Cargando…

Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework

Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot ref...

Descripción completa

Detalles Bibliográficos
Autores principales: Wen, Tzai-Hung, Jiang, Joe-Air, Sun, Chih-Hong, Juang, Jehn-Yih, Lin, Tzu-Shiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881120/
https://www.ncbi.nlm.nih.gov/pubmed/24287859
http://dx.doi.org/10.3390/ijerph10126380
_version_ 1782298166330130432
author Wen, Tzai-Hung
Jiang, Joe-Air
Sun, Chih-Hong
Juang, Jehn-Yih
Lin, Tzu-Shiang
author_facet Wen, Tzai-Hung
Jiang, Joe-Air
Sun, Chih-Hong
Juang, Jehn-Yih
Lin, Tzu-Shiang
author_sort Wen, Tzai-Hung
collection PubMed
description Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management.
format Online
Article
Text
id pubmed-3881120
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-38811202014-01-06 Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework Wen, Tzai-Hung Jiang, Joe-Air Sun, Chih-Hong Juang, Jehn-Yih Lin, Tzu-Shiang Int J Environ Res Public Health Article Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management. MDPI 2013-11-27 2013-12 /pmc/articles/PMC3881120/ /pubmed/24287859 http://dx.doi.org/10.3390/ijerph10126380 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0/ This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Wen, Tzai-Hung
Jiang, Joe-Air
Sun, Chih-Hong
Juang, Jehn-Yih
Lin, Tzu-Shiang
Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework
title Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework
title_full Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework
title_fullStr Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework
title_full_unstemmed Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework
title_short Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework
title_sort monitoring street-level spatial-temporal variations of carbon monoxide in urban settings using a wireless sensor network (wsn) framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881120/
https://www.ncbi.nlm.nih.gov/pubmed/24287859
http://dx.doi.org/10.3390/ijerph10126380
work_keys_str_mv AT wentzaihung monitoringstreetlevelspatialtemporalvariationsofcarbonmonoxideinurbansettingsusingawirelesssensornetworkwsnframework
AT jiangjoeair monitoringstreetlevelspatialtemporalvariationsofcarbonmonoxideinurbansettingsusingawirelesssensornetworkwsnframework
AT sunchihhong monitoringstreetlevelspatialtemporalvariationsofcarbonmonoxideinurbansettingsusingawirelesssensornetworkwsnframework
AT juangjehnyih monitoringstreetlevelspatialtemporalvariationsofcarbonmonoxideinurbansettingsusingawirelesssensornetworkwsnframework
AT lintzushiang monitoringstreetlevelspatialtemporalvariationsofcarbonmonoxideinurbansettingsusingawirelesssensornetworkwsnframework