Coastal Water Quality Monitoring Research Articles

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.

Hydrodynamic and anthropogenic disturbances co-shape microbiota rhythmicity and community assembly within intertidal groundwater-surface water continuum

Tidal hydrodynamics drive the groundwater-seawater exchange and shifts in microbiota structure in the coastal zone. However, how the coastal water microbiota structure and assembly patterns respond to periodic tidal fluctuations and anthropogenic disturbance remains unexplored in the intertidal groundwater-surface water (GW-SW) continuum, although it affects biogeochemical cycles and coastal water quality therein. Here, through hourly time-series sampling in the saltmarsh tidal creek, rhythmic patterns of microbiota structure in response to daily and monthly tidal fluctuations in intertidal surface water are disentangled for the first time. The similarity in archaeal community structures between groundwater and ebb-tide surface water (R2=0.06, p = 0.2) demonstrated archaeal transport through groundwater discharge, whereas multi-source transport mechanisms led to unique bacterial biota in ebb-tide water. Homogeneous selection (58.6%-69.3%) dominated microbiota assembly in the natural intertidal GW-SW continuum and the presence of 157 rhythmic ASVs identified at ebb tide and 141 at flood tide could be attributed to the difference in environmental selection between groundwater and seawater. For intertidal groundwater in the tidal creek affected by anthropogenically contaminated riverine inputs, higher microbial diversity and shift in community structure were primarily controlled by increased co-contribution of dispersal limitation and drift (jointly 57.8%) and enhanced microbial interactions. Overall, this study fills the knowledge gaps in the tide-driven water microbial dynamics in coastal transition zone and the response of intertidal groundwater microbiota to anthropogenic pollution of overlying waters. It also highlights the potential of microbiome analysis in enhancing coastal water quality monitoring and identifying anthropogenic pollution sources (e.g., pathogenic Vibrio in aquaculture) through the detection of rhythmic microbial variances associated with intertidal groundwater discharge and seawater intrusion.

Spatial and temporal variation of physicochemical parameters of coastal waters in the southwestern region of Sri Lanka

Physicochemical parameters of coastal water in the southwestern area of Sri Lanka were studied from January to November 2016. Surface water samples from 15 selected locations were analysed according to standard methods. The ranges of the values recorded were; water temperature (WT) 26.5-33.9 ºC, pH 7.1-8.4, dissolved oxygen (DO) 4.4-7.4 mg L-1, salinity 14-38 ppt, electrical conductivity (EC) 27.1-53.8 mS cm-1, total suspended solids (TSS) 50.2-403.4 mg L-1, turbidity 1.2-64.9 NTU, nitrate nitrogen (NO3--N) 0.00-0.57mg L-1, nitrite nitrogen (NO2--N) 0.00-0.035mg L-1, ammoniacal nitrogen (NH4+-N) 0.00-1.72mg L-1, dissolved inorganic phosphorous (DIP) 0.00-0.65 mg L-1, biochemical oxygen demand (BOD) 0.1-11.6 mg L-1 and chemical oxygen demand (COD) 40-768 mg L-1. Results of Two-way ANOVA showed that there was a seasonal variability in parameters such as water temperature (p<0.001), dissolved oxygen (p<0.001), BOD (p<0.001), COD (p<0.001), nitrite nitrogen (p<0.001), nitrate nitrogen (p<0.001), dissolved inorganic phosphorous (p<0.001), turbidity (p<0.001), TSS (p<0.001) and Chlorophyll a (p<0.001). Spatial variation was identified in parameters, pH (p<0.001), turbidity (p<0.001), dissolved oxygen (p=0.004) and nitrite nitrogen (p<0.001) whereas the interaction between time and location was significant in pH (p=0.005) and turbidity (p=0.007). However, EC, salinity and ammoniacal nitrogen were not significant by either of the factors of consideration. In conclusion, the physicochemical characteristics of water are highly variable over time and location due to both natural and human factors. Water quality near fish processing areas such as fishery harbours and fish markets relatively deteriorated in terms of pH, DO, turbidity and nitrite nitrogen compared to recreational beaches. It is recommended to regulate any discharges of sewage and industrial wastewater directly into the sea. A long-term coastal water quality monitoring programme is vitally important to comprehensively characterise the coastal segments for a long-term strategic plan for sustainable development.

Coastalwql for Stitching, Pre-Processing and Visualization of Push Broom Hyperspectral Imagery for Uav-Based Coastal Water Quality Monitoring

Coastalwql for Stitching, Pre-Processing and Visualization of Push Broom Hyperspectral Imagery for Uav-Based Coastal Water Quality Monitoring

Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring 2021

Global warming together with environmental pollution threatens marine habitats and causes an increasing number of environmental disasters. Periodic monitoring of coastal water quality is of critical importance for the effective management of water resources and the sustainability of marine ecosystems. The use of remote sensing technologies provides significant benefits for detecting, monitoring, and analyzing rapidly occurring and displaced natural phenomena, including mucilage events. In this study, five water indices estimated from cloud-free and partly cloudy Sentinel-2 images acquired from May to July 2021 were employed to effectively map mucilage aggregates on the sea surface in the Izmit Bay using the cloud-based Google Earth Engine (GEE) platform. Results showed that mucilage aggregates started with the coverage of about 6 km² sea surface on 14 May, reached the highest level on 24 May and diminished at the end of July. Among the applied indices, the Adjusted Floating Algae Index (AFAI) was superior for producing the mucilage maps even for the partly cloudy image, followed by Normalized Difference Turbidity Index (NDTI) and Mucilage Index (MI). To be more specific, indices using green channel were found to be inferior for extracting mucilage information from the satellite images.

Evaluating Landsat-8 and Sentinel-2 Data Consistency for High Spatiotemporal Inland and Coastal Water Quality Monitoring

The synergy of fine-to-moderate-resolutin (i.e., 10–60 m) satellite data of the Landsat-8 Operational Land Imager (OLI) and the Sentinel-2 Multispectral Instrument (MSI) provides a possibility to monitor the dynamics of sensitive aquatic systems. However, it is imperative to assess the spectral consistency of both sensors before developing new algorithms for their combined use. This study evaluates spectral consistency between OLI and MSI-A/B, mainly in terms of the top-of-atmosphere reflectance (ρt), Rayleigh-corrected reflectance (ρrc), and remote-sensing reflectance (Rrs). To check the spectral consistency under various atmospheric and aquatic conditions, near-simultaneous same-day overpass images of OLI and MSI-A/B were selected over diverse coastal and inland areas across Mainland China and Hong Kong. The results showed that spectral data obtained from OLI and MSI-A/B were consistent. The difference in the mean absolute percentage error (MAPE) of the OLI and MSI-A products was ~8% in ρt and ~10% in both ρrc and Rrs for all the matching bands, whereas the MAPE for OLI and MSI-B was ~3.7% in ρt, ~5.7% in ρrc, and ~7.5% in Rrs for all visible bands except the ultra-blue band. Overall, the green band was the most consistent, with the lowest MAPE of ≤ 4.6% in all the products. The linear regression model suggested that product difference decreased significantly after band adjustment with the highest reduction rate in Rrs (NIR band) and Rrs (red band) for the OLI–MSI-A and OLI–MSI-B comparison, respectively. Further, this study discussed the combined use of OLI and MSI-A/B data for (i) time series of the total suspended solid concentrations (TSS) over coastal and inland waters; (ii) floating algae area comparison; and (iii) tracking changes in coastal floating algae (FA). Time series analysis of the TSS showed that seasonal variation was well-captured by the combined use of sensors. The analysis of the floating algae bloom area revealed that the algae area was consistent, however, the difference increases as the time difference between the same-day overpasses increases. Furthermore, tracking changes in coastal FA over two months showed that thin algal slicks (width < 500 m) can be detected with an adequate spatial resolution of the OLI and the MSI.

Detection and Sourcing of CDOM in Urban Coastal Waters With UV-Visible Imaging Spectroscopy

Ultraviolet (UV)-visible imaging spectroscopy is an emerging and highly anticipated technology, expected to improve the remote sensing of coastal waters and expand its range of applications. Upcoming NASA satellite missions including PACE and GLIMR will feature imaging spectrometers capable of measuring hyperspectral remote-sensing reflectance (Rrs) across the visible range and well into the near-infrared and ultraviolet domains. The availability of UV reflectance is expected to facilitate the remote sensing of chromophoric dissolved organic matter (CDOM) in optically complex waters, thereby improving coastal water-quality monitoring. Although this argument is well supported by the dominance of CDOM absorption in the UV domain, few studies have directly evaluated the potential advantages conferred by UV reflectance for monitoring CDOM-related coastal water quality. Here, we took advantage of a 6-week wastewater diversion event in Santa Monica Bay, California in 2015 and the availability of Portable Remote Imaging SpectroMeter (PRISM) imagery acquired during the diversion to assess if UV-visible imaging spectroscopy could facilitate the detection of CDOM and help differentiate wastewater effluent-derived CDOM from other sources. A comparison of local empirical algorithms with varying amounts of spectral information implemented on PRISM data showed that incorporating UV Rrs as a predictor significantly improved retrieval of CDOM absorption coefficients (ag). Optimal performance was reached when combining Rrs(365), Rrs(400), and Rrs(700) as predictors of ag in a multiple linear regression. The use of the entire UV-visible spectrum (365–700 nm) in a partial-least-squares regression (PLSR) did not improve retrievals, indicating that a few carefully chosen predictors in the UV-visible domain were sufficient to empirically differentiate CDOM from phytoplankton in coastal waters minimally influenced by sediments or bottom reflectance. Finally, the development of a new fluorescence-based indicator of effluent-derived CDOM (effluent fluorescence ratio, EFR) helped demonstrate the feasibility of remotely detecting CDOM from wastewater. A PLSR-based algorithm using Rrs(365–700) provided reasonable EFR retrievals and successfully identified effluent-derived CDOM at the wastewater outfall when implemented on PRISM imagery. Although further work should investigate the influence of effluent-CDOM fluorescence on Rrs more mechanistically, these results confirmed that UV-visible imaging spectrometers can facilitate coastal CDOM-related water quality monitoring and expand its range of applications.

Coastal Surveillance and Water Quality monitoring in the Rejiche Sea-Tunisia.

The aim of this study is to determine physiochemical and bacteriological properties of seawater and sediments from the coast of Tunisia during six successive months. The conductivity was measured using previously calibrated Hach Conductivity meter. Total suspended solid was determined using Whatman GF/C glass fiber filter (Sigma Aldrich), and the turbidity was assessed using a spectrophotometer (UV/Vis). The pH was measured using pH electrodes. Other physiochemical parameters were determined using Pastel UV multiparameter water analyzer (Secomam, aqualabo). Bacterial analysis was displayed by membrane filtration method, and isolates were identified to the species level by Api strips. Susceptibility to antibiotics was determined by disk diffusion methods. Total suspended solid, turbidity, adsorbable organic halogen, chemical oxygen demand, and biochemical oxygen demand were higher than accepted norms in Tunisia. Enterococcus faecalis was detected in all the sites indicating a human fecal contamination, and all the isolates were highly resistant to rifampicin, gentamicin, chloramphenicol, ampicillin, and azithromycin. The situation in Mahdia coast is alarming particularly with the isolation of multidrug resistance strains. It is recommended that the local government provide restrict policies for the treatment and the assessment of municipal wastewater before its discharge into sea. PRACTITIONER POINTS: Coastal Surveillance and Water Quality monitoring in the Rejiche Sea - Tunisia is crucial. Enterococcus faecalis was detected in sea water and sediment samples in the coast of Rejiche. Isolated strains were highly resistant to rifampicin, gentamicin, chloramphenicol, ampicillin and azithromycin.

Remote sensing of coastal hydro-environment with portable unmanned aerial vehicles (pUAVs) a state-of-the-art review

Urbanization together with climate change is imposing growing stresses on the sustainable development of major coastal cities around the world, particularly in developing countries. Thus, the need for regular monitoring of coastal water quality is becoming increasingly vital, and remote sensing is deemed to be a viable approach particularly due to its cost-effectiveness. Over the past two decades, remote sensing using satellite imaging has already been adopted in various tasks related to the monitoring of coastal hydro-enviro nment, including the detection of oil spills, identification of turbid plumes, mapping of marine ecosystems, etc. Despite their usefulness, space-borne sensors are subjected to constraints such as low spatial resolution, clouds and atmospheric interference, sparse frequency, and inflexible scheduling. Recently, remote sensing with portable Unmanned Aerial Vehicles (pUAVs) is emerging as a promising alternative for high spatial-resolution monitoring of coastal hydro-environment, with the key advantages that they can minimize the effects of atmospheric disruptions and be implemented with a frequent schedule to perform on-demand monitoring. In addition, with the technological advances in the sensor's design, UAVs are now capable of capturing data with fine spectral resolutions, ranging from several (multispectral) to hundreds (hyperspectral) of spectral bands. Data analytic is also improving rapidly with the advancement in artificial intelligence (AI) and deep learning algorithms. In this paper, we present a systematic review of the recent developments in the utilization of UAV-based remote sensing for the sustainable development of the coastal hydro-environment, particularly with pUAVs. The key technical issues, including the design of UAV systems and the processing of data, are examined for specific applications. Future prospects for implementation are also discussed.

Application of ddPCR for detection of Enterococcus spp. in coastal water quality monitoring

Droplet digital polymerase chain reaction (ddPCR) was evaluated for the detection of fecal indicator bacteria (FIB), Enterococcus spp., in San Diego County beach water samples collected under diverse conditions, from multiple pollution sources, as part of regulatory monitoring activities over 20 months. Two US EPA-approved methods, qPCR (EPA 1609.1) and Enterolert (SM9230D), were used as reference comparator methods. A total of 361 samples were assayed by both ddPCR and qPCR and yielded an acceptable Index of Agreement (IA) of 0.89, based on EPA Site-Specific analysis guidelines. A Pearson's correlation coefficient of r = 0.87 (p < 0.001), further indicated a strong relationship between the methods results. From the 361 samples, 185 split samples with ddPCR and Enterolert values within the limits of quantification, were used as a ‘training’ data set to derive an intrinsic copy number equation (ICE) for scaling ddPCR gene copy number to Enterolert most probable number (MPN). Of the 1993 samples that comprised the complete ‘test’ data set assayed by ddPCR and Enterolert, 1086 generated results that fell within the limits of quantification for Enterolert and yielded an overall IA of 0.64. Re-analysis using median as a measure of central tendency to account for significant skewing of Enterolert data yielded an IA of 0.72. Beach grouping-specific IA values ranged from 0.63 to 0.93. Pearson's correlation coefficient, r, ranged from 0.13 to 0.94 within beach groupings and generated a combined value of 0.60 for all groupings. Using the ICE, a ddPCR advisory threshold of 1413 DNA copy number/100 mL was empirically determined to be the equivalent to the California Enterolert beach action threshold of 104 MPN/100 mL, based on comparison with all 1993 paired ddPCR and Enterolert results. Using the 1413 DNA copy number/100 mL as a beach action threshold for ddPCR resulted in a 90.4% agreement with Enterolert (6.0% false negative and 3.7% false positive). Together these findings support the conclusion that ddPCR readouts align closely with Enterolert MPN for identifying FIB exceedance levels of Enterococcus spp. in coastal waters of San Diego, CA.

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

An end-to-end process to achieve a complete framework methodology for Harmful Algal Bloom (HAB) growth prediction is crucial for water management, especially in implementing robust predictive modelling of HAB to prevent water pollution. Previous works have separately focused on the prediction part or the implementation of the water monitoring system that involves the integration of sensors through the Internet of Things (IoT). These studies lack in terms of discussion of both IoT with the algae ecological domain and prediction method. Therefore, this paper takes the initiative to provide a wider coverage on the end-to-end process including the assembly and integration of sensors, data acquisition and predictive modelling using data-driven approaches, for example, machine learning, deep learning and deep time series forecasting algorithm for future algal bloom outbreak mitigation. This paper believes that discussion in a complete framework perspective based on the execution of each phase is important besides providing a true understanding of the algae growth factors and prediction problems to achieve a robust prediction algorithm for algal growth. In the end, this paper presents proof that selecting the right features and utilising time series with deep learning are much better for tackling the issues of highly non-linear and dynamic algae ecological data that are briefly introduced in this paper. Among all the algorithms selected, Long Short-term Memory (LSTM) is the best fit for the prediction method and has outperformed other basic machine learning methods in accurately predicting algal growth through the prediction of chlorophyll-a (Chl-a) as a strong indicator of algal presence for coastal studies.

A Semi-Analytical Optical Remote Sensing Model to Estimate Suspended Sediment and Dissolved Organic Carbon in Tropical Coastal Waters Influenced by Peatland-Draining River Discharges off Sarawak, Borneo

Coastal water quality degradation is a global challenge. Marine pollution due to suspended sediments and dissolved matter impacts water colour, biogeochemistry, benthic habitats and eventually human populations that depend on marine resources. In Sarawak (Malaysian Borneo), peatland-draining river discharges containing suspended sediments and dissolved organic carbon influence coastal water quality at multiple locations along the coast. Optical remote sensing is an effective tool to monitor coastal waters over large areas and across remote geographic locations. However, the lack of regional optical measurements and inversion models limits the use of remote sensing observations for water quality monitoring in Sarawak. To overcome this limitation, we have (1) compiled a regional spectral optical library for Sarawak coastal waters, (2) developed a new semi-analytical remote sensing model to estimate suspended sediment and dissolved organic carbon in coastal waters, and (3) demonstrated the application of our remote sensing inversion model on satellite data over Sarawak. Bio-optical data analysis revealed that there is a clear spatial variability in the inherent optical properties of particulate and dissolved matter in Sarawak. Our optical inversion model coupled with the Sarawak spectral optical library performed well in retrieving suspended sediment (bias = 3% and MAE = 5%) and dissolved organic carbon (bias = 3% and MAE = 8%) concentrations. Demonstration products using MODIS Aqua data clearly showed the influence of large rivers such as the Rajang and Lupar in discharging suspended sediments and dissolved organic carbon into coastal waters. The bio-optical parameterisation, optical model, and remote sensing inversion approach detailed here can now help improve monitoring and management of coastal water quality in Sarawak.

Analysis and Classification of Stormwater and Wastewater Runoff From the Tijuana River Using Remote Sensing Imagery

Urban runoff represents the primary cause of marine pollution in the Southern California coastal oceans. This study focuses on water quality issues originating from the Tijuana River watershed, which spans the southwest border of the United States and Mexico. Frequent discharge events into the coastal ocean at this boundary include stormwater and wastewater. This study focuses on differences in spectral features, as assessed by RapidEye, Sentinel-2 A/B, and Landsat-8 satellite data, along with physical and biological in situ data, to characterize and classify plumes into four key categories: stormwater, wastewater, open ocean/no plume, and mixed (when both types of plumes are present). Key spectral differences in the visible to NIR bands showed that stormwater had elevated reflectance (0.02 to 0.09), followed by mixed (0 to 0.08), wastewater (0 to 0.05), and open ocean/no plume (0 to 0.03) events. We also examined biophysical parameters and found that stormwater events had the highest values in remote sensing based estimates of colored dissolved organic matter (CDOM) (0.98 to 2.1 m–1) and turbidity (12.4 to 45.7 FNU) and also had a large range for in situ variables of enterococcus bacteria and flow rates. This study also finds that the use of spectral features in a hierarchical cluster analysis can correctly classify stormwater from wastewater plumes when there is a dominant type. These results of this study will enable improved determination of the transport of both types of plumes and transboundary monitoring of coastal water quality across the Southern California/Baja California region.

Spatial downscaling of MODIS Chlorophyll-a with machine learning techniques over the west coast of the Yellow Sea in South Korea

Effective water quality monitoring of coastal areas through the measurement of Chlorophyll-a (Chl-a) has remarkably progressed by ocean color remote sensing. Among different sensors, Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 products provide reliable global representations of the Chl-a concentration. On the other hand, due to the coarse spatial resolution of MODIS data, its applicability is limited for spatially complex coastal regions. To overcome this limitation, a few downscaling techniques have been suggested based on the polynomial regression method. However, this type of regression has some restrictions, such as sensitivity to outliers, and nonlinear types of machine learning algorithms have not been tested in downscaling Chl-a datasets. Therefore, three machine learning (ML) techniques, support vector regression (SVR), random forest regression (RFR), and long short-term memory (LSTM), were developed using the Sentinel-2A/MSI bands as predictors and MODIS Chl-a as a predictand and compared their results with the results of multiple polynomial regression (MPR), to find the most suitable model for downscaling MODIS Chl-a in coastal area of South Korea. The obtained results showed that the 2nd degree MPR and SVR-Radial Basis Function (RBF) illustrate the best performance in the winter and summer days, respectively. In addition, LSTM is less sensitive to the changes in all variables (sensitivity index range from 0.31 to 0.48). Overall, we conclude that the downscaling approach based on ML models, especially SVR-RBF, can serve as a suitable alternative in some cases to produce high-resolution Chl-a maps, especially for coastal marine water quality monitoring.

Spatiotemporal river flux and composition of nutrients affecting adjacent coastal water quality in Hainan Island, China

Human activities have changed global nutrients cycling and appreciably altered marine ecosystems in riverine-estuarine systems. This is evident by the significant shifts in the supply of nitrogen and phosphorus. However, very little is known about the concentrations and fluxes and of these nutrients and their seasonal changes in Hainan Island, China. We examined Hainan Island, which is the second largest island in China, to assess the spatiotemporal concentrations and fluxes of nutrients using seasonal data on a whole-of-system scale covering river estuaries and adjacent coastal water (2017). The annual average total dissolved nitrogen (TDN) and total dissolved phosphorus (TDP) concentrations were 77.23 ± 31.36 μmol/L and 3.25 ± 1.67 μmol/L, respectively. The annual fluxes of pollutants from the discharge of coastal rivers on Hainan Island into coastal waters were 1.8 × 104 t for TDN and 1.1 × 103 t for TDP, respectively. The month with the largest contribution to the annual pollutants load was October, which contributed 13.60% of TDN and 17.79% of TDP. In addition, the largest river fluxes were Nandu River, which contributed 7.5 × 103 t of TDN and 364 t of TDP. The annual average concentrations of pollutants in the surface coastal water were 7.60 ± 10.71 μmol/L for dissolved inorganic nitrogen (DIN) and 0.24 ± 0.31 μmol/L for dissolved inorganic phosphorus (DIP). The annual average N/P ratios in coastal rivers and seawater were 30 and 65, which were significantly higher than the Redfield ratio (N/P = 16). The DIN and DIP concentrations of most monitoring stations met the national first-class seawater standards, but the problem of nutrient pollution in some local coastal water areas was still outstanding. The coastal water quality monitoring should consider the influence of tropical storms influence on Hainan Island. The effective improvement of coastal water quality should be based on land–ocean coordination monitoring of coastal water quality and the riverine nutrient flux of Hainan Island.

Web-Based Decision Support System for Coastal Water Quality

Sujith Kumar, S.; Panda, U.S.; Pradhan, U.K; Mishra, P., and Ramana Murthy, M.V., 2020. Web-based decision support system for coastal water quality. In: Sheela Nair, L.; Prakash, T.N.; Padmalal, D., and Kumar Seelam, J. (eds.), Oceanic and Coastal Processes of the Indian Seas. Journal of Coastal Research, Special Issue No. 89, pp. 139-144. Coconut Creek (Florida), ISSN 0749-0208.Operational systems on real-time monitoring of coastal water quality can play a key role to counter and reduce the pollution causing hazardous events in the coastal region. An automated alert system is required to provide relevant information to the coastal authorities, water quality managers and other coastal stakeholders on a real-time basis to avoid any lead time or potential human errors. This paper presents an architecture for coastal water quality prediction and alert system by integrating different platforms and services to achieve scalability, provisioning of resources in real time, simplified deployment and management of resources and applications. The forecast system was developed through data assimilation from coupled hydrodynamic water quality models and field observations and archived in an integrated Database Management System (DBMS). A web-service is programmed to fetch the data from server on demand in order to disseminate the information to the public. Web applications use an architecture that breaks applications into discrete parts, using multiple programming languages and deploying the applications on several layers of technology. The other features of the website include Query builder as per user's specification, Fetch and display latest time series data on water quality parameters based on locations and graphical representation.

An Evaluation of Citizen Science Smartphone Apps for Inland Water Quality Assessment

Rapid and widespread monitoring of inland and coastal water quality occurs through the use of remote sensing and near-surface water quality sensors. A new addition is the development of smartphone applications (Apps) to measure and record surface reflectance, water color and water quality parameters. In this paper, we present a field study of the HydroColor (HC, measures RGB reflectance and suspended particulate matter (SPM)) and EyeOnWater (EoW, determines the Forel–Ule scale—an indication to the visual appearance of the water surface) smartphone Apps to evaluate water quality for inland waters in Eastern Australia. The Brisbane river, multiple lakes and reservoirs and lagoons in Queensland and New South Wales were visited; hyperspectral reflection spectra were collected and water samples were analysed in the laboratory as reference. Based on detailed measurements at 32 sites, covering inland waters with a large range in sediment and algal concentrations, we find that both water quality Apps are close, but not quite on par with scientific spectrometers. EoW is a robust application that manages to capture the color of water with accuracy and precision. HC has great potential, but is influenced by errors in the observational procedure and errors in the processing of images in the iPhone. The results show that repeated observations help to reduce the effects of outliers, while implementation of camera response functions and processing should help to reduce systematic errors. For both Apps, no universal conversion to water quality composition is established, and we conclude that: (1) replicated measurements are useful; (2) color is a reliable monitoring parameter in its own right but it should not be used for other water quality variables, and; (3) tailored algorithms to convert reflectance and color to composition could be developed for lakes individually.

Seasonal change in trace element concentrations of Paracentrotus lividus: Its use as a bioindicator

Abstract An assessment of classical and emerging trace element contamination was conducted on gonads of the sea urchin Paracentrotus lividus (Lamarck, 1819), in Corsica (Western Mediterranean). The aim of this study was to evaluate the contamination levels at different sites by following the seasonal variation of 22 trace elements. The sea urchins analyzed were taken in 2017 from reference and more impacted sites in four Corsican areas. The results obtained shown the importance of biotic factors such as gender, reproduction and the way of life. Variations have been highlighted with lower trace element concentrations during the summer season. This is mainly due to a dilution phenomenon resulting from gametogenesis. The pollution index (TEPI) was determined and highlighted differences in contamination levels at the various sites. This work could provide additional support for other tools for the diagnosis and monitoring of coastal water quality. It provides useful new data to enable managers to act at the source and reduce degradation in order to improve the ecological quality of marine waters.

Rapid pre-concentration of Escherichia coli in a microfluidic paper-based device using ion concentration polarization.

We report a microfluidic paper based analytical device implementing ion concentration polarization (ICP) for rapid pre-concentration of Escherichia coli in water. The fabricated device consists of a paper channel with a Nafion® membrane and in-built micro wire electrodes to supply electric voltage to induce the ICP effect. E. coli cells were stained with SYTO 9 and fluorescence was used as a sensing method. The device achieved high concentration factor up to 2×105 within minutes. The effect of total ion concentration, on ICP and fluorescence intensity was studied. The reported device and method are suitable and effective for detection of E. coli during ballast water quality monitoring, coastal water quality monitoring where high salinity water is present.

Retrieval Algorithm of Chlorophyll-a Concentration in Turbid Waters from Satellite HY-1C Coastal Zone Imager Data

Chen, X.-Y.; Zhang, J.; Tong, C.; Liu, R.-J.; Mu, B., and Ding, J., 2019. Retrieval algorithm of chlorophyll-a concentration in turbid waters from satellite HY-1C coastal zone imager data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 146-155. Coconut Creek (Florida), ISSN 0749-0208.The satellite HY-1C was successfully launched in September 2018 with the Coastal Zone Imager (CZI) onboard, which can provide optical images of 50-m resolution at four broad bands (blue, green, red and near-infrared). In this study, 305 sets of in-situ measurements collected between 2005 and 2015 in the Bohai, Yellow and East China seas were used to assess the performance of HY-1C in retrieving chlorophyll-a concentration in the coastal waters. The results showed that two algorithms based on blue-green band ratios (OC3 and OC3L) performed well. The median of the ratio of retrieved to in-situ values was about 1.1, and the median absolute percentage difference was about 40 %. After applying the two algorithms to HY-1C CZI data of Jiaozhou Bay (Qingdao, China), the spatial distribution of retrieved Chl-a data was found to be in good agreement with results based on data from another satellite (Landsat-8 OLI) with a median absolute percentage difference less than 15 %. The results showed that the broadband channels of the HY-1C CZI can retrieve Chl-a concentration in turbid waters, which indicates potential for its application in coastal water quality and eutrophication monitoring.