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This article proposes a methodology for the estimation of emissions in real driving conditions, based on board diagnostics data and machine learning, since it has been detected that there are no models for estimating pollutants without large measurement campaigns. For this purpose, driving data are obtained by means of a data logger and emissions through a portable emissions measurement system in a real driving emissions test. The data obtained are used to train artificial neural networks that estimate emissions, having previously estimated the relative importance of variables through random forest techniques. Then, by the application of the K-means algorithm, labels are obtained to implement a classification tree and thereby determine the selected gear by the driver. These models were loaded with a data set generated covering 1218.19 km of driving. The results generated were compared to the ones obtained by applying the international vehicle emissions model and with the results of the real driving emissions test, showing evidence of similar results. The main contribution of this article is that the generated model is stronger in different traffic conditions and presents good results at the speed interval with small differences at low average driving speeds because more than half of the vehicle’s trip occurs in urban areas, in completely random driving conditions. These results can be useful for the estimation of emission factors with potential application in vehicular homologation processes and the estimation of vehicular emission inventories.

Air quality is a growing concern worldwide because of its impacts on both the environment and the human health. The road transport sector is a major contributor to this poor air quality. To reduce the emission of particulate matter, all diesel passenger cars were equipped with diesel particulate filters since the EURO5b emission standard. Unfortunately, these filters can be damaged or intentionally removed during the lifetime of a vehicle. This work presents the particle number emission factors for EURO5 and EURO6 diesel passenger cars, based on the measurements of 757 vehicles. These measurements were performed at low idle, which shows a high correlation to particle number emission factors obtained during homologation cycles or real-driving emission measurements. The results show that the average Particle Number (PN) emission factors are highly impacted by high emitters present in the fleet and that the mileage has a significant impact on the PN emission factors. Finally, the estimated PN emission factors based on low idle measurements were higher by a factor 5.6 for EURO5a, 2.5 for EURO5b and 5.5 for EURO6, compared to their respective HBEFA (Handbook Emission Factors for Road, Transport) emission factors.

Soil can be resuspended in the atmosphere due to wind or mechanical disturbances, such as agricultural activities (sowing, tilling, etc.), producing fine particulate matter (PM). Agriculture is estimated to be the third PM10-emitting sector in Europe, emitting more than the transportation sector. However, very few emission figures are available for the different cropping operations. Moreover, soil Emission Potential (EP) is extremely variable, since is influenced by factors such as humidity, texture, chemical composition, and wind speed. Due to their similarity to tilling emission mechanisms, Soil Resuspension Chambers (SRC) are the most suitable method to estimate the impacts of these factors on soil susceptibility to emit PM10 during cropping operations (Emission Potential, EP). The main objective of this work is to assess the EP of different agricultural soils used for maize cropping in North-Western Italy, studying the influence of soil moisture and physico-chemical characteristics. Therefore, a SRC was developed, based on previous studies, with the goal of being relatively small, easy to operate and low-cost. Using the gathered data, a log-linear multiple regression model was developed to allow soil EP estimation from few physico-chemical parameters (moisture, sand/silt ratio and organic carbon content). The model allows to tailor field Emission Factors (EF) of specific cropping operations to different soil and moisture conditions and was applied to an EF for rotary harrowing, defined in a previous study. The concentration of Potentially Toxic Elements (PTE) in soil-emitted PM10 was determined, founding an enrichment up to 16 times higher than in the original soil, evidencing a possible cause of concern for operator's safety during agricultural activities.

The maritime containerised transport continues to grow; thus, an efficient seaport inland access becomes crucial for the performance of the entire intermodal transport chain. Dry ports are inland intermodal terminals directly connected to seaports via rail, which operate as trans-shipment hubs and provide services, such as: storage, consolidation, depot and custom clearance. In addition to offering benefits in terms of operational costs and efficiency, dry port concept implementation can produce positive effects also under an environmental point of view, promoting the modal shift from road to rail. This paper presents a simulation-based method to estimate the environmental benefits, in terms of emissions reduction, of a dry port implementation. This method is organized into three parts: (i) traffic demand analysis, (ii) traffic supply model, (iii) assignment and emission model. The methodology is applied to a real-world case study in Northern Italy, where the current scenario is compared to a future scenario in which a new railway connection is established between a seaport (Port of Venice) and an existing intermodal freight hub (Interporto of Padua). According to our findings, in the future scenario emissions of the main pollutants are reduced by 17%. In terms of CO2 this corresponds to about 8000 tons per year.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-01-12, 資料番号: PA2110061000

Mitigation of methane emission plays a very important role in carbon mitigation. A model which simulates the whole process of methane emissions can be developed based on research on the methane emission mechanism. According to this model, the methane emission is affected by all the factors, which affected the disoxidation of methanogenus and oxidation in the emission process. Therefore, the mitigation of methane emission can be achieved by selection of rice cultivar, water regime and fertilization.

A grid-oriented Biogenic Emission Model (BEM) has been developed to calculate Non-Methane Volatile Organic Compound (NMVOC) emissions from vegetation in high spatial and temporal resolutions. The model allows the emissions calculation for any modeling domain covering Europe on the basis of: 1) the U.S. Geological Survey 1-km resolution land-use database, 2) a land-use specific, monthly isoprene, monoterpene and Other Volatile Organic Compound (OVOC) emission potentials and foliar biomass densities database, 3) temperature and solar radiation data provided by the mesoscale meteorological model MM5. The model was applied for Europe in 30-km spatial resolution for the year 2003. The European total emissions for 2003 consist of 33.0% isoprene, 25.5% monoterpenes and 41.5% OVOC. BEM results are compared with those from the well-documented global Model of Emissions of Gases and Aerosols from Nature (MEGAN). The BEM total emissions compare well with the MEGAN ones. In July 2003, the results of both models agree within a factor of 1.2 for total isoprene emissions and within a factor of 2 for total monoterpene emissions. The comparison of the spatial distributions of the July 2003 isoprene and monoterpene emissions calculated with BEM and MEGAN shows that, in the greater part of the study area, the differences are below the current uncertainty limit for the estimation of spatially-resolved biogenic VOC emissions in Europe being equal to about +/-600 kg km^-^2 month^-^1. Differences that are above this limit are found mainly in the eastern European countries for isoprene and in the Mediterranean countries for monoterpenes.

Does consideration of average speed distributions on roads-as compared to single mean speed-lead to different results in emission modelling of large road networks? To address this question, a post-processing method is developed to predict mean speed distributions using available traffic data from a dynamic macroscopic traffic model (Indy) that was run for an actual test network (Amsterdam). Two emission models are compared: a continuous (COPERT IV) and a discrete model (VERSIT+macro). Computations show that total network emissions of CO, HC, NOx, PM10 and CO2 are generally (but not always) increased after application of the mean speed distribution method up to +9%, and even up to +24% at sub-network level (urban, rural, motorway). Conventional computation methods thus appear to produce biased results (underestimation). The magnitude and direction of the effect is a function of emission model (type), shape of the composite emission factor curve and change in the joint distribution of (sub)-network VKT (vehicle kilometres travelled) and speed. Differences between the two emission models in predicted total network emissions are generally larger, which indicates that other issues (e.g., emission model validation, model choice) are more relevant. © 2007 Elsevier Ltd. All rights reserved.

Atmospheric pollutant dispersion near sources is typically simulated by Gaussian models because of their efficient compromise between reasonable accuracy and manageable computational time. However, the standard Gaussian dispersion formula applies downwind of a source under advective conditions with a well-defined wind direction and cannot calculate air pollutant concentrations under calm conditions with fluctuating wind direction and/or upwind of the emission source. Attempts have been made to address atmospheric dispersion under such conditions. This work evaluates the performance of standard and modified Gaussian plume models using measurements of NO2, PM10, PM2.5, five inorganic ions and seven metals conducted near a freeway in Grenoble, France, during 11–27 September 2011. The formulation for calm conditions significantly improves model performance. However, it appears that atmospheric dispersion due to vehicle-induced turbulence is still underestimated. Furthermore, model performance is poor for particulate species unless road dust resuspension by traffic is explicitly taken into account.

Energy shortage and atmospheric pollution problems are getting more serious in China, and transportation is the main source of energy consumption, pollutants, and carbon emissions. This study combined the activity-based analysis method with emission models, and investigated the influence mechanism of people’s activity travel scheduling on transportation energy consumption and emissions on holidays. Based on the holiday travel behavior survey data, the multinomial logistic regression model was first applied to explore the decision mechanisms of individual travel-mode choices in holidays. Next, the emission model was integrated with an activity-based travel demand model to calculate and compare transportation energy consumption and emissions under different policy scenarios. The results showed that socio-demographic characteristics had significant effects on holiday activity–travel patterns, and combined mode chains had a larger number of activity points than single mode chains. With an increase in the trip time of cars, and decrease of travel distance and the number of activity points, transportation energy consumption and emissions could be reduced greatly with an adjustment of holiday activity–travel patterns. The reduced portion is mainly attracted by slow traffic and public transport. However, the effects of a single policy strategy are very limited, thus portfolio policies need to be considered by policy makers.

Pharmaceuticals in the environment are of growing public health concern. The main objectives of this study were to develop a new emission estimation model, identify factors critical to reducing emission, and demonstrate the model's applicability for screening and priority setting. A new emission estimation model was developed covering the life cycle pathways of pharmaceuticals from supply to discharge into surface water. The emission estimates of the model were assessed by coupling with SimpleBox to give predicted concentrations and by comparing the predicted concentrations with measured concentrations in Korean surface waters for five selected pharmaceuticals (acetaminophen, cephradine, ibuprofen, mefenamic acid, and naproxen). The sensitivity analysis revealed that the biodegradation rate in the sewage treatment plant and the excretion rate of pharmaceuticals were the most important factors influencing the emission rate. The uncertainty of the emission estimate was found to increase with increases in the value of the emission estimate. Once the intrinsic properties of a pharmaceutical (excretion rate, biodegradation rate, and removal rate by sludge separation) were given, the patient behavior parameters, such as participation in a Take-back program and rate of administration, were determined to have a strong influence on the emission estimate. In our study, the predicted and measured concentrations agreed with each other within one order of magnitude. Several management implications were drawn from the analysis of model outcomes. The model outcomes, alone or in combination with toxicity data, may potentially be used for the purposes of screening, priority setting, and the design of management programs.

In the last 20 years the attention of international organizations towards air pollution has been improved, leading to definition of laws and regulations. In order to evaluate strategies and policies, forecasting tools have been adopted by institutions. Currently, the estimation of traffic emissions is based on static models, in which the amount of pollutant is computed as a function of average parameters obtained on a single road stretch. The well-known traffic increase of recent years has significantly changed the actual flow conditions, producing a strong rise of interferences. As this facet affects the operating condition of each vehicle, the use of a standard emission model at high traffic interference can lead to some inaccuracies. In such cases, instantaneous emission models introduce deeper capabilities; essentially, the pollutant prediction is directly tied to the engine vehicle operation point in reallike traffic condition. This second modelling approach has been adopted in the current work. A complete lumped parameter vehicle model has been built to be used as a virtual on-road emission/fuel consumption test unit. Investigations have highlighted the dependence of emission level and fuel consumption on drivers’ behaviour; indeed, the analysis took advantage of the experiments carried out in the virtual reality laboratory: on a typical highway geometry, characterized by a dual lane carriageway with three lanes each, three different flow conditions have been simulated. Once the relationship between highway interference level and drivers’ behaviour has been evidenced (in terms of emissions and fuel consumption), a relation between highway interference level and emissions/fuel consumption has been highlighted. Finally, in order to assess the differences between static and instantaneous emission models, a comparative analysis has been carried out.