A trait-based investigation into evergreen woody plants for traffic-related air pollution mitigation over time

Air pollution abatement from Green-Blue-Grey infrastructure

Green-blue-grey infrastructure (GBGI) offers environmental benefits in urban areas, yet its impact on air pollution is under-researched, and the literature fragmented. This review evaluates quantitative studies on GBGI's capability to mitigate air pollution, compares their specific pollutant removal processes, and identifies areas for further investigation. Of the 51 GBGI types reviewed, only 22 provided quantitative pollution reduction data. Street trees and mixed-GBGI are the most studied GBGIs, with efficacy influenced by wind, GBGI type vegetation characteristics, and urban morphology. Negative percentages denote worsening air quality, while positive reflect improvement. The 22 different GBGI grouped into eight main categories provide an average (± s.d.) reduction in air pollution of 16 ± 21%, with substantial reduction shown by linear features (23 ± 21%), parks (22 ± 34%), constructed GI (14 ± 25%), and other non-sealed urban areas (14 ± 20%). Other individual GBGI reducing air pollutants include woodlands (21 ± 38%), hedges (14 ± 25%), green walls (14 ± 27%), shrubland (12 ± 20%), green roofs (13 ± 23%), parks (9±36%), and mixed-GBGI (7 ± 23 %). On average, GBGI reduced PM1, PM2.5, PM10, UFP and BC by 13 ± 21%, 1 ± 25%, 7 ± 42%, 27 ± 27%, and 16 ± 41%, respectively. GBGI also lowered gaseous pollutants CO, O3 and NOx by 10 ± 21%, 7 ± 21%, and 12 ± 36%, on average, respectively. Linear (e.g., street trees and hedges) and constructed (e.g., green walls) features can impact local air quality, positively or negatively, based on the configuration and density of the built environment. Street trees generally showed adverse effects in street canyons and beneficial outcomes in open-road conditions. Climate change could worsen air pollution problems and impact GBGI effectiveness by shifting climate zones. In Europe and China, climate shifts are anticipated to affect 8 of the 22 GBGIs, with the rest expected to remain resilient. Despite GBGI's potential to enhance air quality, the meta-analysis highlights the need for a standardised reporting structure or to enable meaningful comparisons and effectively integrate findings into urban pollution and climate strategies.

Understanding the impacts of street greening patterns and wind directions on the dispersion of fine particles

Inappropriate planting patterns can increase pollutant concentrations and threaten human health. This study examined three greening patterns (trees, trees + hedges, and hedges) using the ENVI-met model to evaluate the different effects of various planting patterns on PM2.5 dispersion within an idealized 3D street canyon under three typical wind directions. Results showed that street greenbelts alter the PM2.5 concentration field within canyons, and the horizontal and vertical distribution characteristics of PM2.5 under different wind directions were significantly different. The arbor-hedge vegetation structure showed the highest total vegetation deposition amount due to larger canopy volumes while hedges have better deposition amounts per unit volume due to their proximity to emission sources. Additionally, this research selected the averaged relative difference in PM2.5 concentration (ARDC) indicator to assess the influence of different green scenarios on the dispersion of PM2.5 concentrations. Wind direction and planting patterns jointly affect the dispersion of PM2.5 in canyons, and the ARDC varied from -4.39 % to 105.36 %. Unilateral-trees on the windward side or two rows of hedges may be the optimal vegetation layout by trade-off with other services. ARDC was significantly correlated (p < 0.01) with most of the 3D green indicators. These results could provide effective suggestions for optimizing the layout of greenbelts in street canyons to improve air quality.

Evaluation of atmospheric particulate matter pollution characteristics in Shanghai based on biomagnetic monitoring technology

Atmospheric particulate matter (PM) pollution is one of the world's most serious environmental challenges, and it poses a significant threat to environmental quality and human health. Biomagnetic monitoring of PM has great potential to improve spatial resolution and provide alternative indicators for large area measurements, with respect and complementary to standard air quality monitoring stations. In this study, 160 samples of evergreen plant leaves were collected from park green spaces within five different functional areas of Shanghai. Magnetic properties were investigated to understand the extent and nature of particulate pollution and the possible sources, and to assess the suitability of various plant leaves for urban particulate pollution monitoring. The results showed that magnetic particles of the plant leaf-adherent PM were predominantly composed of pseudo-single domain (PSD) and multi-domain (MD) ferrimagnetic particles. Magnolia grandiflora, as a large evergreen arbor with robust PM retention capabilities, proved to be a more suitable candidate for monitoring urban particulate pollution compared to Osmanthus fragrans, a small evergreen arbor, and Aucuba japonica Thunb. var. variegata and Photinia serratifolia, evergreen shrubs. Meanwhile, there were significant differences in the spatial distribution of the magnetic particle content and heavy metal enrichment of the samples, mainly showing regional variations of industrial area > traffic area > commercial area > residential area > clean area. Additionally, the combination with the results of scanning electron microscopy, shows that industrial production (metal smelting, coal burning), transport and other activities are the main sources of particulate pollution. Plant leaves can be used as an effective tool for urban particulate pollution monitoring and assessment of atmospheric particulate pollution characteristics, and the technique provided useful information on particle size, mineralogy and possible sources.

Evaluating the impact of evolving green and grey urban infrastructure on local particulate pollution around city square parks

The relationship between green and grey urban infrastructure, local meteorological conditions, and traffic-related air pollution is complex and dynamic. This case study examined the effect of evolving morphologies around a city square park in Dublin and explores the twin impacts of local urban development (grey) and maturing parks (green) on particulate matter (PM) pollution. A fixed air quality monitoring campaign and computational fluid dynamic modelling (ENVI-met) were used to assess current (baseline) and future scenarios. The baseline results presented the distribution of PM in the study area, with bimodal (PM2.5) and unimodal (PM10) diurnal profiles. The optimal vegetation height for air quality within the park also differed by wind direction with 21 m vegetation optimal for parallel winds (10.45% reduction) and 7 m vegetation optimal for perpendicular winds (30.36% reduction). Increased building heights led to higher PM2.5 concentrations on both footpaths ranging from 25.3 to 37.0% under perpendicular winds, whilst increasing the height of leeward buildings increased PM2.5 concentrations by up to 30.9% under parallel winds. The findings from this study provide evidence of the importance of more in-depth analysis of green and grey urban infrastructure in the urban planning decision-making process to avoid deteriorating air quality conditions around city square parks.

Exploring the interplay between particulate matter capture, wash-off, and leaf traits in green wall species

The study investigated inter-species variation in particulate matter (PM) accumulation, wash-off, and retention on green wall plants, with a focus on leaf characteristics. Ten broadleaf plant species were studied in an experimental green wall. Ambient PM concentrations remained relatively stable throughout the measurement period: PM1: 16.60 ± 9.97 μgm-3, PM2.5: 23.27 ± 11.88 μgm-3, and PM10: 39.59 ± 25.72 μgm-3. Leaf samples were taken before and after three rainfall events, and PM deposition was measured using Scanning Electron Microscopy (SEM). Leaf micromorphological traits, including surface roughness, hair density, and stomatal density, exhibited variability among species and leaf surfaces. Notably, I.sempervirens and H.helix had relatively high PM densities across all size fractions. The study underscored the substantial potential of green wall plants for atmospheric PM removal, with higher Wall Leaf Area Index (WLAI) species like A.maritima and T.serpyllum exhibiting increased PM accumulation at plant level. Rainfall led to significant wash-off for smaller particles, whereas larger particles exhibited lower wash-off rates. Leaf micromorphology impacted PM accumulation, although effects varied among species, and parameters such as surface roughness, stomatal density, and leaf size did not consistently affect PM deposition. The composition of deposited particles encompassed natural, vehicular, salt, and unclassified agglomerates, with minimal changes after rainfall. Air Pollution Tolerance Index (APTI) assessments revealed that I.sempervirens displayed the highest air pollution tolerance, while O.vulgare had the lowest. APTI showed a moderate positive correlation with PM deposition across all fractions. The study concluded that the interplay of macro and micromorphology in green wall plant species determines their PM removal potential. Further research is needed to identify the key leaf characteristics for optimal green wall species selection for effective PM removal.