On campus air quality surveys:
Mobile monitoring campaigns were carried out by bicyclists surrounding a university campus using a low-cost particle sensor and a GPS data logger. Geotagged 1-minute PM2.5 measurements were collected along a fixed route at fixed time schedule.
Spatio-temporal pattern visualization:
Spatial-temporal patterns of PM2.5 concentrations are quantified and the significance levels of within-day and inter-day differences are tested.
Identifying driving landscape factors:
The factors influencing PM2.5 dynamics will be identified, including meteorological factors, traffic, landscape horizontal compositions (e.g., the proportion of green spaces, building footprints) and vertical structure (e.g., average building and tree height) that are derived from earth observation data.
UNT master student, Shannon Hart, is leading the GIS analysis, Lidar data processing, and air quality data survey.
UNT undergraduate student in Geography, Cody Hooten, is working on the land cover classification on 1-m airborne image, to extract the spatial distribution of green spaces in the study area.
Please find more details about this project in our published IJERPH paper.
Hart, R., Liang, L. and Dong, P., 2020. Monitoring, Mapping, and Modeling Spatial–Temporal Patterns of PM2. 5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies. International Journal of Environmental Research and Public Health, 17(14), p.4914.