U.S. NO₂ trends (2005–2013): EPA air quality system (AQS) data versus improved observations from the Ozone Monitoring Instrument (OMI)

Journal Article · Mon Jun 01 00:00:00 EDT 2015 · Atmospheric Environment (1994)

DOI:https://doi.org/10.1016/j.atmosenv.2015.03.055· OSTI ID:1196339

Lamsal, Lok N. ^[1]; Duncan, Bryan N. ^[2]; Yoshida, Yasuko ^[3]; Krotkov, Nickolay A. ^[2]; Pickering, Kenneth E. ^[2]; Streets, David G. ^[4]; Lu, Zifeng ^[4]

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1. Universities Space Research Association, Columbia, MD (United States); NASA Goddard Space Flight Center, Greenbelt, MD (United States)
2. NASA Goddard Space Flight Center, Greenbelt, MD (United States)
3. Science Systems and Applications, Inc., Lanham, MD (United States); NASA Goddard Space Flight Center, Greenbelt, MD (United States)
4. Argonne National Lab. (ANL), Argonne, IL (United States)

Emissions of nitrogen oxides (NO_x) and, subsequently, atmospheric levels of nitrogen dioxide (NO₂) have decreased over the U.S. due to a combination of environmental policies and technological change. Consequently, NO₂ levels have decreased by 30–40% in the last decade. We quantify NO₂ trends (2005–2013) over the U.S. using surface measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) and an improved tropospheric NO₂ vertical column density (VCD) data product from the Ozone Monitoring Instrument (OMI) on the Aura satellite.We demonstrate that the current OMI NO₂ algorithm is of sufficient maturity to allow a favorable correspondence of trends and variations in OMI and AQS data. Our trend model accounts for the non-linear dependence of NO₂ concentration on emissions associated with the seasonal variation of the chemical lifetime, including the change in the amplitude of the seasonal cycle associated with the significant change in NO_x emissions that occurred over the last decade. The direct relationship between observations and emissions becomes more robust when one accounts for these non-linear dependencies. We improve the OMI NO₂ standard retrieval algorithm and, subsequently, the data product by using monthly vertical concentration profiles, a required algorithm input, from a high-resolution chemistry and transport model (CTM) simulation with varying emissions (2005-2013). The impact of neglecting the time-dependence of the profiles leads to errors in trend estimation, particularly in regions where emissions have changed substantially. For example, trends calculated from retrievals based on time-dependent profiles offer 18% more instances of significant trends and up to 15% larger total NO₂ reduction versus the results based on profiles for 2005. Using a CTM, we explore the theoretical relation of the trends estimated from NO₂ VCDs to those estimated from ground-level concentrations. The model-simulated trends in VCDs strongly correlate with those estimated from surface concentrations (r = 0.83, N = 355). We then explore the observed correspondence of trends estimated from OMI and AQS data. We find a significant, but slightly weaker, correspondence (i.e., r = 0.68, N = 208) than predicted by the model and discuss some of the important factors affecting the relationship, including known problems (e.g., NO_z interferents) associated with the AQS data. This significant correspondence gives confidence in trend and surface concentration estimates from OMI VCDs for locations, such as the majority of the U.S. and globe, that are not covered by surface monitoring networks. Using our improved trend model and our enhanced OMI data product, we find that both OMI and AQS data show substantial downward trends from 2005 to 2013, with an average reduction of 38% for each over the U.S. The annual reduction rates inferred from OMI and AQS measurements are larger (–4.8 ± 1.9%/yr, –3.7 ± 1.5%/yr) from 2005 to 2008 than 2010 to 2013 (–1.2 ± 1.2%/yr, –2.1 ± 1.4%/yr). We quantify NO₂ trends for major U.S. cities and power plants; the latter suggest larger negative trend (–4.0 ± 1.5%/yr) between 2005 and 2008 and smaller or insignificant changes (–0.5 ± 1.2%/yr) during 2010-2013.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE; National Aeronautics and Space Administration (NASA)
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1196339
• Journal Information: Atmospheric Environment (1994), Vol. 110, Issue C; Conference: Melbourne (Australia), 19-25 Apr 2015; ISSN 1352-2310
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES