Air in Your Neighborhood: Fine-Grained AQI Forecasting Using Mobile Sensor Data

2025/06/12 by Sharma, Aaryam
Computer Science · Environmental Science · #Air Quality Monitoring and Forecasting #Air Quality and Health Impacts #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mobile Crowdsensing and Crowdsourcing

paper · pdf · doi:10.48550/arxiv.2506.10332

openalex publication_date 2025/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Air pollution has become a significant health risk in developing countries. While governments routinely publish air-quality index (AQI) data to track pollution, these values fail to capture the local reality, as sensors are often very sparse. In this paper, we address this gap by predicting AQI in 1 km² neighborhoods, using the example of AirDelhi dataset. Using Spatio-temporal GNNs we surpass existing works by 71.654 MSE a 79% reduction, even on unseen coordinates. New insights about AQI such as the existence of strong repetitive short-term patterns and changing spatial relations are also discovered. The code is available on GitHub.

Citations

• AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
• Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing
• Graph Attention Networks

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