the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A long-term high-resolution air quality reanalysis with public facing air quality dashboard over the Contiguous United States (CONUS)
Abstract. We present a 14-year 12-km hourly air quality dataset created by assimilating satellite observations of aerosol optical depth (AOD) and carbon monoxide (CO) in an air quality model to fill gaps in the contiguous United States (CONUS) air quality monitoring network and help air quality managers understand long-term changes in county level air quality. Specifically, we assimilate the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and the Measurement of Pollution in the Troposphere (MOPITT) CO observations in the Community Multiscale Air Quality Model (CMAQ) every day from 01 Jan 2005 to 31 Dec 2018 to produce this dataset. The Weather Research and Forecasting (WRF) model simulated meteorological fields are used to drive CMAQ offline and to generate meteorology dependent anthropogenic emissions. Both the weather and air quality (surface fine particulate matter (PM2.5) and ozone) simulations are subjected to a comprehensive evaluation against multi-platform observations to establish the credibility of our dataset and characterize its uncertainties. We show that our dataset captures regional hourly, seasonal, and interannual variability in meteorology very well across the CONUS. The correlation coefficient between the observed and simulated surface ozone and PM2.5 concentrations for different Environmental Protection Agency (EPA) defined regions across CONUS are 0.77–0.91 and 0.49–0.79, respectively. The mean bias and root mean squared error for modeled ozone are 3.7–6.8 ppbv and 7–9 ppbv, respectively, while the corresponding values for PM2.5 are -0.9–5.6 µg/m3 and 3.0–8.3 µg/m3, respectively. We estimate that annual CONUS averaged maximum daily 8-hour average (MDA8) ozone and PM2.5 trends are -0.30 ppb/year and -0.24 μg/m3/year, respectively. Wintertime MDA8 ozone shows an increasing but statistically insignificant trend at several sites. We also found a decreasing trend in the 95th percentile of MDA8 ozone but an increasing trend in the 5th percentile. Most of the sites in the Pacific Northwest show an increasing but statistically insignificant trend during summer. An ArcGIS air quality dashboard has been developed to enable easy visualization and interpretation of county level air quality measures and trends by stakeholders, and a Python-based Streamlit application has been developed to allow the download of the air quality data in simplified text and graphic formats.
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RC1: 'Comment on essd-2024-180', Anonymous Referee #1, 22 Jul 2024
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The paper assimilated a long-term data which is critical for air quality managers and researchers to understand the long term air quality trends and changes at a fine scale county level. Also made that long-term data available to the stake holders through a dashboard which makes it very easy to visualize the data.
Specific Comments:
Line 107: Is the methodology any different than any of the previous studies? If so the author should provide that information and highlight the differences.
Line 188: What is the rationale to perform 9 simulations every day rather than the every hour of the day?
Line 189: The first simulation seems to cover more time period that the subsequent simulations. Will that compromise any of the model predictions?
Line 212: Did the author perform any trace gas species performance for the with and without assimilation of the MOPITT to show the indirect effect of CO on the trace gas species?
Line 275: Can the author specify the 10 regions in the text or in the supplementary document? The 10 regions information will be helpful for the reader to understand the regional changes in the data.
Line 279: Figure 2-5: The time series for all the parameters appears too clustered and it is not clear to see the red and black lines. Is it possible to reduce the temporal resolution to something like daily while plotting?
Line 323: Figure 7: Again the hourly time series looks very overcrowded, can this be changed to daily MDA8?
Line 325-327: The author made a point about the nighttime ozone but there is no figure or data to support the statement. Can the author include figure or a table in the appendix to show this finding?
Line 373: Explain what is trend analysis here, so that the reader can understand the context clearly.
Line 379: Why did the author pick 2-sigma level for this analysis?
Line 391: Include the number of sites in the parenthesis.
Line 509: Reword this line "millions of people in counties with no monitors"
Citation: https://doi.org/10.5194/essd-2024-180-RC1
Data sets
CONUS air quality reanalysis dataset (2005-2018) Rajesh Kumar and Cenlin He https://doi.org/10.5065/cfya-4g50
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