Combining Observations From a Low-Cost Sensor Ensemble with Machine Learning Techniques to Predict Real-Time Measurements of Criteria Atmospheric Pollutants
Kate Smith1, Alastair C Lewis1, Pete Edwards1, Peter Ivatt2, Mathew J Evans3, Thomas B Ryerson4, Zachary Decker5, James D Lee6, Jeff Peischl7, Freya Anne Squires3, Chengliang Dai1, Yele Sun8 and Wei Zhou9, (1)Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, United Kingdom, (2)Goddard Earth Sciences Technology and Research, Greenbelt, United States, (3)University of York, Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, York, United Kingdom, (4)Scientific Aviation, Boulder, CO, United States, (5)Cooperative Institute for Research in Environmental Sciences, CU Boulder, Boulder, United States, (6)University of York, Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, York, YO10, United Kingdom, (7)CIRES, University of Colorado Boulder & NOAA Global Monitoring Laboratory, Boulder, United States, (8)Institute of Atmospheric Physics, Chinese Academy of Sciences, State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Beijing, China, (9)Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
