scholarly journals Participatory process in environmental monitoring design: examples from the Port of Limassol

2021 ◽  
Vol 899 (1) ◽  
pp. 012045
Author(s):  
R Abualhaija ◽  
D Hayes ◽  
J Reodica ◽  
T Pieri ◽  
M Michaelides
Keyword(s):  
Water Quality ◽  
Air Quality ◽  
Environmental Monitoring ◽  
Environmental Data ◽  
Quality Monitoring ◽  
Participatory Process ◽  
Sea Transport ◽  
Monitoring Design ◽  
Port Area ◽  
Monitoring Stations

Abstract Sea transport and seaborne trade have increased significantly in the past few decades. As sea traffic hubs, ports have high risks because of the limitation in manoeuvrability, number of vessels, and land-based port activities. In the coastal city of Limassol, water and air pollution has been anecdotally attributed to port activities. The STEAM project (Sea Traffic Management in the Eastern Mediterranean, INTEGRATED/0916/0063, [1]) aims to set up a monitoring plan to aid in the identification and mitigation of pollution sources. The project followed a participatory process, where port stakeholders and scientists were consulted and included in the ideation, design and implementation process. This participatory process developed a greater sense of stakeholder ownership in the environmental monitoring programs and facilitated their adoption. According to the consultation process, air and water quality are the most important factors to monitor. Five static and one mobile multi-sensor monitoring stations make up the air quality monitoring design for the Port of Limassol. Three air quality stations were installed within the port area along with two stations near the anchorage area. Two environmental data buoys and two oil detectors make up the water quality monitoring stations. The oil detectors will be placed within the port. One environmental data buoy will be placed downstream of the port, while the second buoy will be placed between the port entrance, the Limassol Marina and the anchorage area.

scholarly journals Environmental Monitoring System by Using Unmanned Aerial Vehicle

2018 ◽  
Vol 9 (3-4) ◽  
pp. 31
Author(s):  
Mohammed Abdelrhman ◽  
Ahmed Balkis ◽  
Ali-Abou Ahmed ElNour ◽  
Mohammed Tarique
Keyword(s):  
Water Quality ◽  
Air Quality ◽  
Environmental Monitoring ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Measured Data ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Wireless System ◽  
Environmental Monitoring System

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.

scholarly journals Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

2021 ◽  
Vol 17 (2) ◽  
pp. 1-44
Author(s):  
Francesco Concas ◽  
Julien Mineraud ◽  
Eemil Lagerspetz ◽  
Samu Varjonen ◽  
Xiaoli Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Environmental Monitoring ◽  
Low Cost ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Great Promise ◽  
Air Quality Monitoring ◽  
Monitoring Stations

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

Identifying redundant monitoring stations in an air quality monitoring network

2018 ◽  
Vol 190 ◽  
pp. 256-268 ◽  
Author(s):  
Chenchen Wang ◽  
Laijun Zhao ◽  
Wenjun Sun ◽  
Jian Xue ◽  
Yujing Xie
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Stations
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