Application of High Resolution Air-Borne Remote Sensing Observations for Monitoring NOx Emissions

Nitrogen oxides (NOx=NO+NO2) are one of the air pollutants, responsible for the formation of tropospheric ozone, acid rain and particulate nitrate. The anthropogenic NOx emissions are commonly estimated based on bottom-up inventories which are complicated by many potential sources of error. One way to improve the emission inventories is to use relevant observations to constrain them. Fortunately, Nitrogen dioxide (NO2) is one of the most successful detected species from remote sensing. Although many studies have shown the capability of using space-borne remote sensing observations for monitoring emissions, the insufficient sample number and footprint of current measurements have introduced a burden to constrain emissions at fine scales. Promisingly, there are several air-borne sensors collected for NASA's campaigns providing high spatial resolution of NO2 columns. Here, we use the well-characterized NO2 columns from the Airborne Compact Atmospheric Mapper (ACAM) onboard NASA's B200 aircraft into a 1×1 km regional model to constrain anthropogenic NOx emissions in the Houston-Galveston-Brazoria area. Firstly, in order to incorporate the data, we convert the NO2 slant column densities to vertical ones using a joint of a radiative transfer model and the 1x1 km regional model constrained by P3-B aircraft measurements. After conducting an inverse modeling method using the Kalman filter, we find the ACAM observations are resourceful at mitigating the overprediction of model in reproducing NO2 on regular days. Moreover, the ACAM provides a unique opportunity to detect an anomaly in emissions leading to strong air quality degradation that is lacking in previous works. Our study provides convincing evidence that future geostationary satellites with high spatial and temporal resolutions will give us insights into uncertainties associated with the emissions at regional scales.