Name
Title | ||
|---|---|---|
Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution |
Abstract | ||
|---|---|---|
Air quality models are important for studying the impact of air pollutant on health conditions at a fine spatiotemporal scale. Existing work typically relies on area-specific, expert-selected attributes of pollution emissions (e,g., transportation) and dispersion (e.g., meteorology) for building the model for each combination of study areas, pollutant types, and spatiotemporal scales. In this paper, we present a data mining approach that utilizes publicly available OpenStreetMap (OSM) data to automatically generate an air quality model for the concentrations of fine particulate matter less than 2.5 μm in aerodynamic diameter at various temporal scales. Our experiment shows that our (domain-) expert-free model could generate accurate PM2.5 concentration predictions, which can be used to improve air quality models that traditionally rely on expert-selected input. Our approach also quantifies the impact on air quality from a variety of geographic features (i.e., how various types of geographic features such as parking lots and commercial buildings affect air quality and from what distance) representing mobile, stationary and area natural and anthropogenic air pollution sources. This approach is particularly important for enabling the construction of context-specific spatiotemporal models of air pollution, allowing investigations of the impact of air pollution exposures on sensitive populations such as children with asthma at scale.
|
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3139958.3140013 | SIGSPATIAL/GIS |
Field | DocType | ISBN |
Meteorology,Data mining,Temporal scales,Particulates,Sensitive Populations,Computer science,Pollution,Pollutant,Air quality index,Air pollution,Image resolution | Conference | 978-1-4503-5490-5 |
Citations | PageRank | References |
3 | 0.49 | 6 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yijun Lin | 1 | 11 | 2.23 |
| Yao-Yi Chiang | 2 | 360 | 31.33 |
| fan pan | 3 | 6 | 1.87 |
| Dimitrios Stripelis | 4 | 3 | 0.49 |
| José Luis Ambite | 5 | 958 | 110.89 |
| Sandrah P. Eckel | 6 | 3 | 0.83 |
| Rima Habre | 7 | 3 | 1.17 |