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• W2952635680 abstract "Elevated levels of airborne particulate matter (PM) pose health risks to populations living in many cities worldwide. To remediate the impact of air pollution, urban greening has been increasingly explored as a possible way to remove PM from the surroundings. However, existing research focuses mainly on species-specific assessments within temperate climates that may not necessarily grow outside of their local regions. To address sampling limitations associated with the great species diversity in the tropics, our study quantified three key functional traits-leaf hairiness, average leaf area (ALA) and specific leaf area (SLA)-each spanning a wide range of values across 20 tropical species. Wind-tunnel experiments were used to assess surface deposition velocity on leaves; the size fractions PM10 and PM2.5 were measured through multiple rounds of filtration and gravimetric analysis. The effects of upper- and lower-surfaces of leaves on deposition velocity and their interactions with hairiness were also investigated. Results show that greater upper-surface hairiness and a low SLA were consistent predictors of higher deposition velocity for both PM-size fractions. Hairs on leaves serve as obstacles while low SLA tends to be associated with smaller and thicker leaves, together favoring the deposition and retention of PM. The possible mechanisms behind important plant traits and their interactions are discussed. By testing quantifiable effects of specific plant traits, we provide generalizable findings that may be applied to urban greening efforts. Future work to consider other gaseous pollutants and plant-scale effects can help ensure a more comprehensive evaluation of plant suitability for pollutant mitigation." @default.
• W2952635680 created "2019-06-27" @default.
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• W2952635680 date "2019-10-01" @default.
• W2952635680 modified "2023-10-16" @default.
• W2952635680 title "Particulate matter mitigation via plants: Understanding complex relationships with leaf traits" @default.
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• W2952635680 doi "https://doi.org/10.1016/j.scitotenv.2019.06.263" @default.
• W2952635680 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31247483" @default.
• W2952635680 hasPublicationYear "2019" @default.
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