Evaluation and application of an online coupled modeling system to assess the interaction between urban vegetation and air quality

Abstract

Vegetation has always been an integral part of the urban scene, affecting ambient air quality through both direct and indirect ways: by enhancing the dry deposition process of air pollutants and by contributing to the formation of ozone due to the emission of biogenic volatile organic compounds (BVOC). In this study, hourly measurements of gaseous dry deposition velocities are used to evaluate the performance of two dry deposition modules. Based on verification against measurements, an online coupled modeling system (RBLM-Chem) is introduced to investigate the dry deposition process of air pollutants (both gas and particles), the diurnal and seasonal variation patterns, and the discrepancy between different vegetation species as well as to assess the role of urban vegetation in affecting local air quality under different greening scenarios. Results indicate that trees are generally more efficient in removing air pollutants than shorter vegetation (e.g., grass). Moreover, conifers exhibit higher dry deposition velocities than broadleaf trees in terms of annual average. The introduction of vegetation (either trees or grass) clearly raises the dry deposition velocity of air pollutants. The air pollutant that is most removed by urban vegetation in Suzhou is PM10, with an annual removal rate of 1484.5 t a–1. The current urban greening scenario within Suzhou contributes to a reduction in daily mean concentration of 8.1% (SO2), 7.1% (NO2), 5.6% (O3), 4.7% (PM10) and 4.4% (PM2.5) in summer, while the reduction in winter is 4.6%, 5.5%, 4.5%, 3.6% and 3.7%, respectively. The improvement in pollutant concentration can be strengthened by increasing vegetation coverage. Additionally, the peri-urban forest ecosystem plays a role in air quality improvement within an urban area. As for the effect of BVOC emissions, the emission from urban trees under 40% coverage results in the consumption of NOx (–3.2%) and the formation of O3 (2.3%).