Detecting Urban PM$_{2.5}$ Hotspots with Mobile Sensing and Gaussian Process Regression

• URL: http://arxiv.org/abs/2509.17175v1
• Date: Sun, 21 Sep 2025 17:50:35 GMT
• Title: Detecting Urban PM$_{2.5}$ Hotspots with Mobile Sensing and Gaussian Process Regression
• Abstract summary: We create the first ever map of PM$_2.5$ pollution in Kigali, Rwanda, at a 200m resolution.<n>We identify the hotspots in Kigali where pollution consistently exceeds the city-wide average.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-cost mobile sensors can be used to collect PM$_{2.5}$ 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 PM$_{2.5}$ hotspots that addresses these challenges, involving four steps: (1) equip citizen scientists with mobile PM$_{2.5}$ 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 PM$_{2.5}$ pollution in Kigali, Rwanda, at a 200m resolution. Our results suggest that the level of ambient PM$_{2.5}$ 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 PM$_{2.5}$ 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.

Related papers

• Air Quality Downscaling with Station-Guided Pseudo-Supervision [60.31045749983062]
  Super-resolving coarse atmospheric fields to local PM$_2.5$ variations is uniquely challenged by a mismatch in spatial support.<n>We present a station-guided framework for high-resolution PM$_2.5$ downscaling over Europe.
  arXiv  Detail & Related papers  (2026-07-06T16:32:25Z)
• Tracking Urban Atmospheric Pollutants using Sentinel-5P Satellite Data [0.7090165638014331]
  Urban nitrogen dioxide ($NO$) is a key indicator of combustion-related air pollution and exhibits strong spatial and temporal variability in cities.<n>This study presents a satellite-based framework for tracking urban $NO$ pollution using tropospheric column observations from Sentinel-5P/TROPOMI over Guayas Province, Ecuador.
  arXiv  Detail & Related papers  (2026-05-21T15:59:04Z)
• Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling [50.3911487821783]
  Air pollution remains a leading global health and environmental risk, particularly in regions vulnerable to episodic air pollution spikes due to wildfires, urban haze and dust storms.<n>Here, we present SynCast, a high-resolution neural forecasting model that integrates meteorological and air composition data to improve predictions of both average and extreme pollution levels.
  arXiv  Detail & Related papers  (2025-10-28T01:18:00Z)
• Causal Links Between Anthropogenic Emissions and Air Pollution Dynamics in Delhi [0.0]
  Delhi-National Capital Region experiences air pollution episodes due to complex interactions between anthropogenic emissions and meteorological conditions.<n>This study investigates the causal links of anthropogenic emissions on $PM_2.5$ and $O_3$ concentrations using predictive modeling and causal inference techniques.
  arXiv  Detail & Related papers  (2025-03-24T17:25:44Z)
• AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment [46.56288727659417]
  Air pollution remains a leading global health risk, exacerbated by rapid industrialization and urbanization.<n>We introduce AirCast, a novel multi-variable air pollution forecasting model.<n>AirCast employs a multi-task head architecture that simultaneously forecasts atmospheric conditions and pollutant concentrations.
  arXiv  Detail & Related papers  (2025-02-25T07:34:18Z)
• Gaussian Processes for Monitoring Air-Quality in Kampala [3.173497841606415]
  We investigate the use of Gaussian Processes for nowcasting the current air-pollution in places where there are no sensors and forecasting the air-pollution in the future at the sensor locations. We focus on the city of Kampala in Uganda, using data from AirQo's network of sensors.
  arXiv  Detail & Related papers  (2023-11-28T09:25:23Z)
• Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning [73.01013149014865]
  Methane is one of the most potent greenhouse gases. Current methane emission monitoring techniques rely on approximate emission factors or self-reporting. Deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data.
  arXiv  Detail & Related papers  (2023-08-21T19:36:50Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Air Pollution Hotspot Detection and Source Feature Analysis using Cross-domain Urban Data [2.458537954999774]
  Areas adjacent to pollution sources often have high ambient pollution concentrations, and those areas are commonly referred to as air pollution hotspots. We propose a two-step approach to detect hotspots from mobile sensing data, which includes local spike detection and sample-weighted clustering. As a soft-validation, we build hotspot inference models for cities with and without mobile sensing data.
  arXiv  Detail & Related papers  (2022-11-15T18:44:03Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery [0.5929956715430166]
  We propose a U-net driven quantile regression model to predict $PM_2.5$ air pollution based on easily obtainable satellite imagery. Such predictions could crucially advise public policy strategies geared to reduce the transmission of and lethality of COVID-19.
  arXiv  Detail & Related papers  (2021-05-06T02:50:54Z)
• HVAQ: A High-Resolution Vision-Based Air Quality Dataset [3.9523800511973017]
  We present a high temporal and spatial resolution air quality dataset consisting of PM2.5, PM10, temperature, and humidity data. We evaluate several vision-based state-of-art PM concentration prediction algorithms on our dataset and demonstrate that prediction accuracy increases with sensor density and image.
  arXiv  Detail & Related papers  (2021-02-18T13:42:34Z)
• Averaging Atmospheric Gas Concentration Data using Wasserstein Barycenters [68.978070616775]
  Hyperspectral satellite images report greenhouse gas concentrations worldwide on a daily basis. We propose using Wasserstein barycenters coupled with weather data to average gas concentration data sets and better concentrate the mass around significant sources.
  arXiv  Detail & Related papers  (2020-10-06T14:31:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.