Quantification of greenhouse gas emissions is receiving a lot of attention because of its relevance for climate mitigation. Complementary to official reported bottom-up emission inventories, ...quantification can be done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such a method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories are, however, not systematically analysed. In this work, a statistically coherent method is used to quantify the uncertainties in a high-resolution gridded emission inventory of CO2 and CO for Europe. We perform a range of Monte Carlo simulations to determine the effect of uncertainties in different inventory components, including the spatial and temporal distribution, on the uncertainty in total emissions and the resulting atmospheric mixing ratios. We find that the uncertainties in the total emissions for the selected domain are 1 % forCO2 and 6 % for CO. Introducing spatial disaggregation causes a significant increase in the uncertainty of up to 40 % for CO2 and 70 % for CO for specific grid cells. Using gridded uncertainties, specific regions can be defined that have the largest uncertainty in emissions and are thus an interesting target for inverse modellers. However, the largest sectors are usually the best-constrained ones (low relative uncertainty), so the absolute uncertainty is the best indicator for this. With this knowledge, areas can be identified that are most sensitive to the largest emission uncertainties, which supports network design.
Sisal fibre can potentially replace glass fibre in natural fibre composites. This study focuses on the environmental performance of sisal fibre production by quantifying the greenhouse gas (GHG) ...emissions and energy use of producing sisal fibre in Tanzania and Brazil using life cycle assessment (LCA), based on region-specific inventory data. The results show that sisal fibre production has much lower GHG emissions (75–95%) and non-renewable energy use (85–95%) compared to glass fibre on a kg-basis, which is in line with published LCAs on natural fibres. Sisal fibre's GHG emissions are strongly influenced by potential methane emissions arising from the wet disposal of sisal leaf residues. Furthermore, because the direct energy and material requirements of sisal fibre production are low, its environmental performance is shown to vary strongly based on local practices such as residue disposal and fertiliser use, and is also sensitive to transportation distances. Several improvement options are explored to understand potential improvements in environmental sustainability. The most attractive option is limiting inadvertent methane emissions occurring at residue disposal sites, for instance by using them for the production of biogas.
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•Sisal fibre produced in Tanzania and Brazil is studied using life cycle assessment.•For both countries, GHG emissions and energy use are 75–95% lower than glass fibre.•Sisal's environmental impacts are shown to depend on location and local practices.•Potential methane emissions from residue disposal strongly affect GHG emissions.
Existing CO2 emissions reported by city inventories usually lag in real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise ...estimation of urban CO2 emissions to support present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO2 observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO2 emissions, i.e., fossil fuel as well as biogenic sources and sinks, over 6 years (2016–2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO2 emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO2 concentrations based on the ability of the Weather Research and Forecasting model with Chemistry (WRF-Chem) transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend of around 2 % ± 0.6 % per year in annual CO2 emissions over the Paris region. The impact of the COVID-19 pandemic led to a 13 % ± 1 % reduction in annual fossil fuel CO2 emissions in 2020 with respect to 2019. Subsequently, annual emissions increased by 5.2 % ± 14.2 % from 32.6 ± 2.2 MtCO2 in 2020 to 34.3 ± 2.3 MtCO2 in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling and high-precision observations, our current capacity can deliver near-real-time CO2 emission estimates at the city scale in less than a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.
We present a modelling framework for fossil fuel CO2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of CO2 and its ...co-emitted species CO, NOx, and SO2. Rather than a static assignment of average emission rates to each unit area of the urban domain, the fossil fuel emissions we use are dynamic: they vary in time and space in relation to data that describe or approximate the activity within a sector, such as traffic density, power demand, 2 m temperature (as proxy for heating demand), and sunlight and wind speed (as proxies for renewable energy supply). Through inverse modelling, we optimize the relationships between these activity data and the resulting emissions of all species within the dynamic fossil fuel emission model, based on atmospheric mole fraction observations. The advantage of this novel approach is that the optimized parameters (emission factors and emission ratios, N=44) in this dynamic emission model (a) vary much less over space and time, (b) allow for a physical interpretation of mean and uncertainty, and (c) have better defined uncertainties and covariance structure. This makes them more suited to extrapolate, optimize, and interpret than the gridded emissions themselves. The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion set-up for the Dutch Rijnmond area at 1km×1km resolution.We find that the fossil fuel emission model approximates the gridded emissions well (annual mean differences <2 %, hourly temporal r2=0.21–0.95), while reported errors in the underlying parameters allow a full covariance structure to be created readily. Propagating this error structure into atmospheric mole fractions shows a strong dominance of a few large sectors and a few dominant uncertainties, most notably the emission ratios of the various gases considered. If the prior emission ratios are either sufficiently well-known or well constrained from a dense observation network, we find that including observations of co-emitted species improves our ability to estimate emissions per sector relative to using CO2 mole fractions only. Nevertheless, the total CO2 emissions can be well constrained with CO2 as the only tracer in the inversion. Because some sectors are sampled only sparsely over a day, we find that propagating solutions from day-to-day leads to largest uncertainty reduction and smallest CO2 residuals over the 14 consecutive days considered. Although we can technically estimate the temporal distribution of some emission categories like shipping separate from their total magnitude, the controlling parameters are difficult to distinguish. Overall, we conclude that our new system looks promising for application in verification studies, provided that reliable urban atmospheric transport fields and reasonable a priori emission ratios for CO2 and its co-emitted species can be produced.
Constraining urban emissions is gaining more attention because of the important role of cities in reaching national climate mitigation targets. Urban inverse modelling studies could constrain ...emissions of large hotspots, but still face many challenges. It has been argued that more detailed information is needed on both atmospheric transport and prior emissions when moving to a higher spatial and temporal resolution. In this work we focus on the description of temporal variability in the prior emissions and examine how it impacts the optimization of urban emissions of CO2, CH4 and CO on a monthly time scale representative for a measurement campaign. Currently, temporal profiles based on long-term average activity data are often applied. However, these average temporal profiles are unable to capture a realistic variability in the emissions, such as those imposed by environmental conditions. Therefore, we created a set of location- and time-specific temporal profiles and compared the optimized emissions using these average and specific temporal profiles. We find that using the specific temporal profiles increases the optimized CO2 emissions with 19%, even though the prior monthly emissions are the same. This suggests a change in the source-receptor relationship that affects comparison of the observed and simulated mixing ratios, leading to a different emission estimate. The impact is also large (~40%) for CH4, but this is mainly due to the increase in prior emissions caused by redistributing agricultural emissions over all months of the year. Moreover, we show that extrapolating monthly emission estimates to annual estimates, required for reporting, using the various sets of temporal profiles can result in differences of max. 26% for CO2, 101% for CH4, and 13% for CO. Therefore, we conclude that an accurate representation of the temporal variability is essential for urban inverse modelling studies.
•Specific temporal profiles capture more of the intrinsic variability in emissions.•Temporal profiles have strong impact on monthly top-down emission estimates.•Temporal profiles affect the source-receptor relationship.•Accurate temporal profiles are essential for extrapolation of monthly emissions.•Observational sampling strategy affects monthly top-down emission estimates.
Quantification of greenhouse gas emissions is receiving a lot of attention because of its relevance for climate mitigation. Complementary to official reported bottom-up emission inventories, ...quantification can be done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such a method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories are, however, not systematically analysed. In this work, a statistically coherent method is used to quantify the uncertainties in a high-resolution gridded emission inventory of CO.sub.2 and CO for Europe. We perform a range of Monte Carlo simulations to determine the effect of uncertainties in different inventory components, including the spatial and temporal distribution, on the uncertainty in total emissions and the resulting atmospheric mixing ratios. We find that the uncertainties in the total emissions for the selected domain are 1 % for CO.sub.2 and 6 % for CO. Introducing spatial disaggregation causes a significant increase in the uncertainty of up to 40 % for CO.sub.2 and 70 % for CO for specific grid cells. Using gridded uncertainties, specific regions can be defined that have the largest uncertainty in emissions and are thus an interesting target for inverse modellers. However, the largest sectors are usually the best-constrained ones (low relative uncertainty), so the absolute uncertainty is the best indicator for this. With this knowledge, areas can be identified that are most sensitive to the largest emission uncertainties, which supports network design.
Quantification of greenhouse gas emissions is receiving a
lot of attention because of its relevance for climate mitigation.
Complementary to official reported bottom-up emission inventories,
...quantification can be done with an inverse modelling framework, combining
atmospheric transport models, prior gridded emission inventories and a
network of atmospheric observations to optimize the emission inventories. An
important aspect of such a method is a correct quantification of the
uncertainties in all aspects of the modelling framework. The uncertainties
in gridded emission inventories are, however, not systematically analysed.
In this work, a statistically coherent method is used to quantify the
uncertainties in a high-resolution gridded emission inventory of CO2
and CO for Europe. We perform a range of Monte Carlo simulations to
determine the effect of uncertainties in different inventory components,
including the spatial and temporal distribution, on the uncertainty in total
emissions and the resulting atmospheric mixing ratios. We find that the
uncertainties in the total emissions for the selected domain are 1 % for
CO2 and 6 % for CO. Introducing spatial disaggregation causes a
significant increase in the uncertainty of up to 40 % for CO2
and 70 % for CO for specific grid cells. Using gridded uncertainties, specific
regions can be defined that have the largest uncertainty in emissions and
are thus an interesting target for inverse modellers. However, the largest
sectors are usually the best-constrained ones (low relative uncertainty), so
the absolute uncertainty is the best indicator for this. With this knowledge,
areas can be identified that are most sensitive to the largest emission
uncertainties, which supports network design.
We present a modelling framework for fossil fuel CO.sub.2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of ...CO.sub.2 and its co-emitted species CO, NO.sub.x, and SO.sub.2 . Rather than a static assignment of average emission rates to each unit area of the urban domain, the fossil fuel emissions we use are dynamic: they vary in time and space in relation to data that describe or approximate the activity within a sector, such as traffic density, power demand, 2 m temperature (as proxy for heating demand), and sunlight and wind speed (as proxies for renewable energy supply). Through inverse modelling, we optimize the relationships between these activity data and the resulting emissions of all species within the dynamic fossil fuel emission model, based on atmospheric mole fraction observations. The advantage of this novel approach is that the optimized parameters (emission factors and emission ratios, N=44) in this dynamic emission model (a) vary much less over space and time, (b) allow for a physical interpretation of mean and uncertainty, and (c) have better defined uncertainties and covariance structure. This makes them more suited to extrapolate, optimize, and interpret than the gridded emissions themselves. The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion set-up for the Dutch Rijnmond area at 1 kmx1 km resolution.
We present a modelling framework for fossil fuel CO2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of CO2 and its ...co-emitted species CO, NOx, and SO2. Rather than a static assignment of
average emission rates to each unit area of the urban domain, the fossil
fuel emissions we use are dynamic: they vary in time and space in relation
to data that describe or approximate the activity within a sector, such as
traffic density, power demand, 2 m temperature (as proxy for heating demand),
and sunlight and wind speed (as proxies for renewable energy supply).
Through inverse modelling, we optimize the relationships between these
activity data and the resulting emissions of all species within the dynamic
fossil fuel emission model, based on atmospheric mole fraction observations.
The advantage of this novel approach is that the optimized parameters
(emission factors and emission ratios, N=44) in this dynamic emission
model (a) vary much less over space and time, (b) allow for a physical
interpretation of mean and uncertainty, and (c) have better defined
uncertainties and covariance structure. This makes them more suited to
extrapolate, optimize, and interpret than the gridded emissions themselves.
The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion set-up for the
Dutch Rijnmond area at 1 km×1 km resolution. We find that the fossil fuel emission model approximates the gridded
emissions well (annual mean differences <2 %, hourly temporal r2=0.21–0.95), while reported errors in the underlying parameters allow a
full covariance structure to be created readily. Propagating this error
structure into atmospheric mole fractions shows a strong dominance of a few
large sectors and a few dominant uncertainties, most notably the emission
ratios of the various gases considered. If the prior emission ratios are either sufficiently
well-known or well constrained from a dense observation network,
we find that including observations of co-emitted species improves our
ability to estimate emissions per sector relative to using CO2 mole
fractions only. Nevertheless, the total CO2 emissions can be
well constrained with CO2 as the only tracer in the inversion. Because some sectors are sampled only sparsely over a day, we find that propagating
solutions from day-to-day leads to largest uncertainty reduction and
smallest CO2 residuals over the 14 consecutive days considered. Although we
can technically estimate the temporal distribution of some emission
categories like shipping separate from their total magnitude, the
controlling parameters are difficult to distinguish. Overall, we conclude
that our new system looks promising for application in verification studies,
provided that reliable urban atmospheric transport fields and reasonable
a priori emission ratios for CO2 and its co-emitted species can be produced.
Quantification of land surface-atmosphere fluxes of carbon dioxide (CO.sub.2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ...ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO.sub.2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO.sub.2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990-2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO.sub.2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO.sub.2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI.
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