CFD modelling of the aerodynamic effect of trees on urban air pollution dispersion

Impact of single and combined local air pollution mitigation measures in an urban environment

Urban air pollution is one of the most important environmental problems for human health and several strategies have been developed for its mitigation. The objective of this study is to assess the impact of single and combined mitigation measures on concentrations of air pollutants emitted by traffic at pedestrian level in the same urban environment. The effectiveness of different scenarios of green infrastructure (GI), the implementation of photocatalytic materials and traffic low emission zones (LEZ) are investigated, as well as several combinations of LEZ and GI. A wide set of scenarios is simulated through Computational Fluid Dynamics (CFD) modelling for two different wind directions (perpendicular (0°) and 45° wind directions). Wind flow for the BASE scenario without any measure implemented was previously evaluated using wind-tunnel measurements. Air pollutant concentrations for this scenario are compared with the results obtained from the different mitigation scenarios. Reduction of traffic emissions through LEZ is found to be the most effective single measure to improve local air quality. However, GI enhances the effects of LEZ, which makes the combination of LEZ + GI a very effective measure. The effectiveness of this combination depends on the GI layout, the intensity of emission reduction in the LEZ and the traffic diversion in streets surrounding the LEZ. These findings, in line with previous literature, suggest that the implementation of GI may increase air pollutant concentrations at pedestrian level for some cases. However, this study highlights that this negative effect on air quality can turn into positive when used in combination with reductions of local traffic emissions.

Associations between community green view index and fine particulate matter from Airboxes

Urban greenery can help to improve air quality, reduce health risks and create healthy livable urban communities. This study aimed to explore the role of urban greenery in reducing air pollution at the community level in Tainan City, Taiwan, using air quality sensors and street-view imagery. We also collected the number of road trees around each air quality sensor site and identified the species that were best at absorbing PM2.5. Three greenness metrics were used to assess community greenery in this study: two Normalized Difference Vegetation Indices (NDVI) from different satellites and the Green View Index (GVI) from Google Street View (GSV) images. Land-use Regression (LUR) was used for statistical analysis. The results showed that a higher GVI within a 500 m buffer was significantly associated with decreased PM2.5. Neither NDVI metrics within a 500 m circular buffer were significantly associated with decreased PM2.5. Evergreen trees were significantly associated with lower ambient PM2.5, compared with deciduous and semi-deciduous trees. Because localized changes in air quality profoundly affect public health and environmental equity, our findings provide evidence for future urban community greenspace planning and its beneficial impacts on reducing air pollution.

The impact of dynamic traffic and wind conditions on green infrastructure performance to improve local air quality

Road traffic represents the dominant source of air pollution in urban street canyons. Local wind conditions greatly impacts the dispersion of these pollutants, yet street trees complicate ventilation in such settings. This case study adopts a novel modelling framework to account for dynamic traffic and wind conditions to identify the optimal street tree configuration that prevents a deterioration in air quality. Measurement data from a shallow to moderately deep street canyon (average 0.5 H/W aspect ratio and four lanes of 1-way traffic) in Dublin, Ireland was used for model calibration. The computational fluid dynamics (CFD) models were used to examine scenarios of dynamic traffic flows within each traffic lane with respect to its impact on local PM2.5 concentrations on adjacent footpaths, segmenting air quality monitoring results based on different wind conditions for model calibration. The monitoring campaign identified higher PM2.5 concentrations on the leeward (north) footpath, with average differences of 14.1 % (2.15 μg/m3) for early evening peaks. The modelling results demonstrated how street trees negatively impacted air quality on the windward footpath in parallel wind conditions regardless of leaf area density (LAD) or tree spacing, with mixed results observed on the leeward footpath in varying traffic flows and wind speeds. Perpendicular wind direction models and high wind speed exacerbated poor air quality on the windward footpath for all tree spacing models, while improving the air quality on the leeward footpath. The findings advise against planting high-LAD trees in this type of street, with a minimum of 20 m spacing for low-LAD trees to balance reducing local air pollution and ventilation capacity in the street. This study highlights the complexities of those in key decision-marking roles and demonstrates the need to adopt a transparent framework to ensure adequate modelling evidence can inform tree planting in city streets.

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD - Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations. The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

Differences in particulate matter retention and leaf microstructures of 10 plants in different urban environments in Lanzhou City

Particulate matter (PM) is a major primary environmental air pollutant and poses a threat to human health. Differences in the environment and leaf microstructures of plants will result in varying abilities to retain PM, but the effects of changes in these factors on PM retention are not yet well understood. This study selected 10 plant species in four urban areas (sports field, park, residential green space, and greenway) as the study objects. The amount of retained PM by the different species was measured, and the leaf microstructures were observed. It was found that the environment significantly affected both PM retention and leaf microstructure. The ranking of PM retention in the 10 species in four areas was greenway > residential green space > park > sports field. The ranking of average stomatal width and length was park > sports field > residential green space > greenway, while that of average stomatal density was greenway > residential green space > park > sports field. Different environments affected the length and density of trichomes in the leaves. These changes represented the adaptation of plant species to the growth environment. The stomata and grooves of the leaf surface significantly affected the ability of plants to retain PM. The amount of PM retained by different species varied. In all four urban areas, Prunus × cistena N. E. Hansen ex Koehne (purple leaf sand cherry), Prunus cerasifera Ehrhart f. atropurpurea (Jacq.) Rehd. (cherry plum), Buxus sinica var. parvifolia M. Cheng (common boxwood), and Ligustrum × vicaryi Rehder (golden privet) showed strong PM retention. The results of this study will provide information for planners and urban managers for the selection of plant species.

Air pollution dispersion in Hail city: Climate and urban topography impact

Due to the rapid urbanization of many cities around the world, industrial manufacturing plants have been expanded quickly, leading to the discharge of large amounts of pollutants into the environment. Consequently, a significant deterioration in local air quality is recorded, representing a high health risk for the city's residents. In this context, the main objective of this work is to understand the dispersion of gas pollution in high-density urban environments, specifically the Hail region of Saudi Arabia. The simulations carried out with Ansys Fluent 19.0 were based on actual climatic conditions, with particular attention paid to accurately reproducing the exact topography of the study area. The main results concern the characterization of flow behavior and the dispersion of gas pollutants emitted by power plants. Several factors, including building geometry and wind speed, are examined. The study reveals that for a reference wind speed of more than 7 m/s, gaseous pollution exhibits a significant tendency to accumulate within buildings, resulting in significant concentrations.

Association of SO2/CO exposure and greenness with high blood pressure in children and adolescents: A longitudinal study in China

Introduction

We aimed to investigate the association between greenness around schools, long-term gaseous air pollution exposure (SO₂ and CO), and blood pressure in children and adolescents.

Methods

From 2006 to 2018, a total of 219,956 Chinese children and adolescents aged 7-17 years in Beijing and Zhongshan were included in this longitudinal study. Annual average concentrations of SO₂ and CO and the mean values of normalized difference vegetation index around schools were calculated. We used the generalized estimation equation model, restricted cubic spline model, and Cox model to analyze the health effects.

Results

Among all the subjects, 52,515 had the first onset of HBP. During the follow-up, HBP's cumulative incidence and incidence density were 23.88% and 7.72 per 100 person-year respectively. Exposures to SO₂ and CO were significantly associated with SBP [β = 1.30, 95% CI: (1.26, 1.34) and 0.78 (0.75, 0.81)], DBP [β = 0.81 (0.79, 0.84) and 0.46 (0.44, 0.48)] and HBP [HR = 1.58 (1.57, 1.60) and 1.42 (1.41, 1.43)]. The risks of HBP attributed to SO₂ and CO pollution would be higher in school-aged children in the low greenness group: the attributable fractions (AFs) were 26.31% and 20.04%, but only 13.90% and 17.81% in the higher greenness group. The AFs were also higher for normal-BMI children and adolescents in the low greenness group (AFs = 30.90% and 22.64%, but 14.41% and 18.65% in the high greenness group), while the AFs were not as high as expected for obese children in the low greenness group (AFs = 10.64% and 8.61%), nor was it significantly lower in the high greenness group (AFs = 9.60% and 10.72%).

Discussion

Greenness could alleviate the damage effects of SO₂/CO exposure on the risks of HBP among children and adolescents, and the benefit is BMI sensitivity. It might offer insights for policymakers in making effective official interventions to prevent and control the prevalence of childhood HBP and the future disease burden caused by air pollution.

Effects of Green Network Management of Urban Street Trees on Airborne Particulate Matter (PM2.5) Concentration

Street trees are crucial for air pollutant reduction in urban areas. Herein, we used computational fluid dynamics (CFD) simulation to identify changes in airborne particulate matter (PM_2.5) concentration based on wind characteristics (direction and velocity) and the green network of street trees. The green network was assessed based on composition of the green area of street trees in the central reserve area and between the motor and pedestrian roads. The PM_2.5 concentration varied according to the presence or absence of major reserve planting and the planting structure of the street trees, but not according to the wind direction or velocity. The concentration was lower when the wind direction was 45° (than when the wind direction was 0°), whereas it showed a more significant decrease as the wind velocity increased. Despite variation at each measurement site, the PM_2.5 reduction was generally higher when the central reserve and street trees had a multi-planting structure. Hence, to ensure an effective reduction in the PM_2.5 concentration on motor roads and reduce its negative impact on pedestrians, both arbors and shrubs should be planted in the central reserve area. The study results will serve as reference for managing the green area network and linear green infrastructure in terms of improving the atmospheric environment.

Assessment of gas dispersion near an operating landfill treated by different intermediate covers with soil alone, low-density polyethylene (LLDPE), or ethylene vinyl alcohol (EVOH) geomembrane

This study evaluated the impact area of odiferous gas (i.e., H₂S) dispersion near an operating landfill installed with three different intermediate covers, including soil alone, linear low-density polyethylene (LLDPE), or ethylene vinyl alcohol (EVOH) geomembrane (GM). By using the finite element method employing Reynolds-averaged Navier-Stokes and Fick's Law coupled equations, the performance of the different cover cases for reducing odor dispersion was comparatively evaluated considering environmental factors, including topographic, meteorology, and gas emission. The odor dispersion patterns and the size of affected residents were analyzed for the twelve different scenarios varied with the cover type and seasonal variation. According to the results, it was found that the wind speed affected the time of odor dispersions more with the relatively flat terrain conditions around the landfill but barely affected the size of the dispersion area. Moreover, it was found that the higher concentration (100 ppb) of odor gas is mainly located within a 5.0-km distance from the landfill. Among four seasons, the odor covers the largest area in summer, which is mainly due to the landfill producing more odor gas and giving a higher source concentration in summer. The gas dispersion simulation for different covers showed that the type of covering layer significantly affects the impact area boundary of gas odor. The results showed that the odor area of the LLDPE GM cover case is 1.3% of soil alone case, and the case of EVOH GM is 14.5% of LLDPE GM case. At the same time, the number of residents that may be affected by the odor of the LLDPE GM case and EVOH GM case is 4.81% and 0.63% of soil alone case, respectively.

Association of residential greenness with incident chronic obstructive pulmonary disease: A prospective cohort study in the UK Biobank

BACKGROUND

Residential greenness has been linked to respiratory mortality, but its long-term effect on incident chronic obstructive pulmonary disease (COPD) has rarely been investigated.

METHODS

This prospective cohort study was based on over 350 000 participants aged 38-70 of the UK Biobank, followed from 2006 to 2010 baseline to 2021. COPD cases were ascertained through linkages to health administrative datasets. Residential greenness was measured by satellite-derived normalized difference vegetation index (NDVI) within the 500- and 1 000-m buffer. Effects of greenness on COPD incidence were assessed using Cox proportional hazards models. We also explored mediation by physical activity, particular matter <2.5 μm in aerodynamic diameter (PM_2.5) and nitrogen oxides (NO_x). Restricted cubic spline models were fit to assess exposure-response relationships.

RESULTS

A total of 363 212 individuals (mean [SD] age, 56.2 [8.1] years; 193 181 [53.2 %] women] were included in the analyses. 8 261 COPD cases occurred over 4 287 926 person-years of follow-up. We observed 8% lower COPD risk per IQR increase in NDVI in the 500-m buffer (95% CI: 0.89, 0.95). The association between greenness in the 500-m buffer and COPD were partially mediated by physical activity (1.0%, 95% CI: 0.2%, 1.8%), PM_2.5 (21.0%, 95% CI: 3.7%, 38.4%) and NO_x (17.0%, 95% CI: 2.8%, 31.2%). Similar results were observed for NDVI within 1 000-m buffer.

CONCLUSIONS

Long-term exposure to residential greenness was associated with lower risk of COPD incidence among UK adults. Our findings provide a rationale for greening policies as part of respiratory health promotion efforts.

Computational fluid dynamics-based optimal installation strategy of air purification system to minimize NOX exposure inside a public bus stop

At public bus stops, NO_X pollutants discharged by regularly stopping buses quickly accumulate, exposing waiting passengers to high levels of air pollutants, which creates a threat to public health. The environmental protection agency (EPA) presents air quality standards for NO_X, a significant pollutant that causes lung diseases such as asthma when exposed to the human body. To handle this problem, air purification systems are installed inside bus stops in many public places. However, it is challenging to maintain a low concentration of NO_X inside public bus stops due to the persistent inflow of bus exhaust gas. Therefore, it is crucial to design an optimal location for an air purification system to meet air environment standards for respiratory areas. This study proposed a computational fluid dynamics (CFD)-based optimal installation strategy for an air purification system to minimize NO_X exposure inside a public bus stop. The CFD model was developed to numerically analyze NO₂ exposure with the actual design value for a public bus stop in Ulsan, South Korea. The local NO₂ concentration was evaluated in the human breathing zone. The case study was performed according to the locations of the inlet and outlet of the air purification system. A transient CFD simulation was performed to analyze the effect of the air purification system on pollutants generated from the stationary bus by time flow in various cases. NO₂ concentration and exposure reduction effectiveness (ERE) were analyzed and compared for each case in the breathing zone. In the optimal case, the ERE of NO₂ was confirmed to be 35.9 %, and the NO₂ concentration according to the air quality standards of EPA could be maintained at 0.1 ppm or less. The theoretical framework proposed in this study can be generalized to design air purification systems for general external facilities.

Investigating the impacts of COVID-19 lockdown on air quality, surface Urban Heat Island, air temperature and lighting energy consumption in City of Melbourne

The COVID-19 pandemic has threatened city economies and residents' public health and quality of life. Similar to most cities, Melbourne imposed extreme preventive lockdown measures to address this situation. It would be reasonable to assume that during the two phases of lockdowns, in autumn (March) and winter (June to August) 2020, air quality parameters, air temperature, ^{Surface Urban Heat Island} (SUHI), and lighting energy consumption most likely increased. As such, to test this assumption, Sentinel 5, ERA-5 LAND, Sentinel 1 and 2, NASA SRTM, MODIS Aqua and Terra, and VIIRS satellite imageries are utilized to investigate the alterations of NO₂, SO₂, CO, ^{UV Aerosol Index} (UAI), air temperature, SUHI, and lighting energy consumption factors in the City of Melbourne. Furthermore, satellite imageries of SentiThe results indicate that the change rates of NO₂ (1.17 mol/m²) and CO (1.64 mol/m²) factors were positive. Further, the nighttime SUHI values increased by approximately 0.417 °C during the winter phase of the lockdown, while during the summer phase of the lockdown, the largest negative change rate was in NO₂ (−100.40 mol/m²). By contrast, the largest positive change rate was in SO₂ and SUHI at night. The SO₂ values increased from very low to 330 μm mol/m^2, and the SUHI nighttime values increased by approximately 4.8 °C. From the spatial point of view, this study also shows how the effects on such parameters shifted based on the urban form and land types across the City of Melbourne by using satellite data as a significant resource to analyze the spatial coverage of these factors. The findings of this study demonstrate how air quality factors, SUHI, air temperature, and lighting energy consumption changed from pre-lockdown (2019) to lockdown (2020), offering valuable insights regarding practices for managing SUHI, lighting energy consumption, and air pollution.

How can the built environment affect the impact of autonomous vehicles' operational behaviour on air quality?

Autonomous vehicles (AVs) are pointed out as the technology that will reshape the concept of mobility, with significant implications for the economy, the environment, and society. This fact will bring new challenge to cities urban planning. Research to anticipate the AVs impacts, maximizing their benefits and reduce trade-offs are currently crucial. This work investigates the potential challenges and benefits of gradually replace internal combustion engine human driven vehicles with different penetration rates of AVs - 10%, 30%, 50%, 70%, 90%, and 100% - in urban roads of different characteristics, either in terms of traffic singularities or volumes, and its related implications on air quality. For that purpose, two urban areas with distinct features, Porto and Aveiro, were selected as case studies, and a modelling setup composed of a traffic model, an emission model, and a local air quality model was applied. The results revealed that the AVs benefits are directly linked with the urban design and the road characteristics. In the Aveiro case study, the AVs promoted positive changes with average reductions in daily NOx emissions (compared with the baseline scenario, without AVs) ranging between -2.1% (for C10%) and -7.7% (for C100%). In line with the emissions impacts, positive effects were found on air quality, with average reductions of NO₂ concentrations up to -4% (for C100%). In Porto urban area, slight differences in NOx emissions were obtained (<2%), which implied no changes in the air quality levels. The distinct impact of AVs in the study areas is mostly explained by the traffic light coordination system and directional split distributions in the main roads. These results provide valuable insights to support decision-makers in the definition of strategies that allow the integration of these new emerging technologies in the road infrastructure, considering the features of the urban design, traffic profile and road characteristics.

Impact of Different Combinations of Green Infrastructure Elements on Traffic-Related Pollutant Concentrations in Urban Areas

Urban air quality is a major problem for human health and green infrastructure (GI) is one of the potential mitigation measures used. However, the optimum GI design is still unclear. The purpose of this study is to provide some recommendation that could help in the design of the GI (mainly, the selection of locations and characteristics of trees and hedgerows). Aerodynamic and deposition effects of each vegetation element of different GI scenarios are investigated. Computational fluid dynamics (CFD) simulations of a wide set of GI scenarios in an idealized three-dimensional urban environment are performed. In conclusion, it was found that trees in the middle of the avenue (median strip) reduce street ventilation, and traffic-related pollutant concentrations increase, in particular for streets parallel to the wind. Trees in the sidewalks act as a barrier for pollutants emitted outside, specifically for a 45° wind direction. Regarding hedgerows, the most important effect on air quality is deposition and the effects of green walls and green roofs are limited to their proximity to the building surfaces.

Estimates of population exposure to atmospheric pollution and health-related externalities in a real city: The impact of spatial resolution on the accuracy of results

Health impacts of atmospheric pollution is an important issue in urban environments. Its magnitude depends on population exposure which have been frequently estimated by considering different approaches relating pollutant concentration and population exposed to it. However, the uncertainties due to the spatial resolution of the model used to estimate the pollutant concentration or due to the lack of representativeness of urban air quality monitoring station (AQMS) have not been evaluated in detail. In this context, NO₂ annual average concentration at pedestrian level in the whole city of Pamplona (Spain) modelled at high spatial resolution (~1 m) by Computational Fluid Dynamic (CFD) simulations is used to estimate the total population exposure and health-related externalities by using different approaches. Air pollutant concentration and population are aggregated at different spatial resolutions ranging from a horizontal grid cell size of 100 m × 100 m to a coarser resolution where the whole city is covered by only one cell (6 km × 5 km). In addition, concentrations at AQMS locations are also extracted to assess the representativeness of those AQMS. The case with a spatial resolution of 100 m × 100 m for both pollutant-concentration distribution and population data is used as a reference (Base case) and compared with those obtained with the other approaches. This study indicates that the spatial resolution of concentration and population distribution in the city should be 1 km × 1 km or finer to obtain appropriate estimates of total population exposure (underestimations <13%) and health-related externalities (underestimations <37%). For the cases with coarser resolutions, a strong underestimation of total population exposure (>31%) and health-related externalities (>76%) was found. On the other hand, the use of AQMS concentrations can induce important errors due to the limited spatial representativeness, in particular in terms of population exposure.

Field measurement and numerical simulation of dust migration in a high-rise building of the mine hoisting system

Dust pollutants generated from the coal transfer process in a high-rise building of the mine hoisting system not only undermine the operating environment but also reduce the surrounding air quality. Therefore, this study aimed to determine the spatiotemporal distribution of coal dust in the high-rise buildings using field measurement and numerical simulation. Based on the discrete phase model (DPM), the dust migration process under the hybrid ventilation system was investigated in detail. Then, the feasibility of the established model to predict the spatiotemporal distribution of dust pollutants was proven through the measurements of both the airflow and the dust concentration. The present study showed that dust distribution is not uniform in time and space, which also differs for different floors. The dust concentration of the 3^rd floor is relatively larger when compared with those of other floors. The dust concentration increases for the upper floors when the upward air velocity increases, while those of the lower floors are not always low due to the backflows, particularly for the 2^nd floor. PM_2.5 takes up more than 20% of all discharged particles.

Analyzing Air Pollutant Reduction Possibilities in the City of Zagreb

This paper aims to present possible areas to plant different vegetation types near traffic jams to reduce air pollution in the capital of Croatia, the city of Zagreb. Based on main traffic road and random forest machine learning using WorldView-2 European cities data, potential areas are established. It is seen that, based on a 10 m buffer, there is a possible planting area of more than 220,000 square meters, and based on 15 m buffer, there is a possible planting area of more than 410,000 square meters. The proposed plants are Viburnum lucidum, Photinia x fraseri, Euonymus japonicus, Tilia cordata, Aesculus hippocastanum, Pinus sp., Taxus baccata, Populus alba, Quercus robur, Betula pendula, which are characteristic for urban areas in Croatia. The planting of proposed trees may result in an increase of 3–5% in the total trees in the city of Zagreb. Although similar research has been published, this paper presents novelty findings from combined machine learning methods for defining green urban areas. Additionally, this paper presents original results for this region.

Effects of press-in airflow rate and the distance between the pressure duct and the side wall on ventilation dust suppression performance in an excavating tunnel

The efficiency of mine excavation has been significantly enhanced by continuing improvements in tunneling capabilities; however, this has also resulted in serious environmental pollution and greater safety risks for workers. To ensure safe production, the focus of this study is on the effect of varying the air pressure and the distance between the air pressure cylinder and the side wall settings on dust dispersion behavior and dust control in excavated tunnels. We also investigated temporal-spatial dust diffusion rules in tunnels by combining numerical simulation data with field measurement results. Through further analysis, when the pressure air volume and the exhaust air volume are both equal to 250 m³/min, the dust diffusion distance could be fitted as: [Formula: see text]. When the exhaust air volume is equal to 250 m³/min, dust control effects were improved as the pressure air volume decreased, becoming optimal when the pressure air volume dropped to 150 m³/min. Under these conditions, areas of high dust pollution were contained within 16 m of the cutting face, and the dust diffusion distance satisfied the formula: [Formula: see text]. When the pressure air volume is fixed, the change of the distance between the pressure air cylinder and the side wall has little effect on the dust diffusion. When the distance is 1.5 m, the dust control effect is the best, and the high dust pollution area is controlled within 14 m of the cutting surface. This alleviated dust pollution to a certain degree, thereby enhancing the air quality and ensuring safer production. This study provides a new understanding of the environmentally sustainable development of tunnels and is of great significance for clean production.

High Spatial Resolution Assessment of the Effect of the Spanish National Air Pollution Control Programme on Street-Level NO2 Concentrations in Three Neighborhoods of Madrid (Spain) Using Mesoscale and CFD Modelling

Current European legislation aims to reduce the air pollutants emitted by European countries in the coming years. In this context, this article studies the effects on air quality of the measures considered for 2030 in the Spanish National Air Pollution Control Programme (NAPCP). Three different emission scenarios are investigated: a scenario with the emissions in 2016 and two other scenarios, one with existing measures in the current legislation (WEM2030) and another one considering the additional measures of NAPCP (WAM2030). Previous studies have addressed this issue at a national level, but this study assesses the impact at the street scale in three neighborhoods in Madrid, Spain. NO2 concentrations are modelled at high spatial resolution by means of a methodology based on Computational Fluid Dynamic (CFD) simulations driven by mesoscale meteorological and air quality modelling. Spatial averages of annual mean NO2 concentrations are only estimated to be below 40 µg/m3 in all three neighborhoods for the WAM2030 emission scenarios. However, for two of the three neighborhoods, there are still zones (4–12% of the study areas) where the annual concentration is higher than 40 µg/m3. This highlights the importance of considering microscale simulations to assess the impacts of emission reduction measures on urban air quality.

Evaluation of Vegetation Configuration Models for Managing Particulate Matter along the Urban Street Environment

As a green infrastructure component, urban street vegetation is increasingly being utilized to mitigate air pollution, control microclimates, and provide aesthetic and ecological benefits. This study investigated the effect of vegetation configurations on particulate matter (PM) flows for pedestrians in road traffic environments via a computation fluid dynamics analysis based on the road width (four and eight-lane) and vegetation configuration (single-, multi-layer planting, and vegetation barrier). Airflow changes due to vegetation influenced PM inflow into the sidewalk. Vegetation between roadways and sidewalks were effective at reducing PM concentrations. Compared to single-layer planting (trees only), planting structures capable of separating sidewalk and roadway airflows, such as a multi-layer planting vegetation barrier (trees and shrubs), were more effective at minimizing PM on the sidewalk; for wider roads, a multi-layer structure was the most effective. Furthermore, along a four-lane road, the appropriate vegetation volume and width for reducing PM based on the breathing height (1.5 m) were 0.6 m3 and 0.4 m, respectively. The appropriate vegetation volume and width around eight-lane roads, were 1.2–1.4 m3 and 0.8–0.93 m, respectively. The results of this study can provide appropriate standards for street vegetation design to reduce PM concentrations along sidewalks.

Understanding the effects of roadside hedges on the horizontal and vertical distributions of air pollutants in street canyons

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, PM_2.5, 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% (PM_2.5) 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 PM_2.5 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% (PM_2.5), 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.

A review of strategies for mitigating roadside air pollution in urban street canyons

Urban street canyons formed by high-rise buildings restrict the dispersion of vehicle emissions, which pose severe health risks to the public by aggravating roadside air quality. However, this issue is often overlooked in city planning. This paper reviews the mechanisms controlling vehicle emission dispersion in urban street canyons and the strategies for managing roadside air pollution. Studies have shown that air pollution hotspots are not all attributed to heavy traffic and proper urban design can mitigate air pollution. The key factors include traffic conditions, canyon geometry, weather conditions and chemical reactions. Two categories of mitigation strategies are identified, namely traffic interventions and city planning. Popular traffic interventions for street canyons include low emission zones and congestion charges which can moderately improve roadside air quality. In comparison, city planning in terms of building geometry can significantly promote pollutant dispersion in street canyons. General design guidelines, such as lower canyon aspect ratio, alignment between streets and prevailing winds, non-uniform building heights and ground-level building porosity, may be encompassed in new development. Concurrently, in-street barriers are widely applicable to rectify the poor roadside air quality in existing street canyons. They are broadly classified into porous (e.g. trees and hedges) and solid (e.g. kerbside parked cars, noise fences and viaducts) barriers that utilize their aerodynamic advantages to ease roadside air pollution. Post-evaluations are needed to review these strategies by real-world field experiments and more detailed modelling in the practical perspective.

Automated Simulation Framework for Urban Wind Environments Based on Aerial Point Clouds and Deep Learning

Computational fluid dynamics (CFD) simulation is a core component of wind engineering assessment for urban planning and architecture. CFD simulations require clean and low-complexity models. Existing modeling methods rely on static data from geographic information systems along with manual efforts. They are extraordinarily time-consuming and have difficulties accurately incorporating the up-to-date information of a target area into the flow model. This paper proposes an automated simulation framework with superior modeling efficiency and accuracy. The framework adopts aerial point clouds and an integrated two-dimensional and three-dimensional (3D) deep learning technique, with four operational modules: data acquisition and preprocessing, point cloud segmentation based on deep learning, geometric 3D reconstruction, and CFD simulation. The advantages of the framework are demonstrated through a case study of a local area in Shenzhen, China.

Estimates of pedestrian exposure to atmospheric pollution using high-resolution modelling in a real traffic hot-spot

Atmospheric pollution is a very relevant risk for the human health, in particular in urban environments, where most people lives and high levels of pollution are found. Population exposure is traditionally estimated through concentration recorded at air quality monitoring stations (AQMS) or modelled at a spatial resolution of the order of 1 km². However, these methodologies have limitations in urban areas where strong gradients of concentration, even in the same street, exist. In addition, the movements of pedestrians make difficult to compute reliable estimates of pollutant concentration to which people are exposed to. In this context, the main objective of this study is to estimate the exposure of pedestrians to ambient nitrogen oxides (NOx) concentrations with high spatial resolution in a real urban traffic hot-spot under different methodologies. To achieve this objective, a novel methodology which combines high-resolution NOx concentrations from computational fluid dynamic (CFD) simulations with the pedestrian flows obtained by pedestrian mobility microsimulations is applied to an urban area of Madrid, Spain. High-resolution maps show pedestrian exposure peaks, at bus stops and crosswalks, that cannot be captured by the simpler methods based on spatial average concentration (SAC) or concentration measured in an AQMS. Total daily exposure obtained is 1.19 · 10⁹ person s μg m^-3, while SAC and AQMS concentration methods yielded 9-23% and 30-40% lower values. In conclusion, the proposed methodology allows to determine the areas with higher exposure in order to design local strategies to reduce the impact on human health. In addition, from a more general point of view, the total exposure in the studied area is better estimated by using spatial average concentration than through concentration recorded by AQMS. The assessment of the spatial representative of AQMS becomes necessary to use AQMS concentration to evaluate air quality and population exposure of an urban area.

Regulating and Cultural Ecosystem Services of Urban Green Infrastructure in the Nordic Countries: A Systematic Review

In the Nordic countries (Denmark, Finland, Iceland, Norway and Sweden), the Urban Green Infrastructure (UGI) has been traditionally targeted at reducing flood risk. However, other Ecosystem Services (ES) became increasingly relevant in response to the challenges of urbanization and climate change. In total, 90 scientific articles addressing ES considered crucial contributions to the quality of life in cities are reviewed. These are classified as (1) regulating ES that minimize hazards such as heat, floods, air pollution and noise, and (2) cultural ES that promote well-being and health. We conclude that the planning and design of UGI should balance both the provision of ES and their side effects and disservices, aspects that seem to have been only marginally investigated. Climate-sensitive planning practices are critical to guarantee that seasonal climate variability is accounted for at high-latitude regions. Nevertheless, diverging and seemingly inconsistent findings, together with gaps in the understanding of long-term effects, create obstacles for practitioners. Additionally, the limited involvement of end users points to a need of better engagement and communication, which in overall call for more collaborative research. Close relationships and interactions among different ES provided by urban greenery were found, yet few studies attempted an integrated evaluation. We argue that promoting interdisciplinary studies is fundamental to attain a holistic understanding of how plant traits affect the resulting ES; of the synergies between biophysical, physiological and psychological processes; and of the potential disservices of UGI, specifically in Nordic cities.

Green infrastructure for air quality improvement in street canyons

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.

Increasing Green Infrastructure in Cities: Impact on Ambient Temperature, Air Quality and Heat-Related Mortality and Morbidity

Urban vegetation provides undeniable benefits to urban climate, health, thermal comfort and environmental quality of cities and represents one of the most considered urban heat mitigation measures. Despite the plethora of available scientific information, very little is known about the holistic and global impact of a potential increase of urban green infrastructure (GI) on urban climate, environmental quality and health, and their synergies and trade-offs. There is a need to evaluate globally the extent to which additional GI provides benefits and quantify the problems arising from the deployment of additional greenery in cities which are usually overlooked or neglected. The present paper has reviewed and analysed 55 fully evaluated scenarios and case studies investigating the impact of additional GI on urban temperature, air pollution and health for 39 cities. Statistically significant correlations between the percentage increase of the urban GI and the peak daily and night ambient temperatures are obtained. The average maximum peak daily and night-time temperature drop may not exceed 1.8 and 2.3 °C respectively, even for a maximum GI fraction. In parallel, a statistically significant correlation between the peak daily temperature decrease caused by higher GI fractions and heat-related mortality is found. When the peak daily temperature drops by 0.1 °C, then the percentage of heat-related mortality decreases on average by 3.0% The impact of additional urban GI on the concentration of urban pollutants is analysed, and the main parameters contributing to decrease or increase of the pollutants’ concentration are presented.

Autonomous vehicles opportunities for cities air quality

The impacts of autonomous vehicles (AV) on safety, energy and atmospheric emissions have been recognised to be important issues, but an air quality impact assessment is missing. In this study, by using a numerical modelling approach, the impact of AV on the air quality of a medium-sized Portuguese urban area was evaluated. For that, the air pollutants nitrogen oxides (NOx) and carbon dioxide (CO₂) were considered and three scenarios were developed: i) a baseline scenario; ii) an autonomous scenario, assuming an AV market penetration rate of 30%; and iii) an electric autonomous scenario, taking into account that those 30% of AV are pure battery electric cars. A modelling system composed by a road traffic model, a road transport emission model and a Computational Fluid Dynamics air quality model was used. The autonomous scenario promoted an increase of both NOx (+1.8%) and CO₂ (+0.7%) emissions, while the electric autonomous scenario resulted in emission reductions of about 30% for both air pollutants. In terms of air quality, distinct patterns were found: i) the autonomous scenario promoted both increases and decreases of NOx concentrations; and ii) the electric autonomous scenario promoted a widespread reduction of NOx concentrations (with an average value of -4%). Overall the results showed that AV have the potential to improve urban air quality, but, further research is needed to enrich the findings of this work.

Large eddy simulation of vehicle emissions dispersion: Implications for on-road remote sensing measurements

On-road remote sensing technology measures the concentration ratios of pollutants over CO₂ in the exhaust plume in half a second when a vehicle passes by a measurement site, providing a rapid, non-intrusive and economic tool for vehicle emissions monitoring and control. A key assumption in such measurement is that the emission ratios are constant for a given plume. However, there is a lack of study on this assumption, whose validity could be affected by a number of factors, especially the engine operating conditions and turbulence. To guide the development of the next-generation remote sensing system, this study is conducted to investigate the effects of various factors on the emissions dispersion process in the vehicle near-wake region and their effects on remote sensing measurement. The emissions dispersion process is modelled using Large Eddy Simulation (LES). The studied factors include the height of the remote sensing beam, vehicle speed, acceleration and side wind. The results show that the measurable CO₂ and NO exhaust plumes are relatively short at 30 km/h cruising speed, indicating that a large percentage of remote sensing readings within the measurement duration (0.5 s) are below the sensor detection limit which would distort the derived emission ratio. In addition, the valid measurement region of NO/CO₂ emission ratio is even shorter than the measurable plume and is at the tailpipe height. The effect of vehicle speed (30-90 km/h) on the measurable plume length is insignificant. Under deceleration condition, the length of the valid NO/CO₂ measurement region is shorter than under cruising and acceleration conditions. Side winds from the far-tailpipe direction have a significant effect on remote sensing measurements. The implications of these findings are discussed and possible solutions to improve the accuracy of remote sensing measurement are proposed.

Association between residential greenness and exposure to volatile organic compounds

Residential proximity to vegetation and plants is associated with many health benefits, including reduced risk of cardiovascular disease, diabetes and mental stress. Although the mechanisms by which proximity to greenness affects health remain unclear, plants have been shown to remove particulate air pollution. However, the association between residential-area vegetation and exposure to volatile organic chemicals (VOCs) has not been investigated. We recruited a cohort of 213 non-smoking individuals and estimated peak, cumulative, and contemporaneous greenery using satellite-derived normalized difference vegetation index (NDVI) near their residence. We found that the urinary metabolites of exposure to VOCs - acrolein, acrylamide, acrylonitrile, benzene, 1-bromopropane, propylene oxide were inversely associated (7-31% lower) with 0.1 higher peak NDVI values within 100 m radius of the participants' home. These associations were significant at radii ranging from 25 to 300 m. Strongest associations were observed within a 200 m radius, where VOC metabolites were 22% lower per 0.1 unit higher NDVI. Of the 18 measured urinary metabolites, 7 were positively associated with variation of greenness within a 200 m radius of homes. The percent of tree canopy and street trees around participants' residence were less strongly associated with metabolite levels. The associations between urinary VOC metabolites and residential NDVI values were stronger in winter than in summer, and in participants who were more educated, White, and those who lived close to areas of high traffic. These findings suggest high levels of residential greenness are associated with lower VOC exposure, particularly in winter.

Effect of Street Canyon Shape and Tree Layout on Pollutant Diffusion under Real Tree Model

Trees have a significant impact on the airflow and pollutant diffusion in the street canyon and are directly related to the comfort and health of residents. In this paper, OpenFOAM is used for simulating the airflow and pollutant diffusion in the street canyon at different height–width ratios and tree layouts. Different from the drag source model in the previous numerical simulation, this study focuses on the characterization of the blocking effect of tree branches on airflow by using more precise and real tree models. It is found that the airflow is blocked by the tree branches in the canopy, resulting in slower airflow and varying velocity direction; the air flows in the pore area between trees more easily, and the vortex centers are different in cases where the street canyon shape and tree layout are different. Low-velocity airflow distributes around and between two tree canopies, especially under the influence of two trees with different spacing. At the height of the pedestrian, the tree branches change the vortex structure of airflow, and thereby high pollutant concentration distribution on both sides of the bottom of the leeward side of the street canyon changes constantly. In the street canyon, the small change in tree spacing has a very limited influence on the pollutant concentration. The street canyon has the lowest average pollutant concentration at the largest y-axis direction spacing between two trees.

A CFD analysis of the pollutant dispersion from cooling towers with various configurations in the lower region of atmospheric boundary layer

The mixing and merging of buoyant plumes originating from multiple small cooling towers into the atmosphere is numerically investigated. The effects of different arrangements of cooling towers as well as outlet geometries on the mixing of the plumes are examined. The side by side and tandem arrangements of two sources and also two types of multi-flue cooling towers are considered. The various ways by which the counter rotating vortex pair, as the dominant mechanism, affect the flow pattern in each aforementioned configuration are investigated. For tandem arrangement, far from sources, the outlet flow of the downstream cooling tower surrounds the plumes originating from the upstream cooling tower and in the region near the cooling towers, the pollutants are mostly originated from the upstream cooling tower. Maximum pollutant concentrations at distances 10 and 40 times the diameter downstream of the leading cooling tower increase by 67% and 29% with respect to those of a single cooling tower, respectively. For the side by side arrangement, the counter rotating vortices are stretched due to the large low pressure area created downstream of the cooling towers. Mixing of the plumes with the surrounding air is reduced as a result of contraction of vortices. Maximum contaminant concentrations at distances 10 and 40 times the diameter downstream of the cooling towers increase by 29% and 41% with respect to those of a single tower, respectively. Finally, the differences between flow fields formed around diamond and square configurations of multi-flue cooling towers are extensively discussed.

Estimating the nitrogen source apportionment of Sophora japonica in roadside green spaces using stable isotope

It is generally accepted that urban vegetation absorbs air pollutants resulting in improved air quality. However, limited work has provided experimental data that can be used to quantify this. In this study, Sophora japonica in the near-road environment was studied, and stable isotopes were used to estimate the proportional contributions of different nitrogen sources to the trees. δ¹⁵N and δ¹⁸O values were quantified for plant (n = 254) and soil samples (n = 86) collected from 12 sampling sites in Beijing. The elemental composition (total nitrogen (TN) and total carbon (TC)) of both samples types, and the ionic concentration (NO₃^- and NH₄⁺) of soil samples were also measured. δ¹⁵N in S. japonica sampled near the road was significantly lower than in samples collected far from the road. Variation of δ¹⁸O, TN, and TC in plant samples could not be explained by the road distance. Using the SIAR Bayesian isotope mixing model and the mixing polygon method, the average proportional contributions of three nitrogen sources for the tree samples among all experiment sites were estimated, with the proportion for each nitrogen source following the order: soil (69.2%) > traffic-related NO_x (19.3%) > dry deposition (11.5%). In addition, the results of the Bayesian model revealed that the nitrogen contribution of traffic-related NO_x at road-adjacent sites (23.0%) was higher than the contribution of traffic-related NO_x at sites far from the road (16.4%). These results indicated that the S. japonica in near-road green spaces was significantly influenced by traffic-related NO_x emissions that were characterized by lower δ¹⁵N values. We found that using the SIAR Bayesian isotope mixing model and mixing polygon method, the potential nitrogen sources of plants could be estimated and the proportional contributions estimated by the model can reflect the plant's ability to absorb air-borne NO_x.

Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models

Green infrastructure (GI) in urban areas may be adopted as a passive control system to reduce air pollutant concentrations. However, current dispersion models offer limited modelling options to evaluate its impact on ambient pollutant concentrations. The scope of this review revolves around the following question: how can GI be considered in readily available dispersion models to allow evaluation of its impacts on pollutant concentrations and health risk assessment? We examined the published literature on the parameterisation of deposition velocities and datasets for both particulate matter and gaseous pollutants that are required for deposition schemes. We evaluated the limitations of different air pollution dispersion models at two spatial scales - microscale (i.e. 10-500 m) and macroscale (i.e. 5-100 km) - in considering the effects of GI on air pollutant concentrations and exposure alteration. We conclude that the deposition schemes that represent GI impacts in detail are complex, resource-intensive, and involve an abundant volume of input data. An appropriate handling of GI characteristics (such as aerodynamic effect, deposition of air pollutants and surface roughness) in dispersion models is necessary for understanding the mechanism of air pollutant concentrations simulation in presence of GI at different spatial scales. The impacts of GI on air pollutant concentrations and health risk assessment (e.g., mortality, morbidity) are partly explored. The i-Tree tool with the BenMap model has been used to estimate the health outcomes of annually-averaged air pollutant removed by deposition over GI canopies at the macroscale. However, studies relating air pollution health risk assessments due to GI-related changes in short-term exposure, via pollutant concentrations redistribution at the microscale and enhanced atmospheric pollutant dilution by increased surface roughness at the macroscale, along with deposition, are rare. Suitable treatments of all physical and chemical processes in coupled dispersion-deposition models and assessments against real-world scenarios are vital for health risk assessments.

Microclimate simulation and model optimization of the effect of roadway green space on atmospheric particulate matter

Urban green spaces have the potential to mitigate and regulate atmospheric pollution. However, existing studies have primarily focused on the adsorption effect of different plants on atmospheric particulate matter (PM), whereas the effect of green space on PM has not been adequately addressed. In this study, the effect of different urban green space structures and configurations on PM was investigated through the 3D computational fluid dynamics (CFD) model ENVI-met by treating the green space as a whole based on field monitoring, and at the same time, the regulatory effect of green space on PM was examined by integrating information about the forest stand, PM concentration, and meteorological factors. The results show that the green space primarily affected wind speed but had no significant effect on relative humidity, temperature, or wind direction (P > 0.05). The PM concentration was significantly positively correlated with the relative humidity (P < 0.01), significantly negatively correlated with temperature (P < 0.05), but not significantly correlated with wind speed and direction (P > 0.05). Comparison with the measured values reveals that the ENVI-met model well reflected the differences in PM concentrations between different green spaces and the effect of green space on PM. In different green space structures, the uniform-type structure performed rather poorly at purifying PM, the concave-shaped structure performed the best, and the purifying effectiveness of the incremental-type and convex-shaped structure of green space was higher in the rear region than in the front region; in contrast, the degressional-type green space structure was prone to cause aggregation of the PM in the middle region. Broadleaf and broadleaf mixed forests had a better purifying effectiveness on PM than did coniferous forests, mixed coniferous forests, and coniferous broadleaf mixed forests. The above results are of great significance for urban planning and maximizing the use of urban green space resources.

How parked cars affect pollutant dispersion at street level in an urban street canyon? A CFD modelling exercise assessing geometrical detailing and pollutant decay rates

Computational fluid dynamics (CFD) is a recognised modelling tool for simulating pollutant dispersion in urban street canyons. These studies require accurate street geometry, sufficient modelling experience and access to software to provide reliable outputs. Parked cars are known to act as a passive barrier and affects pollutant dispersion in an urban street canyon. This modelling study examines the impact of accurate geometrical detailing and suitable mesh sizing on air pollution concentrations measured at street level. Mean steady state concentrations of carbon monoxide (CO) were measured in a reference scenario (without parked cars) and three parked car scenarios. A comparison of the results indicated that individual rectangular blocks and generic car scenarios overestimated pollutant concentrations on the footpaths by up to 25% when compared to the results from the five distinct car designs scenario, with the parked cars acting as a passive barrier in each scenario under different wind conditions. The most notable finding demonstrated how the five distinct car designs scenario presented an increase in CO concentrations on the leeward footpath as opposed to a reduction in the results in the other coarser geometrically detailed scenarios. The parked cars trapped pollutants in each scenario with low wind speeds for perpendicular wind conditions, as higher concentrations were measured on the footpaths. A further examination of pollutant decay rates demonstrated how the geometrical detailing of the parked cars also impacted upon the duration taken for pollutants to escape from the footpath zones. These findings demonstrate how parked cars affects pollutant dispersion and concentrations measured on the footpaths at street level, and the adoption of a suitable meshing scheme is vital to capture results in the zone of interest in an urban street canyon.

Potential of Thirteen Urban Greening Plants to Capture Particulate Matter on Leaf Surfaces across Three Levels of Ambient Atmospheric Pollution

The potential of urban greening plants to capture particulate matter (PM) from the ambient atmosphere is contingent on interactions between the level of pollution and leaf surfaces. For this study, thirteen plant species were investigated to quantify their capacity of PM accumulation under three atmospheric environments, that is, industrial, traffic and university campus (control), in Kunming City (Southwest China). The sampled sites represented different pollution levels (that is, high pollution, slight pollution and clean air, respectively). The plant species differed in their accumulation of PM by six- to eight-fold across the three sites. Magnolia grandiflora was the most efficient evergreen tree species, whereas Platanus acerifolia had the highest capture of PM among deciduous trees. The accumulation capacity of the same species varied with the degree of pollution. For example, Osmanthus fragrans, Loropetalum chinense and Cinnamomum japonicum were highly efficient for the capture of PM in the traffic and university campus areas; however, they exhibited medium accumulation in the industrial area. Prunus majestica demonstrated an intermediate accumulation capacity in the industrial area, but was low in the traffic and university campus areas. The capturing capacity of the same genus was also different among the different levels of pollution. For example, C. japonicum had a 2.9⁻4.2-times higher PM accumulation than did C. camphora across the three sites. There were significant differences in leaf surface area, stomata density/length, guard cell area, and trichome density/length among these species. The species-specific efficacy of PM capture was primarily contributed to by leaf size and surface roughness, stomata density, and trichome length. In particular, hairy-leaf leaves with medium stomatal density exhibited higher PM capture. Therefore, leaf micromorphology, leaf size and longevity appeared to be significant predictive factors for the accumulation of PM, which may aid in the selection of greening plant species for the remediation of pollutants in urban areas.

How Outdoor Trees Affect Indoor Particulate Matter Dispersion: CFD Simulations in a Naturally Ventilated Auditorium

This study used computational fluid dynamics (CFD) models, coupling with a standard k-ε model based on the Reynolds-averaged Navier-Stokes (RANS) approach and a revised generalized drift flux model, to investigate effects of outdoor trees on indoor PM1.0, PM2.5, and PM10 dispersion in a naturally ventilated auditorium. Crown volume coverage (CVC) was introduced to quantify outdoor trees. Simulations were performed on various CVCs, oncoming wind velocities and window opening sizes (wall porosities were 3.5 and 7.0%, respectively, for half and fully opened windows). The results were as follows: (1) A vortex formed inside the auditorium in the baseline scenario, and the airflow recirculation created a well-mixed zone with little variation in particle concentrations. There was a noticeable decrease in indoor PM10 with the increasing distance from the inlet boundary due to turbulent diffusion. (2) Assuming that pollution sources were diluted through the inlet, average indoor particle concentrations rose exponentially with increasing oncoming wind speed. PM10 changed most significantly due to turbulent diffusion and surface deposition reduction intensified by the increased wind velocity. (3) Increasing the window opening improved indoor cross-ventilation, thus reducing indoor particle concentrations. (4) When 2.87 m³/m² ≤ CVC ≤ 4.73 m³/m², indoor PM2.5 could meet requirements of the World Health Organization's air quality guidelines (IT-3) for 24-hour mean concentrations; and (5) average indoor particle concentrations had positive correlations with natural ventilation rates (R² = 0.9085, 0.961, 0.9683 for PM1.0, PM2.5, and PM10, respectively, when the wall porosity was 3.5%; R² = 0.9158, 0.9734, 0.976 for PM1.0, PM2.5, and PM10, respectively, when the wall porosity was 7.0%).

Use of high-resolution metabolomics for the identification of metabolic signals associated with traffic-related air pollution

BACKGROUND

High-resolution metabolomics (HRM) is emerging as a sensitive tool for measuring environmental exposures and biological responses. The aim of this analysis is to assess the ability of high-resolution metabolomics (HRM) to reflect internal exposures to complex traffic-related air pollution mixtures.

METHODS

We used untargeted HRM profiling to characterize plasma and saliva collected from participants in the Dorm Room Inhalation to Vehicle Emission (DRIVE) study to identify metabolic pathways associated with traffic emission exposures. We measured a suite of traffic-related pollutants at multiple ambient and indoor sites at varying distances from a major highway artery for 12 weeks in 2014. In parallel, 54 students living in dormitories near (20 m) or far (1.4 km) from the highway contributed plasma and saliva samples. Untargeted HRM profiling was completed for both plasma and saliva samples; metabolite and metabolic pathway alternations were evaluated using a metabolome-wide association study (MWAS) framework with pathway analyses.

RESULTS

Weekly levels of traffic pollutants were significantly higher at the near dorm when compared to the far dorm (p < 0.05 for all pollutants). In total, 20,766 metabolic features were extracted from plasma samples and 29,013 from saliva samples. 45% of features were detected and shared in both plasma and saliva samples. 1291 unique metabolic features were significantly associated with at least one or more traffic indicator, including black carbon, carbon monoxide, nitrogen oxides and fine particulate matter (p < 0.05 for all significant features), after controlling for confounding and false discovery rate. Pathway analysis of metabolic features associated with traffic exposure indicated elicitation of inflammatory and oxidative stress related pathways, including leukotriene and vitamin E metabolism. We confirmed the chemical identities of 10 metabolites associated with traffic pollutants, including arginine, histidine, γ‑linolenic acid, and hypoxanthine.

CONCLUSIONS

Using HRM, we identified and verified biological perturbations associated with primary traffic pollutant in panel-based setting with repeated measurement. Observed response was consistent with endogenous metabolic signaling related to oxidative stress, inflammation, and nucleic acid damage and repair. Collectively, the current findings provide support for the use of untargeted HRM in the development of metabolic biomarkers of traffic pollution exposure and response.

Environmental determinants of polycyclic aromatic hydrocarbons exposure at home, at kindergartens and during a commute

The aim of this study was to assess the potential health risk of exposure to polycyclic aromatic hydrocarbons (PAHs) at home and kindergarten for pre-school children. The urine samples were taken from 200 pre-school children aged 5-7 years and analyzed for 1-OHP as a biomarker of PAHs. Mixed effect models were applied to investigate the association between effective environmental parameters (mode of transport, distance to major roads, traffic density, greenness, tobacco exposure, home ventilation, and grill foods) and urinary 1-OHP levels. A Monte-Carlo simulation technique was applied to calculate the risk of exposure to PAHs and to check the uncertainty of input variables and the sensitivity of the estimated risk. The median and inter quartile range (IQR) of 1-OHP was 257 (188.5) ng L^-1. There was a positive significant association between distance from the kindergartens to the green space with surface area ≥5000 m² and 1-OHP concentration (β = 0.844, 95% CI: 0.223, 1.46, P-value = 0.009). Also, urinary 1-OHP was found to be inversely associated with the time the window was open at the home (β = -12.56, 95% CI: -23.52, -1.596, P-value = 0.025) and normalized difference vegetation index (NDVI) in a 100 m buffer around the homes. The mean (9.76 E-3) and 95th percentile (3.28 E-2) of the hazard quotient (HQ) indicated that the concentration of urinary 1-OHP is at a safe level for the target population (HQ < 1). According to the sensitivity analysis results, the concentration of 1-OHP is the most influential variable in the estimated risk. Our findings indicated that the proximity of homes and kindergartens to green space areas and their remoteness from the main streets and heavy traffic areas are associated with reduced exposure to PAHs.

Are Green Walls Better Options than Green Roofs for Mitigating PM10 Pollution? CFD Simulations in Urban Street Canyons

To examine the effect of green roofs (GRs) and green walls (GWs) on coarse particle (PM10) dispersion in urban street canyons, a computational fluid dynamics (CFD) simulation was conducted with a Reynolds-averaged Navier-Stokes (RANS) model and a revised generalized drift flux model. Simulations were performed with different aspect ratios (H/W = 0.5, 1.0, and 2.0), greenery coverage areas (S = 300, 600, and 900 m2), and leaf area densities (LADs = 1.0, 3.5, 6.0 m2/m3). Results indicate that: (1) GRs and GWs all had the reduction ability of PM10 at the pedestrian level; (2) Averaged concentrations of PM10 in GWs and GRs varied little as LAD changed in H/W = 0.5 and 1.0. When H/W = 2.0, the aerodynamic effects of GRs increased since airflow was enhanced within street canyons, resulting in the increasing concentrations in GRs compared with non-greening scenarios; (3) Given equal greenery coverage area and aspect ratio, GWs are more effective in reducing street-canyon PM10, and the averaged concentrations declined with increasing LADs and greenery coverage areas, especially the H/W; (4) At the pedestrian level, the reduction ratio of GRs is greater than that of GWs with the maximum value of 17.1% for H/W = 0.5. However, where H/W = 1.0 and 2.0, the concentrations within GWs are lower than GRs, with maximum reduction ratios of 29.3% and 43.8%, respectively.

Assessment for the Age-Of-The-Air and Ventilation Efficiency in Confined Outdoor Spaces through Computational Fluid Dynamics Techniques

An experimentally validated numerical method for evaluating the suitability of an urban design with regard to its capacity to provide the optimum air quality for its occupants through the confined outdoor spaces is proposed. Eight possible confined outdoor spaces are defined according to their position with regard to the built elements, laterally delimited by the envelope surfaces of the neighbouring buildings. This work focuses on the definition of a procedure capable of analysing outdoor air change quality according to the age-of-the-air concept. The obtained results show that the confined outdoor spaces that are exposed to the wind action are more predisposed to reduce the mean age-of-the-air that they contain. For the analysed cases, a considerable improvement of up to 78.68% in the air change quality can be obtained by taking into account wind exposure criteria in relation to its shape and urban density, which is classified for this purpose.

Land cover and air pollution are associated with asthma hospitalisations: A cross-sectional study

BACKGROUND

There is increasing policy interest in the potential for vegetation in urban areas to mitigate harmful effects of air pollution on respiratory health. We aimed to quantify relationships between tree and green space density and asthma-related hospitalisations, and explore how these varied with exposure to background air pollution concentrations.

METHODS

Population standardised asthma hospitalisation rates (1997-2012) for 26,455 urban residential areas of England were merged with area-level data on vegetation and background air pollutant concentrations. We fitted negative binomial regression models using maximum likelihood estimation to obtain estimates of asthma-vegetation relationships at different levels of pollutant exposure.

RESULTS

Green space and gardens were associated with reductions in asthma hospitalisation when pollutant exposures were lower but had no significant association when pollutant exposures were higher. In contrast, tree density was associated with reduced asthma hospitalisation when pollutant exposures were higher but had no significant association when pollutant exposures were lower.

CONCLUSIONS

We found differential effects of natural environments at high and low background pollutant concentrations. These findings can provide evidence for urban planning decisions which aim to leverage health co-benefits from environmental improvements.

The impact of traffic-flow patterns on air quality in urban street canyons

We investigated the effect of different urban traffic-flow patterns on pollutant dispersion in different winds in a real asymmetric street canyon. Free-flow traffic causes more turbulence in the canyon facilitating more dispersion and a reduction in pedestrian level concentration. The comparison of with and without a vehicle-induced-turbulence revealed that when winds were perpendicular, the free-flow traffic reduced the concentration by 73% on the windward side with a minor increase of 17% on the leeward side, whereas for parallel winds, it reduced the concentration by 51% and 29%. The congested-flow traffic increased the concentrations on the leeward side by 47% when winds were perpendicular posing a higher risk to health, whereas reduced it by 17-42% for parallel winds. The urban air quality and public health can, therefore, be improved by improving the traffic-flow patterns in street canyons as vehicle-induced turbulence has been shown to contribute significantly to dispersion.