Recently the EPA collaborated with the NIEHS Superfund Research Program (SRP) for the Risk eLearning webinar three-part series on “Using GIS Tools to Analyze, Compute, and Predict Pollution“.
This final session focused on Community Engagement and included a presentation by one of our trainees, Andy Larkin, entitled Making models personal: increasing the impact of atmospheric pollutant models by predicting pollutant levels at Android and iPhone locations.
Over 110 people participated on the webinar. Andy provided an outstanding overview of the mobile app he developed and included future directions and needs.
Presenting as part of this Risk eLearning Series let us demonstrate how GIS chips in smartphones could be used to provide personalized information about air quality. ~Andy Larkin
Key points from Larkin’s presentation
- Smartphones are one of the newest methods available for collecting location-based information. There are currently more than one billion active smartphone users in the world (source: CBSNews.com).
- Smartphones can identify a person’s location and pollutant models can predict pollution levels at a given location. By linking smartphones with pollutant models, it is hypothesized that multiple pollutants can be predicted at smartphone locations. Geographical constraints are based on the constraint of the underlying pollutant models, and can conceivably cover the extent of the entire world.
- Sampling and retaining locations at regular intervals can provide a well documented past of predicted pollutant levels at smartphone locations. Input from the smartphone user about intended future locations can potentially be used to predict pollutant levels at future locations.
- Sampling data acquired from a group representative of the population can be used to make inferences about spatial and temporal trends regarding pollution level conditions for the entire population
- To test the proof of principle that smartphones can be linked with environmental maps, Larkin created PM2.5, PM10, and ozone hourly forecast maps for the state of Oregon. Maps forecast predicted exposure levels at air monitoring stations using Seasonal Integrated Moving Average (SIMA) time series models. Forecasts at air monitoring stations are then interpolated to cover the entire state using universal Kriging for PM2.5 and PM10, and inverse distance weighing for ozone. These modeling methods were chosen because they can be validated and evaluated using prediction errors.
The future in personal monitoring is combining complementary technologies.