High-resolution Carbon Dioxide Research Articles

Generating daily high-resolution and full-coverage XCO2 across China from 2015 to 2020 based on OCO-2 and CAMS data

China has set a goal to achieve carbon neutrality by 2060, and satellite remote sensing allows for acquiring large-range and high-resolution carbon dioxide (CO2) data, which can aid in achieving this goal. However, satellite-derived column-averaged dry-air mole fraction of CO2 (XCO2) products often suffer from substantial spatial gaps due to the impacts of narrow swath and clouds. Here, this paper generates daily full-coverage XCO2 data at a high spatial resolution of 0.1° over China during 2015–2020, by fusing satellite observations and reanalysis data in a deep neural network (DNN) framework. Specifically, DNN constructs the relationships between Orbiting Carbon Observatory-2 satellite XCO2 retrievals, Copernicus Atmosphere Monitoring Service (CAMS) XCO2 reanalysis data, and environmental factors. Then, daily full-coverage XCO2 data can be generated based on CAMS XCO2 and environmental factors. Results show that a satisfactory performance is reported in multiform validations, with RMSE and R2 of 0.99 ppm and 0.963 in terms of the sample-based cross-validation, respectively. The independent in-situ validation also indicates high consistency (R2 = 0.866 and RMSE = 1.71 ppm) between XCO2 estimates and ground measurements. Based on the generated dataset, spatial and seasonal distributions of XCO2 across China are investigated, and a growth rate of 2.71 ppm/yr is found from 2015 to 2020. This paper generates long time series of full-coverage XCO2 data, which helps promote our understanding of carbon cycling. The dataset is available from https://doi.org/10.5281/zenodo.7793917.

Near-real-time global gridded daily CO2 emissions

Precise and high-resolution carbon dioxide (CO2) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO2 Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO2 emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO2) retrievals. Our study on the global CO2 emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO2 emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

CO2 and CH4 Emissions from an Arid Fluvial Network on the Chinese Loess Plateau

The emissions of greenhouse gases (GHGs) from inland waters are an important component of the global carbon (C) cycle. However, the current understanding of GHGs emissions from arid river systems remains largely unknown. To shed light on GHGs emissions from inland waters in arid regions, high-resolution carbon dioxide (CO2) and methane (CH4) emission measurements were carried out in the arid Kuye River Basin (KRB) on the Chinese Loess Plateau to examine their spatio-temporal variability. Our results show that all streams and rivers were net C sources, but some of the reservoirs in the KRB became carbon sinks at certain times. The CO2 flux (FCO2) recorded in the rivers (91.0 mmol m−2 d−1) was higher than that of the reservoirs (10.0 mmol m−2 d−1), while CH4 flux (FCH4) in rivers (0.35 mmol m−2 d−1) was lower than that of the reservoirs (0.78 mmol m−2 d−1). The best model developed from a number of environmental parameters was able to explain almost 40% of the variability in partial pressure of CO2 (pCO2) for rivers and reservoirs, respectively. For CH4 emissions, at least 70% of the flux occurred in the form of ebullition. The emissions of CH4 in summer were more than threefold higher than in spring and autumn, with water temperature being the key environmental variable affecting emission rates. Since the construction of reservoirs can alter the morphology of existing fluvial systems and consequently the characteristics of CO2 and CH4 emissions, we conclude that future sampling efforts conducted at the basin scale need to cover both rivers and reservoirs concurrently.

Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data

Accurate geospatial modeling of greenhouse gas (GHG) emissions is an essential part of the future of global GHG monitoring systems. Our previous work found a systematic displacement in the high-resolution carbon dioxide (CO2) emission raster data of the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) emission product. It turns out this displacement is due to geolocation bias in the Defense Meteorological Satellite Program (DMSP) nighttime lights (NTL) data products, which are used as a spatial emission proxy for estimating non-point source emissions distributions in ODIAC. Mitigating such geolocation error (~1.7 km), which is on the same order of the size of the carbon observing satellites field of view, is especially critical for the spatial analysis of emissions from cities. In this paper, there is proposed a method to mitigate the geolocation bias in DMSP NTL data that can be applied to DMSP NTL-based geospatial products, such as ODIAC. To identify and characterize the geolocation bias, we used the OpenStreetMap repository to define city boundaries for a large number of global cities. Assumption is that the total emissions within the city boundaries are at the maximum if there is no displacement (geolocation bias) in NTL data. Therefore, it is necessary to find an optimal vector (distance and angle) that maximizes the ODIAC total emissions within cities by shifting the emission fields. In the process of preparing annual composites of the nighttime stable lights data, some pixels of the DMSP data corresponding to water bodies were zeroed, which due to the geolocation bias unreasonably distorted the ODIAC emission fields. Hence, an original approach for restoring data in such pixels is considered using elimination of the factor that distorted the ODIAC emission fields. It is also proposed a bias correction method for shifted high-resolution emission fields in ODIAC. The bias correction was applied to multiple cities from the different continents. It is shown that the bias correction to the emission data (elimination of geolocation error in non-point emission source fields) increases the total CO2 emissions within city boundaries by 4.76% on average, due to reduced emissions from non-urban areas to which these emissions were likely to be erroneously attributed.

Priming mechanisms with additions of pyrogenic organic matter to soil

Additions of pyrogenic organic matter (PyOM) to soil have been shown to both increase and decrease mineralization of native soil organic carbon (nSOC). This change in mineralization rate is referred to as priming, and may have important implications for carbon (C) turnover in soil. This study quantifies both positive and negative priming mechanisms using high-resolution carbon dioxide (CO2) measurements using a series of short-term incubation experiments with 13C-labeled PyOM added to a temperate forest soil. Moisture, nutrient availability and pH were adjusted to minimize any differential effects on nSOC mineralization. Irrespective of pyrolysis temperature (200–750 °C), addition of 10 mg PyOM g−1 soil significantly decreased mineralization of nSOC. Dilution was measured against inorganic bulking materials with different surface areas and accounted for 19% of negative priming observed at day 7, and 13% at day 35. In comparison, substrate switching caused only 1% of negative priming assumed to be equivalent to PyOM mineralization itself. Inhibition did not explain reductions in nSOC mineralization since the microbial biomass did not significantly decrease (p > 0.05). Sorptive protection of dissolved organic carbon (DOC) was responsible for the majority of negative priming observed with PyOM additions based on adsorption isotherm experiments as well as co-location of nSOC on PyOM surfaces shown by nanoSIMS. Maximum sorption of soil DOC was 29 times higher to PyOM pyrolyzed at 450 °C than to topsoil, and tripled with an increase in pyrolysis temperature to 750 °C. This tripling in DOC sorption potential to PyOM produced at 750 °C in comparison to 450 °C was only reflected in a less than twice lower nSOC mineralization. Sorptive protection was with 80% the dominant negative priming mechanism on monthly timescales and likely beyond. These results have implications for long-term SOC storage, because sorption has more persistent effects than substrate switching or dilution.