Quantifying Urban Nitrogen Dioxide Emission From Space Based on Cross-Sectional Flux Method and Satellite Data

Urban nitrogen dioxide nitric oxide (NO2) emission is a major source of total NO2 emission, yet brings large uncertainty of emission estimates because of its complicated internal environment. Current top-down methods, for example, exponentially modified Gaussian (EMG) have strict theoretical assumptions of atmospheric dispersion condition and simplify the whole urban region as an isolated point source, which differs from actual emission situation, resulting in poor temporal representativeness and large uncertainty. This article constructs a remote sensing estimation method for urban NO2 emissions based on the cross-sectional flux method which is insensitive to meteorological assumptions, and improved it with considerations of NO2 lifetime. Compared with ground-based observation, the mean absolute percentage error (MAPE) of this work decreases by 42.58% compared with EMG’s MAPE. On the total scale of annual stocktake, the MAPE of this work is reduced by 39.79% compared with the EMG method. This method weakens the impact of meteorology condition and provides higher temporal representativeness. The retrieved NO2 emission based on this method of New York City, Las Vegas, Chicago, Wuhan, Xi’an, and Paris shows a $61.55~\pm ~28.25$ kt/yr differences compared with Emissions Database for Global Atmospheric Research (EDGAR) inventory results, with overestimation up to 135.39 kt/yr (Xi’an). For all study regions, a clearly temporal pattern of NO2 emission is found, with the monthly emission during ozone season increases 5.76 kt/month compared with nonozone season. The cross-sectional flux method, once improved, demonstrates greater accuracy in estimating urban NO2 emissions and is expecting to be applied to different gaseous emission to provide more reliable results.