Design of Real-Time Monitoring System for Surface Water Pollution Based on GPRS

To implement the remote and real-time monitoring of surface water pollution, a design scheme of water quality monitoring system based on GPRS technology is put forward, which is composed of monitoring terminal, monitoring center and communication network. The various parameters of surface water are acquired using water quality detection sensor terminal and uploaded to the remote monitoring center via GPRS module by monitoring, and then the water quality parameters acquisition, processing and wireless transmission are realized. Water quality parameters are received through the internet network by the monitoring center, to realize its remote monitoring and management. According to the practice result, the system has materialized functions on GPRS service platform, such as real-time water quality parameters acquisition, procession, wireless transmission, remote monitoring and management, which is suitable for surface water pollution continuous monitoring and has the good application in the future.

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A WSN-Based Remote Monitoring System for Water Quality

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.1571 ◽

2014 ◽

Vol 926-930 ◽

pp. 1571-1575 ◽

Cited By ~ 1

Author(s):

Hui Feng ◽

Zi Jing Pan

Keyword(s):

Water Quality ◽

Remote Monitoring ◽

Quality Parameters ◽

Water Quality Parameters ◽

Sensor Nodes ◽

Wireless Sensor ◽

Wireless Sensor Nodes ◽

Remote Monitoring System ◽

Power Mode ◽

Monitoring Center

As a result of bad real-time character of detecting water quality by traditional method, in this paper, we illustrate a new remote water quality system which is designed based on WSN, and consists of wireless sensor nodes, sink nodes and a monitoring center. All nodes are built by CC2530, designed in lower power mode, and organized by Zigbee network to gather water quality parameters such as: PH, DO and turbidity. GPRS module, which is combined in the sink node, is responsible to send managed data to monitoring center which analyses, process and display the water quality parameters to users.

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Spatiotemporal Analysis of Surface Water Quality in Dong Thap Province, Vietnam Using Water Quality Index and Statistical Approaches

Water ◽

10.3390/w13030336 ◽

2021 ◽

Vol 13 (3) ◽

pp. 336

Author(s):

Nguyen Thanh Giao ◽

Phan Kim Anh ◽

Huynh Thi Hong Nhien

Keyword(s):

Water Pollution ◽

Water Quality ◽

Surface Water ◽

Water Quality Index ◽

Quality Index ◽

Oxygen Demand ◽

Surface Water Quality ◽

Quality Parameters ◽

Water Quality Parameters ◽

E Coli

The study was conducted to spatiotemporally analyze the quality, location and critical water variables influencing water quality using water monitoring data from the Department of Environment and Natural Resources, Dong Thap province in 2019. The water quality parameters including turbidity, pH, temperature, dissolved oxygen (DO), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrite (N-NO2−), nitrate (N-NO3−), ammonium (N-NH4+), total nitrogen (TN), orthophosphate (P-PO43−), chloride (Cl−), oil and grease, sulfate (SO42−), coliforms, and Escherichia coli (E. coli) were collected at 58 locations with the frequency of four times per year (February, May, August, and November). These parameters were compared with national technical regulation on surface water quality—QCVN 08-MT: 2015/BTNMT. Water quality index (WQI) was calculated and spatially presented by geographical information system (GIS) tool. Pearson correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the correlation among water quality parameters, group and reduce the sampling sites, and identify key parameters and potential water pollution sources. The results showed that TSS, BOD, COD, N-NH4+, P-PO43−, coliforms, and E. coli were the significant concerns impairing the water quality. Water quality was assessed from poor to medium levels by WQI analysis. CA suggested that the current monitoring locations could be reduced from 58 sites to 43 sites which can be saved the total monitoring budget up to 25.85%. PCA showed that temperature, pH, TSS, DO, BOD, COD, N-NH4+, N-NO2−, TN, P-PO43−, coliforms, and E. coli were the key water parameters influencing water quality in Dong Thap province’s canals and rivers; thus, these parameters should be monitored annually. The water pollution sources were possibly hydrological conditions, water runoff, riverbank erosion, domestic and urban activities, and industrial and agricultural discharges. Significantly, the municipal and agricultural wastes could be decisive factors to the change of surface water quality in the study area. Further studies need to focus on identifying sources of water pollution for implementing appropriate water management strategies.

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Assessement of Water Quality Parameters in Some Potential Pollutant Areas from Romania

Revista de Chimie ◽

10.37358/rc.18.8.6470 ◽

2018 ◽

Vol 69 (8) ◽

pp. 2045-2049

Author(s):

Catalina Gabriela Gheorghe ◽

Andreea Bondarev ◽

Ion Onutu

Keyword(s):

Water Quality ◽

Surface Water ◽

Surface Water Quality ◽

Quality Parameters ◽

Water Quality Parameters ◽

Quality Data ◽

Chemical Parameters ◽

Water Quality Data ◽

Pollutant Sources ◽

Water Quality Forecasting

Monitoring of environmental factors allows the achievement of some important objectives regarding water quality, forecasting, warning and intervention. The aim of this paper is to investigate water quality parameters in some potential pollutant sources from northern, southern and east-southern areas of Romania. Surface water quality data for some selected chemical parameters were collected and analyzed at different points from March to May 2017.

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Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽

10.3390/w13111547 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1547

Author(s):

Jian Sha ◽

Xue Li ◽

Man Zhang ◽

Zhong-Liang Wang

Keyword(s):

Neural Network ◽

Water Quality ◽

Deep Learning ◽

Real Time ◽

Best Management Practices ◽

Input Data ◽

Management Practices ◽

Quality Parameters ◽

Water Quality Parameters ◽

Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

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Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

Wireless Communications and Mobile Computing ◽

10.1155/2021/9403963 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Wei Chen ◽

Xiao Hao ◽

JianRong Lu ◽

Kui Yan ◽

Jin Liu ◽

...

Keyword(s):

Water Quality ◽

Water Temperature ◽

Real Time ◽

Monitoring System ◽

Water Environment ◽

Quality Parameters ◽

Water Quality Parameters ◽

Environment Monitoring ◽

Long Distance ◽

Internet Of Things Technology

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

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A Spatial Analysis to Define Data Requirements for Hydrological and Water Quality Models in Data-Limited Regions

Water ◽

10.3390/w11020267 ◽

2019 ◽

Vol 11 (2) ◽

pp. 267 ◽

Cited By ~ 4

Author(s):

Ersilia D’Ambrosio ◽

Anna De Girolamo ◽

Marinella Spanò ◽

Vera Corbelli ◽

Gennaro Capasso ◽

...

Keyword(s):

Water Quality ◽

Spatial Analysis ◽

Surface Water ◽

Source Parameters ◽

Quality Parameters ◽

Point Sources ◽

Water Quality Parameters ◽

Limited Area ◽

Climatic Data ◽

Basin Scale

The objective of the present work is a spatial analysis aimed at supporting hydrological and water quality model applications in the Canale d’Aiedda basin (Puglia, Italy), a data-limited area. The basin is part of the sensitive environmental area of Taranto that requires remediation of the soil, subsoil, surface water, and groundwater. A monitoring plan was defined to record the streamflow and water quality parameters needed for calibrating and validating models, and a database archived in a GIS environment was built, which includes climatic data, soil hydraulic parameters, groundwater data, surface water quality parameters, point-source parameters, and information on agricultural practices. Based on a one-year monitoring of activities, the average annual loads of N-NO3 and P-PO4 delivered to the Mar Piccolo amounted to about 42 t year−1, and 2 t year−1, respectively. Knowledge uncertainty in monthly load estimation was found to be up to 25% for N-NO3 and 40% for P-PO4. The contributions of point sources in terms of N-NO3 and P-PO4 were estimated at 45% and 77%, respectively. This study defines a procedure for supporting modelling activities at the basin scale for data-limited regions.

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