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China's Fossil Fuel CO 2 Emissions Estimated Using Surface Observations of Coemitted NO 2 .
- Source :
-
Environmental science & technology [Environ Sci Technol] 2024 May 14; Vol. 58 (19), pp. 8299-8312. Date of Electronic Publication: 2024 May 01. - Publication Year :
- 2024
-
Abstract
- Accurate estimates of fossil fuel CO <subscript>2</subscript> (FFCO <subscript>2</subscript> ) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO <subscript>2</subscript> observations, allowing us to combine observation-constrained NO <subscript> x </subscript> emissions coemitted with FFCO <subscript>2</subscript> and grid-specific CO <subscript>2</subscript> -to-NO <subscript> x </subscript> emission ratios to infer the daily FFCO <subscript>2</subscript> emissions over China. The estimated national total for 2016 was 11.4 PgCO <subscript>2</subscript> ·yr <superscript>-1</superscript> , with an uncertainty (1σ) of 1.5 PgCO <subscript>2</subscript> ·yr <superscript>-1</superscript> that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO <subscript>2</subscript> -to-NO <subscript> x </subscript> emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO <subscript>2</subscript> related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO <subscript>2</subscript> estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO <subscript>2</subscript> emissions in China.
Details
- Language :
- English
- ISSN :
- 1520-5851
- Volume :
- 58
- Issue :
- 19
- Database :
- MEDLINE
- Journal :
- Environmental science & technology
- Publication Type :
- Academic Journal
- Accession number :
- 38690832
- Full Text :
- https://doi.org/10.1021/acs.est.3c07756