Temporal Distribution Of Emissions Research Articles

A global catalogue of CO2 emissions and co-emitted species from power plants, including high-resolution vertical and temporal profiles

Abstract. We present a high-resolution global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors for CO2 and emission ratios for co-emitted species (e.g. the amount of NOx emitted relative to CO2: NOx/CO2). The resulting catalogue contains annual emission information for more than 16 000 individual facilities at their exact geographical locations. Each facility is linked to a country- and fuel-dependent temporal profile (i.e. monthly, day of the week and hourly) and a plant-level vertical profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows us to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including Carbon Monitoring for Action (CARMA), the Global Infrastructure emission Database (GID) and the Emissions Database for Global Atmospheric Research (EDGAR), as well as officially reported emission data. Overall good agreement is observed between datasets when comparing the CO2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to a lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-, country- and region-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (https://doi.org/10.24380/0a9o-v7xe, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO2 service (CoCO2) European Union (EU)-funded project to support the development of the Copernicus CO2 Monitoring and Verification Support capacity (CO2MVS).

Carbon emissions of water supply systems in China: Characteristic, right, right balance and reduction potential assessment

In light of the global consensus on low-carbon development and the imperative to reduce greenhouse gas emissions, coupled with China's strategic objective of achieving “carbon neutrality” and reaching its carbon peak, it becomes increasingly essential to focus on areas that necessitate adaptation for low-carbon development. One such critical area is the water supply system, known for its substantial contribution to carbon emissions. To assess the situation, this study constructed an evaluation framework for carbon emissions of water supply system, which comprised of emission characteristic investigation, emission right allocation and right balance assessment. For emission characteristic investigation, data on water supply quantity, pipe network pressure, and energy consumption was utilized to calculate carbon emissions produced by China's water supply system and investigated the spatial and temporal distribution of emissions. The analysis revealed a minor increase in carbon emissions from 2010 to 2018, with a significant turning point occurring in 2016. Specifically, carbon emissions in 2018 ranged between 1.03 × 107 kgCO2 and 3.15 × 109 kgCO2. Coastal areas, such as Guangdong and Jiangsu, exhibited a higher level of carbon emissions. This study constructed a provincial allocation model of carbon emission rights, utilizing the shadow price model and entropy method while adhering to the principle of fairness and efficiency. By comparing the value of emission right and emission amount, the provincial balance of carbon emission right in 2018 was evaluated. It was discovered that 14 regions demonstrated varying levels of carbon allowance surpluses. Conversely, 12 regions exhibited insufficient spatial balance, which were subsequently classified as exhibiting mild, moderate, or severe deficiency. Additionally, emission reduction pressures among these regions were assessed through the analysis of the ratio between theoretical emission right and actual amount, as well as levels of economic development. The carbon emission reduction potential scores of different regions were calculated using principal component analysis based on five dimensions, namely policy driver, engineering test and research level, level of economic development, forestry carbon sink and emission reduction efficiency. Our findings indicated that the degree of insufficient balance between emission reduction potential and carbon emission allowance showed a certain convergence. Ultimately, we proposed tailored emission reduction paths for water supply systems in each region, which may serve as theoretical and technical references towards achieving the “double carbon goals”.

Simulation of the influence of residential biomass burning on air quality in an urban area

Residential biomass burning is often one of the dominant local sources of air pollution in urban areas during the winter months. However, the corresponding particle emissions are quite uncertain and chemical transport models (CTMs) often have difficulties reproducing the observed winter particulate matter (PM) concentrations. In this work, we combine measurements from a low-cost PM sensor network and the CTM PMCAMx (Particulate Matter Comprehensive Air quality Modeling with extensions) to estimate the spatial and temporal distribution of biomass burning emissions in an urban area.An estimate of 8100 kg d−1 (40 g d−1 per person) PM2.5 emissions from residential biomass burning was calculated for the city of Patras including organic compounds with effective saturation concentrations C* up to 104 μg m−3. The spatial distribution of the emissions was based on the density of fireplaces in the city. The temporal distribution of emissions was based on measurements of the biomass burning organic aerosol (bbOA) and are higher from 18:00 to 22:00 LT peaking at 21:00 LT. The nighttime (18:00-22:00 LT) bbOA emissions were 4 times higher than the corresponding morning (8:00-13:00 LT) ones. Estimated biomass burning emissions vary from day to day based on ambient temperature, with higher emissions during the colder days. The Volatility Basis Set (VBS) was used to simulate bbOA that is treated as semi-volatile and chemically reactive.PMCAMx predicts that bbOA concentration reaches on average 15 μg m−3 in the high-density bbOA emission area during the simulated period, while the corresponding peak hourly concentrations are higher than 40 μg m−3 most of the nights. The average predicted bbOA concentration in the city center is 3–6 μg m−3 and is lower than 1 μg m−3 at the suburbs. The model predicts that bbOA concentrations peak at 9:00 LT in the morning and at 21:00 LT during the nighttime, reproducing the bbOA measurements.

High-Resolution Emissions from Wood Burning in Norway—The Effect of Cabin Emissions

Emissions from wood burning for heating in secondary homes or cabins is an important part in the development of high-resolution emissions in specific areas. Norway is used as case study as 20% of the national wood consumption for heating occurs in cabins. Our study first shows a method to estimate emissions from cabins based on traffic data to derive cabin occupancy, which combined with heating need allows for the spatial and temporal distribution of emissions. The combination of residential (RWC) and cabin wood combustion (CWC) emissions shows large spatial and temporal differences, and a temporally “cabin population” can in areas be orders of magnitude larger than the registered population. While RWC emissions have been steadily reduced, CWC have kept relatively constant or even increased, which results in an increase in the cabin share to total heating emissions up to 25–35%. When comparing with regional emission inventories, our study shows that the gradient between rural and urban areas is not well-represented in regional inventories, which resembles a population-based distribution and does not allocate emissions in cabin municipalities. CWC emissions may become an increasing environmental concern as higher densification trends in mountain areas are observed.

Statistical Comparison and Assessment of Four Fire Emissions Inventories for 2013 and a Large Wildfire in the Western United States.

Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations.

The potential of satellite spectro-imagery for monitoring CO<sub>2</sub> emissions from large cities

Abstract. This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on observing system simulation experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ∼ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6 h mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular, the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO2 emissions for urban areas like Paris with CO2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO2 and in the inversion systems that exploit it.

Estimation of observation errors for large-scale atmospheric inversion of CO2 emissions from fossil fuel combustion

National annual inventories of CO2 emitted during fossil fuel consumption (FFCO2) bear 5–10% uncertainties for developed countries, and are likely higher at intra annual scales or for developing countries. Given the current international efforts of mitigating actions, there is a need for independent verifications of these inventories. Atmospheric inversion assimilating atmospheric gradients of CO2 and radiocarbon measurements could provide an independent way of monitoring FFCO2 emissions. A strategy would be to deploy such measurements over continental scale networks and to conduct continental to global scale atmospheric inversions targeting the national and one-month scale budgets of the emissions. Uncertainties in the high-resolution distribution of the emissions could limit the skill for such a large-scale inversion framework. This study assesses the impact of such uncertainties on the potential for monitoring the emissions at large scale. In practice, it is more specifically dedicated to the derivation, typical quantification and analysis of critical sources of errors that affect the inversion of FFCO2 emissions when solving for them at a relatively coarse resolution with a coarse grid transport model. These errors include those due to the mismatch between the resolution of the transport model and the spatial variability of the actual fluxes and concentrations (i.e. the representation errors) and those due to the uncertainties in the spatial and temporal distribution of emissions at the transport model resolution when solving for the emissions at large scale (i.e. the aggregation errors). We show that the aggregation errors characterize the impact of the corresponding uncertainties on the potential for monitoring the emissions at large scale, even if solving for them at the transport model resolution. We propose a practical method to quantify these sources of errors, and compare them with the precision of FFCO2 measurements (i.e. the measurement errors) and the errors in the modelling of atmospheric transport (i.e. the transport errors). The results show that both the representation and measurement errors can be much larger than the aggregation errors. The magnitude of representation and aggregation errors is sensitive to sampling heights and temporal sampling integration time. The combination of these errors can reach up to about 50% of the typical signals, i.e. the atmospheric large-scale mean afternoon FFCO2 gradients between sites being assimilated by the inversion system. These errors have large temporal auto-correlation scales, but short spatial correlation scales. This indicates the need for accounting for these temporal auto-correlations in the atmospheric inversions and the need for dense networks to limit the impact of these errors on the inversion of FFCO2 emissions at large scale. More generally, comparisons of the representation and aggregation errors to the errors in simulated FFCO2 gradients due to uncertainties in current inventories suggest that the potential of inversions using global coarse-resolution models (with typical horizontal resolution of a couple of degrees) to retrieve FFCO2 emissions at sub-continental scale could be limited, and that meso-scale models with smaller representation errors would effectively increase the potential of inversions to constrain FFCO2 emission estimates.

Gridded National Inventory of U.S. Methane Emissions.

We present a gridded inventory of US anthropogenic methane emissions with 0.1° × 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 US Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) for 2012. The EPA inventory is available only as national totals for different source types. We use a wide range of databases at the state, county, local, and point source level to disaggregate the inventory and allocate the spatial and temporal distribution of emissions for individual source types. Results show large differences with the EDGAR v4.2 global gridded inventory commonly used as a priori estimate in inversions of atmospheric methane observations. We derive grid-dependent error statistics for individual source types from comparison with the Environmental Defense Fund (EDF) regional inventory for Northeast Texas. These error statistics are independently verified by comparison with the California Greenhouse Gas Emissions Measurement (CALGEM) grid-resolved emission inventory. Our gridded, time-resolved inventory provides an improved basis for inversion of atmospheric methane observations to estimate US methane emissions and interpret the results in terms of the underlying processes.

Temporal Emission Distribution Corresponding to Operating Quantity in Thermal Power Plant

Temporal Emission Distribution Corresponding to Operating Quantity in Thermal Power Plant

A first European scale multimedia fate modelling of BDE-209 from 1970 to 2020

The European Variant Berkeley Trent (EVn-BETR) multimedia fugacity model is used to test the validity of previously derived emission estimates and predict environmental concentrations of the main decabromodiphenyl ether congener, BDE-209. The results are presented here and compared with measured environmental data from the literature. Future multimedia concentration trends are predicted using three emission scenarios (Low, Realistic and High) in the dynamic unsteady state mode covering the period 1970–2020. The spatial and temporal distributions of emissions are evaluated. It is predicted that BDE-209 atmospheric concentrations peaked in 2004 and will decline to negligible levels by 2025. Freshwater concentrations should have peaked in 2011, one year after the emissions peak with sediment concentrations peaking in 2013. Predicted atmospheric concentrations are in good agreement with measured data for the Realistic (best estimate of emissions) and High (worst case scenario) emission scenarios. The Low emission scenario consistently underestimates measured data. The German unilateral ban on the use of DecaBDE in the textile industry is simulated in an additional scenario, the effects of which are mainly observed within Germany with only a small effect on the surrounding areas. Overall, the EVn-BTER model predicts atmospheric concentrations reasonably well, within a factor of 5 and 1.2 for the Realistic and High emission scenarios respectively, providing partial validation for the original emission estimate. Total mean MEC:PEC shows the High emission scenario predicts the best fit between air, freshwater and sediment data. An alternative spatial distribution of emissions is tested, based on higher consumption in EBFRIP member states, resulting in improved agreement between MECs and PECs in comparison with the Uniform spatial distribution based on population density. Despite good agreement between modelled and measured point data, more long-term monitoring datasets are needed to compare predicted trends in concentration to determine the rate of change of POPs within the environment.

Methods for attributing land-use emissions to products

Roughly one-third of anthropogenic GHG emissions are caused by agricultural and forestry activities and landuse change (collectively, ‘land-use emissions’). Understanding the ultimate drivers of these emissions requires attributing emissions to specific land-use activities and products. Although quantities of land-use emissions are matters of fact, the methodological choices and assumptions required to attribute those emissions to activities and products depend on research goals and data availability. In this review, we explore several possible accounting methods. Our results highlight the sensitivity of accounting to temporal distributions of emissions and the consequences of replacing spatially-explicit data with aggregate proxies such as production or harvested area data. Different accounting options emphasize different causes of land-use emissions (e.g., proximate or indirect drivers of deforestation). To support public policies that effectively balance competing objectives, analysts should carefully consider and communicate implications of accounting choices.

Modeling of Emissions from Open Biomass Burning in Asia Using the BlueSky Framework

Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asian region. Estimation of emissions from fires, however, has been problematic, primarily because of uncertainty in the size and location of sources and in their temporal and spatial variability. Hence, more comprehensive tools to estimate wildfire emissions and that can characterize their temporal and spatial variability are needed. Furthermore, an emission processing system that can generate speciated, gridded, and temporally allocated emissions is needed to support air-quality modeling studies over Asia. For these reasons, a biomass-burning emissions modeling system based on satellite imagery was developed to better account for the spatial and temporal distributions of emissions. The BlueSky Framework, which was developed by the USDA Forest Service and US EPA, was used to develop the Asian biomass-burning emissions modeling system. The sub-models used for this study were the Fuel Characteristic Classification System (FCCS), CONSUME, and the Emissions Production Model (EPM). Our domain covers not only Asia but also Siberia and part of central Asia to assess the large boreal fires in the region. The MODIS fire products and vegetation map were used in this study. Using the developed modeling system, biomass-burning emissions were estimated during April and July 2008, and the results were compared with previous studies. Our results show good to fair agreement with those of GFEDv3 for most regions, ranging from 9.7% in East Asia to 52% in Siberia. The SMOKE modeling system was combined with this system to generate three-dimensional model-ready emissions employing the fire-plume rise algorithm. This study suggests a practicable and maintainable methodology for supporting Asian air-quality modeling studies and to help understand the impact of air-pollutant emissions on Asian air quality.

Bottom up approach to estimate air pollution of rice residue open burning in Thailand

Rice residue open burning is a farmer activity potentially contributes to global warming. This study was conducted with the objective of examining the spatial and temporal distribution of emissions from rice residue open burning in Thailand by using questionnaire survey and field experimentation. A sample of 1000 Thai farmers was interviewed in order to study the fire behaviours of farmers. One hundred and twenty rice sampling plots were selected for measuring rice residue characteristics. Of the farmer’s fire behaviour, 45% of farmer regularly uses prescribed burning technique for land preparation activities. The amount of rice residue was approximately 117.7 Mt. Although nearly 60% of total residue was subjected to burning in the fields, only 15% of rice residue is actually burned in the fields because the residue and soil have high moisture content. The burning emissions are computed at 1.67 Mt of CO, 0.04 Mt of NOx, 0.35 Mt of PM2.5, 0.12 Mt of PM10, and 0.01 Mt of BC. Approximately 30%, 26%, and 17% of all emissions are contributed by the lower-northern, central, and western regions of Thailand, respectively. Moreover, 31% and 30% of all emissions are annually emitted from December to January and April to May over one month periods following each harvesting season. The comparisons of rice residue burning emissions provided by this study and previous studies have found the emissions discovered in this study to range from one to five times higher than the finding of previous studies. This finding demonstrates the importance of the assessment of activity data specific to farming fire characteristics.

High resolution numerical simulation of sulphur-dioxide emission from a power plant building

Most sulphur-dioxide (SO2) emission calculations have been performed on an annual basis and do not take into account spatial and temporal variations on fine scale. The main objective of this study is to illustrate spatio-temporal variation of SO2 emission pattern using computational fluid dynamic (CFD) tools of the area surrounding the Yatagan Power Plant (YPP) building in Mugla, Turkey. In order to simulate the atmospheric conditions, wind speed, wind direction and the emission of SO2 from 3 stacks of YPP including the simplified model of the power plant building, a commercial CFD, FLUENT is used. The results involve 75 km2 areas surrounding the YPP with a fine mesh resolution of 5 m × 5 m. SO2 emission shows that the CFD tool is able to simulate the emission with fine mesh digital elevation model (DEM). The DEM highly influences the local magnitude and direction of the wind in the domain which effects both spatial and temporal emission distribution of the SO2 gases. It is noted that the hills around the YPP cause the flue gas emissions to move away from the ground for 4 main wind directions when compared with the constant elevation model.

Application of probability distributions to the modeling of biogenic CO2 fluxes in life cycle assessment

AbstractIn life cycle assessment (LCA), the same characterization factors are conventionally applied irrespective of when the emissions occur (the same importance is given to emissions in the past, present, and future). When the assessment is constrained by fixed timeframes, the appropriateness of this paradigm is questioned and the temporal distribution of emissions becomes of relevance. One typical example is the accounting for biogenic CO2 emissions and removals. This article proposes a methodology for assessing the climate impact of time‐distributed CO2 fluxes using probability distributions. Three selected wood applications, such as fuel, nonstructural panels, and housing construction materials are assessed. In all the cases, CO2 sequestration in growing trees is modeled with an appropriate forest growth function, whereas CO2 emissions from wood oxidation are modeled with different probability distributions, such as the delta function, the uniform distribution, the exponential distribution, and the chi‐square distribution. The combination of these CO2 fluxes with the global carbon cycle provides the respective changes caused in CO2 atmospheric concentration and hence in the radiative forcing. The latter is then used as basis for climate impact metrics. Results demonstrate the utility of using emission and removal functions rather than single pulses, which generally overestimate the climate impact of CO2 emissions, especially in presence of short time horizons. Characterization factors for biogenic CO2 are provided for selected combinations of biomass species, rotation periods, and probability distributions. The time discrepancy between biogenic CO2 emissions and capture through regrowth results in a certain climate impact, even for a system that is carbon neutral over time. For the oxidation rate of wooden products, the use of a chi‐square distribution appears the most reliable and appropriate option under a methodological perspective. The feasibility of its adoption in LCA and emission accounting from harvested wood products deserves further scientific considerations.

Central power generation versus distributed generation – An air quality assessment in the South Coast Air Basin of California

This study assesses the air quality impacts of central power generation and compares them with the impacts of distributed generation (DG). The central power plant emissions factors used are from a newly installed combined cycle gas turbine system. Because location of power plants is a key parameter affecting air quality impacts, this study considers three potential locations for the installation of central power plants. Air quality impacts are evaluated for the South Coast Air Basin of California, in the year 2010, using a three-dimensional air quality model. Results are compared to air quality impacts from two potential DG scenarios to meet the same power demand as that of the central power plant case. Even though emissions from central generation are lower than emissions from the DG technology mix considered herein, central generation concentrates emissions in a small area, whereas DG spreads emissions throughout a larger cross-section of the air basin. As a result, air quality impacts from central generation are more significant than those from DG. The study also shows that assessment of air quality impacts from distributed and central generation should not only consider emissions levels, but also the spatial and temporal distribution of emissions and the air quality that results from atmospheric chemistry and transport – highly non-linear processes. Finally, analysis of population exposure to ozone and PM 2.5 shows that central generation located in coastal areas upwind from populated areas would cause the highest population exposure and even though emissions from central generation are considerably lower than DG emissions spread throughout the basin, results show that central generation causes a higher pollutant exposure than DG.

Inverse modeling of European CH<sub>4</sub> emissions: sensitivity to the observational network

Abstract. Inverse modeling is widely employed to provide "top-down" emission estimates using atmospheric measurements. Here, we analyze the dependence of derived CH4 emissions on the sampling frequency and density of the observational surface network, using the TM5-4DVAR inverse modeling system and synthetic observations. This sensitivity study focuses on Europe. The synthetic observations are created by TM5 forward model simulations. The inversions of these synthetic observations are performed using virtually no knowledge on the a priori spatial and temporal distribution of emissions, i.e. the emissions are derived mainly from the atmospheric signal detected by the measurement network. Using the European network of stations for which continuous or weekly flask measurements are available for 2001, the synthetic experiments can retrieve the "true" annual total emissions for single countries such as France within 20%, and for all North West European countries together within ~5%. However, larger deviations are obtained for South and East European countries due to the scarcity of stations in the measurement network. Upgrading flask sites to stations with continuous measurements leads to an improvement for central Europe in emission estimates. For realistic emission estimates over the whole European domain, however, a major extension of the number of stations in the existing network is required. We demonstrate the potential of an extended network of a total of ~60 European stations to provide realistic emission estimates over the whole European domain.

Entanglement of unstable atoms: modifications of the emission properties

We analyse the influence of entanglement on the emission properties of atoms. To this end, we firstly propose a scheme for the preparation of a pair of entangled helium atoms, one in the ortho- and the other in the para-spin configuration. We discuss a realistic scenario for this process, based on the double ionization of He by intense laser fields. These states are used to analyse disentanglement and the role of entanglement in the spontaneous emission from the pair. In particular, we show that the decaying rate of an entangled atom is different from that in a product state, modifying the temporal emission distribution and lifetime of the atoms.

High-Resolution Vehicular Emission Inventory Using a Link-Based Method: A Case Study of Light-Duty Vehicles in Beijing

We propose a new link-based, bottom-up vehicle emission inventory method for Chinese cities using available on-road emission measurement data and activity survey data. This study uses Beijing as a case study and focuses on CO, HC, and NOx emissions from hot-stabilized activities of light-duty vehicles. The target year is 2004. The total quantity of emissions, emission intensity, temporal distribution of emissions by hour, and spatial distribution of emissions at 1 km x 1 km resolution are presented and compared with results from other inventory methods commonly used in China. The results show that the total emissions from light-duty vehicles in the Beijing urban area in 2004 were 1141 Mg of CO per day, 48 Mg of HC per day, and 32 Mg of NOx per day. Pollutant emissions from mobile sources show temporal and spatial variation trends that are consistent with the characteristics of people's activities. The inventory developed in this study reflects the influences of vehicle driving patterns in traffic on vehicle emissions on a road link basis, and it could be used to evaluate the impacts of traffic-related emission control measures in China.

Transient environmental effects of light alloy substitutions in transport vehicles

Materials indices and exchange constants are combined with Field et al.’s fleet analysis [Field F, Kirchain R, Clark J. Life-cycle assessment and temporal distributions of emissions: developing a fleet-based analysis. J Indust Ecol 2000;4:71–91, doi:10.1162/108819800569816] to examine the time-dependent CO 2 emissions attached to the production of the Al and Mg alloys used to reduce the mass of transport vehicles. The model is used to breakdown the temporal pattern of upfront emissions of passenger cars according to the mass and CO 2-footprint efficiency of typical automotive structural substitutions (castings, stiff panels and stiff beams), accounting for the effect of recycling. The fleet’s upfront emissions of Al and Mg castings with high content of secondary metal are offset by the increased fuel efficiency after 4 years of driving. Al beams and panels and electrolytic Mg panels require between 8 and 15 years, whereas for panels and beams of Pidgeon Mg no environmental benefits ever materialise.