# Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing

arXiv: https://arxiv.org/abs/1904.12303 · HTML: https://mlsanity.com/paper/1904.12303/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review](https://mlsanity.com/paper/2401.01788/) (2024)
- [SPO-VCS: An End-to-End Smart Predict-then-Optimize Framework with Alternating Differentiation Method for Relocation Problems in Large-Scale Vehicle Crowd Sensing](https://mlsanity.com/paper/2411.18432/) (2024)
- [Air in Your Neighborhood: Fine-Grained AQI Forecasting Using Mobile Sensor Data](https://mlsanity.com/paper/2506.10332/) (2025)
- [Detecting Urban PM2.5 Hotspots with Mobile Sensing and Gaussian Process Regression](https://mlsanity.com/paper/2509.17175/) (2025)

## Abstract (arXiv)

Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demonstrates its scientific and financial values in the following aspects. (1) Based on a network of fixed and mobile air quality sensors, we perform spatial inference of PM2.5 concentrations in Beijing (3,025 km2, 19 Jun-16 Jul 2018) for a spatial-temporal resolution of 1km-by-1km and 1 hour, with over 85% accuracy. (2) We leverage urban big data to generate insights regarding the potential cause of pollution, which facilitates evidence-based sustainable urban management. (3) To achieve such spatial-temporal coverage and accuracy, Deep-MAPS can save up to 90% hardware investment, compared with ubiquitous sensing that relies primarily on fixed sensors.