Understanding Spatial and Temporal Variability in Ozone Levels within a Remote-sensing Scale Grid Cell using Data Collected with Low-cost, Next Generation Monitoring Systems

Friday, 19 December 2014
Ashley Monika Collier, Michael Hannigan, Nicholas Masson, Ricardo Piedrahita, Joanna Lynn Gordon and Michael Russel, University of Colorado at Boulder, Boulder, CO, United States
For the past several years, our research group has been developing low-cost (for reference, each unit costs under $1000) next generation air quality monitors, which utilize metal-oxide semiconductor sensors and non-dispersive infrared sensors to collect data on various gaseous pollutants. The pollutants of focus for this deployment were CO2, O3, and NO2. Additional data collected by the monitors includes temperature, humidity, wind speed and direction, and some information on hydrocarbon levels. A main focus of our research has been sensor characterization and exploring research applications of the technology. During summer 2014, the DISCOVER-AQ and FRAPPE sampling campaigns provided our group with the opportunity to deploy twenty monitors throughout the sampling region with the Boulder Atmospheric Observatory Tower in Erie CO at the center of our monitoring area. Thirteen of these monitors were located at ground-level within an approximately 10 by 10 km grid cell, and the rest were outside of this area at various distances. This placement was intended to provide information on pollutant variability, specifically ozone, within a remote-sensing sized grid cell. Additionally, the availability of reference monitors in the field provided opportunities for co-location during the deployment and hence, opportunities to quantify monitor performance. Analysis will include both an evaluation of low-cost sensor performance and a look at temporal and spatial variability. For example, land-use regression modeling will be used to explore population density, distance to roadways, and distance to oil and gas activity as covariates. Additionally, we will explore how the spatial distribution varies with time and look for temporal patterns.