Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea.

ABSTRACT: Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM_2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared

SUBMITTER: Lim CC 

PROVIDER: S-EPMC6728172 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature