Local Mortality Impacts Due to Future Air Pollution Under Climate Change Scenarios

Vijendra Ingole, Asya Dimitrova, Jon Sampedro, Charfudin Sacoor, Sozinho Acacio, Sanjay Juvekar, Sudipto Roy, Paula Moraga, Xavier Basagaña, Joan Ballester, Josep M. Antó, Cathryn Tonne

Research output: Contribution to journalArticlepeer-review

Abstract

IntroductionThe health impacts of global climate change mitigation will affect local populations differently. We aimed to quantify the local health impacts due to fine particles (PM 2.5 ) under the governance arrangements embedded in the Shared Socioeconomic Pathways (SSPs1-5) under two greenhouse gas concentration scenarios (Representative Concentration Pathways (RCPs) 2.6 and 8.5) in local populations of Mozambique, India, and Spain.MethodsWe simulated the SSP-RCP scenarios using the Global Change Analysis Model, which was linked to the TM5-FASST model to estimate PM 2.5 levels. PM 2.5 levels were calibrated with local measurements. We used comparative risk assessment methods to estimate attributable premature deaths due to PM 2.5 linking local population and mortality data with PM 2.5 –mortality relationships from the literature. We incorporated population projections under the SSPs in sensitivity analysis.ResultsPM 2.5 attributable burdens in 2050 differed across SSP-RCP scenarios, and scenario-sensitivity varied across populations. Future attributable mortality burden of PM 2.5 was highly sensitive to assumptions about how populations will change according to SSP. SSPs reflecting high challenges for adaptation (SSPs 3 and 4) consistently resulted in the highest PM 2.5 attributable burdens mid-century.DiscussionOur analysis of local PM 2.5 attributable premature deaths under SSP-RCP scenarios in three local populations highlights the importance of both socioeconomic development and climate policy in reducing the health burden from air pollution. Sensitivity of future PM 2.5 mortality burden to SSPs was particularly evident in low- and midlle- income country settings due either to high air pollution levels or dynamic populations.
Original languageEnglish (US)
JournalSSRN Electronic Journal
DOIs
StatePublished - Dec 10 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-12-15
Acknowledgements: We thank Samir KC for data and technical assistance related to population projections according to SSP.

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