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Using the modified i-Tree Eco model to quantify air pollution removal by urban vegetation

Author:
Wu, Jiansheng, Wang, Yi, Qiu, Sijing, Peng, Jian
Source:
The Science of the total environment 2019 v.688 pp. 673-683
ISSN:
0048-9697
Subject:
air, air pollution, air quality, broadleaved evergreen forests, case studies, cities, coniferous forests, evergreen trees, humans, models, particulates, planting, pollution control, spatial distribution, surface area, urbanization, vegetation cover, China
Abstract:
Fine particulate matter (PM2.5) can pose health problems for humans following urbanization. Because urban vegetation has a large surface area to filter PM2.5 out of the air, it can be an effective long-term way to mitigate air pollution. Various studies have quantified PM2.5 removal by vegetation in cities, but the spatial variability of removal within cities and future scenarios have not been well documented. To ensure more reasonable and effective urban tree planting regimes, we used the spatiotemporal i-Tree Eco model combined with the vertical distribution of vegetation in a case study in Shenzhen City, China. The results indicated that the PM2.5 removal by urban vegetation in 2015 was 1000.1 tons, and the average removal rate by vegetation was 1.6 g m−2 year−1. A maximum hourly local air quality improvement of up to 3% could be achieved, with an average of 1%, which differed significantly with elevation. In terms of vegetation type, evergreen shrubs, evergreen broadleaved forests, and evergreen needle-leaved forests had the highest removal efficiency within <100, 100–300, and >300 m, respectively. For five future planting scenarios, by increasing vegetation cover by 5% in different elevation zones (<100, 100–300, and >300 m), an annual amount of 1220.6–1308 tones could be achieved. Specifically, it was estimated that an increase in evergreen shrubs cover in the developed area (<100 m) would have the best removal potential.
Agid:
6461095