Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method(21 page pdf, Elizabeth Nethery Gary Mallach, Daniel Rainham. Mark S Goldberg, Amanda J Wheeler, Environmental Health, May 8, 2014)
Today we review research that looks at the advantages offered by a GPS and a PM2.5 particulate sensor to monitor 70 children and the pollution sources and durations they are exposed to over 10 days. The pollution sources vary between indoors and outdoors, using transit or driving, as well as in proximity to roadside emissions in the large metropolitan area that Montreal is. This approach improves upon the data that can be collected from a personal pollution exposure sensor that only produces the total pollution exposure over a given time period by breaking down the exposure by location. The use of a GPS also precludes the need to keep a diary as well as offering more convenience and accuracy, and possibly an effective way of monitoring larger populations for longer periods- if smart phones with a sensor were used for example..
Key Quotes:
“A pollutant mixture that varies within small spatial distances is urban air pollution. For example, specific traffic-related air pollutants are often higher on busy city streets in comparison to suburban ones [1]. Understanding personal exposure to air pollution is complicated by an individuals’ activities and where they are physically located, and the resultant interactions with different levels of air pollution”
“we describe an automated classification of GPS data into location-based categories that makes use of temperature data to assist in discriminating indoor from outdoor locations.”
“Mean times spent in different locations as categorized by a GPS-based method were comparable to those from a time-activity diary, but there were differences in estimates of exposure to PM2.5 from the two methods. An automated GPS-based time-activity method will reduce participant burden, potentially providing more accurate and unbiased assessments of location.”
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