Work

Forecasting urban air quality in Paris using ensemble machine learning: A scalable framework for environmental management

Year: 2025

Type: article

Abstract: Urban air pollution poses a significant threat to public health and urban sustainability in megacities like Paris. We cast forecasting as a short-term, next-hour prediction task for PM2.5, NO, and CO, using hourly meteorology and recent pollutant history as inputs. We develop a data-driven framework based on hyperparameter-tuned ensembles (Random Forest, Gradient Boosting, and a Stacked Ensemble) and benchmark against a Long Short-Term Memory (LSTM) model, alongside persistence baselines. All evaluation metrics (RMSE/MAE) are reported in physical units (µg/m³) with R² unitless. Results show that tree ensembles deliver the lowest errors for PM2.5 and CO, while LSTM is competitive for NO; stacking offers gains when base-model errors are complementary but does not universally dominate. The framework is designed for real-time deployment and integration into smart city pipelines, supporting proactive air quality management. By providing accurate, unit-consistent short-term forecasts, this study informs urban planning, risk mitigation, and public-health protection.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0336897

Authors: Somia A. Asklany, Doaa Mohammed, Ismail K. Youssef, Majed Nawaz, Wajdan Al Malwi

Institutions: Northern Border University, Islamic University of Madinah, King Khalid University

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action

Open Access status: gold

Estimated APC paid: $2,382