Search NASA⌕ Search

Engineering topics

Benjamin de Foy

Publications and source records attributed to Benjamin de Foy.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Enhanced Capabilities of TROPOMI NO2: Estimating NOX from North American Cities and Power Plants

The TROPOspheric Monitoring Instrument (TROPOMI) is used to derive top-down NOXemissions for twolarge power plants and three megacities in North America. Wefirst re-process the vertical column NO2with an improved airmass factor to correct for a known systematic low bias in the operational retrieval near urban centers. For the two power plants,top-down NOXemissions agree to within 10% of the emissions reported by the power plants. We then derive top-down NOXemissions rates for New York City, Chicago, and Toronto, and compare them to projected bottom-up emissions inventories. Inthis analysis of 2018 NOXemissions, wefind a +22% overestimate for New York City, a−21% underestimate in Toronto, andgood agreement in Chicago in the projected bottom-up inventories when compared to the top-down emissions. Top-downNOXemissions also capture intraseasonal variability, such as the weekday versus weekend effect (emissions are +45% larger onweekdays versus weekends in Chicago). Finally, we demonstrate the enhanced capabilities of TROPOMI, which allow us toderive a NOXemissions rate for Chicago using a single overpass on July 7, 2018. The large signal-to-noise ratio of TROPOMI iswell-suited for estimating NOXemissions from relatively small sources and for sub-seasonal timeframes.

TROPOspheric Monitoring Instrument (TROPOMI)↗
NASA NTRS2014

Model Evaluation of Methods for Estimating Surface Emissions and Chemical Lifetimes from Satellite Data

Column densities from satellite retrievals can provide valuable information for estimating emissions and chemical lifetimes objectively across the globe. To better understand the uncertainties associated with these estimates, we test four methods using simulated column densities from a point source: a box model approach, a 2D Gaussian fit, an Inverse Radius fit and an Exponentially-Modified Gaussian fit. The model results were simulated using the WRF and CAMx models for the year 2005, for a single point source outside Atlanta in Georgia, USA with specified emissions and three chemical scenarios: no chemical reactions, 12 h chemical lifetime and 1 h chemical lifetime. No other sources were included in the simulations. We find that the box model provides reliable estimates irrespective of plume speed and plume direction, if the plume speed and the chemical lifetime are known accurately. The 2D Gaussian fit was found to be sensitive to plume speed and direction, and requires omnidirectional dispersion in order to have a decent fit. However, the 2D Gaussian fit is only an approximate fit to the data, and the discrepancies mean that the results are dependent on the geographical domain used for the optimization. An Inverse Radius fit is introduced to correct this issue, which is found to provide improved emissions and lifetime estimates. The Exponentially-Modified Gaussian fit also gave improved estimates. It is however dependent on accurate plume rotation such that reported chemical lifetimes with this method could be significantly underestimated.

Emission inventory↗
NASA NTRSDate not supplied

Illustrating the Spatiotemporal Complexity of No2 Columns Using A Multi-Perspective Observing System: Moving Toward Geostationary Product Validation and Applications

As a precursor to secondary pollutants like ozone and PM2.5, nitrogen dioxide (NO2) is crucial to understand when addressing air quality issues. However, due to NO2’s short lifetime during the daytime and complexity of emission sources in urbanized regions, interpreting datasets from ground or satellite perspectives alone are challenged by variance in spatial and temporal resolutions. High resolution airborne mapping (< 1 km) of NO2 column densities across morning, midday, and afternoon add a unique perspective toward interpreting satellite data with respect to ground-measurements. This presentation focuses on the interpretation of spatiotemporal complexity of NO2 columns from the Synergistic TEMPO Air Quality Science Study (STAQS). The mission’s goal is to integrate geostationary observations from Tropospheric Emissions: Monitoring of Pollution (TEMPO) with traditional and enhanced air quality monitoring to improve the understanding of air quality science for increased societal benefit. We will demonstrate the interweaved perspective of NO2 columns from ground-based Pandora spectrometers and satellite-based observations (e.g., TROPOMI) as compared to high spatial resolution airborne observations from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS). This includes the evaluation of each dataset through comparison to each other to identify potential biases in data products and the impact of heterogeneity on these comparisons. Airborne data will also be used as a proxy for geostationary observations with morning, midday, and afternoon raster maps collected over four cities (Los Angeles, Chicago, Toronto, and New York City). Finally, recent research outcomes will be presented to demonstrate how airborne and geostationary observations can be used to evaluate emission inventories and air quality models.

Laura Judd↗