Air Pollution Mitigation Capacity of an Urban Forest in Densely Road-Networked Area: Findings from High-Resolution Sensor Monitoring

The dense roadway network leads to high emissions of air pollutants per unit area, causing adverse environmental and health impacts. Various approaches to mitigating these air pollutions have been proposed, and one of the notable strategies is the use of vegetation for air purification. Vegetation is known to remove particulate and gaseous pollutants through various processes such as stomatal uptake, physical impaction and adsorption, and blocking by surfaces. Additionally, urban forests can contribute to improving air quality by reducing the urban heat island effect, potentially leading to slower chemical reactivities for secondary pollutants. However, air pollutant removal efficiencies of urban forests can be controlled by various factors such as vegetation type, leaf density, size, location, and seasonal and meteorological conditions. Moreover, the complex urban canopy and built environments can lead to spatiotemporally heterogeneous distributions of air pollutants, adding uncertainty to the assessment of the air purification capacity of urban forests.

In this study, we conducted high-resolution measurements with an air quality sensor network to evaluate air pollution mitigation capacity of an urban forest that sits in a densely road-networked area. Air quality sensors were installed inside and outside the urban forests to monitor concentrations of both gaseous (CO, NO, NO₂, O₃) and particulate pollutants (PM_2.5 and number density) across different seasons. Here, we present the preliminary results of our findings obtained from four intensive measurement campaigns in different seasons.

Acknowledgments

This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)

Keywords: urban forest, air pollution mitigation, sensor network, field measurements, seasonal variations