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

arXiv: https://arxiv.org/abs/2506.10332 · HTML: https://mlsanity.com/paper/2506.10332/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction](https://mlsanity.com/paper/2402.03784/) (2024)
- [Predicting air quality via multimodal AI and satellite imagery](https://mlsanity.com/paper/2211.00780/) (2022)
- [Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing](https://mlsanity.com/paper/1904.12303/) (2019)

## Abstract (arXiv)

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 km^2 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.