Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO2 and CO2

Abstract

Abstract:We present joint analyses of satellite-observed combustion products to examine bulk characteristics of combustion in megacities and fire regions. We use retrievals of CO, NO 2 and CO 2 from NASA/Terra Measurement of Pollution In The Troposphere, NASA/Aura Ozone Monitoring Instrument, and JAXA Greenhouse Gases Observing Satellite to estimate atmospheric enhancements of these co-emitted species based on their spatiotemporal variability (spread, σ) within 14 regions dominated by combustion emissions. We find that patterns in σ XCO /σ XCO 2 and σ XCO /σ XNO 2 are able to distinguish between combustion types across the globe. These patterns show distinct groupings for biomass burning and the developing/developed status of a region that are not well represented in global emissions inventories. We show here that such multi-species analyses can provide constraints on emission inventories, and be useful in monitoring trends and understanding regional-scale combustion.

Biomass burning combustion efficiency observed from space using measurements of CO and NO<sub>2</sub> by TROPOMI

“…that is piled together at the surface. These spatial differences in combustion efficiency between deforestation and savanna fires agree with the study of Silva and Arellano (2017), however, a one-on-one comparison between the two studies is difficult. They derived estimates of MDR based on the ratio of ΔXCO/ΔXNO2 (instead of ΔXNO2/ΔXCO), and they did their analysis for a different year, probably under somewhat different meteorological and chemistry regimes.…”

“…The MDR between ΔXNO2 and ΔXCO is therefore equal to the ratio between two standard deviations σXNO2/σXCO. As discussed by Silva and Arellano (2017), this assumption is only valid if both species are highly correlated with each other. This is the case for this study given the strong co-location of the sampled XCO and XNO2 data, the daily sampling interval for both species, and because we carefully selected strong biomass burning source regions.…”

Modification of Local Urban Aerosol Properties by Long-Range Transport of Biomass Burning Aerosol

“…The results of the NAAPS model simulations (Navy Aerosol Analysis and Prediction System [69]; http://www.nrlmry.navy.mil/aerosol) over Poland on [24][25][26][27][28][29][30] August 2016 were used to interpret the results. For brevity, in The AOD values over Warsaw are listed in Table 2, where the passive satellite remote sensors (5:45 UTC pixel of SEVIRI at 635 nm and daily mean pixel of MODIS at 500 nm, both representative for the chosen location), the ground based passive remote sensor (the closest in time to 5:45 UTC columnar AOD derived by MFR-7 at 500 nm), the active remote sensor (the closest in time to 5:45 UTC columnar AOD derived from PollyXT lidar signals at 532 nm), as well as the AOD simulated by models (daily mean pixel of CAMS at 550 nm and NAAPS at 500 nm) are in a good and expected general agreement.…”

“…The effects of wildfire aerosols on the PM air quality are expected to be significant [29]. Based on available satellite data, distinguishing between combustion types and their separation from biomass burning is possible [30]. Interaction between high levels of air pollution and high temperatures (≥30 • C) are reported as statistically significant for sulphur dioxide (due to primary combustion pollution) and as a suggestive for black [31].…”

Satellite Data Reveals a Common Combustion Emission Pathway for Major Cities in China

“…CC BY 4.0 License. reasonable to interpret the long-term changes in spatial covariations between these observed pollutant enhancements within the megacity to reflect dominant shifts in bulk combustion characteristics (e.g., changes in fuel mixture and technology practice), which can then be indicative of an emission pathway for a given megacity (e.g., Parrish et al, 2002;Parrish, 2006;Russell et al, 2012;Silva et al, 2013;Hassler et al, 2016;Silva and Arellano, 2017). Data sampling and collocation issues, as well as retrieval 5 information content and chemical nonlinearities between these pollutants, do not quite manifest at decadal scales more than emission changes, especially when treated as a smokestack in the analysis.…”