What question did this study set out to answer?

This study aims to evaluate how different anthropogenic emission inventories affect the simulation of CO2 concentrations in Jiangsu Province, China.

June 17, 2026

The Differences in Anthropogenic Emission Inventories and Their Impacts on High‐Resolution CO 2 Simulation in Jiangsu Province, China

Key Points

This study aims to evaluate how different anthropogenic emission inventories affect the simulation of CO2 concentrations in Jiangsu Province, China.
Used a 3-km WRF-Chem-VPRM framework for simulations in July and December 2022.
Conducted sensitivity experiments to assess impacts of inventory selection, emission magnitude, spatial distribution, and vertical allocation on modeled CO2 fields.
All simulations controlled for identical meteorological conditions to ensure reliability.
Inventory selection contributed to a standard deviation of 8.2 ppm in average nighttime CO2 levels.
Spatial allocation differences had a greater effect on CO2 variability than total emission magnitude.
Vertical allocation produced differences of 25–50 ppm in surface CO2 under stable boundary-layer conditions.

Abstract

Abstract Accurate simulation of regional carbon dioxide (CO 2 ) concentrations is critical for urban carbon monitoring, inverse modeling and mitigation. However, large uncertainties persist due to differences in anthropogenic emission inventories in China. Focusing on Jiangsu Province in the Yangtze River Delta, where the mean inter‐inventory spread in annual city‐level total emissions exceeds 60% and spatial discrepancies are substantial, we assessed how six widely used inventories affect modeled CO 2 fields. Using a 3‐km WRF‐Chem‐VPRM framework, we performed simulations for July and December 2022. To isolate the impact of inventory‐related uncertainties, we designed sensitivity experiments that separately perturbed inventory selection, emission magnitude, spatial distribution, and temporal and vertical allocation used in the inventory‐to‐model matching. All experiments were driven by identical meteorological conditions to ensure comparability. Model outputs showed good consistency with meteorological observations, CarbonTracker near‐surface CO 2 , and OCO‐2 XCO 2 ( R ≈ 0.83), while also capturing physically reasonable near‐surface diurnal behavior. Inventory selection led to a nighttime urban domain‐averaged standard deviation of 8.2 ppm. Sensitivity results indicated that spatial allocation differences contributed more to modeled CO 2 variability than total emission magnitude. Under stable boundary‐layer conditions, vertical allocation emerged as a key uncertainty source, producing 25–50 ppm differences in surface CO 2 . These results demonstrate that inventory‐to‐model matching, especially vertical allocation, can exceed the impact of inventory selection for high‐resolution urban CO 2 simulations under stable nighttime conditions. This study provides a quantitative basis for diagnosing inventory‐induced variability and supports the development of fine‐resolution, vertically resolved inventories for robust urban CO 2 modeling and future inversion efforts.

AI에게 질문

Bookmark