AERMOD for near-road pollutant dispersion: Evaluation of model performance with different emission source representations and low wind options

The performance of the regulatory dispersion model AERMOD in simulating vehicle-emitted pollutant concentrations near-roadway using area or volume source representation of emissions and with different low wind options was assessed using the SF₆ tracer data from the General Motors (GM) Sulfur Dispersion Experiment. At downwind receptor locations, AERMOD, using either area or volume source emissions, can reasonably predict the tracer concentrations near the surface (0.5 m) but the model performance decreases at higher elevations (3.5m and 9.5m above the surface). For upwind receptors, using an area source representation leads to significant under-predictions due to AERMOD’s lack of treatment of lateral plume meander, but using volume source representation leads to over-predictions of upwind concentrations regardless of the low wind options for plume meander. Among the three low wind options currently available in AERMOD, best model performance is obtained with low wind option 3, which treats plume meander with a higher minimal standard deviation of the horizontal crosswind component (σ_v,min = 0.3 m s⁻¹), eliminates upwind component of dispersion and uses an effective lateral dispersion parameter (σ_y) to replicate centerline concentration. The optional adjustment of the surface friction velocity in the meteorological preprocessor AERMET does not lead to obvious improvements in predicted near-road concentrations for this application.     

Spatial variation of roadside C2–C6 hydrocarbon concentrations during low wind speeds: Validation of CALINE4 and COPERT III modelling

The CALINE4 model is widely used to predict the effect of vehicle emissions on ambient concentrations close to roadways. It requires an evaluation of the rate at which different air pollutants are emitted by vehicles, taking into account things such as vehicle flow, velocity, type and age. For Europe the databases of the COmputer Program to calculate Emissions from Road Transport (COPERT) are combined with local vehicle details to obtain site-specific emission factors for dispersion modelling. The ability of CALINE4 to predict the spatial variation of hydrocarbon concentrations downwind of a motorway is assessed, as is the accuracy of COPERT III composite emission factors for several hydrocarbon compounds. The concentrations of seven traffic-associated compounds is found at three locations downwind and upwind of a motorway. Modelled and measured background-corrected downwind concentrations are compared on three bases: daily peak hour concentrations, mean concentrations, and a set of model evaluation parameters.     

Comparison and evaluation of several mobile-source and line-source models in Israel

This study compares several transportation-related air quality models. Two line-source models were used: Caline4 (California Department of Transportation) and Hiway2 (US Environmental Protection Agency). Two mobile-source models, Mobile5b (US Environmental Protection Agency) and COPERT3 (European Environment Agency), along with real-world emission factors were used and evaluated as well. Model predictions of NO_x concentrations were compared to measured values at two sites in Israel, differing by fleet composition and physical layout (‘at-grade’ and a ‘cut/depressed’ road sections). The process indicated that emission factors generated by COPERT3 are the most appropriate for free flowing traffic situations in Israel. Predictions by both line-source models were similar when applied to ‘at-grade’ road sections. When applied to ‘cut/depressed’ sections, Hiway2 better predicted concentrations during unstable conditions, while Caline4 better predicted concentrations during stable conditions and peak concentrations.     

基于CALINE4模式的平原地区高速公路机动车尾气污染水平研究

为了研究我国平原地区高速公路两侧的机动车尾气污染水平和污染物的分布规律,本文以监测数据为基础,结合当地的具体气象特点,利用CALINE4模式,对国内平原地区的一段高速公路上机动车排放的污染物CO的浓度进行模拟,并将模拟结果和实测结果进行对比分析,结果表明CALINE4模式可以判断平原地区高速公路两侧机动车尾气CO污染状况,动态地估计平原地区高速公路环境交通容量,间接地评价该区域的空气质量。     

Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM_2.5 emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM_2.5 pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM_2.5 pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. EPA’s MOVES and AERMOD models. The heavy-duty truck released PM_2.5 pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using our methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM_2.5 emissions are caused by trucks and also 21 percent of the urban overall ambient PM_2.5 concentrations can be attributed to trucks in Cincinnati urban area.     

Traffic emission pollution sampling and analysis on urban streets with high-rising buildings

Air pollution at many types of intersections and other roadside “hot spots” is not accurately characterized by state-of-the-practice models. In this study, data were collected on traffic flows, second-by-second CO and NO₂ ambient concentrations in Shanghai, China. The sampled data were compared with CAL3QHC modeling results. We found that: (1) intersection hot spot emission concentrations were explained primarily by queuing activities of motor vehicles; (2) air quality concentrations are difficult to predict because of complex dispersion processes near high-rise buildings; and (3) screening models such as CAL3QHC are prone to large errors in dense cities with mixed traffic and high-rising buildings. Suggestions are made for improved models relevant to dense developing cities.     

Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach

This paper applies artificial neural network to predict hourly air pollutant concentrations near an arterial in Guangzhou, China. Factors that influence pollutant concentrations are classified into four categories: traffic-related, background concentration, meteorological and geographical. The hourly averages of these influential factors and concentrations of carbon monoxide, nitrogen dioxide, particular matter and ozone were measured at three selected sites near the arterial using vehicular automatic monitoring equipments. Models based on back-propagation neural network were trained, validated and tested using the collected data. It is demonstrated that the models are able to produce accurate prediction of hourly concentrations of the pollutants respectively more than 10 h in advance. A comparison study shows that the neural network models outperform multiple linear regression models and the California line source dispersion model.     

A system based approach to develop hybrid model predicting extreme urban NOx and PM2.5 concentrations

One of the major drawbacks of conventional air quality models is their inability in accurately predicting extreme air pollutant concentrations. Hybrid modelling is one of the techniques that estimates/predicts the ‘entire range’ of the distribution of pollutant concentrations by combining the deterministic based models (capable in predicting average range) with suitable statistical (probability) distribution models (capable in predicting extreme range). This research paper describes system based approach in developing hybrid model to predict hourly averages as well as extreme percentile ranges of NO_x and PM_2.5 concentrations at two urban locations having complex traffic heterogeneity, highly variable tropical meteorology and different geographical characteristics. At one of the selected locations i.e. Delhi megacity, during winters, hybridization of AERMOD and Lognormal predicts NO_x and PM_2.5 concentrations satisfactorily with index of agreement ‘d’ values of 0.98–0.99, respectively; however, during summers, AERMOD-Log-logistic and AERMOD-Lognormal are best predicting NO_x and PM_2.5 concentrations with d values of 0.98–0.96, respectively. In another, i.e., Chennai, a coastal megacity, AERMOD-Lognormal predicts PM_2.5 concentrations satisfactorily with d values of 0.98 and 0.99 during winter and summer seasons, respectively. Further, hybrid model has also been used to evaluate regulatory compliance.     

Atmospheric dispersion modeling near a roadway under calm meteorological conditions

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, PM_2.5, 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.     

Including passengers’ response to crowding in the Dutch national train passenger assignment model

Transit passengers’ response to crowded conditions has been studied empirically, yet is limitedly included in transport models currently used in the design of policy and infrastructure investments. This has consequences for the practical applicability of these models in studies on, for instance, timetabling, train capacity management strategies, project appraisal, and passenger satisfaction. Here we propose four methods to include the effect of crowding, based on existing studies on passengers’ perception and response as well as often-used crowding indicators. These four alternative methods are implemented in the train passenger assignment procedure of the Dutch national transport model, and evaluated with respect to their impacts on the model results for the Dutch railway network. The four methods relate to four different ways in which an additive trip penalty and/or time-multiplier can be incorporated in the train utility function for different travel purposes, to capture the disutility of crowding as measured by the load factor. The analyses of the test case favor the hybrid method using both a boarding penalty (capturing seat availability upon boarding) and a time-multiplier (capturing physical comfort and safety throughout the trip). This method produces consistent results, while the additional computational effort that it imposes is acceptable. Further empirical underpinning is needed to conclusively show which of these methods best captures passengers’ response behavior quantitatively (for different travel purposes and conditions).     

Modeling NOx and NO2 emissions from mobile sources: A case study for Hamilton, Ontario, Canada

This study characterizes the dispersion of emissions of oxides of nitrogen and nitrogen dioxide from traffic sources in the Hamilton census metropolitan area. The Integrated Model of Urban Landuse and Transportation for Environmental Analysis software was used to estimate emissions of oxides of nitrogen from traffic sources. The Air Pollution Model, was used to simulate dispersion of the traffic emissions over the city. The models account for the two primary wind directions in this region – the prevailing southwest and a secondary northeast direction. The results show a prominent triangle of high pollution defined by major roads and highways along the periphery of the Hamilton Harbour, at peak-hour. With southwest winds, residential areas along the northern shoreline of the Harbour are affected, while the western sections of the city are affected by northeast winds. High concentrations are persistent in some areas, for both wind directions. The resulting dispersion surfaces characterize the spatial distribution of traffic emissions and thus provide a means of assessing population exposure over the Hamilton area.     

Modelling of vehicular exhausts – a review

A review of the air pollution studies carried out to study the dispersion of vehicular exhaust emissions (VEEs) has been made. The review includes the modelling studies in the domain, primarily, of analytical modelling – deterministic mathematical models and numerical models, and statistical models. Various model performance evaluation and comparative assessment studies have also been discussed. Further, the studies conducted to model VEEs at the urban road intersection and urban street canyons have also been reviewed.     

输气管道泄漏率计算与扩散模拟方法述评

分析了研究输气管道泄漏率计算与扩散模拟对管道泄漏事故后果评价和事故处理的重要意义,综述了国内外输气管道泄漏率的稳态、瞬态计算模型,指出其适用范围;总结了输气管道泄漏扩散模拟的各种方法及其优缺点,建议建立较简单的气体扩散模型,应用三维计算模型及湍流统计、模式理论模拟输气管道泄漏情形.     

Heterogeneous platoon flow dispersion model based on truncated mixed simplified phase‐type distribution of travel speed

Existing research on platoon dispersion models either describe homogeneous traffic flow feature, or are in lack of analytical solutions. By analyzing the field data, the truncated mixed simplified phase‐type distribution is proved to be capable of capturing the characteristics of heterogeneous traffic flow with an excellent fitting result. In light of this, we derive a generic heterogeneous platoon dispersion model with truncated mixed simplified phase‐type of speed in the forms of integrable functions. Numerical case studies are conducted to compare the performance of the proposed model and the conventional models (i.e., the Robertson model and truncated mixed Gaussian model). The results show that the proposed model not only better captures the platoon dispersion laws of heterogeneous traffic flow, but also presents higher computational efficiency, which provides practical implications on traffic signal control. Copyright © 2017 John Wiley & Sons, Ltd.     

Computer simulation of impacts of a chlorine tanker truck accident

The aim of the study was to present the author’s original modelling programs of pollution dispersion in an urban environment. The programs outlined herein are based on a diagnostic model of air velocity field and Langrangian particle model. In both modelling programs author’s elements of mathematical modelling were applied. The models have been validated and verified. Geographic Information System (GIS) Idrisi Andes was applied in the pre-processing stage.The development of author’s original computer program and stages of hazardous material propagation modelling were discussed.The results of computations and analysis of pollution dispersion caused by a chlorine tanker truck collision and chlorine release into atmosphere were presented. It was simulated that the accident took place in the city of Bielsko-Biała, on a bypass in a built-up area causing leakage of 10 t of chlorine. The computer simulations conducted for two wind direction scenarios allow to conclude that the total area affected by the pollutant cloud with concentrations above threshold levels could be over 2 km², with 5000 lethally exposed people.     

Allocation of onroad mobile emissions to road segments for air toxics modeling in an urban area

Dispersion models are useful tools for setting emission control priorities and developing strategies for reducing air toxics emissions. Previous methodologies for modeling hazardous air pollutant emissions for onroad mobile sources are based on using spatial surrogates to allocate county level emissions to grid cells. A disadvantage of this process is that it spreads onroad emissions throughout a grid cell instead of along actual road locations. High local concentrations may be underestimated near major roadways, which are often clustered in urban centers. Here, we describe a methodology which utilizes a Geographic Information System to allocate benzene emissions to major road segments in an urban area and model the segments as elongated area sources. The Industrial Source Complex Short Term dispersion model is run using both gridded and link-based emissions to evaluate the effect of improved spatial allocation of emissions on ambient modeled benzene concentrations. Allocating onroad mobile emissions to road segments improves the agreement between modeled concentrations when compared with monitor observations, and also results in higher estimated concentrations in the urban center.     

Simulations of pollutant dispersion at toll plazas using three-dimensional CFD models

Toll plazas are particularly susceptible to build-ups of vehicle-emitted pollutants because vehicles pass through in low gear. To look at this, three-dimensional computational fluid dynamics simulations of pollutant dispersion are used on the standard k − ε turbulence model. The effects of wind speed, wind direction and topography on pollutant dispersion were discussed. The Wuzhuang toll plaza on the Hefei-Nanjing expressway is considered, and the effects of the retaining walls along both sides of the plaza on pollutant dispersion is analysed. There are greater pollutant concentrations near the tollbooths as the angle between the direction of the wind and traffic increases implying that retaining walls impede dispersion. The slope of the walls has little influence on the variations in pollutant concentration.     

Uncertainty analysis of the large zone economic module of the simple,efficient, elegant,and effective model (SE3M) of land use and transportation

Integrated land use and transportation forecasting models are used to assist decision-makers in the policy analysis and infrastructure capital improvement selection process. These models are typically given precise, point-estimate inputs that are mathematically linked, through a series of submodels, to forecasted model outputs. These point-estimate inputs represent an unrealistic level of precision and a growing body of research is focusing on statistical techniques to model uncertainty in model inputs and parameters and tracking the effects of this uncertainty through the various submodels to the model outputs. This paper presents an uncertainty analysis of the Large Zone Economic Module (LZEM) of the Simple, Efficient, Elegant, and Effective Model (SE³M) of land use and transportation. Three case-study implementations of the model are used to obtain a reasonably sound approximation of how uncertainty affects LZEM outputs: Guam, Puerto Rico, and Oahu, Hawaii. These case studies were the subject of an early transferability study with SE³M and were selected based on both their insularity and diverse physical, economic, and demographic geographies. The findings of this research demonstrate that LZEM has a robust framework, with the potential to estimate error both in the positive and negative direction under uncertain input/parameter conditions.     

Intra-household bargaining for school trip accompaniment of children: A group decision approach

This paper tests a group decision-making model to examine the school travel behavior of students 6–18 years old in the Minneapolis-St. Paul Metropolitan area. The school trip information of 1737 two-parent families with a student is extracted from Travel Behavior Inventory data collected by the Metropolitan Council between the Fall 2010 and Spring 2012. The model has four distinct characteristics including: (1) considering the student explicitly in the model, (2) allowing for bargaining or negotiation within households, (3) quantifying the intra-household interaction among family members, and (4) determining the decision weight function for household members. This framework also covers a household with three members, namely, a father, a mother, and a student, and unlike other studies it is not limited to dual-worker families. To test the hypotheses we build two models, each with and without the group-decision approach. The models are separately built for different age groups, namely students 6–12 and 12–18 years old. This study considers a wide range of variables such as work status of parents, age and gender of students, mode of travel, and distance to school. The findings of this study demonstrate that the elasticities of the two modeling approaches differ not only in the value, but in the sign in some cases. In 63% of the cases the unitary household model underestimates the results. More precisely, the elasticities of the unitary household model are as much as 2 times more than that of the group-decision model in 20% of cases. This is a direct consequence of model misspecification that misleads both long- and short-term policies where the intra-household bargaining and interaction is overlooked in travel behavior models.