Estimating the concentration of physico chemical parameters in hydroelectric power plant reservoir

The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines the amazon region and adjacent areas, such as the Pantanal, as world heritage territories, since they possess unique flora and fauna and great biodiversity. Unfortunately, these regions have increasingly been suffering from anthropogenic impacts. One of the main anthropogenic impacts in the last decades has been the construction of hydroelectric power plants. As a result, dramatic altering of these ecosystems has been observed, including changes in water levels, decreased oxygenation and loss of downstream organic matter, with consequent intense land use and population influxes after the filling and operation of these reservoirs. This, in turn, leads to extreme loss of biodiversity in these areas, due to the large-scale deforestation. The fishing industry in place before construction of dams and reservoirs, for example, has become much more intense, attracting large populations in search of work, employment and income. Environmental monitoring is fundamental for reservoir management, and several studies around the world have been performed in order to evaluate the water quality of these ecosystems. The Brazilian Amazon, in particular, goes through well defined annual hydrological cycles, which are very importante since their study aids in monitoring anthropogenic environmental impacts and can lead to policy and decision making with regard to environmental management of this area. The water quality of amazon reservoirs is greatly influenced by this defined hydrological cycle, which, in turn, causes variations of microbiological, physical and chemical characteristics. Eutrophication, one of the main processes leading to water deterioration in lentic environments, is mostly caused by anthropogenic activities, such as the releases of industrial and domestic effluents into water bodies. Physico-chemical water parameters typically related to eutrophication are, among others, chlorophyll-a levels, transparency and total suspended solids, which can, thus, be used to assess the eutrophic state of water bodies. Usually, these parameters must be investigated by going out to the field and manually measuring water transparency with the use of a Secchi disk, and taking water samples to the laboratory in order to obtain chlorophyll-a and total suspended solid concentrations. These processes are time- consuming and require trained personnel. However, we have proposed other techniques to environmental monitoring studies which do not require fieldwork, such as remote sensing and computational intelligence. Simulations in different reservoirs were performed to determine a relationship between these physico-chemical parameters and the spectral response. Based on the in situ measurements, empirical models were established to relate the reflectance of the reservoir measured by the satellites. The images were calibrated and corrected atmospherically. Statistical analysis using error estimation was used to evaluate the most accurate methodology. The Neural Networks were trained by hydrological cycle, and were useful to estimate the physicalchemical parameters of the water from the reflectance of visible bands and NIR of satellite images, with better results for the period with few clouds in the regions analyzed. The present study shows the application of wavelet neural network to estimate water quality parameters using concentration of the water samples collected in the Amazon reservoir and Cefni reservoir, UK. Sattelite imagens from Landsats and Sentinel-2 were used to train the ANN by hydrological cycle. The trained ANNs demonstrated good results between observed and estimated after Atmospheric corrections in satellites images. The ANNs showed in the results are useful to estimate these concentrations using remote sensing and wavelet transform for image processing. Therefore, the techniques proposed and applied in the present study are noteworthy since they can aid in evaluating important physico-chemical parameters, which, in turn, allows for identification of possible anthropogenic impacts, being relevant in environmental management and policy decision-making processes. The tests results showed that the predicted values have good accurate. Improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs. This thesis contributes to the evaluation of the accuracy of different methods in the estimation of physical-chemical parameters, from satellite images and artificial neural networks. For future work, the accuracy of the results can be improved by adding more satellite images and testing new neural networks with applications in new water reservoirs

Sustainable Approaches for Highway Runoff Management During Construction and Operation

publishedVersio

Sustainable Approaches for Highway Runoff Management During Construction and Operation

Paper V and paper VI have not been published yet.Environmentally friendly approaches for highway runoff management during construction and operation are considered in this project. First, the state of the art in runoff management in terms of characterization, treatment, and modeling approaches were surveyed, and knowledge gaps were identified. Then, the characterization and treatment of tunneling wastewater (by natural and chemical coagulants) was investigated. In the next stage, the vulnerability of water quality to road construction activities was investigated by analyzing field monitoring data. In addition, two different approaches, involving information theory and gamma test theory, were suggested to optimize the water quality monitoring network during road construction. Lastly, the application of satellite data (i.e., Sentinel-2 Multi-Spectral Imager satellite imagery products) for water quality monitoring was examined. Based on the results, it can be shown that site-specific parameters (e.g., climate, traffic load) cause spatiotemporal variation in the characterization of highway runoff and performance of best management practices (BMP) to protect water quality. There is a knowledge gap regarding the characterization of highway runoff under different climatic scenarios, as well as the continuous monitoring and assessment of roadside water bodies. Analysis of the field monitoring data indicates that blasting, area cleaning, and construction of water management measures have the highest impact on surface water quality during road construction. Additionally, the application of information theory and gamma test theory indicate that the primary monitoring network assessed here is not optimally designed. The number and spatial distribution of monitoring stations could be modified and reduced, as the construction activities vary over time. Additionally, the suggested remote sensing techniques applied in this project are able to estimate water quality parameters (i.e., turbidity and chlorophyll-a) in roadside water bodies with a reliability consistent with field observations, reflecting the spatiotemporal effects of road construction and operations on water quality. Finally, an efficient two-step treatment strategy (15 min sedimentation followed by chemical coagulation and 45 min sedimentation) is suggested for the treatment of tunneling wastewater. The optimum coagulant dosages in the jar test exhibit high treatment efficiency (92-99%) for both turbidity and suspended solids (SS), especially for particle removal in the range of 10-100 μm, which is hard to remove by sedimentation ponds and may pose serious threats to the aquatic ecosystem. It is hoped the knowledge generated by this project will help decision-makers with management strategies and support UN Sustainable Development Goals (SDGs). The proposed approaches directly contribute to managing highway runoff and achieving SDG 6 (clean water and sanitation) and especially target 6.3 (water quality).publishedVersio

Monitoring, Modelling and Management of Water Quality

Different types of pressures, such as nutrients, micropollutants, microbes, nanoparticles, microplastics, or antibiotic-resistant genes, endanger the quality of water bodies. Evidence-based pollution control needs to be built on the three basic elements of water governance: Monitoring, modeling, and management. Monitoring sets the empirical basis by providing space- and time-dependent information on substance concentrations and loads, as well as driving boundary conditions for assessing water quality trends, water quality statuses, and providing necessary information for the calibration and validation of models. Modeling needs proper system understanding and helps to derive information for times and locations where no monitoring is done or possible. Possible applications are risk assessments for exceedance of quality standards, assessment of regionalized relevance of sources and pathways of pollution, effectiveness of measures, bundles of measures or policies, and assessment of future developments as scenarios or forecasts. Management relies on this information and translates it in a socioeconomic context into specific plans for implementation. Evaluation of success of management plans again includes well-defined monitoring strategies. This book provides an important overview in this context

Remote sensing for water quality studies: test of Suspended Particulate Matter and turbidity algorithms for Portuguese transitional and inland waters

Tese de mestrado em Ciências do Mar, Universidade de Lisboa, Faculdade de Ciências, 2020As partículas em Suspensão (SPM) é um dos principais constituintes da água nos estuários e, juntamente com a turbidez (T), é um parâmetro chave para a avaliação da qualidade da água. Através da absorção e difusão da luz, a concentração de SPM reduz a penetração da irradiância solar na coluna de água e limita a radiação fotossinteticamente disponível (PAR) para os produtores primários. Uma vez que a turbidez é altamente correlacionada com a concentração de SPM, para fins de monitorização da qualidade da água, a turbidez é listada como parâmetro obrigatório a ser medido pelos estados membros da União Europeia na Diretiva-Quadro Estratégia Marinha. Portanto, a quantificação destes dois parâmetros, a sua distribuição geográfica e o modo como se relacionam são de interesse crucial para o estudo dos ecossistemas, assim como para a investigação de diferentes processos, como transporte de sedimentos, produção primária e funcionamento de comunidades bentónicas. A monitorização dos parâmetros da qualidade da água, é geralmente alcançado através de programas de amostragem in situ. No entanto, a realização regular de amostragens exige trabalho intensivo e é dispendioso. Além disso, é necessário assumir que as amostras analisadas, que estão limitadas em termos espaciais e temporais, são representativas da área total de interesse. Neste âmbito, a deteção remota da cor do oceano é uma ferramenta eficiente para monitorizar os parâmetros da qualidade da água. O crescente interesse em entender o potencial desta técnica é impulsionado pelos custos reduzidos e pela alta resolução espacial que permite obter resultados para grandes áreas, mas também pela grande frequência temporal dos dados. No entanto, a complexidade das águas costeiras, transitórias e interiores dificulta a deteção das variáveis de interesse devido à proximidade da terra e aos elevados níveis de reflectância causados pela alta concentração de SPM nas regiões espectrais do visível e infravermelho próximo. Não obstante, algoritmos têm vindo a ser desenvolvidos para estimar a concentração de SPM e turbidez, que são geralmente calibrados regionalmente para as características óticas dos diferentes locais. Neste contexto, a presente dissertação teve como foco o teste de diferentes algoritmos com aplicabilidade global para estimar o SPM e a turbidez, bem como a avaliação de diferentes modelos de correção atmosférica. O principal objetivo deste trabalho foi determinar o esquema de processamento mais apropriado para quantificar o SPM e a turbidez em águas de transição e interiores em Portugal, determinando as incertezas associadas aos algoritmos de aplicabilidade global (Nechad et. al. (2009) para o SPM e Dogliotti et. al. (2015) para a turbidez) quando aplicados fora da sua região de calibração. Para este fim, o estuário do Tejo e do Sado e cinco albufeiras na região do Alentejo em Portugal, foram utilizados como casos de estudo para testar o uso de imagens de satélite na monitorização da turbidez e SPM. A base de dados in situ foi adquirida no contexto de diferentes projetos (PLATAGUS, NIPOGES, Valor Sul, AQUASado, GAMEFISH) entre julho de 2017 e julho de 2019, dependendo do projeto. Os dados de satélite testados foram obtidos pelos Sentinel-2 MultiSpectral Instrument (S2-MSI) e o Sentinel-3 Ocean and Land Colour Instrument (S3-OLCI), missões do programa de Observação da Terra da Comissão Europeia - Copernicus. No estuário do Tejo, as medições radiométricas in situ realizadas no contexto do projeto PLATAGUS permitiram também testar diretamente diferentes processadores para a correção atmosférica, nomeadamente o Acolite (S2-MSI), C2RCC (S2-MSI e S3-OLCI), L2 padrão MSI (Sen2Cor), L2 padrão OLCI (BAC / BPAC) e Polymer (S2-MSI e S3-OLCI). Tendo-se obtido melhores resultados com o Polymer e o C2RCC utilizando dados do S2-MSI, e resultados inconclusivos na avaliação dos dados com o S3-OLCI devido ao reduzido número de dados disponíveis. Na avaliação dos algoritmos de SPM e turbidez, os resultados obtidos sugerem que o produto de turbidez é mais fácil de estimar com menores incertezas associadas. Em relação à estimativa do SPM através dos dados S2-MSI e S3-OLCI, as correlações e erros associados indicam que ainda há uma forte necessidade de desenvolvimento de novos algoritmos, com uma calibração regional específica para as características óticas das áreas de estudo ou para encontrar uma relação local entre SPM e turbidez, como já sugerido anteriormente na literatura. Além disso, o sensor S3-OLCI, que apresentou resultados satisfatórios para o estuário do Tejo, mostrou resultados discordantes para o estuário do Sado, sugerindo uma menor adequação da resolução espacial do OLCI (300 m) para estuários de menor dimensão. No território português, as técnicas de deteção remota para monitorização da qualidade da água já estão em uso, mas têm sido testadas e aplicadas principalmente em águas costeiras. Este trabalho é um primeiro esforço para validar produtos de qualidade da água em águas de transição e interiores em Portugal. A importância destes ecossistemas, assim como o papel crucial da validação de produtos de deteção remota para monitorização ambiental e a principal motivação deste trabalho, e determinantes na definição das principais questões abordadas.Suspended particulate matter (SPM) is one of the main water constituents in estuaries and along with turbidity (T), which is highly correlated with SPM concentration, are key parameters to evaluate water quality. Through light absorption and scattering, the SPM concentration reduces the penetration of solar irradiance within the water column and limits the photosynthetically available radiation (PAR) for primary producers making it a relevant indicator for water quality monitoring. In fact, regarding water quality monitoring, turbidity is listed as a mandatory parameter to be measured by EU member states in the Marine Strategy Framework Directive. Therefore, the quantification of these two parameters, their geographical distribution and their relationship are of crucial interest for ecosystems studies and to understand different processes such as sediment transport, primary production and the functioning of benthic communities. Monitoring water quality parameters is usually achieved through field sampling programs. However, conducting regular field sampling is labor intensive and expensive and it is often necessary to assume that field samples, which are limited both spatially and temporally, are representative of the total area of interest. Satellite Ocean Colour Remote Sensing is an efficient tool to monitor these two parameters and the incrementing interest on understanding the potential of this technique is driven by the reduced costs and the high spatial and temporal resolution that allows obtaining results for large areas. However, remote sensing of coastal, transitional and inland waters is a complicated issue due to the proximity of the land and the high levels of reflectance caused by high SPM concentration in the visible and near infrared spectral regions. Many algorithms to retrieve SPM and T already exist and are often calibrated regionally for the optical characteristics of the different sites. In this context, this thesis focuses on the test of different algorithms with global applicability for SPM and turbidity retrieval, as well as different atmospheric corrections. The main aim of the present work is to determine the most appropriate processing scheme to retrieve SPM and turbidity for Portuguese transitional and inland waters and to determine the accuracy of retrieval algorithms with global applicability (Nechad et. al, 2009 for SPM retrieval and Dogliotti et. al., 2015 for turbidity) outside their calibration region. For this purpose, Tagus and Sado estuary, and five small water reservoirs in the Alentejo region in Portugal have been used as case-studies to test satellite imagery capability to monitor SPM and turbidity products. The in situ data for reference has been collected within the context of different projects (PLATAGUS, NIPOGES, Valor Sul, AQUASado, GAMEFISH) from July 2017 to July 2019 depending on the project. The satellite data used were obtained from the Sentinel-2 MultiSpectral Instrument (S2- MSI) and the Sentinel-3 Ocean and Land Colour Instrument. (S3-OLCI), missions from the European Commission Earth Observation program, Copernicus. In the Tagus estuary, in situ radiometric measurements conducted within the context of the PLATAGUS project allowed also to directly test different atmospheric corrections processors, namely (S2-MSI), C2RCC (S2-MSI and S3-OLCI), L2 standard MSI (Sen2Cor), L2 standard OLCI (BAC/BPAC) and Polymer (S2-MSI and S3-OLCI). Being Polymer and C2RCC the best performing algorithms for S2- MSI, while no definite results was found for S3-OLCI given the low number available data. Results suggested that turbidity is easier to retrieve with smaller uncertainties associated. Regarding the SPM retrieval from S2-MSI and S3-OLCI data, the associated correlations and errors indicate that there is still a strong need of algorithms development perhaps with a regional calibration specific for the optical characteristics of the study areas or finding a local relationship between SPM and turbidity as has been previously suggested. Moreover, the S3-OLCI sensor, which gave satisfactory results for the Tagus estuary, showed discordant results for the Sado estuary suggesting a poor suitability of the OLCI spatial resolution (300m) for smaller estuaries. In the Portuguese territory, remote sensing techniques have been tested and are in place for water quality monitoring mostly for coastal application. This work is a first effort to validate satellite-derived water quality products for monitoring transitional and inland waters in Portugal. The well-known importance of such ecosystems and the crucial role of satellite-data validation for reliable monitoring activities through remote sensing techniques drove the motivations and helped defining the main questions addressed in the present work

Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes

With the increasing occurrence and growing public health concern that cyanobacteria blooms pose, it is crucial that we continue to explore ways to improve our ability to accurately, efficiently, and safely monitor water quality in impacted lakes. Also known as blue-green algae, cyanobacteria are naturally occurring in many waters globally. Cyanobacteria harmful algal blooms (CHABs) release various toxins which can cause skin irritations and dog fatalities while the effects of long-term exposure to the neurotoxins for humans has stirred additional studies. Although possibly harmful if CHABs are present, monitoring this biological component is nonetheless an integral factor when studying freshwater ecosystems. The use of an unmanned aerial system (UAS), equipped with a high resolution multispectral dual imaging sensor, provides a novel and full waterbody approach to quantify CHABs. Using a DJI M300 RTK unmanned aerial copter equipped with a MicaSense dual camera system collecting data in 10 wavelengths, six NH lakes were monitored from May-September 2022. Five of these six lakes experienced cyanobacteria blooms during this field season. Using the UAS collected spectral data coupled with collected in-situ water quality data, we used the random forest algorithm to classify the remotely sensed data and predict water quality classification categories. The analysis yielded very high overall accuracies for cyanobacteria cell concentration (93%), chlorophyll-a concentration (87%), and phycocyanin concentration (92%). Reflectance data from the 475 nm wavelength, the normalized green blue difference index – version 4 (NGBDI_4), and the normalized green-red difference index – version 4 (NGRDI_4) indices in addition to a few others were the most important features for these classifications. Additionally, simple logarithmic regressions illuminated relationships between single bands and indices with water quality data. Particularly, cell concentration with NGBDI_4 (R2 = 0.31), chlorophyll-a concentration with 475 nm (R2 = 0.24), and phycocyanin concentration with NGBDI_4 (R2 = 0.27). Therefore, our proposed monitoring approach successfully classified cyanobacteria cell, chlorophyll-a, and phycocyanin concentrations in the sampled NH lakes using UAS multispectral data while identifying the multispectral properties most important for cyanobacteria identification

Retrieval of Lake Erie Water Quality Parameters from Satellite Remote Sensing and Impact on Simulations with a 1-D Lake Model

Lake Erie is a freshwater lake, and the most southern of the Laurentian Great Lakes in North America. It is the smallest by volume, the fourth largest in surface area (25,700 km2), and the shallowest of the Laurentian Great Lakes. The lake’s high productivity and warm weather in its watershed has attracted one-third of the total human population of the Great Lake’s basin. The industrial and agricultural activities of this huge population has caused serious environmental problems for Lake Erie namely harmful algal blooms, dissolved organic/inorganic matters from river inputs, and sediment loadings. If these sorts of water contaminations exceed a certain level, it can seriously influence the lake ecosystem. Hence, an effective and continuous water quality monitoring program is of outmost importance for Lake Erie. The use of Earth observation satellites to improve monitoring of environmental changes in water bodies has been receiving increased attention in recent years. Satellite observations can provide long term spatial and temporal trends of water quality indicators which cannot be achieved through discontinuous conventional point-wise in situ sampling. Different regression-based empirical models have been developed in the literature to derive the water optical properties from a single (or band ratio of) remote sensing reflectance (radiance). In situ measurements are used to build these regressions. The repeated in situ measurements in space and/or time causes clustered and correlated data that violates the assumption of regression models. Considering this correlation in developing regression models was one of the topics examined in this thesis. More complicated semi-analytical models are applied in Case II waters, aiming to distinguish several constituents confounding water-leaving signals more effectively. The MERIS neural network (NN) algorithms are the most widely used among semi-analytical models. The applicability of these algorithms to derive chl-a concentration and Secchi Disk Depth (SDD) in Lake Erie was assessed for the first time in this thesis. Satellite-observations of water turbidity were then coupled with a 1-D lake model to improve its performance on Lake Erie, where the common practice is to use a constant value for water turbidity in the model due to insufficient in situ measurements of water turbidity for lakes globally. In the first chapter, four well-established MERIS NN algorithms to derive chl-a concentration as well as two band-ratio chl-a related indices were evaluated against in situ measurements. The investigated products are those produced by NN algorithms, including Case 2 Regional (C2R), Eutrophic (EU), Free University of Berlin WeW WATER processor (FUB/WeW), and CoastColour (CC) processors, as well as from band-ratio algorithms of fluorescence line height (FLH) and maximum chlorophyll index (MCI). Two approaches were taken to compare and evaluate the performance of these algorithms to predict chl-a concentration after lake-specific calibration of the algorithms. First, all available chl-a matchups, which were collected from different locations on the lake, were evaluated at once. In the second approach, a classification of three optical water types was applied, and the algorithms’ performance was assessed for each type, individually. The results of this chapter show that the FUB/WeW processor outperforms other algorithms when the full matchup data of the lake was used (root mean square error (RMSE) = 1.99 mg m-3, index-of-agreement (I_a) = 0.67). However, the best performing algorithm was different when each water optical type was investigated individually. The findings of this study provide practical and valuable information on the effectiveness of the already existing MERIS-based algorithms to derive the trophic state of Lake Erie, an optically complex lake. Unlike the first chapter, where physically-based and already trained algorithms were implemented to evaluate satellite derived chl-a concentration, in the next chapter, two lake-specific, robust semi-empirical algorithms were developed to derive chl-a and SDD using Linear Mixed Effect (LME) models. LME considers the correlation that exists in the field measurements which have been repeatedly performed in space and time. Each developed algorithm was then employed to investigate the monthly-averaged spatial and temporal trends of chl-a concentration and water turbidity during the period of 2005-2011. SDD was used as the indicator of water turbidity. LME models were developed between the logarithmic scale of the parameters and the band ratio of B7:665 nm to B9:708.75 nm for log10chl-a, and the band ratio of B6:620 nm to B4:510 nm for log10SDD. The models resulted in RMSE of 0.30 for log10chl-a and 0.19 for log10SDD. Maps produced with the two LME models revealed distinct monthly patterns for different regions of the lake that are in agreement with the biogeochemical properties of Lake Erie. Lastly the water turbidity (extinction coefficient; Kd) of Lake Erie was estimated using the globally available satellite-based CC product. The CC-derived Kd product was in a good agreement with the SDD field observations (RMSE=0.74 m-1, mean bias error (MBE)=0.53 m-1, I_a=0.53). CC-derived Kd was then used as input for simulations with the 1-D Freshwater Lake (FLake) model. An annual average constant Kd value calculated from the CC product improved simulation results of lake surface water temperature (LSWT) compared to a “generic” constant value (0.2 m-1) used in previous studies (CC lake-specific yearly average Kd value: RMSE=1.54 ºC, MBE= -0.08 ºC; generic constant Kd value: RMSE=1.76 ºC, MBE= -1.26 ºC). Results suggest that a time-independent, lake-specific, and constant Kd value from CC can improve FLake LSWT simulations with sufficient accuracy. A sensitivity analysis was also conducted to assess the performance of FLake to simulate LSWT, mean water column temperature (MWCT) and mixed layer depth (MLD) using different values of Kd. Results showed that the model is very sensitive to the variations of Kd, particularly when Kd value is below 0.5 m-1. The sensitivity of FLake to Kd variations was more pronounced in simulations of MWCT and MLD. This study shows that a global mapping of the extinction coefficient can be created using satellite-based observations of lakes optical properties to improve the 1-D FLake model. Overall, results from this thesis clearly demonstrate the benefits of remote sensing measurements of water quality parameters (such as chl-a concentration and water turbidity) for lake monitoring. Also, this research shows that the integration of space-borne water clarity (extinction coefficient) measurements into the 1-D FLake model improves simulations of LSWT

Unfolding the interaction between microplastics and (trace) elements in water: A critical review

Plastic and microplastic pollution is an environmental and societal concern. The interaction of plastic with organic chemicals in the environment has attracted scientific interest. New evidences have highlighted an unexpectedly high affinity of environmental plastics also for metal ions. The degree and typology of plastic ageing (including from mechanical, UV and biological degradations) appear as a pivotal factor determining such an interaction. These earlier evidences recently opened a new research avenue in the plastic pollution area. This review is the first to organize and critically discuss knowledge developed so far. Results from field and laboratory studies of metal accumulation on plastic are presented and the environmental factors most likely to control such an interaction are discussed. On the light of this knowledge, a generalist conceptual model useful for building hypotheses on the mechanisms at stake and directing future studies was elaborated and presented here. Furthermore, all available data on the thermodynamics of the plastic-metal interaction obtained from laboratory experiments are inventoried and discussed here, highlighting methodological and technical challenges that can potentially affect cross-comparability of data and their relevance for environmental settings. Finally, insights and recommendations on experimental approaches and analytical techniques that can help overtaking current limitations and knowledge gaps are proposed

Integrated Data Fusion And Mining (idfm) Technique For Monitoring Water Quality In Large And Small Lakes

Monitoring water quality on a near-real-time basis to address water resources management and public health concerns in coupled natural systems and the built environment is by no means an easy task. Furthermore, this emerging societal challenge will continue to grow, due to the ever-increasing anthropogenic impacts upon surface waters. For example, urban growth and agricultural operations have led to an influx of nutrients into surface waters stimulating harmful algal bloom formation, and stormwater runoff from urban areas contributes to the accumulation of total organic carbon (TOC) in surface waters. TOC in surface waters is a known precursor of disinfection byproducts in drinking water treatment, and microcystin is a potent hepatotoxin produced by the bacteria Microcystis, which can form expansive algal blooms in eutrophied lakes. Due to the ecological impacts and human health hazards posed by TOC and microcystin, it is imperative that municipal decision makers and water treatment plant operators are equipped with a rapid and economical means to track and measure these substances. Remote sensing is an emergent solution for monitoring and measuring changes to the earth’s environment. This technology allows for large regions anywhere on the globe to be observed on a frequent basis. This study demonstrates the prototype of a near-real-time early warning system using Integrated Data Fusion and Mining (IDFM) techniques with the aid of both multispectral (Landsat and MODIS) and hyperspectral (MERIS) satellite sensors to determine spatiotemporal distributions of TOC and microcystin. Landsat satellite imageries have high spatial resolution, but such application suffers from a long overpass interval of 16 days. On the other hand, free coarse resolution sensors with daily revisit times, such as MODIS, are incapable of providing detailed water quality information because of low spatial resolution. This iv issue can be resolved by using data or sensor fusion techniques, an instrumental part of IDFM, in which the high spatial resolution of Landsat and the high temporal resolution of MODIS imageries are fused and analyzed by a suite of regression models to optimally produce synthetic images with both high spatial and temporal resolutions. The same techniques are applied to the hyperspectral sensor MERIS with the aid of the MODIS ocean color bands to generate fused images with enhanced spatial, temporal, and spectral properties. The performance of the data mining models derived using fused hyperspectral and fused multispectral data are quantified using four statistical indices. The second task compared traditional two-band models against more powerful data mining models for TOC and microcystin prediction. The use of IDFM is illustrated for monitoring microcystin concentrations in Lake Erie (large lake), and it is applied for TOC monitoring in Harsha Lake (small lake). Analysis confirmed that data mining methods excelled beyond two-band models at accurately estimating TOC and microcystin concentrations in lakes, and the more detailed spectral reflectance data offered by hyperspectral sensors produced a noticeable increase in accuracy for the retrieval of water quality parameters