Low-cost air quality sensors for air pollution monitoring have shown huge potential in enhancing the spatial and temporal resolution of much needed pollution data at a lower cost, greater flexibility in use with less maintenance than air quality monitoring stations. Air quality sensors provide opportunities to extend a range of existing air pollution monitoring capabilities and provide ideas for new monitoring applications. The widespread data sources combined with sensors improve people’s understanding of air quality research areas. However, severe gaps existed between the guarantee of sensor data quality and derivation of meaningful information from sensor applications in air quality monitoring. This study provides avenues from sensor calibration to applications in urban air quality m...[ Read more ]

Low-cost air quality sensors for air pollution monitoring have shown huge potential in enhancing the spatial and temporal resolution of much needed pollution data at a lower cost, greater flexibility in use with less maintenance than air quality monitoring stations. Air quality sensors provide opportunities to extend a range of existing air pollution monitoring capabilities and provide ideas for new monitoring applications. The widespread data sources combined with sensors improve people’s understanding of air quality research areas. However, severe gaps existed between the guarantee of sensor data quality and derivation of meaningful information from sensor applications in air quality monitoring. This study provides avenues from sensor calibration to applications in urban air quality monitoring. Three steps were divided as follows: Firstly, the air quality sensors were tested in the laboratory-controlled environment to understand the sensor performance and influence factors. The ambient factor influence was identified and quantified to build correction algorithms. Based on the understanding of the sensor working principle, a principle-based correction algorithm was built. The new algorithm proves the sensor sensitivity variation trend during different seasons. Meanwhile, compared with commonly used correction models, it shows the best performance and spatial representativeness in the sensor network.

Secondly, a mobile sensor network was deployed in Hong Kong to monitor traffic-related air pollutants (TRAP) along bus routes. To estimate local and background contributions, a robust baseline extraction algorithm was developed and evaluated. The result indicates NO and NO₂ are locally dominated air pollutants and varied within roads. Background concentrations primarily arose from CO and PM_2.5 and decreased during summertime. The regional transport pollution is the primary contributor during high pollution episodes. For the traffic induced local concentration, the most polluted road segments cluster at tunnel entrances and congested points. Some of these polluted locations were observed in Low Emission Zones and suggest limitations to the existing control strategies. These finding gives insights in the importance of regional cooperation to control background air pollution combined with local control strategies to improve roadside air quality in Hong Kong.

Finally, based on the mobile measurement, we build machine learning based land use regression (LUR) models to estimate the street-level pollutions distribution in complex urban environments. In addition to traditional geographic variables, two innovative variables from Google maps were integrated as candidate predictors: street view image and traffic congestion index. These two variables can be used to represent the pollutant dispersion status and traffic activities which is more relevant to vehicle emissions. The result indicates the integrated machine learning models perform well in transportation dominated local urban environments. The traffic-related predictor variables were identified based on the feature importance evaluation that indicates the traffic and transport infrastructure are major concerns both for individual exposure and urban planning. Furthermore, the estimated pollution maps can help identify environmental hotspots that need to be improved by taking appropriate urban planning measures and management strategies.

Keywords: Air quality, traffic-related pollutants, air quality sensor, correction algorithm, traffic congestion, machine learning, land use, crowdsourcing data