Loading…

Exploring urban planning as a lever for emission and exposure control: Analysis of master plan actions over greater Paris

In this paper we set up a modeling chain to study the impact of different urban planning scenarios on air quality and ultimately the exposure of the population. The analysis relates to the intensity of the polluting activities associated with each scenario, as well as their environmental and health...

Full description

Saved in:
Bibliographic Details
Published in:Atmospheric Environment: X 2024-04, Vol.22, p.100250, Article 100250
Main Authors: Elessa Etuman, Arthur, Coll, Isabelle, Viguié, Vincent, Coulombel, Nicolas, Gallez, Caroline
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we set up a modeling chain to study the impact of different urban planning scenarios on air quality and ultimately the exposure of the population. The analysis relates to the intensity of the polluting activities associated with each scenario, as well as their environmental and health impact. The implementation of a 2030 prospective scenario on Ile-de-France allows us to assess the magnitude of the leverage effect of the actions recommended in the regional master plan. The objective is to quantify the importance of emission reductions, but also the gain in terms of exposure to pollutants, which can be obtained when we transcribe into the model the implementation of regulatory texts on the metropolis of Greater Paris. The results allow us to debate the paradox between reducing emissions and increasing the exposure created by situations of high urban densification. [Display omitted] •Modeling of urban planning scenarios by bottom-up approach.•Integration of the land use component, transport, emissions and air quality.•Prospective simulation of the Paris megacity by 2030.•Taking into account daily mobility, residential mobility, modal choices, emissions in air quality modeling.
ISSN:2590-1621
2590-1621
DOI:10.1016/j.aeaoa.2024.100250