Robust Environmental Sensing Using UAVs

2021 ◽  
Vol 2 (4) ◽  
pp. 1-20
Author(s):  
Ahmed Boubrima ◽  
Edward W. Knightly
Keyword(s):  
Air Pollution ◽  
Gas Sensors ◽  
High Performance ◽  
Weather Conditions ◽  
Ambient Air ◽  
Mission Planning ◽  
Pollution Monitoring ◽  
Compound Pollution ◽  
Planning Algorithm

In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.

scholarly journals Air Pollution Monitoring and Prediction System

2019 ◽  
Vol 8 (2S3) ◽  
pp. 648-651
Keyword(s):  
Air Pollution ◽  
Gas Sensors ◽  
Pollution Monitoring ◽  
Warning Systems ◽  
Major Objective ◽  
Air Pollution Monitoring ◽  
The Public ◽  
The People ◽  
Environmental Air

Environmental Air Pollution Monitoring System is used for monitoring the concentrations of major air pollutants using gas sensors. The main target of this project is to monitor the air quality using sensors and analyze the existing trends in air pollution and make prediction about future. The major objective is to inform the public about the quality of air, raise the awareness and also to develop warning systems for the prevention of undesired air pollution episodes and to create awareness in order to reduce the amount of air pollution caused due to various sources. The system is also used to get the approximate quantity of pollutants present in air thereby giving awareness to the people of that specific region. Thus, the amount of pollution caused due to various sources can be reduced, leading a healthier and safer environment

scholarly journals A review on Assessment of Air Pollution due to Vehicular Emission in Traffic Area

2018 ◽  
Vol 8 (02) ◽  
Author(s):  
Aneri A. Desai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Ambient Air ◽  
Pollution Monitoring ◽  
Motor Vehicles ◽  
Monitoring And Control ◽  
Ambient Air Quality ◽  
Vehicular Emission ◽  
Rapid Urbanization ◽  
Air Pollution Monitoring

In Indian metropolitan cities, the extensive growth of the motor vehicles has resulted in the deterioration of environmental quality and human health. The concentrations of pollutants at major traffic areas are exceeding the permissible limits. Public are facing severe respiratory diseases and other deadly cardio-vascular diseases In India. Immediate needs for vehicular air pollution monitoring and control strategies for urban cities are necessary. Vehicular emission is the main source of deteriorating the ambient air quality of major Indian cities due to rapid urbanization. Total vehicular population is increased to 15 Lacks as per recorded data of Regional Transport Organization (RTO) till 2014-2015. This study is focused on the assessment of major air pollution parameters responsible for the air pollution due to vehicular emission. The major air pollutants responsible for air pollution due to vehicular emissions are PM10, PM2.5, Sox, Nox, HC, CO2 and CO and Other meterological parameters like Ambient temperature, Humidity, Wind direction and Wind Speed. Sampling and analysis of parameters is carried out according to National Ambient Air Quality Standards Guidelines (NAAQS) (2009) and IS 5128.

A dynamic evaluation framework for ambient air pollution monitoring

2019 ◽  
Vol 65 ◽  
pp. 52-71 ◽  
Author(s):  
Ranran Li ◽  
Yuqi Dong ◽  
Zhijie Zhu ◽  
Chen Li ◽  
Hufang Yang
Keyword(s):  
Air Pollution ◽  
Ambient Air ◽  
Evaluation Framework ◽  
Pollution Monitoring ◽  
Ambient Air Pollution ◽  
Dynamic Evaluation ◽  
Air Pollution Monitoring

scholarly journals Using Ultrasound-Treated Washout from Conifer Needles and Fresh Snow Samples in Air Pollution Monitoring

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
A. Kholodov ◽  
M. Tretyakova ◽  
K. Golokhvast
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Ambient Air ◽  
Atmospheric Particulate Matter ◽  
Pollution Monitoring ◽  
Ambient Air Pollution ◽  
Air Pollution Monitoring ◽  
Conifer Needles ◽  
Fresh Snow ◽  
Snow Precipitation

Snow precipitation and snowpack are commonly used to assess the condition of the aerial environment. Another way to monitor air quality is to study trees and shrubs, which are natural barriers for capturing air pollution, including atmospheric particulate matter. The hypothesis of the current study was that using fresh snow precipitation and washout from vegetation for the monitoring of air pollution can produce comparable results. In this study, we compared the results of laser diffraction analysis of suspended particular matter in melted fresh snow and ultrasound-treated washout from conifer needles. The samples were collected at several sites in Primorsky Krai, Russian Federation, and analyzed according to the same scheme. We observed that the content of particulate matter with a smaller aerodynamic diameter in the ultrasound-treated washout from conifer needles was higher than that in the melted fresh snow. The content of PM10 in the ultrasound-treated washout from conifers was increased by 6–27% depending on the site, showing greater efficacy of this method. This method can be used as an alternative to the sampling of snow for the monitoring of ambient air pollution, taking into account several limitations.

scholarly journals Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africa

2021 ◽  
Vol 18 (7) ◽  
pp. 3374
Author(s):  
Oluwaseyi Olalekan Arowosegbe ◽  
Martin Röösli ◽  
Nino Künzli ◽  
Apolline Saucy ◽  
Temitope Christina Adebayo-Ojo ◽  
...  
Keyword(s):  
South Africa ◽  
Air Pollution ◽  
Ground Level ◽  
Ambient Air ◽  
Pollution Monitoring ◽  
Monitoring Data ◽  
Ambient Air Pollution ◽  
Site Specific ◽  
The Impact ◽  
Daily Pm10

Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete.

scholarly journals IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5313
Author(s):  
Santanu Metia ◽  
Huynh A. D. Nguyen ◽  
Quang Phuc Ha
Keyword(s):  
Air Pollution ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fractional Order ◽  
Kalman Filtering ◽  
High Performance ◽  
Pollution Monitoring ◽  
Wireless Sensor ◽  
Quality Data ◽  
Air Pollution Monitoring

This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019–2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area.

scholarly journals A study of the chemical quality of ambient air at selected intersections in the Durban Metropolis

2003 ◽  
Author(s):  
◽  
Shalini Singh
Keyword(s):  
Nitric Oxide ◽  
Air Pollution ◽  
Nitrogen Dioxide ◽  
Ambient Air ◽  
Urban Environments ◽  
Motor Vehicles ◽  
Chemical Quality ◽  
Oxides Of Nitrogen ◽  
Combustion Processes

Motor vehicles are considered a major source of air pollution in urban environments. Nitrogen dioxide (N02) and nitric oxide (NO) which are collectively referred to as oxides of nitrogen (NOx) are formed at high temperatures during combustion processes in the engines of motor vehicles and are emitted via the exhaust into the atmosphere. Nitrogen dioxide is regarded as an irritant of the respiratory system.

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