surveys (2009 and 2015). In support of the hypothesis, ZSD was negatively related to picocyanobacteria after accounting for the effect of chlorophyll-a. We then fitted a “historical” dataset using hierarchical GAMs to compare ZSD conditional to chlorophyll-a, before and after 2000. We estimated that ZSD levels during 2000–2021 were, on average, only about half as deep as those during 1980–1999. We conclude that the adoption of glyphosate-resistant crops has stimulated outbreaks of picocyanobacteria populations, resulting in lower water transparency.

Nutrient Improvements in Chesapeake Bay: Direct Effect of Load Reductions and Implications for Coastal Management

In Chesapeake Bay in the United States, decades of management efforts have resulted in modest reductions of nutrient loads from the watershed, but the corresponding improvements in estuarine water quality have not consistently followed. Generalized additive models were used to directly link river flows and nutrient loads from the watershed to nutrient trends in the estuary on a station-by-station basis, which allowed for identification of exactly when and where responses are happening. Results show that Chesapeake Bay's total nitrogen and total phosphorus conditions are mostly improving after accounting for variation in freshwater flow. Almost all of these improving nutrient concentrations in the estuary can be explained by reductions in watershed loads entering through 16 rivers and 145 nearby point sources, with the nearby point source reductions being slightly more effective at explaining estuarine nutrient trends. Overall, these two major types of loads from multiple locations across the watershed are together necessary and responsible for the improving estuarine nutrient conditions, a finding that is highly relevant to managing valuable estuarine resources worldwide.

OUP accepted manuscript

Diatoms commonly set off the spring-bloom in temperate coastal environments. However, their temporal offset may change in regions subject to nutrient enrichment, and by peaking earlier, such populations can maintain their position in the vernal plankton succession. We tested whether the marine keystone diatom Skeletonema marinoi can accomplish this through thermal evolutionary adaptation. Eight geographically separated subpopulations, representing hydromorphologically and climatologically similar inlets displaying a range of trophic states, were compared in a common-garden experiment. At early-spring temperatures, both doubling times and variation coefficients thereof, correlated negatively with the trophic state of the environment of origin, indicating selection for fast growth due to eutrophication. At mid-spring temperatures, the relationships were reversed, indicating selection in the opposite direction. At late-spring temperatures, no significant relationships were detected, suggesting relaxed selection. Subsequent field observations reflected these findings, where blooming temperatures decreased with trophic state. Natural selection thus moves along with eutrophication towards colder temperatures earlier in the spring, favouring genotypes with the capacity to grow fast. The thermal niche shift demonstrated herein may be an evolutionary mechanism essentially leading to trophic changes in the local ecosystem.

Spatiotemporal BME characterization and mapping of sea surface chlorophyll in Chesapeake Bay (USA) using auxiliary sea surface temperature data

Improving the spatiotemporal coverage of remote sensing (RS) products, such as sea surface chlorophyll concentration (SSCC), can offer a better understanding of the spatiotemporal SSCC distribution for ocean management purposes. In the first part of this work, 834 in-situ SSCC measurements of the SeaBASS-NASA (National Aeronautics and Space Administration) during 2002-2016 served as the empirical dataset. A moving window with ±3 days and ±0.5° centered at each of the in-situ SSCC measurements established a search neighborhood for Moderate Resolution Imaging Spectroradiometer Level 2 (MODIS L2) SSCC and MODIS L2 sea surface temperature (SST) data, and the matched SSCC and SST data were used for building a linear SSCC-SST relationship. The unmatched SST was introduced to the linear model for generating soft SSCC data with uniform distributions. The inherent spatiotemporal dependency of the SSCC distribution was then represented by the Bayesian maximum entropy (BME) method, which incorporated the soft SSCC data as auxiliary variable for SSCC estimation and mapping purposes. The results showed that a 75.3% accuracy improvement of remote SSCC retrieval in terms of R² can be achieved by BME-based method compared to the original MODIS L2 product. Subsequently, the BME-based method was applied to obtain daily SSCC dataset in Chesapeake Bay (USA) during the period 2010-2019. It was found that the SSCC distribution exhibited a decreasing spatial trend from the upper bay to the outer bay, whereas decreasing and increasing temporal trends were detected during the periods 2011-2014 and 2016-2019, respectively. The generalized Cauchy process was used to quantitatively describe the autocorrelation SSCC function in the Chesapeake Bay. The results showed that the outer bay exhibited the strongest long-range dependence among the four sub-regions, whereas the middle bay exhibited the weakest long-range dependence. Finally, one-point and two-point stochastic site indicators (SSIs) were employed to explore the spatiotemporal SSCC characteristics in Chesapeake Bay. The one-point SSI results showed that nearly 100% of the upper, middle and the lower bay areas experienced a high SSCC level (>5 mg/m³) during the entire study period. The area with SSCC >5 mg/m³ in the outer bay increased a lot during the winter season, but the area with SSCC >10 or 20 mg/m³ decreased significantly in the upper, middle and lower bay. Simultaneously, the SSCC dispersion in these areas was rather small during the winter season. On the other hand, the two-point SSI results showed that although the SSCC levels differ among the four sub-regions, but the SSCC connectivity structures between pairs of points also displayed some similarities in terms of their spatiotemporal dependency. In conclusion, the proposed BME-based method was shown to be a promising remote SSCC mapping technique that exhibited a powerful ability to improve both accuracy and coverage of RS products. The SSIs can be also used to explore the spatiotemporal characteristics of a variety of natural attributes in waters.

Interactions of microalgae-bacteria consortia for nutrient removal from wastewater: A review

Nitrogen and phosphorus pollution can cause eutrophication, resulting in ecosystem disruption. Wastewater treatment systems employing microalgae-bacteria consortia have the potential to enhance the nutrient removal efficiency from wastewater through mutual interaction and synergetic effects. The knowledge and control of the mechanisms involved in microalgae-bacteria interaction could improve the system's ability to transform and recover nutrients. In this review, a critical evaluation of recent literature was carried out to synthesize knowledge related to mechanisms of interaction between microalgae and bacteria consortia for nutrient removal from wastewater. It is now established that microalgae can produce oxygen through photosynthesis for bacteria and, in turn, bacteria supply the required metabolites and inorganic carbon source for algae growth. Here we highlight how the interaction between microalgae and bacteria is highly dependent on the nitrogen species in the wastewater. When the nitrogen source is ammonium, the generated oxygen by microalgae has a positive influence on nitrifying bacteria. When the nitrogen source is nitrate, the oxygen can have an inhibitory effect on denitrifying bacteria. However, some strains of microalgae have the capability to supply hydrogen gas for hydrogenotrophic denitrifiers as an energy source. Recent literature on biogranulation of microalgae and bacteria and its application for nutrient removal and biomass recovery is also discussed as a promising approach. Significant research challenges remain for the integration of microalgae-bacteria consortia into wastewater treatment processes including microbial community control and process stability over long time horizons.

Spatial and temporal variation in the occurrence of bottlenose dolphins in the Chesapeake Bay, USA, using citizen science sighting data

Bottlenose dolphins (Tursiops truncatus) are migratory marine mammals that live in both open-ocean and coastal habitats. Although widely studied, little is known about their occurrence patterns in the highly urbanized estuary of the Chesapeake Bay, USA. The goal of this study was to establish the spatial and temporal distribution of bottlenose dolphins throughout this large estuarine system and use statistical modeling techniques to determine how their distribution relates to environmental factors. Three years (April-October 2017–2019) of dolphin sighting reports from a citizen-science database, Chesapeake DolphinWatch, were analyzed. The dolphins had a distinct temporal pattern, most commonly sighted during summer months, peaking in July. This pattern of observed occurrence was confirmed with systematic, passive acoustic detections of dolphin echolocation clicks from local hydrophones. Using spatially-exclusive Generalized Additive Models (GAM), dolphin presence was found to be significantly correlated to spring tidal phase, warm water temperature (24–30°C), and salinities ranging from 6–22 PPT. We were also able to use these GAMs to predict dolphin occurrence in the Bay. These predictions were statistically correlated to the actual number of dolphin sighting reported to Chesapeake DolphinWatch during that time. These models for dolphin presence can be implemented as a predictive tool for species occurrence and inform management of this protected species within the Chesapeake Bay.

Spatial analysis of water quality parameters in Hilo Bay, Hawai'i, using a combination of interpolated surfaces and hot spot analysis

Hilo Bay estuary, located on the northeastern side of Hawai'i Island, experiences variability in water quality parameters due to its numerous water inputs. This estuary experiences influxes of water from three sources: groundwater to the east, marine water from the north, and surface water from the Wailuku River to the west. High rainfall and river flow impacts Hilo Bay's water quality including salinity, turbidity, and chlorophyll a concentration. Here, maps of Hilo Bay water quality were examined to assess spatial patterns of these important parameters. Exploring the patterns of these water quality parameters by creating inverse distance weighted (IDW) interpolation surfaces of survey points and clusters based on hot spot analyses during low- and high-flow conditions showed statistically significant differences in spatial water quality in Hilo Bay. Water quality maps after a storm show (1) an overall decrease in salinity, (2) a river plume from the Wailuku River associated with a turbidity hot spot, and (3) a chlorophyll a hot spot offset from the river plume in the center of the bay. Using spatial analysis to analyze water quality throughout the entirety of Hilo Bay before and after storm events can lead to a better understanding of how this ecosystem is affected during these types of events, and furthermore, adopting this method of sampling and analysis allows for a greater representation of water quality all over the bay and can improve the monitoring of water quality in this important ecosystem.

Water masses seasonality and meteorological patterns drive the biogeochemical processes of a subtropical and urbanized watershed-bay-shelf continuum

Understanding the different scales of temporal variability is crucial to improve the knowledge of the biogeochemical processes in the land-ocean interface. In this study, we evaluated the role of continental runoff and intrusion of oceanic water masses in the trophic state of the Bay of Santa Catarina Island (BSCI) over the last three decades (1993-2019) by using multiple biogeochemical and eutrophication assessment tools. The sub-watersheds of BSCI showed high concentrations of nutrients, fecal coliform and chlorophyll-a, directly correlated to the number of inhabitants. Worst-case scenarios were found in summer and fall seasons due to sewage inputs caused by mass tourism and the inefficiency or even absence of treatment systems, boosted by strong rainfall. The intrusion of the South Atlantic Central Water and the Plata Plume Water into the BSCI favored autotrophy in the summer and heterotrophy in the winter, coupled with low and high residence time, respectively. El Niño events enhanced rainfall and continental runoff, exporting elevated nutrients and phytoplankton biomass loads from the eutrophic rivers to the continental shelf. The pattern reverses during La Niña, when chlorophyll and nutrient peaks were detected inside the bay. Eutrophication evaluation indicated that the trophic state oscillated from moderate to high and that these conditions tend to remain the same in future scenarios due to the moderate residence time of the water, anthropogenic pressures, periodic algal blooms and the intrusion of nutrient-rich oceanic water masses. Management actions, such as the improvement of the wastewater treatment system and wetlands restoration, are needed in order to mitigate eutrophication and the loss of ecosystem services and functions.

Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest

Fifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQI_NSF) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

Seasonal to Inter-Annual Variability of Primary Production in Chesapeake Bay: Prospects to Reverse Eutrophication and Change Trophic Classification

Estuarine-coastal ecosystems are rich areas of the global ocean with elevated rates of organic matter production supporting major fisheries. Net and gross primary production (NPP, GPP) are essential properties of these ecosystems, characterized by high spatial, seasonal, and inter-annual variability associated with climatic effects on hydrology. Over 20 years ago, Nixon defined the trophic classification of marine ecosystems based on annual phytoplankton primary production (APPP), with categories ranging from “oligotrophic” to “hypertrophic”. Source data consisting of shipboard measurements of NPP and GPP from 1982 to 2004 for Chesapeake Bay in the mid-Atlantic region of the United States supported estimates of APPP from 300 to 500 g C m⁻² yr⁻¹, corresponding to “eutrophic” to “hypertrophic” categories. Here, we developed generalized additive models (GAM) to interpolate the limited spatio-temporal resolution of source data. Principal goals were: (1) to develop predictive models of NPP and GPP calibrated to source data (1982 to 2004); (2) to apply the models to historical (1960s, 1970s) and monitoring (1985 to 2015) data with adjustments for nutrient loadings and climatic effects; (3) to estimate APPP from model predictions of NPP; (4) to test effects of simulated reductions of phytoplankton biomass or nutrient loadings on trophic classification based on APPP. Simulated 40% decreases of euphotic-layer chl-a or TN and NO₂ + NO₃ loadings led to decreasing APPP sufficient to change trophic classification from “eutrophic’ to “mesotrophic” for oligohaline (OH) and polyhaline (PH) salinity zones, and from “hypertrophic” to “eutrophic” for the mesohaline (MH) salinity zone of the bay. These findings show that improved water quality is attainable with sustained reversal of nutrient over-enrichment sufficient to decrease phytoplankton biomass and APPP.

Satellite Estimation of Chlorophyll-a Using Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor in Shallow Coastal Water Bodies: Validation and Improvement

The size and distribution of Phytoplankton populations are indicators of the ecological status of a water body. The chlorophyll-a (Chl-a) concentration is estimated as a proxy for the distribution of phytoplankton biomass. Remote sensing is the only practical method for the synoptic assessment of Chl-a at large spatial and temporal scales. Long-term records of ocean color data from the MODIS Aqua Sensor have proven inadequate to assess Chl-a due to the lack of a robust ocean color algorithm. Chl-a estimation in shallow and coastal water bodies has been a challenge and existing operational algorithms are only suitable for deeper water bodies. In this study, the Ocean Color 3M (OC3M) derived Chl-a concentrations were compared with observed data to assess the performance of the OC3M algorithm. Subsequently, a regression analysis between in situ Chl-a and remote sensing reflectance was performed to obtain a green-red band algorithm for coastal (case 2) water. The OC3M algorithm yielded an accurate estimate of Chl-a for deep ocean (case 1) water (RMSE = 0.007, r2 = 0.518, p < 0.001), but failed to perform well in the coastal (case 2) water of Chesapeake Bay (RMSE = 23.217, r2 = 0.009, p = 0.356). The algorithm developed in this study predicted Chl-a more accurately in Chesapeake Bay (RMSE = 4.924, r2 = 0.444, p < 0.001) than the OC3M algorithm. The study indicates a maximum band ratio formulation using green and red bands could improve the satellite estimation of Chl-a in coastal waters.