In this paper, a low cost, real-time water quality monitoring system which can be applied in remote rivers, lakes, coastal areas and other water bodies is presented. The main hardware of the system consists of off-the-shelf electrochemical sensors, a microcontroller, a wireless communication system and the customized buoy. It detects water temperature, dissolved oxygen and pH in a pre-programmed time interval. The developed prototype disseminates the gathered information in graphical and tabular formats through a customized web-based portal and preregistered mobile phones to better serve relevant end-users. To check the system effectivity, the buoy's stability in harsh environmental conditions, system energy consumption, data transmission efficiency and webbased display of information were carefully evaluated. The experimental results prove that the system has great prospect and can be practically used for environmental monitoring by providing stakeholders with relevant and timely information for sound decision making.
Remote sensing provides a synoptic view of the earth surface that can provide spatial and temporal trends necessary for comprehensive water quality (WQ) monitoring and assessment. This study explores the applicability of Landsat 8 and regression analysis in developing models for estimating WQ parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), turbidity, and conductivity. The input image was radiometrically-calibrated using fast line-of-sight atmospheric analysis (FLAASH) and then atmospherically corrected to obtain surface reflectance (SR) bands using FLAASH and dark object subtraction (DOS) for comparison. SR bands derived using FLAASH and DOS, water indices, band ratio, and principal component analysis (PCA) images were utilized as input data. Feature vectors were then collected from the input bands and subsequently regressed together with the WQ data. Forward regression results yielded significant high R2 values for all WQ parameters except TSS and conductivity which had only 60.1% and 67.7% respectively. Results also showed that the regression models of pH, BOD, TSS, TDS, DO, and conductivity are highly significant to SR bands derived using DOS. Furthermore, the results of this study showed the promising potential of using RS-based WQ models in performing periodic WQ monitoring and assessment.
This study implements remote sensing (RS) and geographic information system techniques in deriving physical and spectral characteristics of a catchment to aid in water quality monitoring. This approach is conducted by utilizing RS datasets like digital elevation model (DEM), satellite images, and on-site spectral measurements. A Shuttle Radar Topography Mission DEM was used for extracting physical profiles while Landsat Operational Land Imager was utilized to extract land cover information. This method was tested in a 22,000-ha catchment with dominant agricultural lands where large-scale mining companies are also operating actively. The land cover classification has an overall accuracy of 97.66%. Forest (50%) and cropland (32%) are the most dominant land cover within the catchment. The spectral signature of waters at designated sampling points was measured to evaluate its correlation to water quality data like pH and dissolved oxygen (DO). The correlation between the level of pH and reflectance implies a positive relationship (R 2 of 0.548) while that of DO and reflectance gives a negative correlation (R 2 of 0. 634). Results of this study demonstrate the practical advantage of exploiting remotely-sensed data in profiling and characterizing a catchment as it provides valuable information in understanding and mitigating contamination in an area. Through these RS-derived catchment profiles, insights on the contaminant's concentration and possible sources can be identified. The graphical and statistical analysis of the spectral data prove its potential in developing water quality models and maps.
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