Daily NO2 Concentrations and Uncertainty Levels for New York State, 1-km Grids, 2015

Overview
We applied bneR to estimate NO2 concentrations and characterize uncertainty levels at high spatio-temporal resolution (daily, 1 km²) over New York State (NYS) for 2015. bneR is an automated workflow for ensemble modeling that applies BNE. BNE uses existing exposure models as inputs, which can be systematically biased in certain regions, and weighs them by their spatio-temporal accuracy to produce concentrations and uncertainties. BNE is trained using EPA AQS measurements to obtain this new model data product that is consistently accurate over space and time, and provides uncertainty estimates. BNE derived concentrations and uncertainties can be used for propagating the uncertainty estimates in air pollution health impacts assessments, among other potential uses.
Download
The Daily NO2 Concentrations and Uncertainty Levels for New York State, 1-km Grids, 2015 data are packaged by month in RDS format. Each file is approximately 200MB in volume. For variable metadata please see this readme file. January February March April May June July August September October November December
Methods
The Bayesian Nonparametric Ensemble (BNE) prediction model combines existing exposure models as inputs to provide air pollution estimates and their spatio-temporal uncertainty. We estimated daily NO2 concentrations and uncertainties over NYS in 2015 using four input models: Ozone Monitoring Instrument (OMI) data are sourced from satellite measurements following the methods of Lamsal et al. (2008, 2010) applied to the latest OMI NO2 tropospheric column retrieval (N. Lamsal et al., 2021) and converted to ground-level estimates using the column:surface relationships derived from MERRA2-GMI Replay simulation at 0.25 × 0.25 degrees. These OMI-based ground-level concentration estimates are originally available in hierarchical data formatting (HDF5), with 0.1 × 0.1 degrees spatial resolution in units of parts per billion (ppb). CAMS is a global reanalysis product from a chemical transport model (Inness et al., 2019), which assimilates satellite retrievals and ground observations, and originally provided as NetCDF files with a 0.75 × 0.75 degrees spatial resolution in units of kg/kg. Data from Di et al. (2020) (GAMML input model) are a combination of three different machine learning approaches grouped using a generalized additive model with penalized splines, trained on ground-level AQS monitors, that incorporate chemical transport model data, satellite data, and land-use variables. EQUATES is a dataset from a specific configuration of a chemical transport model with data in CSV formatting over a 12 km × 12 km grid in ppb.
Citation
Data set: Benavides, J.¹, C. Carrillo-Gallegos², V. Kumar¹, S. T. Rowland^1,3, L. G. Chillrud^1,4, T. Adeyeye^5,6, J. Paisley⁷, B. Coull⁸, D. K. Henze⁹, R. V. Martin¹⁰, A. M. Fiore^2,11, and M-A. Kiomourtzoglou¹. 2025. Daily NO2 Concentrations and Uncertainty Levels for New York State, 1-km Grids, 2015 (Preliminary Release). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/cfm8-7k22. Accessed DAY MONTH YEAR. Peer-reviewed publication: TBD. xxx, In press. https://doi.org/xxxxx ¹ Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY ² Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA< ³ PSE Healthy Energy, Oakland, CA, USA ⁴ Department of Electrical and Computer Engineering, Northwestern University, IL, USA ⁵ Bureau of Environmental and Occupational Epidemiology, New York State Department of Health, Albany, NY, USA ⁶ Department of Environmental Health Sciences, College of Integrated Health Sciences, University at Albany, SUNY, NY, USA ⁷ Department of Electrical Engineering and Data Science Institute Columbia, Columbia University, New York, NY, USA ⁸ Department of Biostatistics Harvard University, Boston, MA, USA ⁹ Department of Mechanical Engineering, University of Colorado, 1111 Engineering Drive, Boulder, CO, USA ¹⁰ Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA ¹¹ Lamont-Doherty Earth Observatory and Columbia University, Palisades, NY, USA
Disclaimer
This is a preliminary open data release, pending peer review of the data and associated journal articles. Following the peer review process, data curation will be completed by the NASA Socioeconomic Data and Applications Center (SEDAC) and the data will be disseminated through the EarthData catalog.