Journal articles on the topic 'Emission models'
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Aamaas, Borgar, Terje K. Berntsen, Jan S. Fuglestvedt, Keith P. Shine, and Nicolas Bellouin. "Regional emission metrics for short-lived climate forcers from multiple models." Atmospheric Chemistry and Physics 16, no. 11 (June 15, 2016): 7451–68. http://dx.doi.org/10.5194/acp-16-7451-2016.
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Abstract. For short-lived climate forcers (SLCFs), the impact of emissions depends on where and when the emissions take place. Comprehensive new calculations of various emission metrics for SLCFs are presented based on radiative forcing (RF) values calculated in four different (chemical-transport or coupled chemistry–climate) models. We distinguish between emissions during summer (May–October) and winter (November–April) for emissions in Europe and East Asia, as well as from the global shipping sector and global emissions. The species included in this study are aerosols and aerosol precursors (BC, OC, SO2, NH3), as well as ozone precursors (NOx, CO, VOCs), which also influence aerosols to a lesser degree. Emission metrics for global climate responses of these emissions, as well as for CH4, have been calculated using global warming potential (GWP) and global temperature change potential (GTP), based on dedicated RF simulations by four global models. The emission metrics include indirect cloud effects of aerosols and the semi-direct forcing for BC. In addition to the standard emission metrics for pulse and sustained emissions, we have also calculated a new emission metric designed for an emission profile consisting of a ramping period of 15 years followed by sustained emissions, which is more appropriate for a gradual implementation of mitigation policies.For the aerosols, the emission metric values are larger in magnitude for emissions in Europe than East Asia and for summer than winter. A variation is also observed for the ozone precursors, with largest values for emissions in East Asia and winter for CO and in Europe and summer for VOCs. In general, the variations between the emission metrics derived from different models are larger than the variations between regions and seasons, but the regional and seasonal variations for the best estimate also hold for most of the models individually. Further, the estimated climate impact of an illustrative mitigation policy package is robust even when accounting for the fact that the magnitude of emission metrics for different species in a given model is correlated. For the ramping emission metrics, the values are generally larger than for pulse or sustained emissions, which holds for all SLCFs. For SLCFs mitigation policies, the dependency of metric values on the region and season of emission should be considered.
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Tsanakas, Nikolaos, Joakim Ekström, and Johan Olstam. "Estimating Emissions from Static Traffic Models: Problems and Solutions." Journal of Advanced Transportation 2020 (February 1, 2020): 1–17. http://dx.doi.org/10.1155/2020/5401792.
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In large urban areas, the estimation of vehicular traffic emissions is commonly based on the outputs of transport planning models, such as Static Traffic Assignment (STA) models. However, such models, being used in a strategic context, imply some important simplifications regarding the variation of traffic conditions, and their outputs are heavily aggregated in time. In addition, dynamic traffic flow phenomena, such as queue spillback, cannot be captured, leading to inaccurate modelling of congestion. As congestion is strongly correlated with increased emission rates, using STA may lead to unreliable emission estimations. The first objective of this paper is to identify the errors that STA models introduce into an emission estimation. Then, considering the type and the nature of the errors, our aim is to suggest potential solutions. According to our findings, the main errors are related to STA inability of accurately modelling the level and the location of congestion. For this reason, we suggest and evaluate the postprocessing of STA outputs through quasidynamic network loading. Then, we evaluate our suggested approach using the HBEFA emission factors and a 19 km long motorway segment in Stockholm as a case study. Although, in terms of total emissions, the differences compared to the simple static case are not so vital, the postprocessor performs better regarding the spatial distribution of emissions. Considering the location-specific effects of traffic emissions, the latter may lead to substantial improvements in applications of emission modelling such as dispersion, air quality, and exposure modelling.
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Wolfire, Mark G., and Ed Churchwell. "Circumstellar dust emission models." Astrophysical Journal 427 (June 1994): 889. http://dx.doi.org/10.1086/174194.
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Winther, Morten. "Petrol passenger car emissions calculated with different emission models." Science of The Total Environment 224, no. 1-3 (December 1998): 149–60. http://dx.doi.org/10.1016/s0048-9697(98)00343-x.
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Rakha, Hesham, Kyoungho Ahn, and Antonio Trani. "Comparison of MOBILE5a, MOBILE6, VT-MICRO, and CMEM models for estimating hot-stabilized light-duty gasoline vehicle emissions." Canadian Journal of Civil Engineering 30, no. 6 (December 1, 2003): 1010–21. http://dx.doi.org/10.1139/l03-017.
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The paper compares the MOBILE5a, MOBILE6, Virginia Tech microscopic energy and emission model (VT-Micro), and comprehensive modal emissions model (CMEM) models for estimating hot-stabilized, light-duty vehicle emissions. Specifically, Oak Ridge National Laboratory (ORNL) and Environmental Protection Agency (EPA) laboratory fuel consumption and emission databases are used for model comparisons. The comparisons demonstrate that CMEM exhibits some abnormal behaviors when compared with the ORNL data, EPA data, and the VT-Micro model estimates. Specifically, carbon monoxide (CO) emissions exhibit abrupt changes at low speeds and high acceleration levels and constant emissions at negative acceleration levels. Furthermore, oxides of nitrogen (NOx) emissions exhibit abrupt drops at high engine loads. In addition, the study demonstrates that MOBILE5a emission estimates compare poorly with EPA field data, while MOBILE6 model estimates show consistency with EPA field data and VT-Micro model estimates over various driving cycles. The VT-Micro model appears to be accurate in estimating hot-stabilized, light-duty, normal vehicle tailpipe emissions. Specifically, the emission estimates of the VT-Micro and MOBILE6 models are consistent in trends with laboratory measurements. Furthermore, the VT-Micro and MOBILE6 models accurately capture emission increases for aggressive acceleration drive cycles in comparison with other drive cycles.Key words: transportation energy, transportation environmental impacts, VT-Micro Model, CMEM, MOBILE5, MOBILE6, fuel consumption models, emission models.
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Denby, Bruce, Matthias Karl, Herdis Laupsa, Christer Johansson, Mia Pohjola, Ari Karppinen, Jaakko Kukkonen, Matthias Ketzel, and Peter Wåhlin. "Estimating domestic wood burning emissions of particulate matter in two Nordic cities by combining ambient air observations with receptor and dispersion models." Chemical Industry and Chemical Engineering Quarterly 16, no. 3 (2010): 237–41. http://dx.doi.org/10.2298/ciceq091214019d.
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The major emission source of primary PM2.5 in many Nordic countries is wood burning for domestic heating. Though direct measurements of wood burning emissions are possible under controlled conditions, emission inventories for urban scale domestic heating are difficult to calculate and remain uncertain. As an alternative method for estimating these emissions this paper makes use of ambient air measurements, chemical analysis of filter samples, receptor models, dispersion models, and simple inverse modelling methods to infer emission strengths. A comparison of dispersion models with receptor models indicates that the dispersion models tend to overestimate the contribution from wood burning. The inverse modelling results are found to agree with those from the receptor modelling. Though both the receptor and inverse modelling point to an overestimation of the wood burning emissions of PM2.5 it is not possible to assign this solely to errors in the emissions inventory as dispersion model error can be significant. It is recommended to improve plume rise and urban canopy meteorological descriptions in the dispersion models before these models will be of sufficient quality to allow quantitative assessments of emission inventories.
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Guo, Dong, Jinbao Zhao, Yi Xu, Feng Sun, Kai Li, Juan Wang, and Yuhang Sun. "THE IMPACT OF DRIVING CONDITIONS ON LIGHT-DUTY VEHICLE EMISSIONS IN REAL-WORLD DRIVING." Transport 35, no. 4 (September 29, 2020): 379–88. http://dx.doi.org/10.3846/transport.2020.12168.
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To accurately estimate the effect of driving conditions on vehicle emissions, an on-road light-duty vehicle emission platform was established based on OEM-2100TM, and each second data of mass emission rate corresponding to the driving conditions were obtained through an on-road test. The mass emission rate was closely related to the velocity and acceleration in real-world driving. This study shows that a high velocity and acceleration led to high real-world emissions. The vehicle emissions were the minimum when the velocity ranged from 30 to 50 km/h and the acceleration was less than 0.5 m/s2. Microscopic emission models were established based the on-road test, and single regression models were constructed based on velocity and acceleration separately. Binary regression and neural network models were established based on the joint distribution of velocity and acceleration. Comparative analysis of the accuracy of prediction and evaluation under different emission models, total error, second-based error, related coefficient, and sum of squared error were considered as evaluation indexes to validate different models. The results show that the three established emission models can be used to make relatively accurate prediction of vehicle emission on actual roads. The velocity regression model can be easily combined with traffic simulation models because of its simple parameters. However, the application of neural network model is limited by a complex coefficient matrix.
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Yu, Tai Yi, I. Cheng Chang, Mei Yin Hwa, and Li Teh Lu. "Estimation of Air Pollutant Emissions from Mobile Sources with Three Emission Factors Models." Advanced Materials Research 550-553 (July 2012): 2378–81. http://dx.doi.org/10.4028/www.scientific.net/amr.550-553.2378.
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Vehicle emissions from mobile sources are major contributors to air pollution and varied with vehicle types, vehicle styles, traveled miles, temperature, oil types and the methods of operation and management. This study performs three emission factor models, Mobile-Taiwan 2, Mobile6.2 and EFDB to calculate emission factor of mobile sources from year 1986 to 2011. The emissions of primary air pollutants, MIRs and CO2emitted from mobile sources were calculated. The contribution ratios of varied vehicle types for different air pollutants would be compared and analyzed. Estimated emissions from mobile sources were 32.2, 177, 643, 197 and 401 kilotons/y for PM10, NOx, CO, THC and MIR for 2000; 31.3, 115, 305, 114 and 227 kilotons/y for 2011. Emissions of traditional air pollutants presented a decreasing trend because of fourth-stage emission standards for mobiles sources and CO2 revealed an increasing trend. According to presented control technology for greenhouse gases on mobile sources, ratio of emission for year 2011 to 2000 would be 1.38-1.49.
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Arneth, A., G. Schurgers, J. Lathiere, T. Duhl, D. J. Beerling, C. N. Hewitt, M. Martin, and A. Guenther. "Global terrestrial isoprene emission models: sensitivity to variability in climate and vegetation." Atmospheric Chemistry and Physics 11, no. 15 (August 8, 2011): 8037–52. http://dx.doi.org/10.5194/acp-11-8037-2011.
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Abstract. Due to its effects on the atmospheric lifetime of methane, the burdens of tropospheric ozone and growth of secondary organic aerosol, isoprene is central among the biogenic compounds that need to be taken into account for assessment of anthropogenic air pollution-climate change interactions. Lack of process-understanding regarding leaf isoprene production as well as of suitable observations to constrain and evaluate regional or global simulation results add large uncertainties to past, present and future emissions estimates. Focusing on contemporary climate conditions, we compare three global isoprene models that differ in their representation of vegetation and isoprene emission algorithm. We specifically aim to investigate the between- and within model variation that is introduced by varying some of the models' main features, and to determine which spatial and/or temporal features are robust between models and different experimental set-ups. In their individual standard configurations, the models broadly agree with respect to the chief isoprene sources and emission seasonality, with maximum monthly emission rates around 20–25 Tg C, when averaged by 30-degree latitudinal bands. They also indicate relatively small (approximately 5 to 10 % around the mean) interannual variability of total global emissions. The models are sensitive to changes in one or more of their main model components and drivers (e.g., underlying vegetation fields, climate input) which can yield increases or decreases in total annual emissions of cumulatively by more than 30 %. Varying drivers also strongly alters the seasonal emission pattern. The variable response needs to be interpreted in view of the vegetation emission capacities, as well as diverging absolute and regional distribution of light, radiation and temperature, but the direction of the simulated emission changes was not as uniform as anticipated. Our results highlight the need for modellers to evaluate their implementations of isoprene emission models carefully when performing simulations that use non-standard emission model configurations.
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Stanković, Stamenka, Vladimir Đorić, and Jelena Kajalić. "Estimation of pollutant emissions from traffic using microsimulation models." Tehnika 76, no. 6 (2021): 801–8. http://dx.doi.org/10.5937/tehnika2106801s.
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Traditional traffic management methods are mainly focused on travel time optimization, ie minimizing control delay. The modern approach is increasingly emphasizing the minimization of negative impacts of traffic on the environment. Therefore, it is important to enable the evaluation of different traffic management strategies from the aspect of pollutant emissions. Microscopic simulation models enable detailed (second-by-second) representation of traffic flow, which creates a precondition for integration with emission models to enable this aspect of evaluating different management strategies. The paper presents the possibility of integrating the microscopic simulation model VISSIM and the emission model MOVES. It was concluded that this integration enables the comparison of different management strategies from the aspect of emissions of various pollutants.
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Tu, Ran, Islam Kamel, Baher Abdulhai, and Marianne Hatzopoulou. "Reducing Transportation Greenhouse Gas Emissions Through the Development of Policies Targeting High-Emitting Trips." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 25 (April 18, 2018): 11–20. http://dx.doi.org/10.1177/0361198118755714.
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Traffic emission inventories have been under development for decades, often relying on data from traffic assignment models, ranging from macroscopic models generating average link speeds, to more detailed microscopic models with instantaneous speed profiles. Policy testing within such frameworks has often focused on identifying changes in total emissions, or in emissions aggregated at a zonal or street level. Emissions from specific trips or trajectories are seldom analyzed, although reductions in greenhouse gas (GHG) emissions can be achieved more efficiently when targeting high emitters. In this paper, we propose a different approach to reducing transportation GHG emissions, by catering policies to specific trips based on their emission burden. We focus on the City of Toronto downtown. Using second-by-second speed data for entire trajectories, GHGs (in CO2eq) and nitrogen oxides (NOx) emissions were estimated. We observe that the destinations attracting the highest trip emissions tend to be in the hospital and financial districts. Trips originating and ending in the downtown area are responsible for a small share of total emissions, although they have high emission intensity. Removing trips with high total emissions and high emission intensity led to significant reductions in CO2eq and NOx emissions, whereas removing shorter trips, did not have a significant influence on total emissions nor emission intensities.
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Lan, Xiangang, Xiaode Zuo, and Xia Tang. "The Impact of Different Carbon Emission Policies on Liner Shipping." Journal of Marine Sciences 2020 (July 31, 2020): 1–12. http://dx.doi.org/10.1155/2020/8956045.
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This paper considers the influence of different carbon emission policies for liner shipping. The transportation optimization models under four different forms of carbon emission policies (no carbon emissions constraints, carbon emissions tax, carbon caps, and carbon cap-and-trade) are developed. A real case is given to demonstrate the effectiveness of the proposed models and comparative analysis of the impact of different carbon emission policies on shipowner’s profit and ship carbon emission. It is shown that the carbon caps form is the most direct method for reducing emission; the form of carbon emissions tax is a mandatory measure, which has the greatest impact on the profit of shipping companies; carbon cap-and-trade forms have weaker emission reduction effects, it is easier for enterprises to actively implement emission reductions and be highly motivated in the long run.
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Zare, A., J. Brandt, J. H. Christensen, and P. Irannejad. "Evaluation of two isoprene emission models for use in a long-range air pollution model." Atmospheric Chemistry and Physics Discussions 12, no. 4 (April 10, 2012): 9247–81. http://dx.doi.org/10.5194/acpd-12-9247-2012.
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Abstract. Knowledge about isoprene emissions and concentration distribution is important for chemistry transport models (CTMs), because isoprene acts as a precursor for tropospheric ozone and subsequently affects the atmospheric concentrations of many other atmospheric compounds. Isoprene has a short lifetime, and hence it is very difficult to evaluate its emission estimates against measurements. For this reason, we coupled two isoprene emission models with the Danish Eulerian Hemispheric Model (DEHM), and evaluated the simulated background ozone concentrations based on different models for isoprene emissions. In this research, results of using the two global biogenic emission models; GEIA (Global Emissions Inventory Activity) and MEGAN (the global Model of Emissions of Gases and Aerosols from Nature) are compared and evaluated. The total annual emissions of isoprene for the year 2006 estimated by using MEGAN is 732 Tg yr−1 for an extended area of the Northern Hemisphere, which is 50% higher than that estimated by using GEIA. The overall feature of the emissions from the two models are quite similar, but significant differences are found mainly in Africa's savannah and the rain forests of South America, and in some subtropical regions, such as the Middle East, India and the southern part of North America. Differences in spatial distribution of emission factors are found to be a key source of these discrepancies. In spite of the short life-time of isoprene, a direct evaluation of isoprene concentrations using the two biogenic emission models has been made against available measurements in Europe. Results show that the two models in general represent the measurements well and that the CTM is able to simulate isoprene concentrations. Additionally, investigation of ozone concentrations resulting from the two biogenic emission models show that isoprene simulated by MEGAN strongly affects the ozone production in the African savannah; the effect is up to 20% more than that obtained using GEIA. In contrast, the impact of using GEIA is higher in the Amazon region with more than 15% higher ozone concentrations compared to that of using MEGAN. Comparing the results for ozone concentrations for Europe obtained by using the two different models with measurements, show that the MEGAN emission model improves the model performance significantly in the Mediterranean area.
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Saedi, Ramin, Rajat Verma, Ali Zockaie, Mehrnaz Ghamami, and Timothy J. Gates. "Comparison of Support Vector and Non-Linear Regression Models for Estimating Large-Scale Vehicular Emissions, Incorporating Network-Wide Fundamental Diagram for Heterogeneous Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (April 16, 2020): 70–84. http://dx.doi.org/10.1177/0361198120914304.
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Estimation of vehicular emissions at network level is a prominent issue in transportation planning and management of urban areas. For large networks, macroscopic emission models are preferred because of their simplicity. However, these models do not consider traffic flow dynamics that significantly affect emissions production. This study proposes a network-level emission modeling framework based on the network-wide fundamental diagram (NFD), via integrating the NFD properties with an existing microscopic emission model. The NFD and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogeneous vehicle types with different emission generation rates in a network-level model. This framework is applied to the large-scale network of Chicago as well as its central business district. Non-linear and support vector regression models are developed using simulated trajectory data of 13 simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from the underlying microscopic emissions model regardless of the simulation tool that is used to calibrate the network-level emissions model. The microscopic traffic simulation is appropriate for smaller networks, while mesoscopic traffic simulation is a proper means to calibrate models for larger networks. The proposed model is also used to demonstrate the relationship between macroscopic emissions and flow characteristics in the form of a network emissions diagram. The results of this study provide a tool for planners to analyze vehicular emissions in real time and find optimal policies to control the level of emissions in large cities.
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Meng, Yang, and Hossain Noman. "Predicting CO2 Emission Footprint Using AI through Machine Learning." Atmosphere 13, no. 11 (November 9, 2022): 1871. http://dx.doi.org/10.3390/atmos13111871.
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Adequate CO2 is essential for vegetation, but industrial chimneys and land, space and oceanic vehicles exert tons of excessive CO2 and are mostly responsible for the greenhouse effect, global warming and climate change. Due to COVID-19, CO2 emission was in 2020 at its lowest level compared to prior decades. However, it is unknown how long it will take to reduce CO2 emission to a tolerable point. Furthermore, it is also unknown to what extent it can increase or change in the future. Accurate forecasting of CO2 emissions has real significance for choosing the optimum ways of reducing CO2 emissions. Although some existing models have noticeable CO2 emission forecasting accuracy, the models implemented in this work have more efficacy in prediction due to incorporating COVID-19’s effect on CO2 emission. This paper implements four prediction models using SARIMA (SARIMAX) based on ARIMA. The four models are based on the time period of the surge of the COVID-19 pandemic. The main objective of this work is to compare these four models to suggest an effective model to predict the total CO2 emissions for the future. The study forecasts global total CO2 emission from 2022 to 2027 for near future prediction, 2022 to 2054 for future prediction and 2022 to 2072 for far future prediction. Among the various error measures, mean absolute percentage error (MAPE) is chosen for accuracy comparison. The calculation yields different accuracy for the four SARIMAX models. The MAPEs for the four methods are: pre-COV (MAPE: 0.32), start-COV (MAPE: 0.28), trans-COV (MAPE: 0.19), post-COV (MAPE: 0.09). The MAPE value is relatively low for post-COV (MAPE: 0.09). Hence, it can be inferred that post-COV are suitable models to forecast the global total CO2 emission for the future. The post-COV predictions for the global total CO2 emission for the years 2022 to 2027 are: 36,218.59, 36,733.69, 37,238.29, 37,260.88, 37,674.01 and 37,921.47 million tons (MT). This study successfully predicts CO2 emission either for the COVID-19 period or the post-COVID-19 normal periods. The Machine Learning (ML) method used in this study has shown good agreement with the IPCC model in predicting the past emissions, the current emissions due to COVID-19 and the emissions of the upcoming future. These prediction results can be an asset for the decision support system to develop a suitable policy for global CO2 emission reduction. For future research, a number of other external influence variables responsible for CO2 emission can be added for finer forecasts. This research is an original work in predicting COVID-19-affected CO2 emission using AI through the ML methodology.
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Grote, R., T. Keenan, A. V. Lavoir, and M. Staudt. "Process-based simulation of seasonality and drought stress in monoterpene emission models." Biogeosciences Discussions 6, no. 5 (September 11, 2009): 8961–9004. http://dx.doi.org/10.5194/bgd-6-8961-2009.
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Abstract. Canopy emissions of volatile hydrocarbons such as isoprene and monoterpenes play an important role in air chemistry. They depend on various environmental conditions, are highly species-specific and are expected to be affected by global change. In order to estimate future emissions of these isoprenoids, differently complex models are available. However, seasonal dynamics driven by phenology, enzymatic activity, or drought stress strongly modify annual ecosystem emissions. Although these impacts depend themselves on environmental conditions, they have yet received little attention in mechanistic modelling. In this paper we propose the application of a mechanistic method for considering the seasonal dynamics of emission potential using the ''Seasonal Isoprenoid synthase Model'' (Lehning et al., 2001). We test this approach with three different models (GUENTHER, Guenther et al., 1993; NIINEMETS, Niinemets et al., 2002a; BIM2, Grote et al., 2006) that are developed for simulating light-dependent monoterpene emission. We also suggest specific drought stress representations for each model. Additionally, the proposed model developments are compared with the approach realized in the MEGAN (Guenther et al., 2006) emission model. Models are applied to a Mediterranean Holm oak (Quercus ilex) site with measured weather data. The simulation results demonstrate that the consideration of a dynamic emission potential has a strong effect on annual monoterpene emission estimates. The investigated models, however, show different sensitivities to the procedure for determining this seasonality impact. Considering a drought impact reduced the differences between the applied models and decreased emissions at the investigation site by approximately 33% on average over a 10 year period. Although this overall reduction was similar in all models, the sensitivity to weather conditions in specific years was different. We conclude that the proposed implementations of drought stress and internal seasonality strongly reduce estimated emissions and indicate measurements are needed to further evaluate the models.
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Grote, R., T. Keenan, A. V. Lavoir, and M. Staudt. "Process-based simulation of seasonality and drought stress in monoterpene emission models." Biogeosciences 7, no. 1 (January 20, 2010): 257–74. http://dx.doi.org/10.5194/bg-7-257-2010.
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Abstract. Canopy emissions of volatile hydrocarbons such as isoprene and monoterpenes play an important role in air chemistry. They depend on various environmental conditions, are highly species-specific and are expected to be affected by global change. In order to estimate future emissions of these isoprenoids, differently complex models are available. However, seasonal dynamics driven by phenology, enzymatic activity, or drought stress strongly modify annual ecosystem emissions. Although these impacts depend themselves on environmental conditions, they have yet received little attention in mechanistic modelling. In this paper we propose the application of a mechanistic method for considering the seasonal dynamics of emission potential using the "Seasonal Isoprenoid synthase Model" (Lehning et al., 2001). We test this approach with three different models (GUENTHER, Guenther et al., 1993; NIINEMETS, Niinemets et al., 2002a; BIM2, Grote et al., 2006) that are developed for simulating light-dependent monoterpene emission. We also suggest specific drought stress representations for each model. Additionally, the proposed model developments are compared with the approach realized in the MEGAN (Guenther et al., 2006) emission model. Models are applied to a Mediterranean Holm oak (Quercus ilex) site with measured weather data. The simulation results demonstrate that the consideration of a dynamic emission potential has a strong effect on annual monoterpene emission estimates. The investigated models, however, show different sensitivities to the procedure for determining this seasonality impact. Considering a drought impact reduced the differences between the applied models and decreased emissions at the investigation site by approximately 33% on average over a 10 year period. Although this overall reduction was similar in all models, the sensitivity to weather conditions in specific years was different. We conclude that the proposed implementations of drought stress and internal seasonality strongly reduce estimated emissions and indicate the measurements that are needed to further evaluate the models.
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Wang, Wei, Hongming Zhong, Yu Zeng, Yachao Liu, and Jun Chen. "A Carbon Emission Calculation Model for Roadside Parking." International Journal of Environmental Research and Public Health 18, no. 4 (February 16, 2021): 1906. http://dx.doi.org/10.3390/ijerph18041906.
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With the sustained and rapid development of China’s national economy, the number of motor vehicles owned by families in cities is rapidly growing. Consequently, problems of traffic congestion and air pollution have also appeared in these cities. Roadside parking traffic has also become an important part of the transportation system in cities. However, there is no specific measurement model for carbon emissions caused by roadside parking in the proposed traffic carbon emission model. Therefore, we aim to establish a carbon emission measurement model for roadside parking. In this paper, we first study the characteristics of the deceleration and maneuvering of parking vehicles and the blocking impact on running vehicles in a typical roadside parking scenario. We then establish and fit models of the direct and indirect carbon emissions during roadside parking. Based on the carbon emission model, we propose a calculation method for roadside parking carbon emissions, including accounting and estimation methods. These models can be used to calculate the carbon emissions from roadside parking in a traffic carbon emissions system. We also hope that these models will help future research on the optimization of roadside parking facilities for energy saving and emission reduction.
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Gunn, Roger N., Steve R. Gunn, and Vincent J. Cunningham. "Positron Emission Tomography Compartmental Models." Journal of Cerebral Blood Flow & Metabolism 21, no. 6 (June 2001): 635–52. http://dx.doi.org/10.1097/00004647-200106000-00002.
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The current article presents theory for compartmental models used in positron emission tomography (PET). Both plasma input models and reference tissue input models are considered. General theory is derived and the systems are characterized in terms of their impulse response functions. The theory shows that the macro parameters of the system may be determined simply from the coefficients of the impulse response functions. These results are discussed in the context of radioligand binding studies. It is shown that binding potential is simply related to the integral of the impulse response functions for all plasma and reference tissue input models currently used in PET. This article also introduces a general compartmental description for the behavior of the tracer in blood, which then allows for the blood volume-induced bias in reference tissue input models to be assessed.
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Harding, Alice K. "Gamma-Ray Pulsar Emission Models." International Astronomical Union Colloquium 160 (1996): 315–22. http://dx.doi.org/10.1017/s0252921100041804.
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AbstractWith the increased sensitivity of gamma-ray detectors on the Compton Gamma-Ray Observatory (CGRO) the number of presently known gamma-ray pulsars has grown. The new detections are beginning to provide clues to the origin of the high-energy radiation in the form of emerging patterns and correlations among observed quantities such as gamma-ray efficiency and spectral index vs. age. But there are still many questions about the location of the emission and its relation to the radio, optical and X-ray pulses. This paper will review models for gamma-ray emission from pulsars and will examine how well the detailed predictions of these models account for the existing observations.
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Ajtay, Delia, Martin Weilenmann, and Patrik Soltic. "Towards accurate instantaneous emission models." Atmospheric Environment 39, no. 13 (April 2005): 2443–49. http://dx.doi.org/10.1016/j.atmosenv.2004.03.080.
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Kohlmaier, G. H. "Environmental models: Emission and consequences." Ecological Modelling 59, no. 1-2 (December 1991): 146–47. http://dx.doi.org/10.1016/0304-3800(91)90135-n.
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Monson, Russell K., Nicole Trahan, Todd N. Rosenstiel, Patrick Veres, David Moore, Michael Wilkinson, Richard J. Norby, et al. "Isoprene emission from terrestrial ecosystems in response to global change: minding the gap between models and observations." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1856 (May 18, 2007): 1677–95. http://dx.doi.org/10.1098/rsta.2007.2038.
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Coupled surface–atmosphere models are being used with increased frequency to make predictions of tropospheric chemistry on a ‘future’ earth characterized by a warmer climate and elevated atmospheric CO 2 concentration. One of the key inputs to these models is the emission of isoprene from forest ecosystems. Most models in current use rely on a scheme by which global change is coupled to changes in terrestrial net primary productivity (NPP) which, in turn, is coupled to changes in the magnitude of isoprene emissions. In this study, we conducted measurements of isoprene emissions at three prominent global change experiments in the United States. Our results showed that growth in an atmosphere of elevated CO 2 inhibited the emission of isoprene at levels that completely compensate for possible increases in emission due to increases in aboveground NPP. Exposure to a prolonged drought caused leaves to increase their isoprene emissions despite reductions in photosynthesis, and presumably NPP. Thus, the current generation of models intended to predict the response of isoprene emission to future global change probably contain large errors. A framework is offered as a foundation for constructing new isoprene emission models based on the responses of leaf biochemistry to future climate change and elevated atmospheric CO 2 concentrations.
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Kauhaniemi, M., A. Stojiljkovic, L. Pirjola, A. Karppinen, J. Härkönen, K. Kupiainen, L. Kangas, et al. "Comparison of the predictions of two road dust emission models with the measurements of a mobile van." Atmospheric Chemistry and Physics Discussions 14, no. 4 (February 17, 2014): 4263–301. http://dx.doi.org/10.5194/acpd-14-4263-2014.
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Abstract. The predictions of two road dust suspension emission models were compared with the on-site mobile measurements of suspension emission factors. Such a quantitative comparison has not previously been reported in the reviewed literature. The models used were the Nordic collaboration model NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and the Swedish–Finnish FORE model (Forecasting Of Road dust Emissions). These models describe particulate matter generated by the wear of road surface due to traction control methods and processes that control the suspension of road dust particles into the air. An experimental measurement campaign was conducted using a mobile laboratory called SNIFFER, along two selected road segments in central Helsinki in 2007 and 2008. The suspended PM10 concentration was measured behind the left rear tyre and the street background PM10 concentration in front of the van. Both models reproduced the measured seasonal variation of suspension emission factors fairly well during both years at both measurement sites. However, both models substantially under-predicted the measured emission values. The results indicate that road dust emission models can be directly compared with mobile measurements; however, more extensive and versatile measurement campaigns will be needed in the future.
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Raginski, I., and Ari Laor. "AGN coronal emission models – I. The predicted radio emission." Monthly Notices of the Royal Astronomical Society 459, no. 2 (April 5, 2016): 2082–96. http://dx.doi.org/10.1093/mnras/stw772.
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Aamaas, B., T. K. Berntsen, J. S. Fuglestvedt, K. P. Shine, and N. Bellouin. "Multimodel emission metrics for regional emissions of short lived climate forcers." Atmospheric Chemistry and Physics Discussions 15, no. 18 (September 25, 2015): 26089–130. http://dx.doi.org/10.5194/acpd-15-26089-2015.
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Abstract. For short lived climate forcers (SLCFs), the impact of emissions depends on where and when the emissions take place. Comprehensive new calculations of various emission metrics for SLCFs are presented based on radiative forcing (RF) values calculated in four different (chemistry-transport or coupled-chemistry climate) models. We distinguish between emissions during summer (May–October) and winter season (November–April) for emissions from Europe, East Asia, as well as the global shipping sector. The species included in this study are aerosols and aerosols precursors (BC, OC, SO2, NH3), and ozone precursors (NOx, CO, VOC), which also influence aerosols, to a lesser degree. Emission metrics for global climate responses of these emissions, as well as for CH4, have been calculated relative to CO2, using Global Warming Potential (GWP) and Global Temperature change Potential (GTP), based on dedicated RF simulations by four global models. The emission metrics include indirect cloud effects of aerosols and the semi-direct forcing for BC. In addition to the standard emission metrics for pulse and sustained emissions, we have also calculated a new emission metric designed for an emission profile consisting of a ramp up period of 15 years followed by sustained emissions, which is more appropriate for a gradual implementation of mitigation policies. For the aerosols, the emission metric values are larger in magnitude for Europe than East Asia and for summer than winter. A variation is also observed for the ozone precursors, with largest values in East Asia and winter for CO and in Europe and summer for VOC. In general, the variations between the emission metrics derived from different models are larger than the variations between regions and seasons, but the regional and seasonal variations for the best estimate also hold for most of the models individually. Further, the estimated climate impact of a mitigation policy package is robust even when accounting for correlations. For the ramp up emission metrics, the values are generally larger than for pulse or sustained emissions, which holds for all SLCFs. For a potential SLCFs mitigation policy, the dependency of metric values on the region and season of emission should be considered.
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Zhu, Sicong, LiSian Tey, and Luis Ferreira. "Genetic Algorithm Based Microscale Vehicle Emissions Modelling." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/178490.
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There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.
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Karl, M., A. Guenther, R. Köble, and G. Seufert. "A new European plant-specific emission inventory of biogenic volatile organic compounds for use in atmospheric transport models." Biogeosciences Discussions 5, no. 6 (December 18, 2008): 4993–5059. http://dx.doi.org/10.5194/bgd-5-4993-2008.
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Abstract. We present a new European plant-specific emission inventory for isoprene, monoterpenes, sesquiterpenes and other VOC (OVOC), with a spatial resolution of 10 km, for implementation in atmospheric transport models. The inventory incorporates new data on emission factors at standard conditions for tree and crop species that became available in the last years and more accurate data on foliar biomass densities coming from several new litterfall databases. In contrast to previous emission inventories, a bioclimatic correction factor was introduced to correct the foliar biomass densities for the different plant growth conditions that can be found in Pan-Europe. The 2004–2005 averaged annual total biogenic volatile organic compound (BVOC) emissions for the Pan-European domain are estimated to be about 15 Tg with a large contribution from the OVOC class of about 6 Tg and from monoterpenes of about 5 Tg. Annual isoprene emissions are found to be about 3 Tg, insensitive to the chosen emission algorithm. For the first time crop-specific land use information and standard emission factors were employed. Contrary to former European inventories, emissions of monoterpenes and OVOC were found to originate to a large extent from agriculture. However, monoterpene standard emission factors for crops are highly uncertain and probably positively biased by measurement artifacts. Further experiments on crop emissions should be carried out to check the validity of the high emission factors for monoterpenes and OVOC. In view of future intensified use of agricultural crops as biofuels, emissions of OVOC and monoterpenes from agriculture need to be evaluated in the field.
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Song, Yuan-yuan, En-jian Yao, Ting Zuo, and Zhi-feng Lang. "Emissions and Fuel Consumption Modeling for Evaluating Environmental Effectiveness of ITS Strategies." Discrete Dynamics in Nature and Society 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/581945.
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Road transportation is a major fuel consumer and greenhouse gas emitter. Recently, the intelligent transportation systems (ITSs) technologies, which can improve traffic flow and safety, have been developed to reduce the fuel consumption and vehicle emissions. Emission and fuel consumption estimation models play a key role in the evaluation of ITS technologies. Based on the influence analysis of driving parameters on vehicle emissions, this paper establishes a set of mesoscopic vehicle emission and fuel consumption models using the real-world vehicle operation and emission data. The results demonstrate that these models are more appropriate to evaluate the environmental effectiveness of ITS strategies with enough estimation accuracy.
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Wozniak, Matthew C., and Allison L. Steiner. "A prognostic pollen emissions model for climate models (PECM1.0)." Geoscientific Model Development 10, no. 11 (November 13, 2017): 4105–27. http://dx.doi.org/10.5194/gmd-10-4105-2017.
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Abstract. We develop a prognostic model called Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1) a taxa-specific land cover database, phenology, and emission potential, and (2) a plant functional type (PFT) land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.
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Wang, Han, Yujie Jin, Xingming Hong, Fuan Tian, Jianxian Wu, and Xin Nie. "Integrating IPAT and CLUMondo Models to Assess the Impact of Carbon Peak on Land Use." Land 11, no. 4 (April 13, 2022): 573. http://dx.doi.org/10.3390/land11040573.
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China’s growth plans include a carbon emission peak policy, which is a restriction that indirectly impacts land use structure. In this study, we simulate different paths for achieving policy objectives, and explore the linkages between those paths and land use change. The IPAT model was used to simulate the carbon emissions generated from a natural development scenario, an ideal policy scenario, and a retributive carbon emission scenario in China from 2020 to 2030. The simulation results were incorporated into the CLUMondo model as a demand driver to simulate the land use change in 2030. The results show that carbon emission peak policy can somewhat reduce carbon emissions and increase building land in a regulated way. However, the policy may also lead to a short-term surge in carbon emissions, a reactive expansion of arable land and building land. This may reduce losses in economic development when carbon emissions are limited, but does not achieve the integration of social, economic, and ecological goals. This study links the carbon emission peak policy with land use change and provides a fresh perspective on the Chinese government’s carbon reduction policy.
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Biliaieva, Viktoriia, Polina Mashykhina, Ivan Kalashnikov, Oleksandr Berlov, and Ivan Kravets. "Risk assessment in case of toxic chemical emission at railway transport." MATEC Web of Conferences 294 (2019): 02008. http://dx.doi.org/10.1051/matecconf/201929402008.
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Risk assessment during emission of toxic chemicals at railway transport is the problem of great scientific interest. To make such assessment we need special computer models. At present, in Ukraine,we have lack of such models. The authors present numerical models for territorial risk assessment in case of organized emissionsat railway transport (for example, emissions during locomotive movement) and in case of accident emissions (accident spills of dangerous cargo, emissions of NH3 from railway tank, etc.). The basis of the developed numerical models is the system of fundamental equations of fluid dynamics. These equations are solved numerically using implicit schemes of splitting. The developed models allow to take into account some important factors which influence the territorial risk value: probability of atmosphere conditions, train route, transport infrastructure at railway stations, probability of emission site.Also the process of pollutant chemical transformation in the atmosphere is taken into account in the developed models. The developed models allow to predict territorial risk in case of moving source of emission (moving damaged railway tank).The results of numerical experiments are presented. These results illustrate territorial risk maps for different sites near Prydniprovska railway.
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Niinemets, Ü., R. K. Monson, A. Arneth, P. Ciccioli, J. Kesselmeier, U. Kuhn, S. M. Noe, J. Peñuelas, and M. Staudt. "The leaf-level emission factor of volatile isoprenoids: caveats, model algorithms, response shapes and scaling." Biogeosciences 7, no. 6 (June 1, 2010): 1809–32. http://dx.doi.org/10.5194/bg-7-1809-2010.
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Abstract. In models of plant volatile isoprenoid emissions, the instantaneous compound emission rate typically scales with the plant's emission potential under specified environmental conditions, also called as the emission factor, ES. In the most widely employed plant isoprenoid emission models, the algorithms developed by Guenther and colleagues (1991, 1993), instantaneous variation of the steady-state emission rate is described as the product of ES and light and temperature response functions. When these models are employed in the atmospheric chemistry modeling community, species-specific ES values and parameter values defining the instantaneous response curves are often taken as initially defined. In the current review, we argue that ES as a characteristic used in the models importantly depends on our understanding of which environmental factors affect isoprenoid emissions, and consequently need standardization during experimental ES determinations. In particular, there is now increasing consensus that in addition to variations in light and temperature, alterations in atmospheric and/or within-leaf CO2 concentrations may need to be included in the emission models. Furthermore, we demonstrate that for less volatile isoprenoids, mono- and sesquiterpenes, the emissions are often jointly controlled by the compound synthesis and volatility. Because of these combined biochemical and physico-chemical drivers, specification of ES as a constant value is incapable of describing instantaneous emissions within the sole assumptions of fluctuating light and temperature as used in the standard algorithms. The definition of ES also varies depending on the degree of aggregation of ES values in different parameterization schemes (leaf- vs. canopy- or region-scale, species vs. plant functional type levels) and various aggregated ES schemes are not compatible for different integration models. The summarized information collectively emphasizes the need to update model algorithms by including missing environmental and physico-chemical controls, and always to define ES within the proper context of model structure and spatial and temporal resolution.
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Kauhaniemi, M., A. Stojiljkovic, L. Pirjola, A. Karppinen, J. Härkönen, K. Kupiainen, L. Kangas, et al. "Comparison of the predictions of two road dust emission models with the measurements of a mobile van." Atmospheric Chemistry and Physics 14, no. 17 (September 8, 2014): 9155–69. http://dx.doi.org/10.5194/acp-14-9155-2014.
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Abstract. The predictions of two road dust suspension emission models were compared with the on-site mobile measurements of suspension emission factors. Such a quantitative comparison has not previously been reported in the reviewed literature. The models used were the Nordic collaboration model NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and the Swedish–Finnish FORE model (Forecasting Of Road dust Emissions). These models describe particulate matter generated by the wear of road surface due to traction control methods and processes that control the suspension of road dust particles into the air. An experimental measurement campaign was conducted using a mobile laboratory called SNIFFER, along two selected road segments in central Helsinki in 2007 and 2008. The suspended PM10 concentration was measured behind the left rear tyre and the street background PM10 concentration in front of the van. Both models reproduced the measured seasonal variation of suspension emission factors fairly well during both years at both measurement sites. However, both models substantially under-predicted the measured emission values. The article illustrates the challenges in conducting road suspension measurements in densely trafficked urban conditions, and the numerous requirements for input data that are needed for accurately applying road suspension emission models.
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Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P. I. Palmer, and C. Geron. "Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)." Atmospheric Chemistry and Physics 6, no. 11 (August 2, 2006): 3181–210. http://dx.doi.org/10.5194/acp-6-3181-2006.
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Abstract. Reactive gases and aerosols are produced by terrestrial ecosystems, processed within plant canopies, and can then be emitted into the above-canopy atmosphere. Estimates of the above-canopy fluxes are needed for quantitative earth system studies and assessments of past, present and future air quality and climate. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is described and used to quantify net terrestrial biosphere emission of isoprene into the atmosphere. MEGAN is designed for both global and regional emission modeling and has global coverage with ~1 km2 spatial resolution. Field and laboratory investigations of the processes controlling isoprene emission are described and data available for model development and evaluation are summarized. The factors controlling isoprene emissions include biological, physical and chemical driving variables. MEGAN driving variables are derived from models and satellite and ground observations. Tropical broadleaf trees contribute almost half of the estimated global annual isoprene emission due to their relatively high emission factors and because they are often exposed to conditions that are conducive for isoprene emission. The remaining flux is primarily from shrubs which have a widespread distribution. The annual global isoprene emission estimated with MEGAN ranges from about 500 to 750 Tg isoprene (440 to 660 Tg carbon) depending on the driving variables which include temperature, solar radiation, Leaf Area Index, and plant functional type. The global annual isoprene emission estimated using the standard driving variables is ~600 Tg isoprene. Differences in driving variables result in emission estimates that differ by more than a factor of three for specific times and locations. It is difficult to evaluate isoprene emission estimates using the concentration distributions simulated using chemistry and transport models, due to the substantial uncertainties in other model components, but at least some global models produce reasonable results when using isoprene emission distributions similar to MEGAN estimates. In addition, comparison with isoprene emissions estimated from satellite formaldehyde observations indicates reasonable agreement. The sensitivity of isoprene emissions to earth system changes (e.g., climate and land-use) demonstrates the potential for large future changes in emissions. Using temperature distributions simulated by global climate models for year 2100, MEGAN estimates that isoprene emissions increase by more than a factor of two. This is considerably greater than previous estimates and additional observations are needed to evaluate and improve the methods used to predict future isoprene emissions.
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Mądziel, Maksymilian, Artur Jaworski, Hubert Kuszewski, Paweł Woś, Tiziana Campisi, and Krzysztof Lew. "The Development of CO2 Instantaneous Emission Model of Full Hybrid Vehicle with the Use of Machine Learning Techniques." Energies 15, no. 1 (December 26, 2021): 142. http://dx.doi.org/10.3390/en15010142.
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Road transport contributes to almost a quarter of carbon dioxide emissions in the EU. To analyze the exhaust emissions generated by vehicle flows, it is necessary to use specialized emission models, because it is infeasible to equip all vehicles on the road in the tested road sections with the Portable Emission Measurement System (PEMS). However, the currently used emission models may be inadequate to the investigated vehicle structure or may not be accurate due to the used macroscale. This state of affairs is especially related to full hybrid vehicles, since there are none of the microscale emission models that give estimated emissions values exclusively for this kind of drive system. Several automakers over the past decade have invested in hybrid vehicles with great opportunities to reduce costs through better design, learning, and economies of scale. In this work, the authors propose a methodology for creating a CO2 emission model, which takes relatively little computational time, and the models created give viable results for full hybrid vehicles. The creation of an emission model is based on the review of the accuracy results of methods, such as linear, robust regression, fine, medium, coarse tree, linear, cubic support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and neural network (NNET). Particularly in the work, the best fit for the road input data for the CO2 emission model creation was the GPR method. PEMS data was used, as well as model training data and model validation. The model resulting from this methodology can be used for the analysis of emissions from simulation tests, or they can be used for input parameters for speed, acceleration, and road gradient.
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Davis, Aika Y., Roger Ottmar, Yongqiang Liu, Scott Goodrick, Gary Achtemeier, Brian Gullett, Johanna Aurell, et al. "Fire emission uncertainties and their effect on smoke dispersion predictions: a case study at Eglin Air Force Base, Florida, USA." International Journal of Wildland Fire 24, no. 2 (2015): 276. http://dx.doi.org/10.1071/wf13071.
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Prescribed burning is practiced to benefit ecosystems but the resulting emissions can adversely affect air quality. A better understanding of the uncertainties in emission estimates and how these uncertainties affect smoke predictions is critical for model-based decision making. This study examined uncertainties associated with estimating fire emissions and how they affected smoke concentrations downwind from a prescribed burn that was conducted at Eglin Air Force Base in Florida, US. Estimated variables used in the modelled emission calculation were compared with field measurements. Fuel loadings, fuel consumption and emission factors were simulated using Photo Series, Consume, and previously published values. A plume dispersion model was used to study the effect of uncertainty in emissions on ground concentration prediction. The fire emission models predicted fuel loading, fuel consumption and emission factor within 15% of measurements. Approximately 18% uncertainty in field measurements of PM2.5 emissions and 36% uncertainty attributed to variability in emission estimating models resulted respectively in 20% and 42% ground level PM2.5 concentration uncertainties in dispersion modelling using Daysmoke. Uncertainty in input emissions influences the concentrations predicted by the smoke dispersion model to the same degree as does the model’s inherent uncertainty due to turbulence.
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Zare, A., J. H. Christensen, P. Irannejad, and J. Brandt. "Evaluation of two isoprene emission models for use in a long-range air pollution model." Atmospheric Chemistry and Physics 12, no. 16 (August 16, 2012): 7399–412. http://dx.doi.org/10.5194/acp-12-7399-2012.
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Abstract. Knowledge about isoprene emissions and concentration distribution is important for chemistry transport models (CTMs), because isoprene acts as a precursor for tropospheric ozone and subsequently affects the atmospheric concentrations of many other atmospheric compounds. Isoprene has a short lifetime, and hence it is very difficult to evaluate its emission estimates against measurements. For this reason, we coupled two isoprene emission models with the Danish Eulerian Hemispheric Model (DEHM), and evaluated the simulated background ozone concentrations based on different models for isoprene emissions. In this research, results of using the two global biogenic emission models; GEIA (Global Emissions Inventory Activity) and MEGAN (the global Model of Emissions of Gases and Aerosols from Nature) are compared and evaluated. The total annual emissions of isoprene for the year 2006 estimated by using MEGAN is 592 Tg yr−1 for an extended area of the Northern Hemisphere, which is 21% higher than that estimated by using GEIA. The overall feature of the emissions from the two models is quite similar, but differences are found mainly in Africa's savannah and in the southern part of North America. Differences in spatial distribution of emission factors are found to be a key source of these discrepancies. In spite of the short life-time of isoprene, a direct evaluation of isoprene concentrations using the two biogenic emission models in DEHM has been made against available measurements in Europe. Results show an agreement between two models simulations and the measurements in general and that the CTM is able to simulate isoprene concentrations. Additionally, investigation of ozone concentrations resulting from the two biogenic emission models show that isoprene simulated by MEGAN strongly affects the ozone production in the African savannah; the effect is up to 10% more than that obtained using GEIA. In contrast, the impact of using GEIA is higher in the Amazon region with more than 8% higher ozone concentrations compared to that of using MEGAN. Comparing the ozone concentrations obtained by DEHM using the two different isoprene models with measurements from Europe and North America, show an agreement on the hourly, mean daily and daily maximum values. However, the average of ozone daily maximum value simulated by using MEGAN is slightly closer to the measured value for the average of all measuring sites in Europe.
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Dashkevich, Zhanna, and Vladimir Ivanov. "Diagnostics of emission intensities and electron density in auroras based on empirical precipitation models." Solar-Terrestrial Physics 8, no. 2 (June 30, 2022): 56–60. http://dx.doi.org/10.12737/stp-82202208.
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We have studied the influence of the precipitating electron spectrum shape on the integral intensity of emissions λ391.4 nm 1NG N⁺₂, λ670.4 mn 1PG N₂, λ337.1 nm 2PG N₂, λ320.0 nm VK N₂, λ127.3 nm LBH N₂, atomic oxygen emissions λ557.7 and λ630.0 nm, total electron content in the vertical column of aurora. The integral characteristics of the emission intensity and the total electron content are shown to weakly depend on the energy spectrum shape and to be determined mainly by average energy values Eev and energy flux value Fᴇ of precipitating electrons. An algorithm is proposed for diagnosing the planetary distribution of emission intensities and total electron content in auroras based on data from empirical electron precipitation models, without making a priori assumptions about the shape of the energy spectrum of precipitating electrons.
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Arneth, A., R. K. Monson, G. Schurgers, Ü. Niinemets, and P. I. Palmer. "Why are estimates of global terrestrial isoprene emissions so similar (and why is this not so for monoterpenes)?" Atmospheric Chemistry and Physics 8, no. 16 (August 8, 2008): 4605–20. http://dx.doi.org/10.5194/acp-8-4605-2008.
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Abstract. Emissions of biogenic volatile organic compounds (BVOC) are a chief uncertainty in calculating the burdens of important atmospheric compounds like tropospheric ozone or secondary organic aerosol, reflecting either imperfect chemical oxidation mechanisms or unreliable emission estimates, or both. To provide a starting point for a more systematic discussion we review here global isoprene and monoterpene emission estimates to-date. We note a surprisingly small variation in the predictions of global isoprene emission rate that is in stark contrast with our lack of process understanding and the small number of observations for model parameterisation and evaluation. Most of the models are based on similar emission algorithms, using fixed values for the emission capacity of various plant functional types. In some cases, these values are very similar but differ substantially in other models. The similarities with regard to the global isoprene emission rate would suggest that the dominant parameters driving the ultimate global estimate, and thus the dominant determinant of model sensitivity, are the specific emission algorithm and isoprene emission capacity. But the models also differ broadly with regard to their representation of net primary productivity, method of biome coverage determination and climate data. Contrary to isoprene, monoterpene estimates show significantly larger model-to-model variation although variation in terms of leaf algorithm, emission capacities, the way of model upscaling, vegetation cover or climatology used in terpene models are comparable to those used for isoprene. From our summary of published studies there appears to be no evidence that the terrestrial modelling community has been any more successful in "resolving unknowns" in the mechanisms that control global isoprene emissions, compared to global monoterpene emissions. Rather, the proliferation of common parameterization schemes within a large variety of model platforms lends the illusion of convergence towards a common estimate of global isoprene emissions. This convergence might be used to provide optimism that the community has reached the "relief phase", the phase when sufficient process understanding and data for evaluation allows models' projections to converge, when applying a recently proposed concept. We argue that there is no basis for this apparent relief phase. Rather, we urge modellers to be bolder in their analysis, and to draw attention to the fact that terrestrial emissions, particularly in the area of biome-specific emission capacities, are unknown rather than uncertain.
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Niinemets, Ü., R. K. Monson, A. Arneth, P. Ciccioli, J. Kesselmeier, U. Kuhn, S. M. Noe, J. Peñuelas, and M. Staudt. "The emission factor of volatile isoprenoids: caveats, model algorithms, response shapes and scaling." Biogeosciences Discussions 7, no. 1 (February 17, 2010): 1233–93. http://dx.doi.org/10.5194/bgd-7-1233-2010.
Full textAbstract:
Abstract. In models of plant volatile isoprenoid emissions, the instantaneous compound emission rate typically scales with the plant's emission capacity under specified environmental conditions, also defined as the emission factor, ES. In the most widely employed plant isoprenoid emission models, the algorithms developed by Guenther and colleagues (1991, 1993), instantaneous variation of the steady-state emission rate is described as the product of ES and light and temperature response functions. When these models are employed in the in atmospheric chemistry modeling community, species-specific ES values and parameter values defining the instantaneous response curves are typically considered as constant. In the current review, we argue that ES is largely a modeling concept, importantly depending on our understanding of which environmental factors affect isoprenoid emissions, and consequently need standardization during ES determination. In particular, there is now increasing consensus that variations in atmospheric CO2 concentration, in addition to variations in light and temperature, need to be included in the emission models. Furthermore, we demonstrate that for less volatile isoprenoids, mono- and sesquiterpenes, the emissions are often jointly controlled by the compound synthesis and volatility, and because of these combined biochemical and physico-chemical properties, specification of ES as a constant value is incapable of describing instantaneous emissions within the sole assumptions of fluctuating light and temperature, as are used in the standard algorithms. The definition of ES also varies depending on the degree of aggregation of ES values in different parameterization schemes (leaf- vs. canopy- or region-level, species vs. plant functional type level), and various aggregated ES schemes are not compatible for different integration models. The summarized information collectively emphasizes the need to update model algorithms by including missing environmental and physico-chemical controls, and always to define ES within the proper context of model structure and spatial and temporal resolution.
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Karl, M., A. Guenther, R. Köble, A. Leip, and G. Seufert. "A new European plant-specific emission inventory of biogenic volatile organic compounds for use in atmospheric transport models." Biogeosciences 6, no. 6 (June 18, 2009): 1059–87. http://dx.doi.org/10.5194/bg-6-1059-2009.
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Abstract. We present a new European plant-specific emission inventory for isoprene, monoterpenes, sesquiterpenes and oxygenated VOC (OVOC), on a spatial resolution of 0.089×0.089 degrees, for implementation in atmospheric transport models. The inventory incorporates more accurate data on foliar biomass densities from several litterfall databases that became available in the last years for the main tree species in Europe. A bioclimatic correction factor was introduced to correct the foliar biomass densities of trees and crops for the different plant growth conditions that can be found in Pan-Europe. Long-term seasonal variability of agriculture and forest emissions was taken into account by implementing a new growing season concept. The 2004–2005 averaged annual total biogenic volatile organic compound (BVOC) emissions for the Pan-European domain are estimated to be about 12 Tg with a large contribution from the OVOC class of about 4.5 Tg and from monoterpenes of about 4 Tg. Annual isoprene emissions are found to be about 3.5 Tg, insensitive to the chosen emission algorithm. Emissions of OVOC were found to originate to a large extent from agriculture. Further experiments on crop emissions should be carried out to check the validity of the applied standard emission factors. The new inventory aims at a fully transparent and verifiable aggregation of detailed land use information and at the inclusion of plant-specific emission data. Though plant-specific land use data is available with relatively high accuracy, a lack of experimental biomass densities and emission data on terpenes, sesquiterpenes and oxygenated VOC, in particular for agricultural plants, currently limits the setup of a highly accurate plant-specific emission inventory.
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Peltola, Olli, Maarit Raivonen, Xuefei Li, and Timo Vesala. "Technical note: Comparison of methane ebullition modelling approaches used in terrestrial wetland models." Biogeosciences 15, no. 3 (February 15, 2018): 937–51. http://dx.doi.org/10.5194/bg-15-937-2018.
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Abstract. Emission via bubbling, i.e. ebullition, is one of the main methane (CH4) emission pathways from wetlands to the atmosphere. Direct measurement of gas bubble formation, growth and release in the peat–water matrix is challenging and in consequence these processes are relatively unknown and are coarsely represented in current wetland CH4 emission models. In this study we aimed to evaluate three ebullition modelling approaches and their effect on model performance. This was achieved by implementing the three approaches in one process-based CH4 emission model. All the approaches were based on some kind of threshold: either on CH4 pore water concentration (ECT), pressure (EPT) or free-phase gas volume (EBG) threshold. The model was run using 4 years of data from a boreal sedge fen and the results were compared with eddy covariance measurements of CH4 fluxes.Modelled annual CH4 emissions were largely unaffected by the different ebullition modelling approaches; however, temporal variability in CH4 emissions varied an order of magnitude between the approaches. Hence the ebullition modelling approach drives the temporal variability in modelled CH4 emissions and therefore significantly impacts, for instance, high-frequency (daily scale) model comparison and calibration against measurements. The modelling approach based on the most recent knowledge of the ebullition process (volume threshold, EBG) agreed the best with the measured fluxes (R2 = 0.63) and hence produced the most reasonable results, although there was a scale mismatch between the measurements (ecosystem scale with heterogeneous ebullition locations) and model results (single horizontally homogeneous peat column). The approach should be favoured over the two other more widely used ebullition modelling approaches and researchers are encouraged to implement it into their CH4 emission models.
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Yu, Qian, Tie Zhu Li, Yan Ming Ren, Na Zhu, and Fang Qian. "Influence of Passenger Load on Diesel Bus Emissions." Applied Mechanics and Materials 694 (November 2014): 13–18. http://dx.doi.org/10.4028/www.scientific.net/amm.694.13.
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The purpose of this paper is to analyze the influence of passenger load on diesel bus emissions based on the real-world on-road emission data collected by the Portable Emission Measurement System (PEMS). It is also analyzed whether passenger load affect the accuracy of emission models based on VSP. The results indicate that the influence of passenger load on emission rates of CO2, CO, NOX and HC is various with different speed and acceleration ranges. As for the distance-based emission factors of CO2, CO, NOX and HC, per-passenger emission factors decrease with the rise of passenger load. In addition, it is found that the influence of passenger load can be omitted properly in the emission models of low and middle speed bins. But that can lead to an error reaching up to 49% if the influence of passenger load is neglected in the models of high speed bins.
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Dulebenets, Maxim A. "Advantages and disadvantages from enforcing emission restrictions within emission control areas." Maritime Business Review 1, no. 2 (June 30, 2016): 107–32. http://dx.doi.org/10.1108/mabr-05-2016-0011.
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Purpose Emissions produced by oceangoing vessels not only negatively affect the environment but also may deteriorate health of living organisms. Several regulations were released by the International Maritime Organization (IMO) to alleviate negative externalities from maritime transportation. Certain polluted areas were designated as “Emission Control Areas” (ECAs). However, IMO did not enforce any restrictions on the actual quantity of emissions that could be produced within ECAs. This paper aims to perform a comprehensive assessment of advantages and disadvantages from introducing restrictions on the emissions produced within ECAs. Two mixed-integer non-linear mathematical programs are presented to model the existing IMO regulations and an alternative policy, which along with the established IMO requirements also enforces restrictions on the quantity of emissions produced within ECAs. A set of linearization techniques are applied to linearize both models, which are further solved using the dynamic secant approximation procedure. Numerical experiments demonstrate that introduction of emission restrictions within ECAs can significantly reduce pollution levels but may incur increasing route service cost for the liner shipping company. Design/methodology/approach Two mixed-integer non-linear mathematical programs are presented to model the existing IMO regulations and an alternative policy, which along with the established IMO requirements also enforces restrictions on the quantity of emissions produced within ECAs. A set of linearization techniques are applied to linearize both models, which are further solved using the dynamic secant approximation procedure. Findings Numerical experiments were conducted for the French Asia Line 3 route, served by CMA CGM liner shipping company and passing through ECAs with sulfur oxide control. It was found that introduction of emission restrictions reduced the quantity of sulfur dioxide emissions produced by 40.4 per cent. In the meantime, emission restrictions required the liner shipping company to decrease the vessel sailing speed not only at voyage legs within ECAs but also at the adjacent voyage legs, which increased the total vessel turnaround time and in turn increased the total route service cost by 7.8 per cent. Research limitations/implications This study does not capture uncertainty in liner shipping operations. Practical implications The developed mathematical model can serve as an efficient practical tool for liner shipping companies in developing green vessel schedules, enhancing energy efficiency and improving environmental sustainability. Originality/value Researchers and practitioners seek for new mathematical models and environmental policies that may alleviate pollution from oceangoing vessels and improve energy efficiency. This study proposes two novel mathematical models for the green vessel scheduling problem in a liner shipping route with ECAs. The first model is based on the existing IMO regulations, whereas the second one along with the established IMO requirements enforces emission restrictions within ECAs. Extensive numerical experiments are performed to assess advantages and disadvantages from introducing emission restrictions within ECAs.
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Barth, Matthew, Feng An, Joseph Norbeck, and Marc Ross. "Modal Emissions Modeling: A Physical Approach." Transportation Research Record: Journal of the Transportation Research Board 1520, no. 1 (January 1996): 81–88. http://dx.doi.org/10.1177/0361198196152000110.
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Mobile source emission models currently used by state and federal agencies (e.g., Environmental Protection Agency's MOBILE and California Air Resources Board's EMFAC) are often inadequate for analyzing the emissions impact of various transportation control measures, intelligent transportation systems, alternative fuel vehicles, and more sophisticated inspection/maintenance programs contained in most state air quality management plans. These emission models are based on the assumption that vehicle running exhaust emissions can be represented as integrated values for a specific driving cycle, and then later adjusted by speed correction factors. What is needed in addition to these “regional-type” mobile source models is an emissions model that considers at a more fundamental level the modal operation of a vehicle (i.e., emissions that directly relate to vehicle operating modes such as idle, steady-state cruise, various levels of acceleration/deceleration, and so forth). A new modal-emissions modeling approach that is deterministic and based on analytical functions that describe the physical phenomena associated with vehicle operation and emissions productions is presented. This model relies on highly time-resolved emissions and vehicle operation data that must be collected from a wide range of vehicles of varying emission control technologies. Current emission modeling techniques are discussed and the modeling approach and implementation plan for a new, three-year NCHRP Project entitled “Development of a Modal Emissions Model” are described.
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Wu, Xiaomeng, Daoyuan Yang, Ruoxi Wu, Jiajun Gu, Yifan Wen, Shaojun Zhang, Rui Wu, et al. "High-resolution mapping of regional traffic emissions using land-use machine learning models." Atmospheric Chemistry and Physics 22, no. 3 (February 10, 2022): 1939–50. http://dx.doi.org/10.5194/acp-22-1939-2022.
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Abstract. On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on road traffic monitoring datasets in the Beijing–Tianjin–Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than other machine learning models on most occasions in this study. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. NOx, fine particulate matter (PM2.5), and black carbon (BC) emissions from heavy-duty trucks (HDTs) generally have a higher emission intensity on the highways connecting to regional ports. The model found a general reduction in light-duty passenger vehicles when traffic restrictions were implemented but a much more spatially heterogeneous impact on HDTs, with some road links experiencing up to 40 % increases in the HDT traffic volume. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, thereby providing a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.
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Skjøth, C. A., and C. Geels. "The effect of climate and climate change on ammonia emissions in Europe." Atmospheric Chemistry and Physics Discussions 12, no. 9 (September 10, 2012): 23403–31. http://dx.doi.org/10.5194/acpd-12-23403-2012.
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Abstract. We present here a dynamical method for modelling temporal and geographical variations in ammonia emissions in regional scale Chemistry Transport Models (CTMs) and Chemistry Climate Models (CCMs). The method is based on the meteorology in the models and gridded inventories. We use the dynamical method to investigating the spatio-temporal variability of the ammonia emissions across part of Europe and study how these emissions are related to geographical and year-to-year variations in atmospheric temperature alone. For simplicity we focus on the emission from a storage related to a Danish standard pig stable with 1000 animals and display how the emission from this source category vary geographically throughout central and northern Europe and from year to year. In view of future climate changes we also evaluate the potential future changes in the emission by including temperature projections from an ensemble of climate models. The results points towards four overall issues: (1) Emissions can easily vary with 20% by changing geographical location within a country due to overall variations in climate. Largest uncertainties are seen for large countries like UK, Germany and France. (2) Annual variations in overall climate can at specific locations cause uncertainties in the range of 20%. (3) Climate change will in general increase the emissions with 0–40%, in central to northern Europe. (4) Gradients in existing emission inventories that are seen along country borders (e.g. between UK and France), can be reduced by using a dynamical methodology for calculating emissions. Acting together these four issues can cause substantial uncertainties in emission. Emissions are generally considered among the largest uncertainties in the model calculations with CTM and CCM models. Efforts to reduce uncertainties are therefore highly relevant. It is therefore recommended that both CCMs and CTMs implement a dynamical methodology for simulating ammonia emissions in a similar way as for biogenic volatile organic compound (BVOCs) – a method that has been used for more than a decade in CTMs.
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Freitas, S. R., K. M. Longo, M. F. Alonso, M. Pirre, V. Marecal, G. Grell, R. Stockler, R. F. Mello, and M. Sánchez Gácita. "PREP-CHEM-SRC – 1.0: a preprocessor of trace gas and aerosol emission fields for regional and global atmospheric chemistry models." Geoscientific Model Development 4, no. 2 (May 10, 2011): 419–33. http://dx.doi.org/10.5194/gmd-4-419-2011.
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Abstract. The preprocessor PREP-CHEM-SRC presented in the paper is a comprehensive tool aiming at preparing emission fields of trace gases and aerosols for use in atmospheric-chemistry transport models. The considered emissions are from the most recent databases of urban/industrial, biogenic, biomass burning, volcanic, biofuel use and burning from agricultural waste sources. For biomass burning, emissions can be also estimated directly from satellite fire detections using a fire emission model included in the tool. The preprocessor provides emission fields interpolated onto the transport model grid. Several map projections can be chosen. The inclusion of these emissions in transport models is also presented. The preprocessor is coded using Fortran90 and C and is driven by a namelist allowing the user to choose the type of emissions and the databases.
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Terlevich, Roberto, Jorge Melnick, and Mariano Moles. "STarburst Models for AGNs." Symposium - International Astronomical Union 121 (1987): 499–519. http://dx.doi.org/10.1017/s0074180900155548.
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A significant fraction of all spiral galaxies exhibit some type of “activity” in their nuclear regions as evidenced by the presence of emission lines in their optical spectrum (Keel, 1982; Cetty-Veron and Veron, 1985). It has become standard to classify emission-line galaxies into two main groups: “active” having Seyfert or Liner nuclei and “inactive”, having Starburst or HII-region like nuclei. Two main classification criteria are used, one based in the widths (Khachikian and Weedman 1974) the other in the intensity ratios of the nuclear emission lines (Baldwin, Phillips and Terlevich, 1981).