Implementation of an automatic and miniature on-line multi-parameter water quality monitoring system and experimental determination of chemical oxygen demand and ammonia-nitrogen

2015 ◽  
Vol 73 (3) ◽  
pp. 697-706 ◽  
Author(s):  
Yingke Xie ◽  
Zhiyu Wen ◽  
Zhihong Mo ◽  
Zhiqiang Yu ◽  
Kanglin Wei
Keyword(s):  
Water Quality ◽  
Chemical Oxygen Demand ◽  
Monitoring System ◽  
Oxygen Demand ◽  
Water Quality Monitoring ◽  
Ammonia Nitrogen ◽  
Quality Monitoring ◽  
National Standard ◽  
Relative Standard ◽  
On Line

An automatic, miniature and multi-parameter on-line water quality monitoring system based on a micro-spectrometer is designed and implemented. The system is integrated with the flow-batch analysis and spectrophotometric detection method. The effectiveness of the system is tested by measuring chemical oxygen demand (COD) and ammonia-nitrogen in water. The results show that the modified system provides a cost-effective, sensitive, reproducible and reliable way to measure COD and ammonia-nitrogen in water samples with automatic operation and low toxic chemical consumption. In addition, the experiment results show that the relative error of the system is less than 10%, the limit of detection is 2 mg/L COD and 0.032 mg/L ammonia-nitrogen, respectively, and the relative standard deviation was 6.6% at 15.0 mg/L COD (n = 7) and 5.0% at 0.300 mg/L ammonia-nitrogen (n = 7). Results from the newly designed system are consistent with the data collected through the Chinese national standard analysis methods.

An On-Line Water Quality Monitoring System for the Reservoirs Based on Android

2014 ◽  
Vol 945-949 ◽  
pp. 2199-2202
Author(s):  
Zi Wen Dai ◽  
Hai Yang Liao
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Early Warning ◽  
Automatic Monitoring ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Water Quality Change ◽  
Long Time ◽  
On Line ◽  
Warning Model

According to the demand of water quality automatic monitoring in many large or medium reservoirs, we proposed an on-line water quality monitoring system. It is composed of wireless sensor networks and an embedded monitoring platform. We built a novel early-warning model to well adapt to the regular pattern of water quality change in the reservoirs. As a result, an Android application with outstanding control experience is achieved for real-time monitoring, water pollution early warning and water quality comprehensive assessment. Experimental results show that the system can work stably for a long time and provide accurate monitoring information continuously. It can also detect the abnormal signals of water quality in time and alarm. The system efficiently satisfies the requirement of water quality on-line automatic monitoring.

An On-Line Multiparametric Water Quality Monitoring System Based on Visible Spectrophotometry

2011 ◽  
Vol 189-193 ◽  
pp. 2801-2804 ◽  
Author(s):  
Hai Yang Liao ◽  
Peng Tian ◽  
Yu Du ◽  
Zhi Yu Wen
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
High Efficiency ◽  
High Reliability ◽  
Pure Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Compact Structure ◽  
Visible Spectrophotometry ◽  
On Line

Water quality monitoring plays an important role in contamination control and environment protection. This paper describes an on-line multiparametric water quality monitoring system based on visible spectrophotometry, which combines embedded technology with GPRS telecommunication technology. This system can realize online wireless monitoring to concentrations of chromium (Cr), plumbum (Pb), A surfactant (AS), chemical oxygen demand (COD), ammonia nitrogen (AN), total phosphorus (TP) and total phenol (TPh) in real time. The mechanical structure, hardware circuit and software design of the system are completed. The absorption spectrums of pure water and MB-SDS complex have been measured. Preliminary experiments using a model machine show that each mechanism of the system runs well. Moreover, the monitor possesses many advantages, such as high degree of automation, high reliability, high efficiency, compact structure, small size, and so on.

scholarly journals Design of On-line Water Quality Monitoring System

2021 ◽  
Vol 271 ◽  
pp. 02009
Author(s):  
Tan Fenfang
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Human Life ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Agricultural Wastewater ◽  
On Line ◽  
Normal Production ◽  
Software And Hardware

Water is the source of human life. However, a large amount of domestic sewage, industrial wastewater and agricultural wastewater produced in human production and life pollute the surface water, threatening normal production and life of people.In order to grasp the water quality fully, the temperature, PH. turbidity and conductivity sensors are adopted to collect various water quality parameters, and necessary software and hardware design of the on-line water quality' monitoring system is completed to provide a basis for subsequent water quality monitoring in various industries.

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

Sensors ◽  
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.

Smart and Low Cost Real Time Water Quality Monitoring System Using IoT

2019 ◽  
Author(s):  
Jeba Anandh S ◽  
Anandharaj M ◽  
Aswinrajan J ◽  
Karankumar G ◽  
Karthik P
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Research on Fishery Water Quality Monitoring System Based on 6LoWPAN

2020 ◽  
Vol 1624 ◽  
pp. 042057
Author(s):  
Xueying Wang ◽  
Yanli Feng ◽  
Jiajun Sun ◽  
Dashe Li ◽  
Huanhai Yang
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Quality Monitoring
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