# Detecting Urban PM2.5 Hotspots with Mobile Sensing and Gaussian Process Regression

arXiv: https://arxiv.org/abs/2509.17175 · HTML: https://mlsanity.com/paper/2509.17175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 100 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [GPflow: A Gaussian process library using TensorFlow](https://mlsanity.com/paper/1610.08733/) (2016)
- [Distributed Gaussian Processes](https://mlsanity.com/paper/1502.02843/) (2015)
- [Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing](https://mlsanity.com/paper/1904.12303/) (2019)
- [Gaussian Processes for Monitoring Air-Quality in Kampala](https://mlsanity.com/paper/2311.16625/) (2023)
- [Comprehensive Monitoring of Air Pollution Hotspots Using Sparse Sensor Networks](https://mlsanity.com/paper/2410.04309/) (2024)
- [Air Pollution Hotspot Detection and Source Feature Analysis using Cross-domain Urban Data](https://mlsanity.com/paper/2211.08400/) (2022)

## Abstract (arXiv)

Low-cost mobile sensors can be used to collect PM2.5 concentration data throughout an entire city. However, identifying air pollution hotspots from the data is challenging due to the uneven spatial sampling, temporal variations in the background air quality, and the dynamism of urban air pollution sources. This study proposes a method to identify urban PM2.5 hotspots that addresses these challenges, involving four steps: (1) equip citizen scientists with mobile PM2.5 sensors while they travel; (2) normalise the raw data to remove the influence of background ambient pollution levels; (3) fit a Gaussian process regression model to the normalised data and (4) calculate a grid of spatially explicit 'hotspot scores' using the probabilistic framework of Gaussian processes, which conveniently summarise the relative pollution levels throughout the city. We apply our method to create the first ever map of PM2.5 pollution in Kigali, Rwanda, at a 200m resolution. Our results suggest that the level of ambient PM2.5 pollution in Kigali is dangerously high, and we identify the hotspots in Kigali where pollution consistently exceeds the city-wide average. We also evaluate our method using simulated mobile sensing data for Beijing, China, where we find that the hotspot scores are probabilistically well calibrated and accurately reflect the 'ground truth' spatial profile of PM2.5 pollution. Thanks to the use of open-source software, our method can be re-applied in cities throughout the world with a handful of low-cost sensors. The method can help fill the gap in urban air quality information and empower public health officials.