Modeling future PM₁₀ concentrations under climate change scenarios
File(s)
Author(s)
Talepour, Nastaran
Birgani, Yaser Tahmasebi
Kelly, Frank J
Jaafarzadeh, Neamatollah
Goudarzi, Gholamreza
Type
Journal Article
Abstract
Climate change poses significant challenges to air quality, particularly in arid regions prone to dust pollution. This study assesses future trends in particulate matter (PM10) concentrations in Ahvaz, Iran, under climate change scenarios defined by the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). Historical climate data (1998–2014) and observed PM10 records (2013–2022) were used to establish a baseline. Future climate variables were statistically downscaled using the LARS-WG 6.0 model, with projections from the MIROC6 model under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0). A Nonlinear Autoregressive Neural Network with Exogenous Inputs (ANN-NARX) was developed to forecast PM10 concentrations for the period 2023–2042, using temperature, precipitation, and solar radiation as predictors. The ANN-NARX model showed strong performance with RMSE values of 8.66 µg/m3, 22.18 µg/m3, and 16.83 µg/m3, and correlation coefficients of 0.95, 0.96, and 0.92 for SSP1-2.6, SSP2-4.5, and SSP3-7.0, respectively. All scenarios indicate an increase in PM10 levels, particularly under the high-emission SSP3-7.0 pathway, with the most pronounced rises during the summer months. Sensitivity analysis identified maximum temperature as the most influential predictor. These findings highlight the urgent need for proactive air quality management and integrated climate adaptation policies to mitigate health risks in dust-prone urban environments.
Date Issued
2025-08-20
Date Acceptance
2025-07-08
Citation
Modeling Earth Systems and Environment, 2025, 11 (6)
ISSN
2363-6203
Publisher
Springer
Journal / Book Title
Modeling Earth Systems and Environment
Volume
11
Issue
6
Copyright Statement
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Subjects
AIR-QUALITY
Climate change
Environmental Sciences
Environmental Sciences & Ecology
IMPACTS
Life Sciences & Biomedicine
Long Ashton research station weather generator
Nonlinear autoregressive with exogenous input
Particulate matter
PM2.5
PREDICTION
REGION
Science & Technology
Shared socioeconomic pathway
Publication Status
Published
Article Number
397
Date Publish Online
2025-08-20