Air Quality Sensor Data Analytics: What Information Can be Gained From Dense Sensor Networks
The research participant selected for this opportunity may collaborate with a multidisciplinary team of EPA scientists devoted to advancing the understanding and use of lower-cost air quality sensors and sensor systems. We have a variety of stakeholders including EPA researchers/policy makers/regulators, regional/tribal/state/local air quality professionals, academics, sensor manufacturers, communities, citizens, etc. Our research efforts provide first-hand knowledge, best practices, techniques, and tools that can inform future sensor use by our stakeholders.
The research participant may collaborate with a multidisciplinary team of EPA scientists devoted to advancing the understanding and use of lower-cost air quality sensors and sensor systems. We have a variety of stakeholders including EPA researchers/policy makers/regulators, regional/tribal/state/local air quality professionals, academics, sensor manufacturers, communities, citizens, etc. Our research efforts provide first-hand knowledge, best practices, techniques, and tools that can inform future sensor use by our stakeholders.
The research participant may conduct research related to a small scale network deployment of lower-cost air quality sensors. The network is part of a remote urban field study being developed in collaboration with a local air quality agency. Data may be used to model the movement of pollutants and to target efforts to reduce local-scale air pollution. The field study design may also allow for the proposal and testing of various network calibration techniques including, but not limited to, reposition-able FEM instruments for collocation. The network deployment may allow for research exploring data processing methods using adjacent nodes or nearby air quality and/or meteorological reference data. Research may involve development of methods for visualizing, animating, and modeling network data and for locating area pollutant sources. Given the large volume of data and measurement artifacts affecting some sensors, the research participant may use programming languages (e.g., R) to analyze data, explore algorithms meant improve data quality, and visualize the data. The research participant may be involved in regular meetings with local air quality professionals and one or more aspects of sensor discovery, planning and implementation of the field deployment, routine monitoring of data from field deployed instruments, and data collection. Commensurate with the level of training, the research participant will have latitude in exercising independent initiative and judgment.