Scale-dependent effects of urban greenspace on particulate matter air pollution

Lei, Yakai; Davies, G. Matt; Jin, Huan; Tian, Guohang; Kim, Gunwoo

doi:10.1016/j.ufug.2021.127089

Cited by 40 publications

(20 citation statements)

References 62 publications

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“…The results of spatial regression analysis showed that the LSI of forest land had a significant negative effect on the concentration of PM 2.5 , which was consistent with the results obtained by previous studies and indicated that increasing the contact area between the edges of the green space patches and surrounding urban areas at large spatial scales significantly reduces PM 2.5 concentrations [ 18 ]. PM 2.5 primarily originates from industrial emissions, traffic emissions, and the burning of biomass, and is mainly present on roads, factories, and surrounding farmland in built-up areas [ 27 ].…”

Section: Discussionsupporting

confidence: 89%

“…Yue et al [ 17 ] studied the quantitative relationship between vegetation coverage and atmospheric particulate matter based on remote sensing inversion and deduced that vegetation coverages of ≤10% and >45% exhibited a significant effect on mitigating atmospheric particulate matter pollution. Lei et al [ 18 ] explored the effect of green spaces on particulate matter pollution by studying the landscape patterns of urban green spaces at multiple spatial scales and determined that increasing the biodiversity of green spaces and increasing the number of large green spaces significantly reduced PM 10 concentrations at almost all scales. Zhao et al [ 19 ] conducted a land use regression (LUR) analysis on the green spaces of lakes and wetlands, including the surrounding 500-m built environment in Wuhan City, and their results demonstrated that the lakes, wetlands, and nearby greenery exerted a positive and significant effect on PM 2.5 concentrations within a buffer zone of 300 m or closer.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Does the Spatial Pattern of Plants and Green Space Affect Air Pollutant Concentrations? Evidence from 37 Garden Cities in China

Wang

Guo

Jin

et al. 2022

Plants

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Relevant studies have demonstrated that urban green spaces composed of various types of plants are able to alleviate the morbidity and mortality of respiratory diseases, by reducing air pollution levels. In order to explore the relationship between the spatial pattern of urban green spaces and air pollutant concentrations, this study takes 37 garden cities with subtropical monsoon climate in China as the research object and selects the urban air quality monitoring data and land use type data in 2019 to analyze the relationship between the spatial pattern and the air pollutant concentration through the landscape metrics model and spatial regression model. Moreover, the threshold effect of the impact of green space on air pollutant concentrations is estimated, as well. The results showed that the spatial pattern of urban green space was significantly correlated with the concentrations of PM2.5 (PM with aerodynamic diameters of 2.5 mmor less), NO2 (Nitrogen Dioxide), and SO2 (Sulfur dioxide) pollutants in the air, while the concentrations of PM10 (PM with aerodynamic diameters of 10 mmor less) pollutants were not significantly affected by the green space pattern. Among them, the patch shape index (LSI), patch density (PD) and patch proportion in landscape area (PLAND) of forest land can affect the concentration of PM2.5, NO2, and SO2, respectively. The PLAND, PD, and LSI of grassland and farmland can also have an additional impact on the concentration of SO2 pollutants. The study also found that there was a significant threshold effect within the impact mechanism of urban green space landscape pattern indicators (LSI, PD, PLAND) on the concentrations of PM2.5, NO2, and SO2 air pollutants. The results of this study not only clarified the impact mechanism of the spatial pattern of urban green space on air pollutant concentrations but also provided quantitative reference and scientific basis for the optimization and updating of urban green space to promote public health.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Does the Spatial Pattern of Plants and Green Space Affect Air Pollutant Concentrations? Evidence from 37 Garden Cities in China

Wang

Guo

Jin

et al. 2022

Plants

View full text Add to dashboard Cite

show abstract

“…For example, the results suggest that an emphasis on conserving (or even enlarging) the area of natural vegetation cover is a means of reducing PM 2.5 pollution [76]. Moreover, adjusting land-use configuration [77], especially in urban areas such as Hangzhou, Shaoxing, and Ningbo, and increasing the proportion of urban green space [78] are known to have moderating effects on atmospheric pollution. Specific measures such as vertical planting, urban wind tunnel design [61], and rational spatial planning of green belts [79] should be encouraged.…”

Section: Discussionmentioning

confidence: 99%

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Meadows

et al. 2021

Remote Sensing

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Fine particulate matter in the lower atmosphere (PM2.5) continues to be a major public health problem globally. Identifying the key contributors to PM2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM2.5 values and offers a reliable reference for pollution control strategies.

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“…Green space plays a role in improving air quality through particle deposition, dispersion, and modification [ 55 ]. Another study conducted in China showed that the overall abundance of green spaces in urban areas can significantly reduce the PM 10 concentrations [ 56 ]. Green space might play a role in mitigating the air pollutant levels and indirectly reduce the incidence of RTA.…”

Section: Discussionmentioning

confidence: 99%

Association of Air Pollution and Weather Factors with Traffic Injury Severity: A Study in Taiwan

Chan

Pai

et al. 2022

IJERPH

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Exposure to air pollutants may elevate the injury severity scores (ISSs) for road traffic injuries (RTIs). This multicenter cross-sectional study aimed to investigate the associations between air pollution, weather conditions, and RTI severity. This retrospective study was performed in Taiwan in 2018. The location of each road traffic accident (RTA) was used to determine the nearest air quality monitoring and weather station, and the time of each RTA was matched to the corresponding hourly air pollutant concentration and weather factors. Five multiple logistic regression models were used to compute the risk of sustaining severe injury (ISS ≥ 9). Of the 14,973 patients with RTIs, 2853 sustained severe injury. Moderate or unhealthy air quality index, higher exposure to particulate matter ≤2.5 μm in diameter, bicyclists or pedestrians, greater road width, nighttime, and higher temperature and relative humidity were significant risk factors for severe injury. Exposure to nitrogen oxide and ozone did not increase the risk. Auto occupants and scene-to-hospital time were the protective factors. Sensitivity analyses showed consistent results between air pollutants and the risk of severe injury. Poor air quality and hot and humid weather conditions were associated with severe RTIs. Active commuters were at higher risk of sustaining severe RTI.

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Scale-dependent effects of urban greenspace on particulate matter air pollution

Cited by 40 publications

References 62 publications

Does the Spatial Pattern of Plants and Green Space Affect Air Pollutant Concentrations? Evidence from 37 Garden Cities in China

Does the Spatial Pattern of Plants and Green Space Affect Air Pollutant Concentrations? Evidence from 37 Garden Cities in China

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Association of Air Pollution and Weather Factors with Traffic Injury Severity: A Study in Taiwan

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