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Assessment of air pollution removal by urban trees based on the i-Tree Eco Model: The case of Tehran, Iran.

Reihaneh RasoolzadehNaghmeh Mobarghaee DinanHassan EsmaeilzadehYousef RashidiSeyed Mohammad Moein Sadeghi
Published in: Integrated environmental assessment and management (2024)
Air quality concerns have become increasingly serious in metropolises such as Tehran (Iran) in recent years. This study aims to assess the contribution of urban trees in Tehran toward mitigating air pollution and to evaluate the economic value of this ecosystem service using the i-Tree Eco model. To accomplish this objective, we utilized Tehran's original land use map, identifying five distinct land use categories: commercial and industrial, parks and urban forests, residential areas, roads and transportation, and urban services. Field data necessary for this analysis were collected from 316 designated plots, each with a radius of 11.3 m, and subsequently analyzed using the i-Tree Eco model. The locations of these plots were determined using the stratified sampling method. The results illustrate that Tehran's urban trees removed 1286.4 tons of pollutants in 2020. Specifically, the annual rates of air pollution removal were found to be 134.8 tons for CO; 299.7 tons for NO 2 ; 270.3 tons for O 3 ; 0.7 tons for PM 2.5 ; 489.4 tons for PM 10 (particulate matter with a diameter size between 2.5 and 10 µm); and 91.5 tons for SO 2 , with an associated monetary value of US$1 536 619. However, despite this significant removal capacity, the impact remains relatively small compared with the total amount of pollution emitted in 2020, accounting for only 0.17%. This is attributed to the high emissions rate and low per capita green space in the city. These findings could serve as a foundation for future research and urban planning initiatives aimed at enhancing green spaces in urban areas, thereby promoting sustainable urban development. Integr Environ Assess Manag 2024;00:1-11. © 2024 SETAC.
Keyphrases
  • air pollution
  • particulate matter
  • heavy metals
  • lung function
  • healthcare
  • risk assessment
  • climate change
  • primary care
  • deep learning
  • wastewater treatment
  • health risk assessment
  • drinking water