Identification

Title

Deep learning to evaluate US NOx emissions using surface ozone predictions

Abstract

Emissions of nitrogen oxides (NOx = NO + NO2) in the United States have declined significantly during the past three decades. However, satellite observations since 2009 indicate total column NO2 is no longer declining even as bottom-up inventories suggest continued decline in emissions. Multiple explanations have been proposed for this discrepancy including (a) the increasing relative importance of nonurban NOx to total column NO2, (b) differences between background and urban NOx lifetimes, and (c) that the actual NOx emissions are declining more slowly after 2009. Here, we use a deep learning model trained by NOx emissions and surface observations of ozone to assess consistency between the reported NOx trends between 2005 and 2014 and observations of surface ozone. We find that the satellite-derived trends best reproduce ozone in low NOx emission (background) regions. The 2010-2014 trend from older satellite-derived emission estimates produced at low spatial resolution results in the largest bias in surface ozone in regions with high NOx emissions, reflecting the blending of urban and background NOx in these low-resolution top-down analyses. In contrast, the trend from higher resolution satellite-based estimates, which are more capable of capturing the urban emission signature, is in better agreement with ozone in high NOx emission regions, and is consistent with the trend based on surface observations of NO2. Our results confirm that the satellite-derived trends reflect anthropogenic and background influences.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d77d2zpr

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2022-02-27T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact