scholarly journals Establishment of an Automatic Real-Time Monitoring System for Irrigation Water Quality Management

2020 ◽  
Vol 17 (3) ◽  
pp. 737 ◽  
Author(s):  
Wei-Jhan Syu ◽  
Tsun-Kuo Chang ◽  
Shu-Yuan Pan
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Quality Management ◽  
Real Time ◽  
Monitoring System ◽  
Irrigation Water ◽  
Water Quality Management ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Irrigation Water Quality

In order to provide the real-time monitoring for identifying the sources of pollution and improving the irrigation water quality management, the integration of continuous automatic sampling techniques and cloud technologies is essential. In this study, we have established an automatic real-time monitoring system for improving the irrigation water quality management, especially for heavy metals such as Cd, Pb, Cu, Ni, Zn, and Cr. As a part of this work, we have first provided several examples on the basic water quality parameters (e.g., pH and electrical conductance) to demonstrate the capacity of data correction by the smart monitoring system, and then evaluated the trend and variance of water quality parameters for different types of monitoring stations. By doing so, the threshold (to initiate early warming) of different water quality parameters could be dynamically determined by the system, and the authorities could be immediately notified for follow-up actions. We have also provided and discussed the representative results from the real-time automatic monitoring system of heavy metals from different monitoring stations. Finally, we have illustrated the implications of the developed smart monitoring system for ensuring the safety of irrigation water in the near future, including integration with automatic sampling for establishing information exchange platform, estimating fluxes of heavy metals to paddy fields, and combining with green technologies for nonpoint source pollution control.

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

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.

scholarly journals Assessment of Irrigation Water Quality Parameters of Nyandungu Wetlands

2021 ◽  
Vol 09 (10) ◽  
pp. 151-160
Author(s):  
Phenias Mukiza ◽  
Jean De Dieu Bazimenyera ◽  
Jean Paul Nkundabose ◽  
Rose Niyonkuru ◽  
Nelly Elias Bapfakurera
Keyword(s):  
Water Quality ◽  
Irrigation Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Irrigation Water Quality

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

Water ◽  
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.

scholarly journals Investigation of surface water quality of the Buriganga river in Bangladesh: Laboratory and spatial analysis approaches

2019 ◽  
Vol 28 (2) ◽  
pp. 147-158
Author(s):  
Mohammad Saiful Islam ◽  
Romana Afroz ◽  
Md Bodruddoza Mia
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Quality Standard ◽  
Quality Parameters ◽  
Average Concentration ◽  
Water Quality Parameters ◽  
Economic Implications ◽  
Permissible Limits ◽  
Buriganga River

This work has been conducted to evaluate the water quality of the Buriganga river. In situ water quality parameters and water samples were collected from 10 locations in January 2016 and analyzed later in laboratory for water quality parameters such as pH, Eh, EC, TDS, cations (Na+, K+, Ca2+, Mg2, As3+), anions (Cl-, HCO3-, NO2-, NO3-, SO42-, F-, Br-, PO43-), heavy metals (Cr2+, Pb2+, Zn2+, Cd+2, Fe2+, Mn2+) to see whether or not the level of these parameters are within the permissible limits. The average values of pH, Eh, EC and temperature were 7.31, –214.9 mV, 928.9 μs/cm and 21.4°C, respectively; the average concentration of Na+, K+, Ca2+, Mg2+, and As3+ were 109.62, 13.38, 46.78, 13.98 and 0.018 mg/l, respectively, while the concentrations of Cl-,HCO3-, PO43-, SO42-, NO3-, NO2-, F and Br -were 79, 331.06, 2.22, 84.32, 0.0254, 0.058, 0.224 and 0.073 mg/l, respectively; and the concentration of heavy metals Pb2+, Zn2+, Fe2+ and Mn2+were 0.28, 0.053, 0.17 and 0.23 mg/l, respectively. The study indicates that most of the parameters are within the permissible limits set by Bangladesh water quality standard. The concentrations of K+, Mn2+, and Pb2+ were beyond the permissible limits meaning that that the water of Buriganga is not safe for drinking. The people living beside Buriganga river should be more cautious about using the polluted/contaminated river water. The concerned authorities should take urgent necessary steps to improve the degraded water quality of the river considering the ecological, environmental and economic implications associated with it. Dhaka Univ. J. Biol. Sci. 28(2): 147-158, 2019 (July)

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