Forecasting Air Quality with AI: Understanding PM10 Changes in Future Climate Scenarios

Climate change is altering our planet, making it crucial to predict air quality accurately. This study looks at how particulate matter (PM10) levels might change under various future climate scenarios from the CMIP6 model. They used a combination of Artificial Neural Networks (ANNs) and Nonlinear Autoregressive models with exogenous inputs (NARX) to make these predictions.

PM10, tiny particles suspended in the air, can seriously affect our health. By understanding how these particles might behave in the future, we can better plan for public health initiatives and environmental strategies. The study focused on different scenarios, called Shared Socioeconomic Pathways, which consider various socio-economic factors and their impact on climate change. Using these advanced models, researchers can anticipate how PM10 levels could increase or decrease in different parts of the world. This helps policymakers prepare for potential air quality issues, ensuring that people stay safe and healthy. The findings can also guide efforts to reduce pollution and mitigate the effects of climate change.

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