Search Results (5)

Reducing anthropogenic fossil fuel CO₂ (FFCO₂) emissions in urban areas is key to mitigating climate change. To better understand the spatial characteristics and temporal variations in urban CO₂ levels in the Beijing (BJ) region, we conducted a long-term CO₂ simulation study by using the Weather Research and Forecasting WRF-Chem model and CO₂ observation data. To assess the model performance, three representative sites with high-precision CO₂ observation data were chosen in this study: the rural regional background Shangdianzi (SDZ) site, the suburban Xianghe (XH) site, and the urban BJ site. The simulation results generally captured the observed variations at these three sites, but the model performed much better at the SDZ and XH sites, with mean biases of −0.7 ppm and −2.3 ppm, respectively, and RMSE of 12.3 ppm and 21.4 ppm, respectively. The diurnal variations in the model results agreed well with those in the observed CO₂ concentrations at the SDZ and XH sites during all seasons. In the meanwhile, the diurnal variations in the modeled FFCO₂ were similar to those in the CO₂ observation with a positive bias at the BJ site, which may have been caused by higher emissions especially in winter. Moreover, both the modeled FFCO₂ and biospheric CO₂ (BIOCO₂) have positive correlations with the observed CO₂ concentration, whereas the planetary boundary layer height (PBLH) and observed CO₂ concentration exhibited negative correlations at all sites. In addition, the contributions of FFCO₂ and BIOCO₂ to CO₂ varies depending on the seasons and the location of sites. Full article

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO₂ emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers. Full article

Fossil fuel carbon dioxide (FFCO₂) is a major source of atmospheric greenhouse gases that result in global climate change. Quantification of the atmospheric concentrations and emissions of FFCO₂ is of vital importance to understand its environmental process and to formulate and evaluate the efficiency of carbon emission reduction strategies. Focusing on this topic, we summarized the state-of-the-art method to trace FFCO₂ using radiocarbon (¹⁴C), and reviewed the ¹⁴CO₂ measurements and the calculated FFCO₂ concentrations conducted in the last two decades. With the mapped-out spatial distribution of ¹⁴CO₂ values, the typical regional distribution patterns and their driving factors are discussed. The global distribution of FFCO₂ concentrations is also presented, and the datasets are far fewer than ¹⁴CO₂ measurements. With the combination of ¹⁴C measurements and atmospheric transport models, the FFCO₂ concentration and its cross-regional transport can be well interpreted. Recent progress in inverse methods can further constrain emission inventories well, providing an independent verification method for emission control strategies. This article reviewed the latest developments in the estimation of FFCO₂ and discussed the urgent requirements for the control of FFCO₂ according to the current situation of climate change. Full article

Transportation accounts for 18% of global fossil fuel carbon dioxide (FFCO₂) emissions, especially in urban areas. An improved understanding of on-road FFCO₂ emissions is essential to both carbon science and mitigation policy. Previous studies have identified the driving factors and quantified their relationship to on-road FFCO₂ emissions. However, they have been primarily based on case studies conducted in individual cities, and the research results remain inconclusive due to the considerable heterogeneity of cities and associated outcomes. In order to achieve more general results and to further understand their uncertainties, this study explored the relationships between socio-economic/urban form data and self-reported on-road FFCO₂ emissions for a sample of global cities based on the adjusted Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. The robustness and sensitivity of these relationships was evaluated by introducing artificial errors, conducting cross-validation, and examining various model specifications. Results indicated that fuel economy (p-value < 3.1 × 10⁻⁸), vehicle ownership (p-value < 3.0 × 10⁻⁴), road density (p-value < 4.4 × 10⁻³) and population density (p-value < 3.1 × 10⁻³) were statistically significant factors that correlate with on-road FFCO₂ emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships. These results offer potential policy insights into on-road FFCO₂ emissions mitigation in cities, in addition to offering a means to generate emissions estimates without detailed bottom-up information. Full article

The ‘Hestia Project’ uses a bottom-up approach to quantify fossil fuel CO₂ (FFCO₂) emissions spatially at the building/street level and temporally at the hourly level. Hestia FFCO₂ emissions are provided in the form of a group of sector-specific vector layers with point, line, and polygon sources to support carbon cycle science and climate policy. Application to carbon cycle science, in particular, requires regular gridded data in order to link surface carbon fluxes to atmospheric transport models. However, the heterogeneity and complexity of FFCO₂ sources within regular grids is sensitive to spatial resolution. From the perspective of a data provider, we need to find a balance between resolution and data volume so that the gridded data product retains the maximum amount of information content while maintaining an efficient data volume. The Shannon entropy determines the minimum bits that are needed to encode an information source and can serve as a metric for the effective information content. In this paper, we present an analysis of the Shannon entropy of gridded FFCO₂ emissions with varying resolutions in four Hestia study areas, and find: (1) the Shannon entropy increases with smaller grid resolution until it reaches a maximum value (the max-entropy resolution); (2) total emissions (the sum of several sector-specific emission fields) show a finer max-entropy resolution than each of the sector-specific fields; (3) the residential emissions show a finer max-entropy resolution than the commercial emissions; (4) the max-entropy resolution of the onroad emissions grid is closely correlated to the density of the road network. These findings suggest that the Shannon entropy can detect the information effectiveness of the spatial resolution of gridded FFCO₂ emissions. Hence, the resolution-entropy relationship can be used to assist in determining an appropriate spatial resolution for urban CO₂ flux studies. We conclude that the optimal spatial resolution for providing Hestia total FFCO₂ emissions products is centered around 100 m, at which the FFCO₂ emissions data can not only fully meet the requirement of urban flux integration, but also be effectively used in understanding the relationships between FFCO₂ emissions and various social-economic variables at the U.S. census block group level. Full article