Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing

TL;DR

Deep-MAPS is a machine learning framework that enables high-resolution, cost-effective mobile air pollution sensing, providing accurate spatial-temporal pollution maps and urban insights.

Contribution

It introduces a novel ML-based mobile sensing framework that reduces hardware costs while achieving high accuracy in urban air quality monitoring.

Findings

Achieves over 85% accuracy in PM2.5 spatial inference.

Reduces hardware investment by up to 90%.

Provides insights into pollution causes for urban management.

Abstract

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.