How can vegetation protect us from air pollution? A critical review on green spaces' mitigation abilities for air-borne particles from a public health perspective - with implications for urban planning

Diener, Arnt; Mudu, Pierpaolo

doi:10.1016/j.scitotenv.2021.148605

Cited by 297 publications

(107 citation statements)

References 133 publications

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“…The average PM 2.5 concentration in the G was 124.5 ± 41.1 µg/m 3 , lower than 132.9 ± 45.4 µg/m 3 in the AG and 133.1 ± 44.6 µg/m 3 in the ASG. The same pattern can be observed for PM 10 . The average PM 10 concentration in the G was 150.3 ± 53.3 µg/m 3 , while in the AG it was 159.2 ± 57.2 µg/m 3 , and in the ASG it was 159.0 ± 57.4 µg/m 3 .…”

Section: Spatial-temporal Variation In the Pm Concentrations And Removal Efficienciessupporting

confidence: 80%

“…However, it can be found that the average PM 2.5 and PM 10 concentrations at 1 m from the river in the G were the lowest along the transect. The concentration was 110.7 ± 32.0 µg/m 3 for PM 2.5 and 138.5 ± 40.8 µg/m 3 for PM 10 .…”

Section: Spatial-temporal Variation In the Pm Concentrations And Removal Efficienciesmentioning

confidence: 93%

“…The coagulation effect on the plant leaf surface is identified as an important mechanism for plants to remove PM [30]. This was summarized as one type of modification of PM by which the size, weight and particle number concentration of toxic PM can be changed [10]. Moreover, particulate reactions to high humidity depend on their chemical composition.…”

Section: The Effect Of the Rivermentioning

confidence: 99%

“…It has long been recognized that the vegetation in urban green spaces contributes to the PM removal from the air, primarily through the deposition, dispersion and the modification of PM [10]. The PM capture capacity of a plant is enhanced by the interception of airborne particles and the uptake by leaf surfaces, resulting in high deposition activity [11].…”

Section: Introductionmentioning

confidence: 99%

“…However, dense trees may impede ventilation and create recirculation zones, keeping PM from dispersion and causing downwind PM accumulation [27][28][29], especially PM 2.5 [26]. Furthermore, small PMs and ultrafine particulates (UFPs) are susceptible to the coagulation effect, which is one type of modification of PM [10]. Yin et al (2020) found an increase in particle size after resuspension of UFPs from plant leaves, and listed influencing factors such as transpiration, wind and thermal diffusion [30].…”

Section: Introductionmentioning

confidence: 99%

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Spatial-Temporal Variation of Air PM2.5 and PM10 within Different Types of Vegetation during Winter in an Urban Riparian Zone of Shanghai

et al. 2021

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Particulate matter (PM) in urban riparian green spaces are undesirable for human participation in outdoor activities, especially PM2.5 and PM10. The PM deposition, dispersion and modification are influenced by various factors including vegetation, water bodies and meteorological conditions. This study aimed to investigate the impact of vegetation structures and the river’s presence on PM in riparian zones. The spatial-temporal variations of PM2.5 and PM10 concentrations in three riparian vegetation communities with different structures (open grassland (G), arbor-grass (AG) and arbor-shrub-grass (ASG) woodlands) were monitored under relatively stable environment. The removal percentages (RP) and ratios of PM2.5 and PM10 were calculated and compared to identify the removal effect of vegetation structures and the river’s presence. It is found that: (1) when the wind was static (hourly wind speed < 0.2 m/s), the RP was ranked as follows: G > AG > ASG. When the wind was mild (0.2 m/s < hourly wind speed < 2 m/s), the RP was ranked as follows: G > ASG > AG. Generally, the G had the best removal effect during the monitoring period; (2) the lowest RP occurred in the middle of the G (–3.4% for PM2.5, 1.8% for PM10) while the highest RP were found in middle of the AG and ASG, respectively (AG: 2.1% for PM2.5, 6.7% for PM10; ASG: 2.4% for PM2.5, 6.3% for PM10). Vegetation cover changed the way of natural deposition and dispersion; (3) compared with static periods, PM removal percentages were significantly reduced under mild wind conditions, and they were positively correlated with wind speed during the mild-wind period. Thus, a piecewise function was inferred between wind speed and PM removal percentage; (4) for all three communities, the 1 m-to-river PM2.5/PM10 ratio was significantly lower than that at 6 m and 11 m, even lower than that in the ambient atmosphere. The river likely promoted the hygroscopic growth of PM2.5 and the generation of larger-sized particles by coagulation effect. Based on these findings, open grassland space is preferred alongside rivers and space for outdoor activities is suggested under canopies in the middle of woodlands.

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Section: Spatial-temporal Variation In the Pm Concentrations And Removal Efficienciessupporting

confidence: 80%

Section: Spatial-temporal Variation In the Pm Concentrations And Removal Efficienciesmentioning

confidence: 93%

Section: The Effect Of the Rivermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial-Temporal Variation of Air PM2.5 and PM10 within Different Types of Vegetation during Winter in an Urban Riparian Zone of Shanghai

et al. 2021

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Structural equation models for simultaneous modeling of air pollutants

Bottazzi Schenone,

Grimaccia,

Vichi

2024

Environmetrics

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This paper provides a new modeling for air pollution, simultaneously taking into account the six main pollutants (PM10 and PM2.5, Sulphate Dioxide, Nitrogen Dioxide, Carbon Monoxide, ground level Ozone concentrations) and their key determinants, employing Structural Equation Models (SEMs). The model is able to estimate the complex links among air pollutants, often neglected in literature, and identifies specific drivers of air pollution. In literature, indexes of air pollution achieved using a fully statistical methodology have not been proposed yet. Indeed, an added value of this proposal is the statistical procedure itself, which can be applied also to obtain indexes modeling different phenomena. In particular, in this study, the new Air Pollution Index (API) is based on a modeling approach that allows to assess, through statistical criteria, the goodness of fit of the SEM in modeling pollutants and the significance of their determinants. The performance of the new index is assessed using air quality data for municipal European areas, which are characterized by different socioeconomic, geographical, and meteorological features. SEMs are estimated and evaluated in terms of best fit and model complexity. The index resulting by the best SEM is compared with the well‐established Air Quality Index (AQI). The new API is validated by means of a sensitivity analysis, performed with a simulation study. Finally, to visualize the meaningfulness of the obtained results, a model‐based cluster analysis is estimated on the municipal areas. The proposed SEM contributes to a better understanding of the relationships between air pollutants and their determinants, and this knowledge can inform policy decisions aimed at reducing air pollution and improving public health.

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Air pollutant removal by four sidewalk tree species in the largest city in Taiwan

Chang

Jien

et al. 2022

J of Env Quality

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Air pollutants pose risks to human health, especially in densely populated cities. We compared the interception of suspended particles and metal elements by four sidewalk tree species with different leaf surface wettability (based on contact angle), leaf area, and phenology in Taipei, Taiwan. Suspended particles were enriched 2.0-2.5 times in throughfall relative to rainfall due to wash-off of suspended particles deposited on leaf surfaces during rainless periods. The enrichment in throughfall was greater in tree species with larger leaf areas. Despite greater concentrations of suspended particles in rainfall during the low-leaf-area period, enrichment was greater in the high-leaf-area period, indicating that leaf area was a key factor affecting canopy interception of pollutants. Throughfall enrichment of suspended particles positively correlated with water quantity, indicating that air pollutants intercepted by tree canopies were not fully washed off by rainfall. Annually, ∼830 g of suspended particles were intercepted and washed off from one tree canopy, with a crown area of 42 m 2 . Scaling up, a rough estimate of 72.7 Mg of suspended particles were intercepted annually by the 90,000 sidewalk trees in Taipei City. Copper, chromium, and aluminum were enriched in throughfall compared with rainfall. However, lead was depleted in throughfall, indicating greater interception than wash-off. Based on our results, leaf area and length of foliated period are key characteristics affecting canopy interception of particulate matter and associated metal elements, whereas leaf surface wettability is of secondary importance.Abbreviations: LAI, leaf area index; PM 10 , particulate matter <10 μm; PM 2.5 , particulate matter <2.5 μm.

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How can vegetation protect us from air pollution? A critical review on green spaces' mitigation abilities for air-borne particles from a public health perspective - with implications for urban planning

Cited by 297 publications

References 133 publications

Spatial-Temporal Variation of Air PM2.5 and PM10 within Different Types of Vegetation during Winter in an Urban Riparian Zone of Shanghai

Spatial-Temporal Variation of Air PM2.5 and PM10 within Different Types of Vegetation during Winter in an Urban Riparian Zone of Shanghai

Structural equation models for simultaneous modeling of air pollutants

Air pollutant removal by four sidewalk tree species in the largest city in Taiwan

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