The dataset was created by University of Houston. For details, please contact Jia Jung at helloiamjia@gmail.com. This dataset is associated with the following publication: Jung, J., Y. Choi, S. Mousavinezhad, D. Kang, J. Park, A. Pouyaei, M. Ghahremanloo, M. Momeni, and H. Kim. Changes in the ozone chemical regime over the contiguous United States inferred by the inversion of NOx and VOC emissions using satellite observation. Atmospheric Research. Elsevier Science BV, Amsterdam, NETHERLANDS, 270: 106076, (2022).

Urban ozone (O3) formation can be limited by NOx, VOCs, or both, complicating the design of effective O3 abatement plans. A satellite-retrieved ratio of formaldehyde to NO2 (HCHO/NO2), developed from theory and modeling, has previously been used to indicate O3 formation chemistry. Here, we connect this space-based indicator to spatiotemporal variations in O3 recorded by on-the-ground monitors over major U.S. cities. High-O3 events vary nonlinearly with OMI HCHO and NO2, and the transition from VOC-limited to NOx-limited O3 formation regimes occurs at higher HCHO/NO2 value (3 to 4) than previously determined from models, with slight intercity variations. To extend satellite records back to 1996, we develop an approach to harmonize observations from GOME and SCIAMACHY that accounts for differences in spatial resolution and overpass time. Two-decade (1996-2016) multisatellite HCHO/NO2 captures the timing and location of the transition from VOC-limited to NOx-limited O3 production regimes in major U.S. cities, which aligns with the observed long-term changes in urban-rural gradient of O3 and the reversal of O3 weekend effect. Our findings suggest promise for applying space-based HCHO/NO2 to interpret local O3 chemistry, particularly with the new-generation satellite instruments that offer finer spatial and temporal resolution. This dataset is not publicly accessible because: The data are publicly available on government-supported servers and are terabytes in size. It can be accessed through the following means: Please refer to the linked publication, visit archives described in the text or contact the corresponding authors for more information. Format: Data are processed as described in the linked publication - 10.1021/acs.est.9b07785 . Data included in the analysis are from the European Quality Assurance for Essential Climate Variables project (QA4ECV; http://www.qa4ecv.eu/ecvs), and EPA/AQS and are publicly available at the time of publication. Satell. This dataset is associated with the following publication: Jin, X., A. Fiore, K.F. Boersma, I. De Smedt, and L. Valin. Inferring changes in summertime surface ozone-NOx-VOC chemistry over U.S. urban areas from two decades of satellite and ground-based observations. International Journal of Environmental Science and Technology. Springer, Heidelburg, GERMANY, 54(11): 6518-6529, (2020).

Urban ozone (O3) formation can be limited by NOx, VOCs, or both, complicating the design of effective O3 abatement plans. A satellite-retrieved ratio of formaldehyde to NO2 (HCHO/NO2), developed from theory and modeling, has previously been used to indicate O3 formation chemistry. Here, we connect this space-based indicator to spatiotemporal variations in O3 recorded by on-the-ground monitors over major U.S. cities. High-O3 events vary nonlinearly with OMI HCHO and NO2, and the transition from VOC-limited to NOx-limited O3 formation regimes occurs at higher HCHO/NO2 value (3 to 4) than previously determined from models, with slight intercity variations. To extend satellite records back to 1996, we develop an approach to harmonize observations from GOME and SCIAMACHY that accounts for differences in spatial resolution and overpass time. Two-decade (1996-2016) multisatellite HCHO/NO2 captures the timing and location of the transition from VOC-limited to NOx-limited O3 production regimes in major U.S. cities, which aligns with the observed long-term changes in urban-rural gradient of O3 and the reversal of O3 weekend effect. Our findings suggest promise for applying space-based HCHO/NO2 to interpret local O3 chemistry, particularly with the new-generation satellite instruments that offer finer spatial and temporal resolution. This dataset is not publicly accessible because: The data are publicly available on government-supported servers and are terabytes in size. It can be accessed through the following means: Please refer to the linked publication, visit archives described in the text or contact the corresponding authors for more information. Format: Data are processed as described in the linked publication - 10.1021/acs.est.9b07785 . Data included in the analysis are from the European Quality Assurance for Essential Climate Variables project (QA4ECV; http://www.qa4ecv.eu/ecvs), and EPA/AQS and are publicly available at the time of publication. Satell. This dataset is associated with the following publication: Jin, X., A. Fiore, K.F. Boersma, I. De Smedt, and L. Valin. Inferring changes in summertime surface ozone-NOx-VOC chemistry over U.S. urban areas from two decades of satellite and ground-based observations. International Journal of Environmental Science and Technology. Springer, Heidelburg, GERMANY, 54(11): 6518-6529, (2020).

This data set contains continuous UV photometric data of surface level ozone collected at 6m above ground level. Data records consist of UTC time, date, and processed ozone mixing ratio (parts per billion). Data is collected from global locations and is provided in 1 minute and 1 hour averages. The data were produced and are available from the NOAA Earth System Research Laboratory (ESRL) Global Monitoring Laboratory, and the data are archived by the NOAA National Centers for Environmental Information (NCEI).

EPA, in collaboration with NASA, collected upward-viewing UV/visible solar radiance measurements at air quality stations in the New York City region. From these radiances, NO2 vertical column densities (molecules cm-2) are retrieved for comparison to retrieved NO2 column from a downward viewing, satellite-based UV/visible instrument, the NOAA Ozone Mapping and Profiler Suite (OMPS) . This dataset is associated with the following publication: Huang, X., K. Yang, S. Kondragunta, Z. Weir, L. Valin, J. Szykman, and M. Goldberg. NO2 retrievals from NOAA-20 OMPS: Algorithm, evaluation, and observations of drastic changes during COVID-19. ATMOSPHERIC ENVIRONMENT. Elsevier B.V., Amsterdam, NETHERLANDS, 290: 119367, (2022).

This file describes the dataset used in the following article: Nolte, C. G., Spero, T. L., Bowden, J. H., Sarofim, M. C., Martinich, J., Mallard, M. S., Fann, N., "Regional Temperature-Ozone Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios," 2020. MODEL VERSION AND CONFIGURATION The Community Multiscale Air Quality (CMAQ) model was used. The model is open source and can be freely downloaded at http://github.com/USEPA/CMAQ. The specific code version used in this study was based on a pre-release version of CMAQ 5.3, with minor modifications to accommodate the USGS28 land-use scheme used in WRF. The model source code is included in the "src" directory. The meteorological input data for CMAQ were derived from outputs of the Community Earth System Model (CESM) and the Coupled Model version 3 (CM3) following Representative Concentration Pathway (RCP) 8.5, which represents a relatively high warming scenario. The CESM and CM3 fields were downscaled to 36-km grid cells over North America using the Weather Research and Forecasting model. The downscaling and air quality modeling procedure are described in the associated manuscript (Nolte et al., submitted manuscript, 2020) and references therein. CMAQ simulations were conducted using the meteorology downscaled from the two climate models and using two different sets of anthropogenic emissions: the 2011 National Emission Inventory and a 2040 projection developed for analysis of the Heavy Duty Greenhouse Gas Rule. This 2040 projection represents significant reductions relative to present-day of pollutant emissions, including nitrogen oxides (NOx), sulfur dioxide, and volatile organic compounds (VOCs). See U.S. EPA (2016) for further information on the anthropogenic emissions. Climate-sensitive VOCs emitted from vegetation, e.g., isoprene, were modeled within CMAQ using the downscaled meteorological projections from WRF. CMAQ was used to simulate air pollutant concentrations over the continental United States using grid cells with 36km x 36km horizontal spacing, with the height of the lowest model layer around 38 m. Further details on the model configuration and input data are described in the manuscript. Figures used in this paper were prepared using version 3.6.1 of the R programming language. R is open source, and can be downloaded at www.r-project.org. The R scripts are labeled according to their figure number, and reference all data needed to generate the figures, which are located in the "figs" folder. This dataset is associated with the following publication: Nolte, C., T. Spero, J. Bowden, M. Sarofim, J. Martinich, and M. Mallard. Regional Temperature-Ozone Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, 71(10): 1251-1264, (2021).

This file describes the dataset used in the following article: Nolte, C. G., Spero, T. L., Bowden, J. H., Sarofim, M. C., Martinich, J., Mallard, M. S., Fann, N., "Regional Temperature-Ozone Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios," 2020. MODEL VERSION AND CONFIGURATION The Community Multiscale Air Quality (CMAQ) model was used. The model is open source and can be freely downloaded at http://github.com/USEPA/CMAQ. The specific code version used in this study was based on a pre-release version of CMAQ 5.3, with minor modifications to accommodate the USGS28 land-use scheme used in WRF. The model source code is included in the "src" directory. The meteorological input data for CMAQ were derived from outputs of the Community Earth System Model (CESM) and the Coupled Model version 3 (CM3) following Representative Concentration Pathway (RCP) 8.5, which represents a relatively high warming scenario. The CESM and CM3 fields were downscaled to 36-km grid cells over North America using the Weather Research and Forecasting model. The downscaling and air quality modeling procedure are described in the associated manuscript (Nolte et al., submitted manuscript, 2020) and references therein. CMAQ simulations were conducted using the meteorology downscaled from the two climate models and using two different sets of anthropogenic emissions: the 2011 National Emission Inventory and a 2040 projection developed for analysis of the Heavy Duty Greenhouse Gas Rule. This 2040 projection represents significant reductions relative to present-day of pollutant emissions, including nitrogen oxides (NOx), sulfur dioxide, and volatile organic compounds (VOCs). See U.S. EPA (2016) for further information on the anthropogenic emissions. Climate-sensitive VOCs emitted from vegetation, e.g., isoprene, were modeled within CMAQ using the downscaled meteorological projections from WRF. CMAQ was used to simulate air pollutant concentrations over the continental United States using grid cells with 36km x 36km horizontal spacing, with the height of the lowest model layer around 38 m. Further details on the model configuration and input data are described in the manuscript. Figures used in this paper were prepared using version 3.6.1 of the R programming language. R is open source, and can be downloaded at www.r-project.org. The R scripts are labeled according to their figure number, and reference all data needed to generate the figures, which are located in the "figs" folder. This dataset is associated with the following publication: Nolte, C., T. Spero, J. Bowden, M. Sarofim, J. Martinich, and M. Mallard. Regional Temperature-Ozone Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, 71(10): 1251-1264, (2021).

This data set contains continuous UV photometric data of surface level ozone collected at 6m above ground level. Data records consist of UTC time, date, and processed ozone mixing ratio (parts per billion). Data is collected from global locations and is provided in 1 minute and 1 hour averages. Data are archived at the NOAA National Climatic Data Center (NCDC), but are produced and available from NOAA Earth System Research Laboratory (ESRL).

The Solar Backscattered Ultraviolet (SBUV) from NOAA-9 Level-2 daily product (SBUV2N09L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.The SBUV2N09L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from February 1985 through January 1998. The SBUV2N09L2 data product was used as input in creating the SBUV2N09L3zm monthly zonal mean data product.

The Solar Backscattered Ultraviolet (SBUV) from NOAA-19 Level-2 daily product (SBUV2N19L2) contains ozone nadir profile and total column data from retrievals generated from the v8.6 SBUV algorithm. The v8.6 SBUV algorithm estimates the ozone nadir profile and total column from SBUV measurements using 1) the Brion-Daumont-Malicet ozone cross sections, 2) an OMI-derived cloud-height climatology, 3) a revised a priori ozone climatology, and 4) inter-instrument calibration based on comparisons with no local time difference.The SBUV2N19L2 product is written as daily files using the HDF5 format, with file sizes ranging from about 1 to 5 Mbytes. Data are available from February 2009 through July 2013. The SBUV2N19L2 data product was used as input in creating the SBUV2N19L3zm monthly zonal mean data product.