Cargando…

Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air...

Descripción completa

Detalles Bibliográficos
Autores principales: Pandya, Sharnil, Ghayvat, Hemant, Sur, Anirban, Awais, Muhammad, Kotecha, Ketan, Saxena, Santosh, Jassal, Nandita, Pingale, Gayatri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571022/
https://www.ncbi.nlm.nih.gov/pubmed/32972037
http://dx.doi.org/10.3390/s20185448
_version_ 1783597080892145664
author Pandya, Sharnil
Ghayvat, Hemant
Sur, Anirban
Awais, Muhammad
Kotecha, Ketan
Saxena, Santosh
Jassal, Nandita
Pingale, Gayatri
author_facet Pandya, Sharnil
Ghayvat, Hemant
Sur, Anirban
Awais, Muhammad
Kotecha, Ketan
Saxena, Santosh
Jassal, Nandita
Pingale, Gayatri
author_sort Pandya, Sharnil
collection PubMed
description Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O(3)). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO(2), and O(3), for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.
format Online
Article
Text
id pubmed-7571022
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75710222020-10-28 Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living Pandya, Sharnil Ghayvat, Hemant Sur, Anirban Awais, Muhammad Kotecha, Ketan Saxena, Santosh Jassal, Nandita Pingale, Gayatri Sensors (Basel) Article Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O(3)). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO(2), and O(3), for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants. MDPI 2020-09-22 /pmc/articles/PMC7571022/ /pubmed/32972037 http://dx.doi.org/10.3390/s20185448 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pandya, Sharnil
Ghayvat, Hemant
Sur, Anirban
Awais, Muhammad
Kotecha, Ketan
Saxena, Santosh
Jassal, Nandita
Pingale, Gayatri
Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living
title Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living
title_full Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living
title_fullStr Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living
title_full_unstemmed Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living
title_short Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living
title_sort pollution weather prediction system: smart outdoor pollution monitoring and prediction for healthy breathing and living
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571022/
https://www.ncbi.nlm.nih.gov/pubmed/32972037
http://dx.doi.org/10.3390/s20185448
work_keys_str_mv AT pandyasharnil pollutionweatherpredictionsystemsmartoutdoorpollutionmonitoringandpredictionforhealthybreathingandliving
AT ghayvathemant pollutionweatherpredictionsystemsmartoutdoorpollutionmonitoringandpredictionforhealthybreathingandliving
AT suranirban pollutionweatherpredictionsystemsmartoutdoorpollutionmonitoringandpredictionforhealthybreathingandliving
AT awaismuhammad pollutionweatherpredictionsystemsmartoutdoorpollutionmonitoringandpredictionforhealthybreathingandliving
AT kotechaketan pollutionweatherpredictionsystemsmartoutdoorpollutionmonitoringandpredictionforhealthybreathingandliving
AT saxenasantosh pollutionweatherpredictionsystemsmartoutdoorpollutionmonitoringandpredictionforhealthybreathingandliving
AT jassalnandita pollutionweatherpredictionsystemsmartoutdoorpollutionmonitoringandpredictionforhealthybreathingandliving
AT pingalegayatri pollutionweatherpredictionsystemsmartoutdoorpollutionmonitoringandpredictionforhealthybreathingandliving