2019
DOI: 10.5194/acp-2019-472
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Inferring the anthropogenic NO<sub><i>x</i></sub> emission trend over the United States during 2003–2017 from satellite observations: Was there a flattening of the emission tend after the Great Recession?

Abstract: <p><strong>Abstract.</strong> We illustrate the nonlinear relationships among anthropogenic NO<sub><i>x</i></sub> emissions, NO<sub>2</sub> tropospheric vertical column densities (TVCDs), and NO<sub>2</sub> surface concentrations using model simulations for July 2011 over the contiguous United States (CONUS). The variations of NO<sub>2</s… Show more

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Cited by 5 publications
(10 citation statements)
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References 52 publications
(81 reference statements)
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“…In contrast, it has been suggested that the slowdown in the reduction rate in the satellite‐derived emission estimates does not indicate a discrepancy with the NEI inventory, but instead is due to the increasing relative influence of nonanthropogenic NO x emissions on atmospheric NO x as captured by the satellite measurements (Silvern et al., 2019). It has also been reported by J. Li and Wang (2019) that the satellite‐derived trends are consistent with the trends in surface observations of NO 2 in high emission regions and that the discrepancy between the top‐down and bottom‐up trends are due to nonlinearity in the relationship between NO x emissions and the satellite observations of NO 2 in low emission “rural” regions. Here, we use a data‐driven deep learning (DL) model that predicts surface ozone abundances across the US, which allows us to assess the consistency of the inferred 2005–2014 trends in NO x emissions with observed surface ozone.…”
Section: Introductionsupporting
confidence: 84%
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“…In contrast, it has been suggested that the slowdown in the reduction rate in the satellite‐derived emission estimates does not indicate a discrepancy with the NEI inventory, but instead is due to the increasing relative influence of nonanthropogenic NO x emissions on atmospheric NO x as captured by the satellite measurements (Silvern et al., 2019). It has also been reported by J. Li and Wang (2019) that the satellite‐derived trends are consistent with the trends in surface observations of NO 2 in high emission regions and that the discrepancy between the top‐down and bottom‐up trends are due to nonlinearity in the relationship between NO x emissions and the satellite observations of NO 2 in low emission “rural” regions. Here, we use a data‐driven deep learning (DL) model that predicts surface ozone abundances across the US, which allows us to assess the consistency of the inferred 2005–2014 trends in NO x emissions with observed surface ozone.…”
Section: Introductionsupporting
confidence: 84%
“…To investigate whether the satellite observations of NO 2 are more representative of nonanthropogenic NO x in rural regions after 2010 (Silvern et al., 2019; Li & Wang, 2019), we segregated the predictions into high‐NO x and low‐NO x regions according to whether the average NO x emission in a given grid box is greater than or less than 1 × 10 11 molec cm −2 s −1 , respectively, following Li and Wang (2019). We assume that the high‐NO x regions are strongly influenced by anthropogenic emissions, whereas the low‐NO x regions are more representative of background NO x conditions (see Figure S6 in Supporting Information for the spatial distribution of these high‐NO x and low‐NO x emission regions).…”
Section: Resultsmentioning
confidence: 99%
“…However, it has also been suggested [44] that the slowdown in the reduction rate in the satellite-derived emission estimates does not indicate a discrepancy with the EPA emission inventory, but instead is due to the increasing relative influence of non-anthropogenic NOx emissions on atmospheric NOx as captured by the satellite measurements. It was also suggested in [27] that the satellite-derived trends are consistent with the trends in surface observations of NOx in high emission regions and that the discrepancy between the top-down and bottom-up trends are due to non-linearity in the relationship between NOx emissions and the satellite observations of NO 2 in rural regions. Here we use the deep learning model to evaluate the recent trends in NOx emissions in the United States.…”
Section: Introductionmentioning
confidence: 70%
“…After 2010, however, the EPA trend produced the largest negative bias in predicted ozone, whereas the top-down trends were in better agreement with observed AQS ozone observations. It was suggested [44,27] that the discrepancy between the top-down and bottom-up NOx emission estimates could be due to the fact that the satellite observations of NO 2 are more representative of non-anthropogenic NOx in rural regions after 2010. In Table 3 the error statistics for the model predictions for 2010-2016 are aggregated in "urban" and "rural" regions, defined according to whether NOx emissions in a given grid box are greater than or less than 1 × 10 11 molec cm −2 s −1 , respectively, following Li and Wang [27].…”
Section: A Deep Learning Model To Predict Mda8mentioning
confidence: 99%
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