High-resolution spatiotemporal patterns of China’s FFCO₂ emissions under the impact of LUCC from 2000 to 2015

Abstract

Fossil fuel carbon dioxide (FFCO 2 ) emissions have become a principal driver behind the increase of atmospheric CO 2 concentration and spatiotemporal variations of atmospheric CO 2 in the urban surface layer. This study quantifies the 2000-2015 urban high-resolution spatiotemporal patterns of China's FFCO 2 emissions under the impact of the land-use and land-cover change. Multi-source data were used together with various up-to-date geostatistics and spatial analysis methods. FFCO 2 emissions were determined to rise over the 15 years in the highest emitting cities in the South and East of China. The high-value clusters inside of all cities expanded outward from their city centers and in some cases transferred to economic development zones or new city centers, while the expansion speeds and variation time were found to differ significantly. We found further that then FFCO 2 emissions spatial distribution is interconnected with diverse factors: urbanization, and either croplands (rainfed, irrigated, and post-flooding) or native vegetation, being the two most important. As expected, the increase in urban areas was associated with increased FFCO 2 emissions, while the wettability in croplands or the increase in native vegetation have an association with the decrease of FFCO 2 emissions. Unlike previous studies, we have found no change associated with changes in water cover. Finally, while the primary source of FFCO 2 emissions is still coal, there has been a gradual move to cleaner energy (natural gas in Beijing) or more efficient industrial processes (Wuxi and Dalian), although diverse industrial structures and energy efficiencies exist. Over time, the current spatial patterns of FFCO 2 emissions in China will conflict with these trends at the macroscale.

Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China

“…SO 2 emissions are generally estimated using the "bottomup" approach, which requires direct observations of the activities and emissions factors from all possible sources (Zhao et al, 2020). However, the estimates are subject to substantial uncertainties because of limited available observations, with the differences among existing inventories as high as 42 % (Granier et al, 2011).…”

Potential remote forcing of North Atlantic SST tripole anomalies on the seesaw haze intensity between late winter months in the North China plain: A case study

“…In this study, we first examined the role of intraseasonal variations in emission anomalies. Regarding anthropogenic emissions, CO 2 emissions are a substitute proxy representing the amount of total fossil energy consumption (Zhao et al, 2020), which is associated with temporal haze pollution variations in China (Xu et al, 2015). From the spatial distribution of anomalous FFCO 2 emissions in central eastern China (Figure 2), we can discern that the estimated major anomalies of anthropogenic CO 2 emissions were concentrated in economically developed subregions of China with frequent haze occurrences (i.e., NCP, the Huang‐Huai basin, Yangtze River Delta [YRD] and Pearl River Delta [PRD] [Yin et al, 2015; Mao et al, 2019; Chang et al, 2020]) and CO 2 emission anomalies intensified around the NCP and the YRD.…”

“…Because visibility observations switched from manual to automatic in 2014 (Wang et al, 2019), for reasonable analysis, the temporal coverage of visibility data was from 1980 to 2013. Gridded fossil fuel CO 2 (FFCO 2 ) emissions over land for 2000–2017, with a horizontal resolution of 1° × 1°, were obtained from the Open‐source Data Inventory for Anthropogenic CO 2 (ODIAC; Oda & Maksyutov, 2011; Oda et al, 2018). ODIAC FFCO 2 emission data are widely deployed to estimate anthropogenic CO 2 emissions from fossil fuel combustion in China (e.g., Zhao et al, 2020). This study selected the ODIAC2020b version, downloaded from https://db.cger.nies.go.jp/dataset/ODIAC/DL_odiac2020b.html. Monthly atmospheric fields derived from the National Centres for Environmental Prediction–Department of Energy (NCEP–DOE) reanalysis 2 (NCEP‐2; Kanamitsu et al, 2002), with a horizontal resolution of 2.5° × 2.5°, covering the range of 1980–2019. The monthly planetary boundary layer (PBL) height (PBLH) from the fifth major global reanalysis produced by the European Centre for Medium‐Range Weather Forecasts (ECMWF) (ERA5; Hersbach et al, 2020), with a horizontal resolution of 1° × 1°, covering the range of 1980–2019. Monthly SST data from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST version 5 (ERSST5; Huang et al, 2017), with a horizontal resolution of 2° × 2°, covering the range of 1979–2019. Global monthly precipitation data from NOAA's precipitation reconstruction (Chen et al, 2002), with a horizontal resolution of 2.5° × 2.5°, covering the range of 1980–2019. The monthly Community Earth System Model Large Ensemble Numerical Simulation (CESM‐LENS) datasets (Kay et al, 2015), from the National Centre for Atmospheric Research (NCAR) Climate Data Gateway ( https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.cesmLE.html).…”

Effects of climate changes on net primary productivity variation in the marsh area of the Sanjiang Plain

“…On one hand, LUCC can directly affect the carbon cycle process by changing the vegetation types (Li et al, 2014). On the other hand, LUCC can cause changes in N deposition and CO 2 concentration, and then indirectly affect the carbon cycle process (Li et al, 2014;Lu et al, 2016;Zhao et al, 2020a). The research on the change of carbon budget caused by LUCC shows that the transformation between forest loss and urbanization usually reduced the carbon budget and farmland expansion can increase carbon budget in some regions (Hu and Wang, 2008;Fu et al, 2009;Yu et al, 2009;Chang et al, 2022b;Zhuang et al, 2022).…”