Coastal Water Quality Management Research Articles

Estimation of Suspended Particulate Matter Using Landsat 9 Imagery: Generating Algorithms and Spatio-Temporal Distributions

The fluctuation of suspended particulate matter (SPM) is essential to the biogeochemical cycle and ecological health of coastal waters. Anthropogenic activities potentially trigger an increase in SPM, so it needs to be monitored continuously. Spatial and temporal monitoring of SPM can be carried out cost-effectively with broad coverage using a remote sensing application. This study aims to build the SPM algorithm and estimate its temporal variability. The algorithm model in this study is based on an empirical formula between field data and reflectance data with the same acquisition. Water samples were taken from 100 stations in July 2022. Half were used for model building and the other for model validation. Suspended Particulate Matter was determined gravimetrically and estimated their temporal variability was based on Landsat 9 image records from December 2021 - November 2022. The results of the analysis show that the best algorithm for SPM estimation can be built based on coastal aerosol canals (B1), blue canals (B2), and green canals (B3) with the accuracy test result of (R2 = 0.68; RMSE = 5.551 mg.L-1; MAPE= 7.07%; Bias= 0.28). The SPM temporal fluctuations were generally higher in the west monsoon and lowered in the east monsoon, ranging from 30 to 70 mg.L-1. On the other hand, the spatial distribution shows a higher magnitude in the estuary than in the offshore waters, with a deviation of about 30 mg.L-1. Regional authorities can use the results obtained to improve the management of coastal water quality and monitoring systems.

Spatiotemporal variation, composition, and implications for transport flux of nitrogen in Leizhou Peninsula coastal water, China

Nitrogen, as the main bioactive element, plays an important role in biological productivity, ecosystem function, and biogeochemical processes in marine environment. In this study, seawater samples collected from China’s Leizhou Peninsula coastal water (LZPCW) during dry, normal, and wet seasons in 2018 were explored to reveal the spatiotemporal variation, composition, and transport flux of dissolved inorganic nitrogen (DIN) linked to hydrographic condition. DIN concentration and speciation showed significantly seasonal variation (P < 0.01), and the concentration of DIN species was significantly higher in dry season than other seasons. The annual mean DIN concentration was 8.01 ± 5.79 μmol L−1. In addition, the N–NO2−, N–NO3− and N–NH4+ were significantly different in the DIN bulk in different seasons. The largest fraction of DIN was N–NO3−, followed by N–NH4+, the lowest was N–NO2−, comprising up to 67.92 ± 23.20%, 23.90 ± 23.19% and 8.18 ± 8.19%, respectively. Besides, the Beibu Gulf was subjected to 7.28 × 1010 mol DIN annual flux through the Qiongzhou Strait. The annual transport fluxes of N–NO2−, N–NO3−, and N–NH4+ accounted for 6.43%, 77.20% and 16.36%, respectively. The DIN concentration and coastal water flow led to the largest DIN flux transport in dry season. This study revealed that the coastal ocean currents, river plumes and human activities jointly drove the dynamic variations of N species in LZPCW. It provides a baseline data for studying the spatiotemporal effects of hydrographic condition on nitrogen distribution and transport flux in the LZPCW, which is implications for understanding nutrients dynamics and coastal water quality management in future.

Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis

Coastal water quality management is a public health concern, as water of poor quality can potentially harbor dangerous pathogens. In this study, we employ routine monitoring data of EscherichiaColi and enterococci across 15 beaches in the city of Rijeka, Croatia, to build machine learning models for predicting E.Coli and enterococci based on environmental features. Cross-validation analysis showed that the Catboost algorithm performed best with R2 values of 0.71 and 0.69 for predicting E.Coli and enterococci, respectively, compared to other evaluated algorithms. SHapley Additive exPlanations technique showed that salinity is the most important feature for forecasting both E.Coli and enterococci levels. Furthermore, for low water quality sites, the spatial predictive models achieved R2 values of 0.85 and 0.83, while the temporal models achieved R2 values of 0.74 and 0.67. The temporal model achieved moderate R2 values of 0.44 and 0.46 at a site with high water quality.

Automation of Water Quality detection using Machine Learning

Coastal water quality management is a public health concern, as poor coastal water quality can potentially harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of Escherichia Coli and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their dynamics and relationships withenvironmental stressors. Gradient Boosting algorithms (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with routine monitoring measurements from all sampling sites and used to predict E: Coli and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R2 values of 0.71 and 0.68 for predicting E: Coli and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks.

Identifying locations of sewage pollution within a Hawaiian watershed for coastal water quality management actions

Study regionPuakō, Hawai‘i Island. Study focusLocations of sewage pollution in the Puakō watershed were identified through measurements of sewage indicators at groundwater wells and within Puakō’s and adjacent resorts’ shoreline waters. Dye tracer tests, water quality, δ15N macroalgal, and δ15N- and δ18O-NO3- measurements, along with stable isotope mixing models, were combined to assess water quality impairment caused by different Onsite Sewage Disposal System (OSDS) types, and used to predict water quality improvements from future management actions. New hydrological insights for the regionSewage indicators were highest within Puakō’s shoreline waters, including: Enterococcus spp., Clostridium perfringens, human-associated Bacteroides, and δ15N-NO3-. Mixing model results using δ15N- and δ18O-NO3- suggest that sewage was a dominant NO3- source, comprising > 40% at 10 of the 16 shoreline stations. δ15N macroalgae measurements confirmed presence of sewage at most stations. In groundwater wells and at adjacent resorts’ shoreline waters, sewage indicators were low, and δ15NO3-was indicative of soils and fertilizers. Puakō dye tracer tests revealed that sewage reached the shoreline within 5 h to 10 d, and that OSDS type did not affect travel time. Water quality was similar in front of homes with different OSDS. In conclusion, sewage is entering the groundwater at Puakō, and the underlying geology, rather than OSDS type, primarily controls the speed at which sewage reaches the shoreline. Our findings highlight the need for improved sewage treatment and collection at Puakō.

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.

Seawater quality conditions of the south Andaman Sea (Bay of Bengal, Indian Ocean) in lustrum during 2010s decade

Andaman and Nicobar islands is one of the major tourism hubs of the World. Most travelers visit these islands for historical attractions, beaches, snorkeling, scuba diving, coral reefs, adventure and recreation. Port Blair is the capital and sole entry/exit point of these islands. The coasts of Port Blair Bay (PBB) and Wandoor Creek (WC) are largely populated due to its services offered to different public/private sectors and for the economic significance. Nevertheless, the global recognition of these islands relies on its healthy ecosystem. Effective management of beaches, bays and their environmental services requires knowledge of coastal water quality. This study assesses the datasets of twenty seawater quality parameters of PBB and WC generated during five years (2011–2015) at eight sites. Multivariate statistical techniques were used for (i) analysis and interpretation of water quality parameters (ii) identification of pollution factors/sources and (iii) understanding spatio-temporal variations valuable for coastal water quality management.

An empirical model to quantify fecal bacterial loadings to coastal areas: Application in a Mediterranean context

In coastal catchments, rainfall events primarily drive bacterial inputs to the sea by causing land runoff, surface leaching and sewer overflow. Under semi-arid climate, extensive dry periods are interspersed with extreme precipitation. This paper aims to assess the impact of intense summer rainstorms events on Fecal Indicator Bacteria loadings to Mediterranean seawaters. Firstly, explanatory relationships were derived between an Antecedent Precipitation Index and the loads of thermo-tolerant coliforms and intestinal enterococci measured at three catchment outlets in the Gulf of Aigues-Mortes (southern France). Secondly, fecal bacterial loadings were simulated during summer season from 2006–2016, with a confidence interval arising from measurements uncertainties. On average, more than two rainstorms per summer season elevate bacterial loads at least by one order of magnitude, potentially leading to the degradation of bathing and fishing water quality observed in regulatory monitoring data. The results highlight the crucial importance of considering hydrological conditions in coastal water quality management.

Developing qualitative ecosystem service relationships with the Driver-Pressure-State-Impact-Response framework: A case study on Cape Cod, Massachusetts

Understanding the effects of environmental management strategies on society and the environment is critical for evaluating their effectiveness but is often impeded by limited data availability. In this article, we present a method that can help scientists to support environmental managers’ thinking about causal effects on ecosystem services in coupled human and natural systems. Our method aims to model qualitative cause-effect relationships between management strategies and ecosystem services, using information provided by knowledgeable participants, and the tradeoffs between strategies. We select and organize management strategies, environmental variables, and ecosystem services as indicators using the Driver-Pressure-State-Impact-Response framework. We evaluate the relationship between indicators using a decision tree and numerical representations of interaction strength. We use a matrix multiplication procedure to model direct and indirect interaction effects, and we provide guidelines for combining effects. Results include several data tables from which information can be visualized to understand the plausible interaction effects of implementing management strategies on ecosystem services. We illustrate our method with a coastal water quality management case study on Cape Cod, Massachusetts.

Bayesian structural equation modeling for coastal management: The case of the Saemangeum coast of Korea for water quality improvements

The data-driven paradigm is widely used in ocean science and includes the statistical modeling of various phenomena in coastal marine environments and data assimilation in numerical models. One of the most important challenges in the data-driven paradigm is finding the model that best approximates the underlying mechanism of a phenomenon with measurement data. In this paper, we propose a Bayesian approach to modeling coastal marine environments using ocean observational data, and we apply it to the Saemangeum coast. There are two main advantages to the Bayesian method: domain knowledge can be encoded to prior probability, and Markov-chain Monte Carlo simulation can be used in model estimation and inference. We apply the method to estimate model parameters and predict coastal water quality and sea current for maintaining optimal coastal water quality. The threshold quantity of sea current is computed to ensure sustainable coastal development. One of the interesting results we have obtained is a flat plateau relationship between the sea current for water exchange and the level of improvement of coastal water quality. This means that coastal water quality is not always being improved, even if the amount of water exchange is increased. The computed results are in good agreement with oceanographic theory, while showing a valid difference compared to the results using the frequentist approach and probabilistic inference using the probabilistic graphical model. These results will be helpful in coastal water quality management, ultimately contributing to sustainable coastal development.

Modeling for Pollution Contribution Rate of Land based Load in Masan Bay

Pollution contribution rate that is effect on water quality from land based load in Masan bay was showed quantifiably for coastal water quality management by using ecological model. It was calculated by difference of water quality concentration at each points t hat is calculated by each scenarios that are presence or absence of each sources (16 points). Results show that, rivers of Northern Masan bay contributed in Masan bay COD is 20 %, T-P is 62 % at northern part and COD is 10 %, T-P is 16 % at middle part. As a result, rivers of Northern Masan bay had effect on water quality of northern Masan bay and middle Masan bay. Also, T-P load affects water quality bigger than COD load, because T-P contribution rate bigger than COD contribution rate of northern rivers. Dukdong WTTP that is land pollution source of southern Masan bay contributed in Masan bay COD is 26 %, T-P is 11% at middle part, COD is 17 %, T-P is 7 % at middle part and COD is 10 %, T-P is 1 % at outer part. It affects water quality bigger at southern and middle of Masan bay than outer bay, because residual flow of bottom flows toward inner of Masan bay nearby Dukdong WTTP.

Land Ocean Interaction - Measuring and Modelling Fluxes from River Basins to Coastal Seas

The UK Land-Ocean Interaction Study (LOIS) provided a major opportunity to measure and model the processes controlling the fluxes of materials between river basins and coastal seas. The main programme of research took place between 1992 and 1998, funded by the UK Natural Environment Research Council, and involved approximately 360 coastal researchers contributing to more than 70 collaborative projects. This book presents accessible summaries of key results of this research. The ten chapters of the book are written by leading contributors to the programme, and cover aspects of the physical, sedimentological, chemical and biological processes controlling fluxes between river catchments and coastal seas in temporate environments. Major overarching themes for all of the chapters include the persistence, storage, degradation and transport of pollutants from catchment to coastal sea, and an emphasis on the relevance of research for river and coastal water quality management. The chapters also describe a number of important technological advances in the measurement of processes and longer term monitoring of the fluvial, estuarine and coastal environments. Approaches to monitoring were also transferred between disciplines, in many cases for the first time. The rivers and coastal programme of LOIS was unique in its scale and inter-disciplinarity and its results are of lasting value. This book provides a useful and important summary of many of its results and an effective introduction to the research for those who want to delve more deeply into the data and published papers. It will be of interest to scientists, environmental managers and scientific policy makers. This title belongs to European Water Research Series ISBN: 9781900222105 (Print) ISBN: 9781780402222 (eBook)

Simple Estimate of Entrainment Rate of Pollutants from a Coastal Discharge into the Surf Zone

Microbial pollutants from coastal discharges can increase illness risks for swimmers and cause beach advisories. There is presently no predictive model for estimating the entrainment of pollution from coastal discharges into the surf zone. We present a novel, quantitative framework for estimating surf zone entrainment of pollution at a wave-dominant open beach. Using physical arguments, we identify a dimensionless parameter equal to the quotient of the surf zone width l(sz) and the cross-flow length scale of the discharge la = M(j) (1/2)/U(sz), where M(j) is the discharge's momentum flux and U(sz) is a representative alongshore velocity in the surf zone. We conducted numerical modeling of a nonbuoyant discharge at an alongshore uniform beach with constant slope using a wave-resolving hydrodynamic model. Using results from 144 numerical experiments we develop an empirical relationship between the surf zone entrainment rate α and l(sz)/(la). The empirical relationship can reasonably explain seven measurements of surf zone entrainment at three diverse coastal discharges. This predictive relationship can be a useful tool in coastal water quality management and can be used to develop predictive beach water quality models.

A New Assessment Model for Coastal Water Quality Based on Nonlinear Mapping Theory

A new assessment model for coastal water quality was proposed based on the nonlinear mapping theory. Taking the water quality monitoring data of Tianjins coastal marine as an example, firstly, the high-dimension water quality data were mapped to the two-dimension plane by nonlinear mapping method. Secondly, the water quality assessment model was established according to the position relationship of mapping points. Then, the water quality was assessed based on the model. Through application we could found the method proposed in this paper was simple and practicable. It is science and effectiveness of applying the nonlinear mapping method to assessment the water quality. It could be used to supply the decision support for the coastal water quality management.

Information theoretic perspective on coastal water-quality monitoring and management near an offshore industrial park

A semi-continuous water-quality monitoring system was installed in Yunlin Offshore Industrial Park (YOIP), the largest industrial park in Taiwan, in 2007 to provide real-time water-quality information such as pH, water depth, dissolved oxygen, temperature, turbidity, conductivity, and chlorophyll. To interpret the large quantities of high-frequency data generated by this system, information theory was applied for data analysis and extraction of useful information for further coastal water-quality management. Information theory is a branch of applied mathematics that involves the quantification of information. Shannon entropy is a key measure of information that was calculated in this study to reveal the inherent uncertainty of water-quality information. The applicability of Shannon entropy for signaling possible coastal pollution events in the YOIP was explored and results showed that it provides new insight into the inherent uncertainty or randomness of the original data. Specially, when Shannon entropy was high, multiple instable readings were observed for turbidity and salinity. This indicates that Shannon entropy may be a useful new tool for exploratory data analysis. It can be used as a supplementary indicator along with the original environmental data to signify some episodes of water-quality degradation.

Stormwater runoff plumes observed by SeaWiFS radiometer in the Southern California Bight

Understanding the factors that influence the incidence and dispersal patterns of freshwater runoff plumes in southern California is important for management of coastal water quality. Significant river discharge is associated with episodic winter rainstorms, leading to turbid pollutant and pathogen-laden stormwater plumes that are clearly visible nearshore in the Southern California Bight. We analyzed 1.1-km spatial resolution sea-spectral reflectance data acquired in 1997–2003 by the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), focusing on four regions with distinctive adjacent watershed properties: Ventura, Santa Monica Bay, San Pedro Shelf, and Orange County/San Diego. The area of each plume was detected by the backscattering characteristics of surface waters, i.e., normalized water-leaving radiation of green–yellow wavelength 555 nm (nLw555). Plume area size was correlated with the rainstorm magnitude, which was estimated from atmospheric precipitation averaged over the total area of the watersheds connected to the seashore. The time lag between rainstorm and maximum plume was one day in San Pedro Shelf and two days in other three regions. Assessing maximum correlation between precipitated rainwater and the plume size, we estimated the optimal nLw555 values best characterizing the plume boundaries in each of the four study regions. Another quantitative characteristic derived from maximum correlation between rainwater and plume size was the “coefficient of persistence”, related to the speed of freshwater discharge and the time of plume water dissipation; it was also different in different watersheds. The primary factors regulating the relationship between rainstorm and plume were watershed land-use characteristics, size, and elevation.

Satellite ocean color observations of stormwater runoff plumes along the San Pedro Shelf (southern California) during 1997–2003

Knowledge of freshwater runoff plume dynamics in southern California is important for management of coastal water quality, because river discharge associated with episodic winter rainstorms can be a major source of pollutants and pathogens to coastal waters. The purpose of this study was to analyze the spatio-temporal dynamics of plumes at the San Pedro Shelf in southern California and identify factors influencing the incidence and dispersal patterns of plumes. We used 1.1-km spatial resolution satellite images collected in 1997–2003 by the SeaWiFS optical radiometer. The area of each plume was detected by the backscattering characteristics of surface waters in the vicinity of the mouths of four rivers that discharge to the San Pedro Basin. The rainstorm magnitude was estimated from atmospheric precipitation averaged over the total area of local watersheds. The plume size estimated from the backscattering coefficient nLw555>1.3 mW cm −2 μm −1 sr −1 was highly correlated with the amount of rainwater precipitated over the watershed area and accumulated during the period preceding the plume. The relation between rainstorm and plume area was linear with zero intercept: that means that the minimum rainstorm magnitude under which no plume occurred in the study area was almost zero, and even small precipitation resulted in a plume. The persistence of the plumes was estimated by fitting the coefficients of the model of plume water dissipation to achieve maximum correlation between the plume area and rainstorm: Twenty-five percent of rainwater dissipated daily, which gradually decreased the plume size. The size, alongshore and cross-shelf translocation of the plumes were modulated slightly by the local circulation, forced remotely by the equatorward wind a few hundred km to the south (along the coast of Baja California). The influence of tidal circulation on the plume area was slight and statistically insignificant.

Bioassays for coastal water quality: an assessment using the larval development of &lt;i&gt;Haliotis midae &lt;/i&gt;L

The United States Environmental Protection Agency (USEPA) has established a suite of methods that use coastal invertebrate species as bioassay organisms to test industrial and domestic effluent as well as coastal waters for potential toxicity. Although these methods are used globally, the potential of such toxicity tests has not been adequately explored for South African coastal waters. This study serves to describe a simple, cost-effective and relatively quick testing procedure using the development of Haliotis midae larvae as a bioassay of coastal water quality. This test is based on the sensitivity of these larvae to low concentrations of zinc (Zn). Its performance in a field trial demonstrates not only that this test has the potential to identify coastal waters of poor quality, but also that such identification could be of value in attempts to restock natural abalone populations, which are under extreme pressure from legal and illegal exploitation. Further work in this line should focus on the refinement of the methodology for this and other local species and should aim to contribute to the development of suitable criteria for the management of coastal water quality in South Africa. WaterSA Vol.28(4) 2002: 457-462

Application of the kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality

Kohonen neural network (KNN) was applied to nutrient data (ammonia, nitrite, nitrate and phosphate) taken from coastal waters in a Spanish tourist area. The activation maps obtained were not sufficient to evaluate and predict the trophic status of coastal waters. To achieve this aim, a new methodology is proposed which uses as its starting point the activation maps obtained from KNN. Firstly, to evaluate the trophic status of the coastal waters, it consists of the development of a quadrat system which enables a better classification than the traditional classification based simply on standardized data. The new classification allows clear differentiation of water quality within the mesotrophic band. Secondly, and in order to use the activation maps as predictive tools, the trophic classification, obtained from activation maps, was transposed onto new activation maps. To do this, the activation maps of the sampling points which defined each trophic group were superimposed. To avoid unnecessary complexity and to facilitate the process, this superimposition was undertaken only where the frequency exceeded 0.05. In this way, four frequency maps related to the trophic status of coastal waters (potentially eutrophic, high mesotrophic, low mesotrophic and oligotrophic) were obtained. There was no loss of relevant information in the new maps thus obtained. These frequency maps served as the basis for the successful prediction of the trophic status of random samples of coastal waters. This methodology, based on KNN, is proposed as a tool to aid the decision-making in coastal water quality management.

Mathematical modeling – a tool for coastal water quality management

This paper suggests an approach to solve the Indian coastal water quality management problem based on waste load allocation studies and application of modelling techniques. The waste load allocation procedure suggested in this paper assists in deriving a quantitative relationship between the waste load discharged and the receiving coastal water concentrations or effects of concern as presented by water quality standards. The approach sequentially addresses the topics of hydrodynamics, mass transport, water quality kinetics and problems of bioaccumulation and toxicity. The water quality modelling section describes the role of water quality modelling in WLA procedure. The paper also discusses the criteria and procedures for formulation of coastal zone management plans. The second part of the paper illustrates the concept with the help of brief case studies wherein simple screening procedures and the water quality modeling techniques have been applied. The first case study describes the application of the WASP model to arrive at the level of treatment required for municipal wastewater being discharged into Mumbai coastal waters so that the designated coastal water quality criteria is not violated and the degree of treatment is optimized. The second case study deals with the study of different options relating to disposal of tailings from an iron ore beneficiation plant keeping in view the impact of these tailings on marine water quality and ecology.