Data fusion for fine-scale ozone mapping in the New York City metropolitan area using low-cost sensors and model information
Hojeily, E., Lu, C. H., Alessandrini, S., Kim, J., Kumar, R., et al. (2026). Data fusion for fine-scale ozone mapping in the New York City metropolitan area using low-cost sensors and model information. Atmospheric Environment, doi:https://doi.org/10.1016/j.atmosenv.2026.121805
| Title | Data fusion for fine-scale ozone mapping in the New York City metropolitan area using low-cost sensors and model information |
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| Genre | Article |
| Author(s) | E. Hojeily, Cheng Hsuan Lu, Stefano Alessandrini, Ju-Hye Kim, Rajesh Kumar, Shih-Wei Wei, L. Sheji, M. A. Bari, S. D. Miller |
| Abstract | Surface ozone (O3) exhibits spatiotemporal variability in the New York City metropolitan area (NYCMA) under the influence of complex mesoscale flows which transport O3 and its precursors. Fine-scale ambient air quality monitoring is critical for estimating air pollution exposure and assessing whether mitigation strategies are sufficient to attain the National Ambient Air Quality Standards. To improve air quality monitoring in the NYCMA, 38 New York State Mesonet (NYSM) sites were outfitted with well-calibrated low-cost O3 sensors. This study applies the Satellite Enhanced Data Interpolation (SEDI) method to bias-correct 1-year of gridded output from the Weather Research and Forecasting with Chemistry (WRF-Chem) through fusion with surface O3 observations from the NYSM low-cost sensor sites and instruments from the U.S. EPA's AirNow monitoring network. Prior to bias correction, WRF-Chem overestimated O3 concentrations at 11 NYCMA AirNow sites. Constraining WRF-Chem using the NYSM low-cost sensor sites alone reduced mean bias error by around 6 ppb at the AirNow sites. At NYSM sites, the gridded O3 dataset constrained by observations from NYSM and AirNow together resulted in a better-performing dataset compared to the dataset constrained using observations from AirNow alone. These results highlight the value added by low-cost sensors in filling observational gaps in existing regulatory monitoring networks. Additionally, the SEDI algorithm is a computationally inexpensive post-processing technique that effectively reduces error and bias and is enhanced by the increase in spatial resolution of air quality monitoring provided by integrating the NYSM low-cost sensor sites with the AirNow network. |
| Publication Title | Atmospheric Environment |
| Publication Date | Apr 15, 2026 |
| Publisher's Version of Record | https://doi.org/10.1016/j.atmosenv.2026.121805 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d7tt4wgd |
| OpenSky Listing | View on OpenSky |
| RAL Affiliations | WISP, WCAP, RALAO |