Abstract
Many people live, work and spend time during their commute in near-road environments (<50 m) where pollutant concentrations usually remain high. We investigated the influence of roadside green infrastructure (GI) on concentrations of particulate matter ≤10 μm (PM10), ≤2.5 μm (PM2.5), ≤1 μm (PM1), black carbon (BC) and particle number concentrations (PNC) under three GI configurations – (i) hedges only, (ii) trees only, and (iii) a mix of trees and hedges/shrubs – separately in close (<1 m) and away (>2 m) road conditions. These configurations gave us a total of six different real-world scenarios for evaluation. The changes in concentrations of PM10, PM2.5, PM1, BC and PNC at all six sites were estimated by comparing simultaneous measurements behind and in front of GI (or adjacent clear area). A portable battery-operated experimental set-up was designed for measuring the pollutant concentrations for 30 full days over a field campaign period of three months. On each day, around 10 h of continuous data were recorded simultaneously behind and in front of GI/adjacent clear area, capturing both morning and evening traffic peaks. Our objectives were to: (i) assess the effectiveness of different types of GI in reducing various pollutants; (ii) evaluate the impact of wind directions and density of vegetation on reducing different pollutant concentrations behind GI; (iii) investigate the changes in fractional composition of sub-micron (PM1), fine (PM2.5) and coarse (PM2.5-10) particles; and (iv) quantify the elemental composition of collected particles before and after the GI. In away-road conditions, all three configurations showed reductions behind the GI for all pollutants. The ‘hedges only’ configuration showed higher pollutant reductions than the other two configurations, with maximum reductions of up to 63% shown for BC. In close-road conditions, the results were mixed. The ‘trees only’ configuration reported increases in most of the pollutant concentrations, whereas the combination of trees and hedges resulted in reduced pollutant concentrations behind the GI. Among all pollutants, the highest relative changes in concentration were observed for BC (up to 63%) and lowest for PM2.5 (14%). Categorising the data based on wind directions showed the highest reduction during along-road wind conditions (i.e., parallel to the road). This was expected due to the sweeping of emissions by the wind and the wake of road vehicles whilst the barrier effect of GI enhanced this cleansing, limiting lateral diffusion of the pollutants. However, cross-road winds that took vehicular emissions to pass through the GI allowed us to assess their influence, showing up to 52, 15, 17, 31 and 30% reduction for BC, PM10, PM2.5, PM1 and PNC, respectively. The largest reductions were consistently noted for the mixed ‘trees and hedges’ configuration in close-road conditions and the ‘hedge only’ configuration in away-road conditions. The assessment of various fractions of PM showed that ‘hedges only’ and a combination of trees and hedges lowered fine particles behind GI. The SEM-EDS analysis indicated the dominance of natural particles (50%) and a reduction in vehicle-related particles (i.e., iron and its oxides, Ba, Cr, Mn) behind GI when compared with the in-front/adjacent clear area. The evidence contributed by this work enhances our understanding of air quality modifications under the influence of different GI configurations, for multiple pollutants. In turn, this will support the formulation of appropriate guidelines for GI design, to reduce the air pollution exposure of those living, working or travelling near busy roads.