Advanced monitoring of water systems using in situ measurement stations: data validation and fault detection

2013 ◽  
Vol 68 (5) ◽  
pp. 1022-1030 ◽  
Author(s):  
Janelcy Alferes ◽  
Sovanna Tik ◽  
John Copp ◽  
Peter A. Vanrolleghem
Keyword(s):  
Water Quality ◽  
Treatment Plant ◽  
Principal Component ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Data Quality Assessment ◽  
Multiple Sensor ◽  
Pertinent Information

In situ continuous monitoring at high frequency is used to collect water quality information about water bodies. However, it is crucial that the collected data be evaluated and validated for the appropriate interpretation of the data so as to ensure that the monitoring programme is effective. Software tools for data quality assessment with a practical orientation are proposed. As water quality data often contain redundant information, multivariate methods can be used to detect correlations, pertinent information among variables and to identify multiple sensor faults. While principal component analysis can be used to reduce the dimensionality of the original variable data set, monitoring of some statistical metrics and their violation of confidence limits can be used to detect faulty or abnormal data and can help the user apply corrective action(s). The developed algorithms are illustrated with automated monitoring systems installed in an urban river and at the inlet of a wastewater treatment plant.

scholarly journals Control limit detection for source apportionment in Perlis River Basin, Malaysia

2017 ◽  
Vol 13 (3) ◽  
Author(s):  
Mohd Saiful Samsudin ◽  
Saiful Iskandar Khalit ◽  
Azman Azid ◽  
Hafizan Juahir ◽  
Ahmad Shakir Mohd Saudi ◽  
...  
Keyword(s):  
River Basin ◽  
Control Process ◽  
Principal Component ◽  
River Pollution ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Hierarchical Agglomerative Cluster ◽  
Limit Detection ◽  
Hierarchical Agglomerative Cluster Analysis

This study presents the application of selected environmetric in the Perlis River Basin. The results show PCA extracted nine principal components (PCs) with eigenvalues greater than one, which equates to about 77.15% of the total variance in the water-quality data set. The absolute principal component scores (APCS)-MLR model discovered BOD and COD as the main parameters, which indicates the measure of the agricultural pollution in the Perlis River Basin, the hierarchical agglomerative cluster analysis (HACA) shows 11 monitoring stations assembled into two clusters in accordance with similarities in the concentration of BOD and COD, which are grouped in P4. The X ̅ control chart shows that the mean concentration of BOD and COD in P4 is in the control process. The capability ratio (Cp) was applied to measure the risk of the concentration in terms of the river pollution in a subsequent period of time using the limit NWQS.

scholarly journals OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

Toxicity and pollutant impact analysis in an urban river due to combined sewer overflows loads

2010 ◽  
Vol 61 (1) ◽  
pp. 207-215 ◽  
Author(s):  
A. Casadio ◽  
M. Maglionico ◽  
A. Bolognesi ◽  
S. Artina
Keyword(s):  
Water Quality ◽  
Impact Analysis ◽  
Waste Water Treatment ◽  
Treatment Plant ◽  
Quality Data ◽  
Waste Water Treatment Plant ◽  
Combined Sewer Overflows ◽  
Water Quality Data ◽  
Combined Sewer ◽  
Sewer Overflows

The Navile Channel (Bologna, Italy) is an ancient artificial water course derived from the Reno river. It is the main receiving water body for the urban catchment of Bologna sewer systems and also for the Waste Water Treatment Plant (WWTP) main outlet. The aim of this work is to evaluate the Combined Sewer Overflows (CSOs) impact on Navile Channel's water quality. In order to collect Navile flow and water quality data in both dry and wet weather conditions, two measuring and sampling stations were installed, right upstream and downstream the WWTP outflow. The study shows that even in case of low intensity rain events, CSOs have a significant effect on both water quantity and quality, spilling a considerable amount of pollutants into the Navile Channel and presenting also acute toxicity effects. The collected data shown a good correlations between the concentrations of TSS and of chemical compounds analyzed, suggesting that the most part of such substances is attached to suspended solids. Resulting toxicity values are fairly high in both measuring points and seem to confirm synergistic interactions between heavy metals.

scholarly journals A Multivariate and Spatiotemporal Analysis of Water Quality in Code River, Indonesia

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mochamad A. Pratama ◽  
Yan D. Immanuel ◽  
Dwinanti R. Marthanty
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Agricultural Land ◽  
Water Quality Management ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data

The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River’s water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.

An approach for determining the most critical among a suite of chemical contaminants at a drinking water intake

2013 ◽  
Vol 13 (3) ◽  
pp. 835-845
Author(s):  
Fei Chen ◽  
William B. Anderson ◽  
Peter M. Huck
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Intake ◽  
Treatment Plant ◽  
Quality Data ◽  
Exceedance Probability ◽  
Raw Water ◽  
Water Quality Data ◽  
Treatment Processes ◽  
Raw Water Quality

An integrated approach for the identification and assessment of the most critical chemical contaminant(s) at a drinking water intake has been developed. It involves the determination of a threshold or critical raw water concentration (CRWC) for target contaminants using the observed overall removal efficiency of a specific water treatment plant (WTP) and regulated drinking water concentrations for the target contaminants. The exceedance probability relative to the CRWC based on historical raw water quality monitoring data is then calculated. Finally, the integration of the raw water quality data and the overall efficiency of a particular WTP sequence allows for identification of the most critical contaminant(s) as well as an advance indication of which contaminants are most likely to challenge a plant. The proactive nature of this approach gives a utility the impetus and time to assess current treatment processes and potential alternatives. In addition, it was found that three- or four-parameter theoretical distributions are more appropriate than two-parameter probability distributions for the fitting of raw water quality data. This study reveals that the reliance on raw and/or treated water contaminant concentrations in isolation or on theoretical removals through treatment processes can, in some circumstances, be misguided.

Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

2020 ◽  
Vol 8 (3) ◽  
pp. 172-185
Author(s):  
Juan G. Arango ◽  
Brandon K. Holzbauer-Schweitzer ◽  
Robert W. Nairn ◽  
Robert C. Knox
Keyword(s):  
Water Quality ◽  
Near Infrared ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Electromagnetic Spectrum ◽  
Water Quality Data ◽  
Ground Control Points ◽  
Bodies Of Water

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

scholarly journals Comprehensive Assessment of Water Quality and Pollution Source Apportionment in Wuliangsuhai Lake, Inner Mongolia, China

2020 ◽  
Vol 17 (14) ◽  
pp. 5054 ◽  
Author(s):  
Rui Shi ◽  
Jixin Zhao ◽  
Wei Shi ◽  
Shuai Song ◽  
Chenchen Wang
Keyword(s):  
Water Quality ◽  
Inner Mongolia ◽  
Principal Component ◽  
Middle Part ◽  
Quality Data ◽  
Quality Factors ◽  
Water Quality Data ◽  
Irrigation Area ◽  
Irrigation Period ◽  
Hetao Irrigation Area

Water quality is a key indicator of human health. Wuliangsuhai Lake plays an important role in maintaining the ecological balance of the region, protecting the local species diversity and maintaining agricultural development. However, it is also facing a greater risk of water quality deterioration. The 24 water quality factors that this study focused on were analyzed in water samples collected during the irrigation period and non-irrigation period from 19 different sites in Wuliangsuhai Lake, Inner Mongolia, China. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to evaluate complex water quality data and to explore the sources of pollution. The results showed that, during the irrigation period, sites in the middle part of the lake (clusters 1 and 3) had higher pollution levels due to receiving most of the agricultural and some industrial wastewater from the Hetao irrigation area. During the non-irrigation period, the distribution of the comprehensive pollution index was the opposite of that seen during the irrigation period, and the degree of pollutant index was reduced significantly. Thus, run-off from the Hetao irrigation area is likely to be the main source of pollution.

scholarly journals Pioneering water quality data on the Lake Victoria watershed: effects on human health

2015 ◽  
Vol 13 (3) ◽  
pp. 920-930 ◽  
Author(s):  
Tamie J. Jovanelly ◽  
Julie Johnson-Pynn ◽  
James Okot-Okumu ◽  
Richard Nyenje ◽  
Emily Namaganda
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Lake Victoria ◽  
Fecal Coliforms ◽  
World Health ◽  
Quality Data ◽  
Water Quality Data ◽  
Data Set ◽  
Forest Reserves ◽  
Elevated Exposure

Four forest reserves within 50 km of Kampala in Uganda act as a critical buffer to the Lake Victoria watershed and habitat for local populations. Over a 9-month period we capture a pioneering water quality data set that illustrates ecosystem health through the implementation of a water quality index (WQI). The WQI was calculated using field and laboratory data that reflect measured physical and chemical parameters (pH, dissolved oxygen, biological oxygen on demand, nitrates, phosphates, fecal coliform, and temperature turbidity). Overall, the WQI for the four forest reserves reflect poor to medium water quality. Results compared with US Environmental Protection Agency and World Health Organization drinking water standards indicate varying levels of contamination at most sites and all designated drinking water sources, with signatures of elevated nitrates, phosphates, and/or fecal coliforms. As critical health problems are known to arise with elevated exposure to contaminants in drinking water, this data set can be used to communicate necessary improvements within the watershed.

scholarly journals Evaluation of water pollution and source identification in Merbok River Kedah, Northwest Malaysia

2020 ◽  
Vol 16 (4) ◽  
pp. 458-463
Author(s):  
Ateshan Msahir Haidr ◽  
Misnan Rosmilah ◽  
Sinang Som Cit ◽  
Koki Baba Isa
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Anthropogenic Activities ◽  
Human Health Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Data ◽  
Toxic Heavy Metals ◽  
Water Quality Data ◽  
Fish And Shellfish

This study investigates the temporal water quality variations and pollution sources identification in Merbok River using principal component analysis. The variables analyzed include As, Cd, Pb, Fe, Cr, Mn, Zn, Ni, Ca, Mg, Na, K, NH4, F, Cl, Br, NO2, NO3, SO4, PO4, pH, BOD, DO, COD, turbidity, and salinity. These variables were analyzed using inductively coupled plasma mass spectrometry, ion chromatography, and YSI multiprobe. Principal component analysis (PCA) was utilized to evaluate the variations of the most significant water quality parameters and identify the probable source of the pollutants. From the results of PCA, 86% of the total variations were observed in the water quality data with strong dominance of toxic heavy metals (As, Pb, and Cr), parameters associated with industrial discharge, domestic inputs, overland runoff (NH4, pH, BOD, DO, COD), agrochemicals (NO2, NO3, SO4, PO4), and weathering of basement rocks (Ca, Mg, Cl, F, K, and Na). Most of these parameters were present in concentrations exceeded the reference standards limits used in this study, indicating pollution of the river water. Together with the presence of microbial contamination, the results suggest potential human health risk due to water uses, fish and shellfish consumption. Moreover, the results revealed that anthropogenic activities and weathering were the main sources of pollutants in Merbok River. 

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