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<p>Climate change poses significant challenges to air quality, particularly in arid regions prone to dust pollution. This study assesses future trends in particulate matter (PM<sub>10</sub>) 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 PM<sub>10</sub> 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 PM<sub>10</sub> 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&#xa0;µg/m<sup>3</sup>, 22.18&#xa0;µg/m<sup>3</sup>, and 16.83&#xa0;µg/m<sup>3</sup>, 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 PM<sub>10</sub> 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.</p>

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Modeling future PM10 concentrations under climate change scenarios

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月20日

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  • Nastaran Talepour,
  • Yaser Tahmasebi Birgani,
  • Frank J. Kelly,
  • Neamatollah Jaafarzadeh,
  • Gholamreza Goudarzi

摘要

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.