Many dispersion models are available to simulate the mass concentrations of particulate matter in an urban environment. Still, fewer are capable of simulating the effect of green infrastructure (GI) on the airborne nanoparticles represented by total particle number concentration (ToNC). We developed an integrated approach capable of simulating the dispersion of airborne nanoparticles under the various scenarios of green infrastructure (GI). We demonstrated the usefulness of this approach by simulating a high-resolution spatial (250 × 250 m) concentration of traffic-emitted airborne nanoparticles at an urban scale under eight GI urban planning scenarios: the base year 2015 (2015-Rl-GI); business-as-usual for 2039 (2039-BAU-GI); three hypothetical future scenarios with maximum possible coniferous (2039-HMax-Con), deciduous (2039-HMax-Dec) trees, and grassland (2039-HMax-Grl) over the available land; and three alternative future scenarios by considering coniferous (2039-HNR-Con), deciduous (2039-HNR-Dec) trees, and grassland (2039-HNR-Grl) around traffic lanes. We assessed both the parametric and structural uncertainties due to particle transformation processes (nucleation, coagulation and deposition) and uncertainty in particle number emission factors (PNEFs) on ToNC, respectively. We also simulated the combined impact of deposition and aerodynamic dispersion of GI on ToNC reduction. The annual average ToN emission (ToNE) reduced from 5.36 × 10²² (2015) to 2.84 × 10²¹ (2039) particles due to the UK's air quality plan in future. Parametric uncertainty due to variable PNEFs might cause variation in annual ToNC from −57% to +60%. However, structural uncertainties in ToNC, due to particle transformation processes were up to −12%, −11% and +0.14% for deposition, coagulation, and nucleation, respectively. The annual ToN deposition (ToND) and concentration were 28–4800 × 10¹⁹ particles and 3.94–19.10 × 10³ # cm⁻³, respectively, depending on the percentage share of GI type and annual traffic emissions. Planting maximum coniferous trees (2039-HMax-Con) simulated maximum reduction in annual ToNC. Coniferous trees near traffic lanes (2039-HNR-Con) also found to be more effective to reduce annual ToNC.
Built-up environments limit air pollution dispersion in street canyons and lead to complex trade-offs between green infrastructure (GI) usage and its potential to reduce near-road exposure. This study evaluated the effects of an evergreen hedge on the distribution of particulate matter (PM1, PM2.5, PM10), black carbon (BC) and particle number concentrations (PNCs) in a street canyon in West London. Instrumentation was deployed around the hedge at 13 fixed locations to assess the impact of the hedge on vertical and horizontal concentration distributions. Changes in concentrations behind the hedge were measured with reference to the corresponding sampling point in front of the hedge for all sets of measurements. Results showed a significant reduction in vertical concentrations between 1 and 1.7 m height, with maximum reductions of -16% (PM1 and PM10) and -17% (PM2.5) at ∼1 m height. Horizontal concentrations revealed two zones between the building façade and the hedge, with opposite trends: (i) close to hedge (within 0.2 m), where a reduction of PM1 and PM2.5 was observed, possibly due to dilution, deposition and the barrier effect; and (ii) 0.2-3 m from the hedge, showing an increase of 13-37% (PM1) and 7-21% (PM2.5), possibly due to the blockage effect of the building, restricting dispersion. BC showed a significant reduction at breathing height (1.5 m) of between -7 and -50%, followed by -15% for PNCs in the 0.02-1 µm size range. The ELPI + analyser showed a peak of ∼30 nm. The presence of the hedge led to a ∼39 ± 32% decrease in total PNCs (0.006-10 µm), suggesting a greater removal in different modes, such as a 83 ± 12% reduction in nucleation mode (0.006-0.030 µm), 74 ± 15% in ultrafine (≤0.1 µm), and 34 ± 30% in accumulation mode (0.03-0.3 µm). These findings indicate graded filtering of particles by GI in a near-road street canyon environment. This insight will guide the improved design of GI barriers and the validation of microscale dispersion models.
There is a lack of clear guidance regarding the optimal configuration and plant composition of green infrastructure (GI) for improved air quality at local scale. This study aimed to co-develop (i.e. with feedback from end-users) a public engagement and decision support tool, to facilitate effective GI design and management for air pollution abatement. The underlying model uses user-directed input data (e.g. road type) to generate output recommendations (e.g. plant species) and pollution reduction projections. This model was computerised as a user-friendly tool named HedgeDATE (Hedge Design for Abatement of Traffic Emissions). A workshop generated feedback on HedgeDATE, which we also discuss. We found that data from the literature can be synthesised to predict air pollutant exposure and abatement in open road environments. However, further research is required to describe pollutant decay profiles under more diverse roadside scenarios (e.g. split-level terrain) and to strengthen projections. Workshop findings validated the HedgeDATE concept and indicated scope for uptake. End-user feedback was generally positive, although potential improvements were identified. For HedgeDATE to be made relevant for practitioners and decision-makers, future iterations will require enhanced applicability and functionality. This work sets the foundation for the development of advanced GI design tools for reduced pollution exposure.
Green infrastructure (GI) is effective in reducing PM concentrations in near-road environments, but how such reductions in concentration compared with relative respiratory deposition doses (RDD) is rarely discussed. We quantified variations in RDD in the presence of three GI types (trees, hedges, and tree-hedge combinations), and compared them with PM reduced by the GI under different wind directions and seasons through the assessment of data collected during multiple field campaigns. We also studied three scenarios (sitting, walking, running) to investigate RDD doses in adults and children during different possible activities in the presence of GI at public parks or gardens or in front of houses. Finally, we illustrated particle mass distribution before and after different GI configurations to explore the reasons for variations in RDD. Changes in RDD displayed a trend of %ΔRDDPM10 > %ΔRDDPM2.5 = %ΔRDDPM1, compared to the changes in PM concentrations of %ΔPM1 >%ΔPM10 >%ΔPM2.5. A maximum reduction (25%) in RDD was observed for PM10 in the presence of the tree-hedge combination, and this combination emerged as the most effective GI type in lowering the RDD dose. The changes in ratios of mass median diameter and deposition fraction of roughly ±0.2 before and after the GI led to differences between %∆PM and %∆RDD. Cross-winds (perpendicular to road direction) led to greater variations between %∆PM and %∆RDD, whereas parallel winds (along the road) led to similar variations in %∆RDD and %∆PM. Particle mass distributions revealed the absence of a peak around particle diameter 2.5µm in the presence of GI. The highest difference in RDD behind GI was observed in the presence of a hedge-tree combination during different physical activities.
Street canyons are generally highly polluted urban environments due to high traffic emissions and impeded dispersion. Green infrastructure (GI) is one potential passive control system for air pollution in street canyons, yet optimum GI design is currently unclear. This review consolidates findings from previous research on GI in street canyons and assesses the suitability of different GI forms in terms of local air quality improvement. Studies on the effects of various GI options (trees, hedges, green walls, green screens and green roofs) are critically evaluated, findings are synthesised, and possible recommendations are summarised. In addition, various measurement methods used for quantifying the effectiveness of street greening for air pollution reduction are analysed. Finally, we explore the findings of studies that have compared plant species for pollution mitigation. We conclude that the influences of different GI options on air quality in street canyons depend on street canyon geometry, meteorological conditions and vegetation characteristics. Green walls, green screens and green roofs are potentially viable GI options in existing street canyons, where there is typically a lack of available planting space. Particle deposition to leaves is usually quantified by leaf washing experiments or by microscopy imaging techniques, the latter of which indicates size distribution and is more accurate. The pollutant reduction capacity of a plant species largely depends on its macromorphology in relation to the physical environment. Certain micromorphological leaf traits also positively correlate with deposition, including grooves, ridges, trichomes, stomatal density and epicuticular wax amount. The complexity of street canyon environments and the limited number of previous studies on novel forms of GI in street canyons mean that offering specific recommendations is currently unfeasible. This review highlights a need for further research, particularly on green walls and green screens, to substantiate their efficacy and investigate technical considerations.
Urban Heat Island (UHI) is posing a significant challenge due to growing urbanisations across the world. Green infrastructure (GI) is popularly used for mitigating the impact of UHI, but knowledge on their optimal use is yet evolving. The UHI effect for large cities have received substantial attention previously. However, the corresponding effect is mostly unknown for towns, where appreciable parts of the population live, in Europe and elsewhere. Therefore, we analysed the possible impact of three vegetation types on UHI under numerous scenarios: baseline/current GI cover (BGI); hypothetical scenario without GI cover (HGI-No); three alternative hypothetical scenarios considering maximum green roofs (HGR-Max), grasslands (HG-Max) and trees (HT-Max) using a dispersion model ADMS-Temperature and Humidity model (ADMS-TH), taking a UK town (Guildford) as a case study area. Differences in an ambient temperature between three different landforms (central urban area, an urban park, and suburban residential area) were also explored. Under all scenarios, the night-time (0200h; local time) showed a higher temperature increase, up to 1.315°C due to the lowest atmospheric temperature. The highest average temperature perturbation (change in ambient temperature) was 0.563°C under HGI-No scenario, followed by HG-Max (0.400°C), BGI (0.343°C), HGR-Max (0.326°C) and HT-Max (0.277°C). Furthermore, the central urban area experienced a 0.371°C and 0.401°C higher ambient temperature compared with its nearby suburban residential area and urban park, respectively. The results allow to conclude that temperature perturbations in urban environments are highly dependent on the type of GI, anthropogenic heat sources (buildings and vehicles) and the percentage of land covered by GI. Among all other forms of GI, trees were the best-suited GI which can play a viable role in reducing the UHI. Green roofs can act as an additional mitigation measure for the reduction of UHI at city scale if large areas are covered.