Environmental Monitoring System by Using Unmanned Aerial Vehicle

This paper presents a reliable and low cost environmental monitoring system. The system uses an Unmanned Ariel Vehicle (UAV) equipped with a set of sensors, microcontroller, wireless system, and other accessories. The system consists of two systems namely air quality monitoring system and water quality monitoring system. The air quality monitoring system consists of a set of gas sensors and microcontroller. This system measures the concentration of greenhouse gases at different altitudes under different environmental conditions. On the other hand, the water quality monitoring system consists of a set of water quality sensors, microcontroller, and water sampling unit. This system collects water samples from off-shore and on-shore water sources and measures water quality parameters. The present system is capable of recording the measured data in an onboard SD card. It is also able to send data to a ground monitoring unit through a wireless system. To ensure reliability in measurement the sensors are calibrated before deployment. Finally, the system is upgradable and reconfigurable. The system has been tested to measure air and water quality at different local areas. Some these measured data are also presented in this paper.

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Design and Deployment of an IoT-Based Air Quality Monitoring System

International Journal of Environmental Science and Development ◽

10.18178/ijesd.2021.12.5.1331 ◽

2021 ◽

Vol 12 (5) ◽

pp. 139-145

Author(s):

Tuyen Phong Truong ◽

◽

Duy Thanh Nguyen ◽

Phong Vu Truong

Keyword(s):

Air Quality ◽

Monitoring System ◽

Data Communication ◽

Quality Monitoring ◽

Battery Life ◽

Total System ◽

Air Quality Monitoring ◽

Quality Factors ◽

Large Area ◽

Environmental Monitoring System

Air quality is getting worse worldwide, especially in cities with high population density and many industrial parks. Raising community awareness and applying science and technology are effective ways to mitigate the negative impacts of industrialization and pollution on the natural environment as well as public health. This work presents the design and deployment of an IoT-based air quality monitoring system, named the Environmental Monitoring System (EnMoS). LoRa (Long-Range) wireless communication technology and innovation sensors being used aim to facilitate the development of data communication network over a large area, improving sensing reliability, extending battery life as well as reducing total system costs. The air quality factors such as particulate matter (PM2.5 and PM10), carbon dioxide (CO2), air temperature and humidity, after being read from the sensors were uploaded to a real-time database server for Air Quality Index (AQI) calculation. In addition, for indicating conveniently obtained AQI values a web page is also developed to provide an interactive map along with corresponding charts. A case study on an actual LoRa network consisting of three sensing nodes and a gateway were conducted for validating the feasibility of the system.

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A LoRa enabled IoT-based Air Quality Monitoring System for Smart City

2021 IEEE World AI IoT Congress (AIIoT) ◽

10.1109/aiiot52608.2021.9454232 ◽

2021 ◽

Author(s):

Evariste Twahirwa ◽

Kambombo Mtonga ◽

Desire Ngabo ◽

Santhi Kumaran

Keyword(s):

Air Quality ◽

Monitoring System ◽

Smart City ◽

Quality Monitoring ◽

Air Quality Monitoring

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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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