Internet of Things-Based Water Quality Control and Monitoring System for Urban Society

In this chapter, an attempt has been made to develop a hardware-based remote water quality monitoring system using a single-chip microcontroller, Atmega328P, in synchrony with some sensor technology and GSM/GPRS module for long-distance data transmission. The proposed system is able to perform a qualitative test on the water, taking into consideration both the chemical behavior as well as physical properties exhibited by the latter. The fluid will be analyzed in terms of its ph value (i.e., the molar concentration of hydrogen ions, the haziness caused by the major suspension of minute particles). The device aims to transmit all the deliberated parameters of the wastewater along with the longitude and latitude information to the concerned authorities for real-time monitoring of that data. The subject aims to devise a robust solution that can be used to analyze the quality of large water bodies and send the analysis report to the authorities of pollution control for further implication.

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Construction and research of water quality monitoring system based on ZigBee technology

E3S Web of Conferences ◽

10.1051/e3sconf/202016503060 ◽

2020 ◽

Vol 165 ◽

pp. 03060

Author(s):

Fan Heng

Keyword(s):

Water Quality ◽

Monitoring System ◽

Ph Value ◽

Water Quality Monitoring ◽

Quality Parameters ◽

Sensor Nodes ◽

Quality Monitoring ◽

Test Model ◽

Hardware System ◽

Zigbee Technology

The water quality monitoring system proposed in this paper is to establish a monitoring system by using the ZigBee technology .The sensor nodes are placed in the monitoring area to form a tree-type network topology, and the data of the water quality parameters (including PH value, turbidity, water temperature, electric conductivity, etc.) are collected and analyzed. This paper mainly expounds the construction of the hardware system of the monitoring system, establishes the test model of the hardware system, simulates the placement of the sensor node to obtain the experimental data, and provides the hardware test result for the overall system design.

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Intelligent Water Quality Monitoring System Based on Multi-Sensor Data Fusion Technology

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2021100103 ◽

2021 ◽

Vol 12 (4) ◽

pp. 43-63

Author(s):

Qiuxia Liu

Keyword(s):

Water Quality ◽

Data Fusion ◽

Monitoring System ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Sensor Data ◽

Single Chip ◽

Single Chip Microcomputer ◽

Fusion Algorithm ◽

Sensor Module

The intelligent water quality monitoring system takes the single chip microcomputer STM32F103C8T6 as the control core to collect signals of each sensor module and converts the collected parameters into effective digital signals by using the internal analog-to-digital converter. The data gathered by the acquisition center is sent to the analysis and processing center through the ZigBee module E18. In the analysis and processing center, data is fused and processed by the single chip microcomputer STC12C5A60S2. The data after fusion is sent to the monitoring management center through the GPRS module SIM800C. For improving the monitoring precision of the system, multi-level data fusion algorithms are used. In the data layer, abnormal values are deleted by abnormal data detection method, and the median average filtering method is used to fuse the data; the algorithm based on weighted estimation fusion is used in the feature layer; the fuzzy control fusion algorithm is used in the decision.

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An Open Source Water Quality Measurement System for Remote Areas

Engineering Proceedings ◽

10.3390/engproc2021012050 ◽

2021 ◽

Vol 12 (1) ◽

pp. 50

Author(s):

Muhammad Owais Tariq ◽

Asif Siddiq ◽

Hafsa Irshad ◽

Muhammad Aman ◽

Muhammad Shahbaz Khan

Keyword(s):

Water Quality ◽

Drinking Water ◽

Monitoring System ◽

Ph Value ◽

Water Quality Monitoring ◽

Ion Concentration ◽

Quality Monitoring ◽

World Health ◽

Average Error ◽

Remote Areas

The unavailability of safe drinking water leads to poor conditions related to mental and physical health. To quantify the quality of water, laboratories testing the water are present in major cities which assess the basic quality parameters of drinking water, e.g., total dissolved salts (TDS), ion concentration (conductivity), turbidity, and pH value as recommended by the World Health Organization (WHO). The unavailability of such testing laboratories at remote locations makes the testing of the drinking water difficult. Establishing such laboratories is a tedious job as it requires a lot of costly equipment and specially trained personnel to operate them, making them difficult to handle. To address these issues, a water quality monitoring system for remote areas was designed which is capable of measuring basic measurable qualities of salt concentration, ion concentration, turbidity, and pH value. With the utilization of such a system, the user can qualify the water present in the vicinity as safe or unsafe for drinking purposes. The results from the proposed system are evaluated based on standard testing results and it is found that our water quality monitoring system is in agreement with the standard lab results with an average error of 2.9%, 1.4%, 1.2%, and 1.2% for pH, turbidity, conductivity, and TDS, respectively.

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Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽

10.3390/s21124118 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4118

Author(s):

Leonardo F. Arias-Rodriguez ◽

Zheng Duan ◽

José de Jesús Díaz-Torres ◽

Mónica Basilio Hazas ◽

Jingshui Huang ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Extreme Learning Machine ◽

Monitoring System ◽

Field Measurements ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Chl A ◽

Learning Machine ◽

Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

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