The Open-Source Data Inventory for Anthropogenic Carbon Dioxide (CO2), Version 2016 (ODIAC2016): A Global, Monthly Fossil-Fuel CO2 Gridded Emission Data Product for Tracer Transport Simulations and Surface Flux Inversions

The Open-source Data Inventory for Anthropogenic CO2 (ODIAC) is a global high-spatial resolution gridded emission data product that distributes carbon dioxide (CO2) emissions from fossil fuel combustion. The emission spatial distributions are estimated at a 1x1 km spatial resolution over land using power plant profiles (emission intensity and geographical location) and satellite-observed nighttime lights. This paper describes the year 2016 version of the ODIAC emission data product (ODIAC2016) and presents analyses that help guiding data users, especially for atmospheric CO2 tracer transport simulations and flux inversion analysis. Since the original publication in 2011, we have made modifications to our emission modeling framework in order to deliver a comprehensive global gridded emission data product. Major changes from the 2011 publication are 1) the use of emissions estimates made by the Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory (ORNL) by fuel type (solid, liquid, gas, cement manufacturing, gas flaring and international aviation and marine bunkers), 2) the use of multiple spatial emission proxies by fuel type such as nightlight data specific to gas flaring and ship/aircraft fleet tracks and 3) the inclusion of emission temporal variations. Using global fuel consumption data, we extrapolated the CDIAC emissions estimates for the recent years and produced the ODIAC2016 emission data product that covers 2000-2015. Our emission data can be viewed as an extended version of CDIAC gridded emission data product, which should allow data users to impose global fossil fuel emissions in more comprehensive manner than original CDIAC product. Our new emission modeling framework allows us to produce future versions of ODIAC emission data product with a timely update. Such capability has become more significant given the CDIAC/ORNL's shutdown. ODIAC data product could play an important role to support carbon cycle science, especially modeling studies with space-based CO2 data collected near real time by ongoing carbon observing missions such as Japanese Greenhouse Observing SATellite (GOSAT), NASA's Orbiting Carbon Observatory 2 (OCO-2) and upcoming future missions. The ODIAC emission data product including the latest version of the ODIAC emission data (ODIAC2017, 2000-2016), is distributed from http://db.cger.nies.go.jp/dataset/ODIAC/ with a DOI

Comparing a global high-resolution downscaled fossil fuel CO2 emission dataset to local inventory-based estimates over 14 global cities

Background: Compilation of emission inventories (EIs) for cities is a whole new challenge to assess the subnational climate mitigation effort under the Paris Climate Agreement. Some cities have started compiling EIs, often following a global community protocol. However, EIs are often difficult to systematically examine because of the ways they were compiled (data collection and emission calculation) and reported (sector definition and direct vs consumption). In addition, such EI estimates are not readily applicable to objective evaluation using modeling and observations due to the lack of spatial emission extents. City emission estimates used in the science community are often based on downscaled gridded EIs, while the accuracy of the downscaled emissions at city level is not fully assessed. Results: This study attempts to assess the utility of the downscaled emissions at city level. We collected EIs from 14 major global cities and compare them to the estimates from a global high-resolution fossil fuel CO2 emission data product (ODIAC) commonly used in the science research community. We made necessary adjustments to the estimates to make our comparison as reasonable as possible. We found that the two methods produce very close area-wide emission estimates for Shanghai and Delhi (< 10% difference), and reach good consistency in half of the cities examined (< 30% difference). The ODIAC dataset exhibits a much higher emission compared to inventory estimates in Cape Town (+ 148%), Sao Paulo (+ 43%) and Beijing (+ 40%), possibly related to poor correlation between nightlight intensity with human activity, such as the high-emission and low-lighting industrial parks in developing countries. On the other hand, ODIAC shows lower estimates in Manhattan (-62%), New York City (-45%), Washington D.C. (-42%) and Toronto (-33%), all located in North America, which may be attributable to an underestimation of residential emissions from heating in ODIAC's nightlight-based approach, and an overestimation of emission from ground transportation in registered vehicles statistics of inventory estimates. Conclusions: The relatively good agreement suggests that the ODIAC data product could potentially be used as a first source for prior estimate of city-level CO2 emission, which is valuable for atmosphere CO2 inversion modeling and comparing with satellite CO2 observations. Our compilation of in-boundary emission estimates for 14 cities contributes towards establishing an accurate inventory in-boundary global city carbon emission dataset, necessary for accountable local climate mitigation policies in the future. © 2020 The Author(s)

A High-Definition Spatially Explicit Modeling Approach for National Greenhouse Gas Emissions from Industrial Processes: Reducing the Errors and Uncertainties in Global Emission Modeling

Spatially-explicit (gridded) emission inventories (EIs) should allow us to analyse sectoral emissions patterns to estimate potential impacts of emission policies and support decisions on reducing emissions. However, such EIs are often based on simple downscaling of national level emissions estimate and the changes in subnational emissions distributions are not necessarily reflecting the actual changes driven by the local emissions drivers. This article presents a high definition,100m resolution bottom-up inventory of greenhouse gas (GHG) emissions from the industrial processes (fuel combustion activities in energy and manufacturing industry, fugitive emissions, mineral products, chemical industry, metal production, food and drink) that is exemplified on data for Poland. We propose an improved emission disaggregation algorithmthat fully utilizes a collection of activity data available at national/provincial level to the level of individual point and diffused (area) emission sources. To ensure the accuracy of the resulting 100m emission fields, the geospatial data used for mapping emission sources (point source geolocation and land cover classification) were subject to thorough human visual inspection.The resulting 100m emission field even hold cadastres of emissions separately for each industrial emission category, while we start with IPCC-compliant national sectoral GHG estimates that we made using Polish official statistics. We aggregated the resulting emissions to the level of administrative units such as municipalities, districts and provinces. We also compiled cadastres in regular grids and then compared them with EDGAR results. Quantitative analysis of discrepancies between both results revealed quite frequent misallocations of point sources used in the EDGAR compilation that considerably deteriorates high resolution inventories. We also propose a Monte-Carlo method-based uncertainty assessment that yields a detailed estimation of the GHG emission uncertainty in the main categories of the analysed processes. We found that the above mentioned geographical coordinates and patterns used for emission disaggregation have the greatest impact on overall uncertainty of GHG inventoriesfrom the industrial processes

Implementation-First Approach of Developing Formal Semantics of a Simulation Language in VDM-SL

Formal specification is a basis for rigorous software implementation. VDM-SL is a formal specification language with an extensive executable subset. Successful cases of VDM-family including VDM-SL have shown that producing a well-tested executable specification can reduce the cost of the implementation phase. This paper introduces and discusses the reversed order of specification and implementation. The development of a multi-agent simulation language called \remobidyc is described and examined as a case study of defining a formal specification after initial implementation and reflecting the specification into the implementation code

A microtubule-dynein tethering complex regulates the axonemal inner dynein f (I1)

Motility of cilia/flagella is generated by a coordinated activity of thousands of dyneins. Inner dynein arms (IDAs) are particularly important for the formation of ciliary/flagellar waveforms, but the molecular mechanism of IDA regulation is poorly understood. Here, we show using cryo-electron tomography and biochemical analyses of Chlamydomonas flagella that a conserved protein FAP44 forms a complex that tethers IDA f (I1 dynein) head domains to the A-tubule of the axonemal outer doublet microtubule. In wild-type flagella, IDA f showed little nucleotide-dependent movement except for a tilt in the fbeta head perpendicular to the microtubule-sliding direction. In the absence of the tether complex, however, addition of ATP and vanadate caused a large conformational change in the IDA f head domains, suggesting that the movement of IDA f is mechanically restricted by the tether complex. Motility defects in flagella missing the tether demonstrates the importance of the IDA f-tether interaction in the regulation of ciliary/flagellar beating

Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data

The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China and worldwide. It has significantly reduced gross domestic product (GDP), power generation, industrial activity and transport volume; thus, it has reduced fossil-related and cement-induced carbon dioxide (CO 2) emissions in China. Due to time delays in obtaining activity data, traditional emissions inventories generally involve a 2–3-year lag. However, a timely assessment of COVID-19's impact on provincial CO 2 emission reductions is crucial for accurately understanding the reduction and its implications for mitigation measures; furthermore, this information can provide constraints for modeling studies. Here, we used national and provincial GDP data and the China Emission Accounts and Datasets (CEADs) inventory to estimate the emission reductions in the first quarter (Q1) of 2020. We find a reduction of 257.7 Mt. CO 2 (11.0%) over Q1 2019. The secondary industry contributed 186.8 Mt. CO 2 (72.5%) to the total reduction, largely due to lower coal consumption and cement production. At the provincial level, Hubei contributed the most to the reductions (40.6 Mt) due to a notable decrease of 48.2% in the secondary industry. Moreover, transportation significantly contributed (65.1 Mt), with a change of −22.3% in freight transport and −59.1% in passenger transport compared with Q1 2019. We used a point, line and area sources (PLAS) method to test the GDP method, producing a close estimate (reduction of 10.6%). One policy implication is a change in people's working style and communication methods, realized by working from home and holding teleconferences, to reduce traffic emissions. Moreover, GDP is found to have potential merit in estimating emission changes when detailed energy activity data are unavailable. We provide provincial data that can serve as spatial disaggregation constraints for modeling studies and further support for both the carbon cycle community and policy makers