Simulation of Climate Change Impact on Emergency Medical Services Clients Caused by Air Pollution
In one sentence. Projects 2020–2050 emergency medical visits in Tehran under RCP scenarios by linking downscaled GCM climate and air pollution to health via an ANN.
Abstract
Background: Climate change and air pollution are linked. Both of them are expected to impact human health. Climate can increase health risks from poor air quality and lead to emergencies. Forecasting health consequences of air pollution episodes is a matter of great concern. Objectives: The current study mainly aimed at simulating the climate change impact on emergency medical services (EMS) clients caused by air pollution to estimate the future trend of EMS clients with cardiovascular and respiratory symptoms by 2050. Methods: Future climate pattern was projected using general circulation model outputs under the scenario of two representative concentration pathways (RCP2.6 and RCP8.5). Statistical downscaling was performed by LARS weather generator to produce high-resolution synthetic time series weather dataset. Simulation was performed using an artificial neural network (ANN). Observed climate and air pollutant variables were tagged as predictors in ANN, and EMS clients were considered as the target. Projected future (2020 - 2050) climate and air pollution were applied to estimate the future trend of EMS clients. Results: The climate pattern was predicted to become warmer and wetter in the study area (Tehran, Iran). Annual trend of EMS clients with cardiovascular and respiratory symptoms increases under both RCP scenarios. Further increase is under RCP8.5 for EMS clients with cardiovascular symptoms, and the least increase is under RCP2.6 for those with respiratory symptoms. Annual and monthly trends of EMS clients with cardiovascular and respiratory problems are more sensitive to different groups of climate and air pollution variables. Conclusions: ANN is an executive tool to simulate the impact of climate change and air pollution on public health to estimate the future trend of related morbidity and forecast short-term cases across the world.
Labels
- Topic
- Applications & impacts
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Local / station
- Data
- CMIP / climate model output, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.5812/jhealthscope.57786 OpenAlex:W2809339527
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.