A deep-learning model for predicting daily PM2.5 concentration in response to emission reduction

TL;DR

CleanAir is a deep-learning model that predicts daily PM2.5 levels responding to emission reductions with high speed and accuracy, significantly outperforming traditional chemical transport models in computational efficiency.

Contribution

This paper introduces CleanAir, a novel deep-learning model that efficiently simulates PM2.5 concentrations in response to emission changes, offering a faster alternative to chemical transport models.

Findings

Predicts PM2.5 concentrations within 10 seconds on a GPU

Achieves accuracy comparable to CMAQ in concentration and emission response

Generalizes well across different meteorological years and emission scenarios

Abstract

Air pollution remains a leading global health threat, with fine particulate matter (PM2.5) contributing to millions of premature deaths annually. Chemical transport models (CTMs) are essential tools for evaluating how emission controls improve air quality and save lives, but they are computationally intensive. Reduced form models accelerate simulations but sacrifice spatial-temporal granularity, accuracy, and flexibility. Here we present CleanAir, a deep-learning-based model developed as an efficient alternative to CTMs in simulating daily PM2.5 and its chemical compositions in response to precursor emission reductions at 36 km resolution, which could predict PM2.5 concentration for a full year within 10 seconds on a single GPU, a speed five orders of magnitude faster. Built on a Residual Symmetric 3D U-Net architecture and trained on more than 2,400 emission reduction scenarios generated by a well-validated Community Multiscale Air Quality (CMAQ) model, CleanAir generalizes well across unseen meteorological years and emission patterns. It produces results comparable to CMAQ in both absolute concentrations and emission-induced changes, enabling efficient, full-coverage simulations across short-term interventions and long-term planning horizons. This advance empowers researchers and policymakers to rapidly evaluate a wide range of air quality strategies and assess the associated health impacts, thereby supporting more responsive and informed environmental decision-making.