An integrated monitoring platform (IMP) was developed for real-time monitoring of traffic flows and related air pollution in urban areas. The IMP includes: (i) an air quality monitoring unit, integrating the "Arduino" open-source technology with lowcost and high-resolution sensors, to measure air pollutant concentrations; (ii) a traffic monitoring device, equipped with a camera sensor and a video analysis software, to detect vehicles' counts, speed and category; (iii) a spatial data infrastructure, composed of a central GeoDatabase, a GIS engine, and a web interface, for data storage and management. The IMP was tested in Florence (Italy) by installing sensor devices at a road site where a 1-year measuring campaign was carried out. A reference meteorological station in the city centre was used to provide observations of wind speed and direction, air temperature, and relative humidity. In this work, a statistical analysis was performed to investigate the influence of local road traffic and meteorological conditions on CO, NO2 and CO2 concentrations. Two different methods were applied: a linear regression model and an artificial neural network. To investigate the role played by emissions from road traffic, the influence of all drivers by period of the year (cold vs. warm months) and day of the week (weekdays vs. weekends) was analysed. As a result, the contribution of local road traffic on pollutant concentrations proved to be lower than meteorological parameters.
An integrated monitoring platform (IMP) was developed for real-time monitoring of traffic flows and related air pollution in urban areas. The IMP includes: (i) an air quality monitoring unit, integrating the "Arduino" open-source technology with low-cost and high-resolution sensors, to measure air pollutant concentrations; (ii) a traffic monitoring device, equipped with a camera sensor and a video analysis software, to detect vehicles' counts, speed and category; (iii) a spatial data infrastructure, composed of a central GeoDatabase, a GIS engine, and a web interface, for data storage and management. The IMP was tested in Florence (Italy) by installing sensor devices at a road site where a 1-year measuring campaign was carried out. A reference meteorological station in the city centre was used to provide observations of wind speed and direction, air temperature, and relative humidity. In this work, a statistical analysis was performed to investigate the influence of local road traffic and meteorological conditions on CO, NO2 and CO2 concentrations. Two different methods were applied: a linear regression model and an artificial neural network. To investigate the role played by emissions from road traffic, the influence of all drivers by period of the year (cold vs. warm months) and day of the week (weekdays vs. weekends) was analysed. As a result, the contribution of local road traffic on pollutant concentrations proved to be lower than meteorological parameters.
An integrated low-cost road traffic and air pollution monitoring platform to assess vehicles' air quality impact in urban areas
Gualtieri G;Camilli F;De Filippis T;Di Lonardo S;Gioli B;Matese A;Rocchi L;Toscano P;Vagnoli C;Zaldei A
2017
Abstract
An integrated monitoring platform (IMP) was developed for real-time monitoring of traffic flows and related air pollution in urban areas. The IMP includes: (i) an air quality monitoring unit, integrating the "Arduino" open-source technology with low-cost and high-resolution sensors, to measure air pollutant concentrations; (ii) a traffic monitoring device, equipped with a camera sensor and a video analysis software, to detect vehicles' counts, speed and category; (iii) a spatial data infrastructure, composed of a central GeoDatabase, a GIS engine, and a web interface, for data storage and management. The IMP was tested in Florence (Italy) by installing sensor devices at a road site where a 1-year measuring campaign was carried out. A reference meteorological station in the city centre was used to provide observations of wind speed and direction, air temperature, and relative humidity. In this work, a statistical analysis was performed to investigate the influence of local road traffic and meteorological conditions on CO, NO2 and CO2 concentrations. Two different methods were applied: a linear regression model and an artificial neural network. To investigate the role played by emissions from road traffic, the influence of all drivers by period of the year (cold vs. warm months) and day of the week (weekdays vs. weekends) was analysed. As a result, the contribution of local road traffic on pollutant concentrations proved to be lower than meteorological parameters.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
