Abstract
Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically these provide at best a few recording locations per city. However, large spatial var-iability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 - 32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM2.5 concentration was 14.1 ± 5.7 μg·m-3 corresponding to periods when the wind was out of the south. The lowest PM2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only “cleanse” the air, leaving a mean PM2.5 concentration as low as 3.0 ± 0.5 μg·m-3, but also leave the region with a more uniform PM2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.
Highlights
Multiple studies have established a strong link between aerosols and health issues [1]-[4]
This study looked at the size distribution, in the size range 0.25 - 32 μm, and the spatial and temporal variability across a 100 km2 area encompassing parts of Richardson, and Garland, TX
To objectively characterize the meteorological regimes, an self organizing map (SOM) was used to classify the meteorological data into 10 different classes
Summary
Multiple studies have established a strong link between aerosols and health issues [1]-[4]. There is a lack of neighborhood-scale observations of airborne particulates, and many towns have no observations at all To help address this issue, a machine learning approach was previously developed, by Lary et al (2014) [12], to estimate daily global abundance of airborne particulates from multiple big, environmental, data sets. One of the goals of this study was to look at the size distribution, in the size range 0.25 - 32 μm, and the spatial and temporal variability across a 10 km pixel of this global daily product. This project used a mobile sensor package to gather data throughout the city mounted 1.5 m (5') above the ground. The question “What is the appropriate spatial resolution required to accurately characterize the PM2.5 abundance at a neighborhood scale?” will be explored
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call