scholarly journals A Rapid UV/Vis Spectrophotometric Method for the Water Quality Monitoring at On-Farm Root Vegetable Pack Houses

2020 ◽  
Vol 10 (24) ◽  
pp. 9072
Author(s):  
Algirdas Radzevičius ◽  
Midona Dapkienė ◽  
Nomeda Sabienė ◽  
Justyna Dzięcioł
Keyword(s):  
Water Quality ◽  
Suspended Solids ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Coliform Bacteria ◽  
Limit Values ◽  
Root Vegetable ◽  
Quality Of Water ◽  
On Farm

Our research aim was to apply UV/Vis spectrophotometric techniques for the rapid monitoring of the quality of water sourced from on-farm root vegetable washing processes. To achieve this goal, the quality assessment of the washing water and wastewater at different stages of the technological processes was performed using physicochemical, biological, and UV/Vis absorbance measurements as well as statistical methods, such as principal component analysis (PCA) and partial least squares (PLS) regression. Limit values of UV/Vis absorbance at specific wavelengths were predicted in order to adapt them for routine testing and water quality monitoring at the farm packhouses. Results of the lab analyses showed, that the main problems of the water quality were caused by suspended solids (470–3400 mg L−1), organic substances (BOD5 215–2718 mg L−1; COD 540–3229 mg L−1), nitrogen (3–52 mg L−1), phosphorus (1–6 mg L−1), and pathogenic microorganisms (TVC > 300 cfu mL−1, E. coli 5.5 × 103–1.0 × 104 cfu mL−1, intestinal enterococci 2.8 × 102–1.5 × 104 cfu mL−1, coliform bacteria 1.6 × 103–2.0 × 104 cfu mL−1). Suspended solids exceeded the limit values by 10–50 times, organic matter by 10–25 times, dissolved organic carbon by 3–5 times, nitrogen by 3–7 times, total phosphorus by 3–12 times, and microorganisms by 3–10 times. UV/Vis limit values calculated were as follows: A210 nm—3.997–4.009 cm−1, A 240 nm—5.193–5.235 cm−1, A254 nm—4.042–4.047 cm−1, A320 nm—7.387–7.406 cm−1, and A 660 nm—3.937–3.946 cm−1. UV/Vis measurements at A320 nm are proposed for the routine water quality monitoring.

Statistical and Data Mining Techniques for Understanding Water Quality Profiles in a Mining-Affected River Basin

2018 ◽  
Vol 9 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jose Simmonds ◽  
Juan A. Gómez ◽  
Agapito Ledezma
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
River Basin ◽  
Suspended Solids ◽  
Water Quality Monitoring ◽  
High Flow ◽  
Quality Monitoring ◽  
Low Flow ◽  
Inverse Relation

This article contains a multivariate analysis (MV), data mining (DM) techniques and water quality index (WQI) metrics which were applied to a water quality dataset from three water quality monitoring stations in the Petaquilla River Basin, Panama, to understand the environmental stress on the river and to assess the feasibility for drinking. Principal Components and Factor Analysis (PCA/FA), indicated that the factors which changed the quality of the water for the two seasons differed. During the low flow season, water quality showed to be influenced by turbidity (NTU) and total suspended solids (TSS). For the high flow season, main changes on water quality were characterized by an inverse relation of NTU and TSS with electrical conductivity (EC) and chlorides (Cl), followed by sources of agricultural pollution. To complement the MV analysis, DM techniques like cluster analysis (CA) and classification (CLA) was applied and to assess the quality of the water for drinking, a WQI.

scholarly journals Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 420 ◽  
Author(s):  
Thuy Hoang Nguyen ◽  
Björn Helm ◽  
Hiroshan Hettiarachchi ◽  
Serena Caucci ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Basin ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Component Analysis ◽  
Quality Monitoring ◽  
Critical Parameters ◽  
Spatial And Temporal Variation ◽  
Effectiveness Monitoring

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

scholarly journals Automatic River Water Quality Monitoring Using IOT

2021 ◽  
Vol 9 (10) ◽  
pp. 1880-1882
Author(s):  
Saheb Tabassum
Keyword(s):  
Water Quality ◽  
Real Time ◽  
River Water ◽  
Water Quality Monitoring ◽  
River Water Quality ◽  
Quality Monitoring ◽  
Grab Sampling ◽  
Quality Of Water ◽  
Economical System ◽  
Aquaculture Water

Abstract: One of the major problems in India is surface water pollution that is in Rivers. For the purpose of drinking, agriculture requirements and for industrial usage, an adequate amount of water quality has to be made sure and for maintaining the balance in aquaculture, water quality has to be monitored in real time. Deteriorated quality of water affects all well living beings. Traditional River water quality monitoring involves grab sampling, testing and analysis which is time consuming. In this project, determined attempts are made to design an economical system for real time monitoring of river water quality. Different physical and chemical parameters of the water are monitored using various water measuring sensor. The parameters such as temperature, hardness, dissolved oxygen; pH, turbidity and flow can be measured through sensors. The system can be enforced with Arduino model as a core controller. WI-FI module, Internet of things and GSM board can be used effectively to monitor the water quality and thereby relevant impacts for using river water safely. Keywords: 1. IOT, 2. GSM, 3. Sensors, 4. E.C.

scholarly journals Water quality evaluation and apportionment of pollution sources: a case study of the Baralia and Puthimari River (India)

2021 ◽  
Author(s):  
Kunwar Raghvendra Singh ◽  
Ankit Pratim Goswami ◽  
Ajay S. Kalamdhad ◽  
Bimlesh Kumar
Keyword(s):  
Water Quality ◽  
Water Conservation ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Pollution Sources ◽  
Quality Monitoring ◽  
Conservation Strategies ◽  
Rapid Urbanization ◽  
Monitoring Programs

Abstract Water quality monitoring programs are indispensable for developing water conservation strategies, but elucidation of large and random datasets generated in these monitoring programs has become a global challenge. Rapid urbanization, industrialization and population growth pose a threat of pollution for the surface water bodies of the Assam, state in northeastern India. This calls for strict water quality monitoring programs, which would thereby help in understanding the status of water bodies.In this study, the water quality of Baralia and Puthimari River of Assam was assessed using cluster analysis (CA), information entropy, and principal component analysis (PCA) to derive useful information from observed data. 15 sampling sites were selected for collection of samples during the period May 2016- June 2017. Collected samples were analysed for 20 physicochemical parameters. Hierarchal CA was used to classify the sampling sites in different clusters. CA grouped all the sites into 3 clusters based on observed variables. Water quality of rivers was evaluated using entropy weighted water quality index (EWQI). EWQI of rivers varied from 61.62 to 314.68. PCA was applied to recognise various pollution sources. PCA identified six principal components that elucidated 87.9% of the total variance and represented surface runoff, untreated domestic wastewater and illegally dumped municipal solid waste (MSW) as major factors affecting the water quality. This study will help policymakers and managers for making better decisions in allocating funds and determining priorities. It will also assist in effective and efficient policies for the improvement of water quality.

scholarly journals A Study on IOT for Smart Water Quality Monitoring Using MQTT Algorithm

2019 ◽  
pp. 869-872
Author(s):  
G. Vadivel ◽  
A. P. Thangamuthu ◽  
A. Priyadharshini
Keyword(s):  
Water Quality ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Smart Water ◽  
Quality Detection ◽  
Quality Of Water ◽  
System Water

The decrease in quality of water resources has become a common problem. The standard methods of water quality surveillance include water sample manual collection from various locations. These water samples were tested in laboratory using intelligence capabilities. Such approaches take time and are no longer considered inefficient. The old method of water quality detection was time consuming, less accurate and expensive. By focusing on the above problems, IOT can be used to monitor water quality in real time, a low cost water quality monitoring system. Water quality parameters in the proposed system are measured by various sensors such as pH, temperature and dissolved oxygen to transfer data on a platform via a microcontroller system. Therefore, to meet these needs, you can use other technologies such as MQTT (Message Sorting Delimiter Transform), allowing the Sensor and End device rankings to publish and subscribe. And the number of data simultaneously between sensors and servers with the help of the MQTT algorithm.

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