Modelling the effectiveness of urban trees and grass on PM2.5 reduction via dispersion and deposition at a city scale

Jeanjean, Antoine; Monks, Paul S.; Leigh, R. J.

doi:10.1016/j.atmosenv.2016.09.033

Cited by 210 publications

(109 citation statements)

References 43 publications

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“…In the model, particles are treated as a continuum, and particle movements are assumed to not affect turbulence. The convective velocity of the particle phase is assumed to be the same as the airflow, which has been widely used in recent studies [55][56][57], and greatly reduced the complexity of the two-phase flow simulation. The model is expressed as follows:…”

Section: Simulation Modelsmentioning

confidence: 99%

Prediction of Wind Environment and Indoor/Outdoor Relationships for PM2.5 in Different Building–Tree Grouping Patterns

2018

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Airflow behavior and indoor/outdoor PM 2.5 dispersion in different building-tree grouping patterns depend significantly on the building-tree layouts and orientation towards the prevailing wind. By using a standard k-ε model and a revised generalized drift flux model, this study evaluated airflow fields and indoor/outdoor relationships for PM 2.5 resulting from partly wind-induced natural ventilation in four hypothetical building-tree grouping patterns. Results showed that: (1) Patterns provide a variety of natural ventilation potential that relies on the wind influence, and buildings that deflect wind on the windward facade and separate airflow on the leeward facade have better ventilation potential; (2) Patterns where buildings and trees form a central space and a windward opening side towards the prevailing wind offer the best ventilation conditions; (3) Under the assumption that transported pollution sources are diluted through the inlet, the aerodynamics and deposition effects of trees cause the lower floors of a multi-storey building to be exposed to lower PM 2.5 compared with upper floors, and lower indoor PM 2.5 values were found close to the tree canopy; (4) Wind pressure differences across each flat showed a poor correlation (R 2 = 0.059), with indoor PM 2.5 concentrations; and (5) Patterns with the long facade of buildings and trees perpendicular to the prevailing wind have the lowest indoor PM 2.5 concentrations.

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Section: Simulation Modelsmentioning

confidence: 99%

Prediction of Wind Environment and Indoor/Outdoor Relationships for PM2.5 in Different Building–Tree Grouping Patterns

2018

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“…As reported previously, it is possible that green spaces can reduce PM 2·5 loads via absorption and deposition, or dispersion in urban street canyons, as well as provide ventilation corridors breaking air pollution flows. 51,52 Our study used objective and detailed building footprint-level spatial data to develop an index of urbanicity reporting 4·6% higher odds of COPD per interquartile increment in urbanicity. The detrimental effects of urban areas have been established in terms of rural -urban differences in self-reported COPD diagnosis 14 and the effects of aggregated population density on COPD mortality.…”

Section: Discussionmentioning

confidence: 99%

Environmental correlates of chronic obstructive pulmonary disease in 96 779 participants from the UK Biobank: a cross-sectional, observational study

Sarkar

Zhang

et al. 2019

The Lancet Planetary Health

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Background The role of environmental exposures in chronic obstructive pulmonary disease (COPD) remains inconclusive. We examined the association between environmental exposures (PM 2·5 , greenness, and urbanicity) and COPD prevalence using the UK Biobank cohort data to identify key built environment correlates of COPD. MethodsIn this cross-sectional, observational study we used baseline data for UK Biobank participants. Included participants were aged 39 years and older, white, had available spirometry data, and had complete data for phenotypes and exposures. COPD was defined by spirometry with the 2017 Global Initiative for Chronic Obstructive Lung Disease criteria. Environmental exposures were PM 2·5 derived from monitoring data and interpolated using land-use regression at the participants' geocoded residential addresses. Built environment metrics of residential greenness were modelled in terms of normalised difference vegetation index from remotely sensed colour infrared data within a 500 m residential catchment, and an urbanicity index derived from spatial analyses and measured with a 1 km buffer around each participant's residential address. Logistic regression models examined the associations between environmental exposures and COPD prevalence adjusting for a range of confounders. Subgroup analyses by urbanicity and effect modification by white blood cell count as an inflammatory marker were also done. FindingsWe assessed 96 779 participants recruited between April 4, 2006, and Oct 1, 2010, of which 5391 participants had COPD with a prevalence of 5·6%. Each 10 µg/m³ increment in ambient PM 2·5 exposure at a participant's residential location was associated with higher odds of COPD (odds ratio 1·55, 95% CI 1·14-2·10). Among the built environment metrics, urbanicity was associated with higher odds of COPD (1·05, 1·01-1·08 per interquartile increment), whereas residential greenness was protective, being associated with lower odds of COPD (0·89, 0·84-0·93 for each interquartile increment in greenness). The results remained consistent in models of COPD defined as per lower limit of normal criteria. The highest quartile of white blood cell count was associated with lower lung function and higher COPD risk with a significant interaction between PM 2·5 and white blood cell count only in the model of lung function (p=0·0003).Interpretation In this study of the built environment and COPD, to our knowledge the largest done in the UK, we found that exposure to ambient PM 2·5 and urbanicity were associated with a higher risk of COPD. Residing in greener areas, as measured by normalised difference vegetation index, was associated with lower odds of COPD, suggesting the potential value of urban planning and design in minimising or offsetting environmental risks for the prevention and management of COPD.

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“…Not all pollution is well-mixed throughout the urban airshed. Pockets of high concentration exist, particularly in poorly ventilated street canyons through which high volumes of traffic flow [10,38,41,42]. We expect the likelihood of the occurrence of pollution hotspots,…”

Section: The Expected Scaling Of Air Pollution Hotspotsmentioning

confidence: 98%

“…The effects of vegetation on surface roughness and, hence, the mean wind speed, u, and the effects of roughness and local heat balance on the mixing height, h [36,38], will be more influential on urban airshed-average pollution concentrations than the effect of deposition on vegetation (see [39] and similar approaches which focus solely or mainly on the deposition term). However, at the local scale, the effect of green infrastructure on pollutant concentrations is a context-dependent balance of fumigation and deposition effects with potentially large impact [10,38,40,41].…”

Section: Airshed-average Pollutant Concentration Scalingmentioning

confidence: 99%

Urban form strongly mediates the allometric scaling of airshed pollution concentrations

MacKenzie

Whyatt

Barnes

et al. 2019

Environ. Res. Lett.

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We present allometric-scaling relationships between non-point-source emissions of air pollutants and settlement population, using 3030 urban settlements in Great Britain (home to ca. 80% of the population of that region). Sub-linear scalings (slope<1.0; standard error on slope ∼0.01; r 2 >0.6) were found for the oxides of nitrogen (NO x ) and microscopic airborne particles (PM 10 and PM 2.5 ). That is, emissions of these pollutants from larger cities are lower per capita than would be expected when compared to the same population dispersed in smaller settlements. The scalings of traffic-related emissions are disaggregated into a component due to under-use of roads in small settlements and a fraction due to congestion in large settlements. We use these scalings of emissions, along with a scaling related to urban form, to explain quantitatively how and why urban airshed-average air pollutant concentrations also scale with population. Our predicted concentration scaling with population is strongly sub-linear, with a slope about half that of the emissions scaling, consistent with satellite measurements of NO 2 columns over large cities across Europe. We demonstrate that the urban form of a particular settlement can result in the airshed-average air pollution of that settlement being much larger or smaller than expected. We extend our analysis to predict that the likelihood of occurrence of local air pollution hotspots will scale super-linearly with population, a testable hypothesis that awaits suitable data. Our analysis suggests that coordinated management of emissions and urban form would strongly reduce the likelihood of local pollutant hotspots occurring whilst also ameliorating the urban heat island effect under climate change.

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Modelling the effectiveness of urban trees and grass on PM2.5 reduction via dispersion and deposition at a city scale

Cited by 210 publications

References 43 publications

Prediction of Wind Environment and Indoor/Outdoor Relationships for PM2.5 in Different Building–Tree Grouping Patterns

Prediction of Wind Environment and Indoor/Outdoor Relationships for PM2.5 in Different Building–Tree Grouping Patterns

Environmental correlates of chronic obstructive pulmonary disease in 96 779 participants from the UK Biobank: a cross-sectional, observational study

Urban form strongly mediates the allometric scaling of airshed pollution concentrations

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