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.     

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.     

Estimation of road transport related air pollution in Saint Petersburg using European and Russian calculation models

The paper analyzes Russian and European emission and dispersion models aimed at the estimation of road transport related air pollution on street and regional scale as exemplified with St. Petersburg, Russia. It demonstrates the results of model calculations of peak concentrations of main harmful substances (NО_X, CO and PM₁₀) along the St. Petersburg Ring Road at high traffic volume and adverse meteorological conditions (calm, temperature inversion) executed by means of a Russian street pollution model, and it evaluates the computed results against the measurements from monitoring stations. The paper also examines the ways of adaptation of the COPERT IV model – a software tool for calculation of air pollutant and greenhouse gas emissions from road transport on regional or country scale – to the inventory conditions of the Russian Federation, compares the COPERT IV numerical estimates with the national inventory data. It also reveals the obstacles and possibilities in the harmonization of the Russian and European approaches.     

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.     

A hybrid model for better predicting capabilities under parallel wind conditions for NOx concentrations from highways in Ireland

Atmospheric dispersion models based on the Gaussian line source formulation tend to give poor results for low wind speed and parallel wind directions. Certain dispersion models like HIWAY-2 have an inbuilt dispersion algorithm to adjust in such situations but no correction exists for mathematical based Gaussian dispersion models. We evaluate the performance of a hybrid model a mathematical model, to assess its suitability as an alternative to the Gaussian based models.     

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.     

Environmental,functional and economic criteria for comparing “target roundabouts” with one- or two-level roundabout intersections

The article describes a criterion based on functional, environmental and economic aspects for comparing conventional roundabouts with innovative one- or two-level roundabouts. We compared the performances of eight roundabout types, differing in geometric layout, number of lanes and traffic flow regulation from each other, with regard to vehicle delays and CO₂, NO_x, PM_2.5 and PM₁₀ pollutant emissions. Recently-designed roundabouts – target roundabouts and flyover roundabouts – have also been studied for their undoubted practical interest. By means of closed-form capacity models and CORINAIR methodology, several traffic simulations were carried out to examine a typical annual traffic demand curve in a suburban context, three different distribution test matrices for traffic flows (ρ1, ρ2, ρ3) and maximum annual traffic flow values Q_max ranging between 1300 and 3300 veh/h.Estimating vehicle delays and annual pollutant emissions, along with construction and management costs, allowed obtaining overall costs for each roundabout examined, in function of traffic demand and several other parameters. Thanks to these analyses, we identified the roundabout types which best suit to each traffic condition.     

Vehicular pollution prediction modelling: a review of highway dispersion models

Air quality modelling plays an important role in formulating air pollution control and management strategies by providing guidelines for better and more efficient air quality planning. Several line source models, mostly Gaussian‐based, have been suggested to predict pollutant concentrations near highways/roads. These models, despite several assumptions and limitations, are used throughout the world, including in India, to carry out air pollution prediction analysis due to vehicular traffic near roads/highways. These models are being continuously upgraded and modified based on field experiments, and numerical and physical modelling results. An effort has been made in the present paper to review briefly the philosophy and basic features of most of the commonly used highway dispersion models. The paper also discusses various theories and techniques that led to the development and modification of these models along with the statistical analysis tools to evaluate the performance of these models. An attempt has also been made to summarize briefly the various line source models currently used in India and to highlight the difficulties being faced while using them in an Indian context.     

Experimental study of the impact of structural geometry and wind direction on vehicle emissions in urban environment

Vehicle emissions inside an urban environment are investigated using a wind-tunnel under neutral atmospheric conditions. The urban environment was formed as street canyon model. The diffusion flow field in the boundary layer inside the street canyon was examined at different locations of varying geometry of the street and wind directions in the downwind distance of the leeward side of the street canyon model. The results show that the vertical velocity increases as the aspect ratio increases and with wind direction increases from θ = 90°. The pollutant concentration increases as the aspect ratio decreases. The pollutant concentration decreases as the wind direction increases from θ = 90°. The pollutant concentration distributions indicate that the variability of the structure, geometry and wind direction inside the street canyon are important parameters for estimating air quality in the urban street canyon.     

Estimating near-road pollutant dispersion: A model inter-comparison

A model inter-comparison study to assess the abilities of steady-state Gaussian dispersion models to capture near-road pollutant dispersion has been carried out with four models (AERMOD, run with both the area-source and volume-source options to represent roadways, CALINE, versions 3 and 4, ADMS and RLINE). Two field tracer studies are used: the Idaho Falls tracer study and the Caltrans Highway 99 tracer study. Model performance measures are calculated using concentrations (observed and estimated) that are paired in time and space, since many of the health related questions involve outcomes associated with spatially and temporally distributed human activities. All four models showed an ability to estimate the majority of downwind concentrations within a factor of two of the observations. RLINE, AERMOD-V, and ADMS, also have the capability to predict concentrations upwind of the roadway that result from low-speed meandering of the plume. Generally, RLINE, ADMS, and AERMOD (both source types) had overall performance statistics that were broadly similar, while CALINE 3 and 4 both produced a larger degree of scatter in their concentration estimates. The models performed best for near-neutral conditions in both tracer studies, but had mixed results under convective and stable conditions.     

Influence of avenue trees on traffic pollutant dispersion in asymmetric street canyons: Numerical modeling with empirical analysis

The dispersion of traffic-related pollutants in urban street canyons is of importance for the health and quality of lives. To reveal the inherent principle, researchers have performed a lot of investigations; many dispersion phenomena have also been assessed during recent years. However, the presence of avenue trees in street canyons and their capacity for pollutant dispersion remains partly addressed. In this study, we investigated the effects of avenue trees in urban street canyons on traffic pollutant dispersion. The dispersion of CO concentration in asymmetric street canyons was simulated under varied situations. The computational results showed a good agreement with the experimental data, and the numerical model was validated to be adequate for investigating the pollutant dispersion in street canyons. Then, the numerical simulations were extended to explore the impacts of the effects of avenue trees on CO dispersion; the results indicated that avenue trees generally increase CO concentrations in asymmetric street canyons. When the wind direction is perpendicular to the street axis, a terraced building raises pollutant concentrations at the windward wall and reduces concentration at the leeward wall on the pedestrian levels. Findings of this study are expected to provide significant insight into urban road design and strategy making for avenue tree planting, particularly under the existing worldwide sustainable low-carbon urban development.     

PM2.5 and NOx from traffic: Human health impacts,external costs and policy implications from the Belgian perspective

This article employs an optimized impact pathway approach to marginal external health costs that relies on high-resolution dispersion models calibrated for Belgium and the surrounding areas. Per tonne, the MEHC_PM2.5 is found to be many times larger than MEHC_NOx, which is currently negative. Further, the impact of Belgian PM_2.5 emissions in the immediate area of generation is significantly larger than the impact on more distant areas; the opposite is true for NO_x. The MEHCs of both pollutants are predicted to increase in the coming years. Further analysis of the impacts of PM_2.5 and NO_x reveals that, on average, modern gasoline vehicles outperform their diesel counterparts as far as future emissions are concerned. This contrasts with findings for 2007, which suggested that Euro 5 diesels had fewer associated health costs because of the potential for ozone reduction offered by their NO_x emissions.     

A model for evaluating the environmental and functional benefits of “innovative” roundabouts

This study looks at the singling out of a multi-parameter criterion for choosing conventional or innovative roundabout layouts, by taking functional, environmental and economic aspects into consideration. The performances of three conventional roundabouts (with different lane number at entries and through the ring), turbo-roundabouts and roundabouts with right-turn bypass lane on all the arms (flower roundabouts) have been compared in terms of vehicle delays and pollutant (carbon dioxide, nitrogen oxides, particle pollution (PM₁₀ and PM_2.5)) emissions. By means of closed-form capacity models and with the help of COPERT IV^© software, several traffic simulations have been carried out, referred to yearly peak flow values Q_max and ranging between 1300 and 3300 veh/h, starting from a typical annual traffic demand curve in urban areas. The estimation of cumulative vehicle delays and annual pollutant emissions, together with construction and maintenance costs has allowed working out overall costs for each roundabout under consideration, depending on the traffic demand. Thus, the proposed model allows finding the most cost-effective geometric solution as to overall costs for a comprehensive case record of traffic values.     

Valuing reductions in environmental pollution in a residential location context

A stated preference ranking experiment is designed to estimate the willingness-to-pay (WTP) for reducing the amount of atmospheric pollution in a group-based residential location context. Important issues are the proper definition of the context and the variable metric for the environmental attribute. Sample members were asked to rank 10 options arising from variations in the attributes travel time to work and to study, rent of the house and an environmental attribute (the number of days of Alert, in terms of concentration of PM₁₀, at a dwelling’s location). Multinomial logit models based on a consistent microeconomic framework were estimated for various stratifications of the data (income, pollution sensitivity, and type of dwelling currently inhabited). From these subjective values of time and WTP were derived for reductions in the number of days of alert and hence the amount of pollutant concentration at a given location. The WTP came out at about 1% of the family income for reducing one contingence day per year; this is approximately 60% higher than an estimate reported for the city of Edmonton, Canada, but the average PM₁₀ concentration in Santiago is about six times higher.     

Characteristics of particulate matter (PM) concentrations influenced by piston wind and train door opening in the Shanghai subway system

More than 9 million passengers take Shanghai’s subway system every work day. The system’s air quality has caused widespread concern because of the potential harm to passengers’ health. We measured the particulate matter (PM) concentrations at three kinds of typical underground platform (side-type, island-type, and stacked-type platforms) and inside the trains in Shanghai’s metro during 7 days of measurements in April and July 2015. Our results demonstrated that the patterns of air quality variation and PM concentrations were similar at the side-type and island-type platforms. We also found that the PM concentrations were higher on the platforms than inside the train and that the PM concentrations in the subway system were positively correlated with those in the ambient air. Piston wind generated by vehicle motion pushes air from the tunnel to the platform, so platform PM concentrations increase when trains approach the platform. However, the piston wind effect varies greatly between locations on the platform. In general, the effect of the piston wind is weaker at the middle of the platform than at both ends. PM concentrations inside the train increase after the doors open, during which time dirty platform air floods into the compartments. PM_1.0 and PM_2.5 were significantly correlated both inside the train and on the platforms. PM_1.0 accounted for 71.9% of PM_2.5 inside the train, which is higher than the corresponding platform values. Based on these results, we propose some practical suggestions to minimize air pollution damage to passengers and staff from the subway system.     

Simulating the air quality impacts of traffic calming schemes in a dense urban neighborhood

In this study, the effects of isolated traffic calming measures and area-wide calming schemes on air quality in a dense neighborhood were estimated using a combination of microscopic traffic simulation, emission, and dispersion modeling. Results indicated that traffic calming measures did not have as large an effect on nitrogen dioxide (NO₂) concentrations as the effect observed on nitrogen oxide (NO_x) emissions. Changes in emissions resulted in highly disproportional changes in pollutant levels due to daily meteorological conditions, road geometry and orientation with respect to the wind. Average NO₂ levels increased between 0.1% and 10% with respect to the base-case while changes in NO_x emissions varied between 5% and 160%. Moreover, higher wind speeds decreased NO₂ concentrations on both sides of the roadway. Among the traffic calming measures, speed bumps produced the highest increases in NO₂ levels.     

Port-city exhaust emission model: An application to cruise and ferry operations in Las Palmas Port

Exhaust emissions cause air pollution and climate change. The exhausts of shipboard fuel combustion are equally damaging particularly, so close to the environmentally sensitive mainland and island coasts, as well as at ports due to their urbanized character. This paper estimates, for the first time, exhaust pollutants related to cruise and ferry operations in Las Palmas Port and, in an island context. Emission assessment is based on a full bottom-up model and messages transmitted by the Automatic Identification System during 2011. Results are described as a breakdown of NOx, SOx, PM_2.5, CO and CO₂, according to ship classes, operative type and time, providing valuable information to environmental policy makers in port-city areas and islands under similar conditions. It is generally concluded that vessel traffic and passenger shipping in particular are a source of air pollution in Las Palmas Port. Emission maps confirm location of hot spots in quays assigned for cruise and ferry operations. Policy recommendations encourage regular monitoring of exhaust emissions and market-based incentives supported by details on polluting and operative profiles. On the other hand, feasibility studies are suggested for automated mooring, LNG bunkering facilities and also shore-side energy services, prioritizing berthing of shipping sectors (or sub-sectors) with the highest share of exhaust emissions once their local effects have been confirmed by a dispersion, exposure and impact assessment.     

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.     

Commuters’ exposure to PM2.5 and CO2 in metro carriages of Shanghai metro system

Air quality inside transportation carriages has become a public concern. A comprehensive measurement campaign was conducted to examine the commuters’ exposure to PM_2.5 (d_p ⩽ 2.5 μm) and CO₂ in Shanghai metro system under different conditions. The PM_2.5 and CO₂ concentrations inside all the measured metro lines were observed at 84 ± 42 μg/m³ and 1253.1 ± 449.1 ppm, respectively. The factors that determine the in-carriage PM_2.5 and CO₂ concentrations were quantitatively investigated. The metro in-carriage PM_2.5 concentrations were significantly affected by the ventilation systems, out-carriage PM_2.5 concentrations and the passenger numbers. The largest in-carriage PM_2.5 and CO₂ concentrations were observed at 132 μg/m³ and 1855.0 ppm inside the carriages equipped with the oldest ventilation systems. The average PM_2.5 and CO₂ concentrations increased by 24.14% and 9.93% as the metro was driven from underground to overground. The average in-carriage PM_2.5 concentrations increased by 17.19% and CO₂ concentration decreased by 16.97% as the metro was driven from urban to the suburban area. It was found that PM_2.5 concentration is proportional to the on-board passenger number at a ratio of 0.4 μg/m³·passenger. A mass-balance model was developed to estimate the in-carriage PM_2.5 concentration under different driving conditions.