Air Pollution Forecasting in Bucharest

TL;DR

This paper compares various machine learning models, including deep learning and large language models, for predicting PM2.5 air pollution levels in Bucharest to enable early warnings and health risk mitigation.

Contribution

It introduces a comprehensive evaluation of multiple ML models, including novel deep learning and LLM approaches, for air pollution forecasting in an urban setting.

Findings

Deep learning models outperform traditional methods in accuracy.

Large language models show promising results in forecasting.

Ensemble methods provide a good balance of performance and computational efficiency.

Abstract

Air pollution, especially the particulate matter 2.5 (PM2.5), has become a growing concern in recent years, primarily in urban areas. Being exposed to air pollution is linked to developing numerous health problems, like the aggravation of respiratory diseases, cardiovascular disorders, lung function impairment, and even cancer or early death. Forecasting future levels of PM2.5 has become increasingly important over the past few years, as it can provide early warnings and help prevent diseases. This paper aims to design, fine-tune, test, and evaluate machine learning models for predicting future levels of PM2.5 over various time horizons. Our primary objective is to assess and compare the performance of multiple models, ranging from linear regression algorithms and ensemble-based methods to deep learning models, such as advanced recurrent neural networks and transformers, as well as large language models, on this forecasting task.