Agricultural lands occupy 37% of the earth's land surface. Agriculture accounts for 52 and 84% of global anthropogenic methane and nitrous oxide emissions. Agricultural soils may also act as a sink or source for CO 2 , but the net flux is small. Many agricultural practices can potentially mitigate greenhouse gas (GHG) emissions, the most prominent of which are improved cropland and grazing land management and restoration of degraded lands and cultivated organic soils. Lower, but still significant mitigation potential is provided by water and rice management, set-aside, land use change and agroforestry, livestock management and manure management. The global technical mitigation potential from agriculture (excluding fossil fuel offsets from biomass) by 2030, considering all gases, is estimated to be approximately 5500-6000 Mt CO 2 -eq. yr K1 , with economic potentials of approximately 1500-1600, 2500-2700 and 4000-4300 Mt CO 2 -eq. yr K1 at carbon prices of up to 20, up to 50 and up to 100 US$ t CO 2 -eq. K1 , respectively. In addition, GHG emissions could be reduced by substitution of fossil fuels for energy production by agricultural feedstocks (e.g. crop residues, dung and dedicated energy crops). The economic mitigation potential of biomass energy from agriculture is estimated to be 640, 2240 and 16 000 Mt CO 2 -eq. yr K1 at 0-20, 0-50 and 0-100 US$ t CO 2 -eq. K1 , respectively.
The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (−1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.
The two-step nitrification process is an integral part of the global nitrogen cycle, and it is accomplished by distinctly different nitrifiers. By combining DNA-based stable isotope probing (SIP) and high-throughput pyrosequencing, we present the molecular evidence for autotrophic growth of ammonia-oxidizing bacteria (AOB), ammonia-oxidizing archaea (AOA) and nitrite-oxidizing bacteria (NOB) in agricultural soil upon ammonium fertilization. Time-course incubation of SIP microcosms indicated that the amoA genes of AOB was increasingly labeled by 13 CO 2 after incubation for 3, 7 and 28 days during active nitrification, whereas labeling of the AOA amoA gene was detected to a much lesser extent only after a 28-day incubation. Phylogenetic analysis of the 13 C-labeled amoA and 16S rRNA genes revealed that the Nitrosospira cluster 3-like sequences dominate the active AOB community and that active AOA is affiliated with the moderately thermophilic Nitrososphaera gargensis from a hot spring. The higher relative frequency of Nitrospira-like NOB in the 13 C-labeled DNA suggests that it may be more actively involved in nitrite oxidation than Nitrobacter-like NOB. Furthermore, the acetylene inhibition technique showed that 13 CO 2 assimilation by AOB, AOA and NOB occurs only when ammonia oxidation is not blocked, which provides strong hints for the chemolithoautotrophy of nitrifying community in complex soil environments. These results show that the microbial community of AOB and NOB dominates the nitrification process in the agricultural soil tested. The ISME Journal (2011) 5, 1226-1236; doi:10.1038/ismej.2011.5; published online 17 February 2011Subject Category: microbial ecology and functional diversity of natural habitats
[1] A biogeochemical model, Denitrification-Decomposition (DNDC), was modified to enhance its capacity of predicting greenhouse gas (GHG) emissions from paddy rice ecosystems. The major modifications focused on simulations of anaerobic biogeochemistry and rice growth as well as parameterization of paddy rice management. The new model was tested for its sensitivities to management alternatives and variations in natural conditions including weather and soil properties. The test results indicated that (1) varying management practices could substantially affect carbon dioxide (CO 2 ), methane (CH 4 ), or nitrous oxide (N 2 O) emissions from rice paddies; (2) soil properties affected the impacts of management alternatives on GHG emissions; and (3) the most sensitive management practices or soil factors varied for different GHGs. For estimating GHG emissions under certain management conditions at regional scale, the spatial heterogeneity of soil properties (e.g., texture, SOC content, pH) are the major source of uncertainty. An approach, the most sensitive factor (MSF) method, was developed for DNDC to bring the uncertainty under control. According to the approach, DNDC was run twice for each grid cell with the maximum and minimum values of the most sensitive soil factors commonly observed in the grid cell. The simulated two fluxes formed a range, which was wide enough to include the ''real'' flux from the grid cell with a high probability. This approach was verified against a traditional statistical approach, the Monte Carlo analysis, for three selected counties or provinces in China, Thailand, and United States. Comparison between the results from the two methods indicated that 61-99% of the Monte Carlo-produced GHG fluxes were located within the MSAproduced flux ranges. The result implies that the MSF method is feasible and reliable to quantify the uncertainties produced in the upscaling processes. Equipped with the MSF method, DNDC modeled emissions of CO 2 , CH 4 , and N 2 O from all of the rice paddies in China with two different water management practices, i.e., continuous flooding and midseason drainage, which were the dominant practices before 1980 and in 2000, respectively. The modeled results indicated that total CH 4 flux from the simulated 30 million ha of Chinese rice fields ranged from 6.4 to 12.0 Tg CH 4 -C per year under the continuous flooding conditions. With the midseason drainage scenario, the national CH 4 flux from rice agriculture reduced to 1.7-7.9 Tg CH 4 -C. It implied that the water management change in China reduced CH 4 fluxes by 4.2-4.7 Tg CH 4 -C per year. Shifting the water management from continuous flooding to midseason drainage increased N 2 O fluxes by 0.13-0.20 Tg N 2 O-N/yr, although CO 2 fluxes were only slightly altered. Since N 2 O possesses a radiative forcing more than 10 times higher than CH 4 , the increase in N 2 O offset about 65% of the benefit gained by the decrease in CH 4 emissions.INDEX TERMS: 0315 Atmospheric Composition and Structure: Biosphere/atmosphere
[1] Validations of the DeNitrification-DeComposition (DNDC) model against field data sets of trace gases (CH 4 , N 2 O, and NO) emitted from cropping systems in Japan, China, and Thailand were conducted. The model-simulated results were in agreement with seasonal N 2 O emissions from a lowland soil in Japan from 1995 to 2000 and seasonal CH 4 emissions from rice fields in China, but failed to simulate N 2 O and NO emissions from an Andisol in Japan as well as NO emissions from the lowland soil. Seasonal CH 4 emissions from rice cropping systems in Thailand were poorly simulated because of site-specific soil conditions and rice variety. For all of the simulated cases, the model satisfactorily simulated annual variations of greenhouse gas emissions from cropping systems and effects of land management. However, discrepancies existed between the modeled and observed seasonal patterns of CH 4 and N 2 O emissions. By incorporating modifications based on the local soil properties and management, DNDC model could become a powerful tool for estimating greenhouse gas emissions from terrestrial ecosystems.
Abstract. We present a new multispectral approach for observing lowermost tropospheric ozone from space by synergism of atmospheric radiances in the thermal infrared (TIR) observed by IASI (Infrared Atmospheric Sounding Interferometer) and earth reflectances in the ultraviolet (UV) measured by GOME-2 (Global Ozone Monitoring Experiment-2). Both instruments are onboard the series of MetOp satellites (in orbit since 2006 and expected until 2022) and their scanning capabilities offer global coverage every day, with a relatively fine ground pixel resolution (12 km-diameter pixels spaced by 25 km for IASI at nadir). Our technique uses altitude-dependent Tikhonov-Phillips-type constraints, which optimize sensitivity to lower tropospheric ozone. It integrates the VLIDORT (Vector Linearized Discrete Ordinate Radiative Transfer) and KOPRA (Karlsruhe Optimized and Precise Radiative transfer Algorithm) radiative transfer codes for simulating UV reflectance and TIR radiance, respectively. We have used our method to analyse real observations over Europe during an ozone pollution episode in the summer of 2009. The results show that the multispectral synergism of IASI (TIR) and GOME-2 (UV) enables the observation of the spatial distribution of ozone plumes in the lowermost troposphere (LMT, from the surface up to 3 km a.s.l., above sea level), in good agreement with the CHIMERE regional chemistry-transport model. In this case study, when high ozone concentrations extend vertically above 3 km a.s.l., they are similarly observed over land by both the multispectral and IASI retrievals. On the other hand, ozone plumes located below 3 km a.s.l. are only clearly depicted by the multispectral retrieval (both over land and over ocean). This is achieved by a clear enhancement of sensitivity to ozone in the lowest atmospheric layers. The multispectral sensitivity in the LMT peaks at 2 to 2.5 km a.s.l. over land, while sensitivity for IASI or GOME-2 only peaks at 3 to 4 km a.s.l. at lowest (above the LMT). The degrees of freedom for the multispectral retrieval increase by 0.1 (40 % in relative terms) with respect to IASI only retrievals for the LMT. Validations with ozonesondes (over Europe during summer 2009) show that our synergetic approach for combining IASI (TIR) and GOME-2 (UV) measurements retrieves lowermost tropospheric ozone with a mean bias of 1 % and a precision of 16 %, when smoothing by the retrieval vertical sensitivity (1 % mean bias and 21 % precision for direct comparisons).Published by Copernicus Publications on behalf of the European Geosciences Union. 9676
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.