Predicting Air Quality by Integrating a Mesoscopic Traffic Simulation Model and Simplified Air Pollutant Estimation Models

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Continuous growth in traffic demand has led to a decrease in the air quality in various urban areas. More than ever, local authorities for environmental protection and urban planners are interested in performing detailed investigations using traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness where necessary. This article is focused on the traffic and air pollution in the eco-neighbourhood “Nancy Grand Cœur”, located in a medium-size city from north-eastern France. The main objective of this work is to build an integrated simulation model which would predict and visualize various environmental changes inside the neighbourhood such as: air pollution, traffic flow or meteorological information. Firstly, we conduct a data profiling analysis on the received data sets together with a discussion on the daily and hourly traffic patterns, average nitrogen dioxide concentrations and the regional background concentrations recorded in the eco-neighbourhood for the study period. Secondly, we build the 3D mesoscopic traffic simulation model using real data sets from the local traffic management centre. Thirdly, by using reliable data sets from the local air-quality management centre, we build a regression model to predict the evolution of nitrogen dioxide concentrations, as a function of the simulated traffic flow and meteorological data. We then validate the estimated results through comparisons with real data sets, with the purpose of supporting the traffic engineering decision-making and the smart city sustainability. The last section of the paper is reserved for further regression studies applied to other air pollutants monitored in the eco-neighbourhood, such as sulphur dioxide and particulate matter and a detailed discussion on benefit and challenges to conduct such studies

Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling

© 2019 Elsevier Ltd Air pollution impact assessment is a major objective for various community councils in large cities, which have lately redirected their attention towards using more low-cost sensing units supported by citizen involvement. However, there is a lack of research studies investigating real-time mobile air-quality measurement through smart sensing units and even more of any data-driven modelling techniques that could be deployed to predict air quality accurately from the generated data-sets. This paper addresses these challenges by: a) proposing a comparative and detailed investigation of various air quality monitoring devices (both fixed and mobile), tested through field measurements and citizen sensing in an eco-neighbourhood from Lorraine, France, and by b) proposing a machine learning approach to evaluate the accuracy and potential of such mobile generated data for air quality prediction. The air quality evaluation consists of three experimenting protocols: a) first, we installed fixed passive tubes for monitoring the nitrogen dioxide concentrations placed in strategic locations highly affected by traffic circulation in an eco-neighbourhood, b) second, we monitored the nitrogen dioxide registered by citizens using smart and mobile pollution units carried at breathing level; results revealed that mobile-captured concentrations were 3–5 times higher than the ones registered by passive-static monitoring tubes and c) third, we compared different mobile pollution stations working simultaneously, which revealed noticeable differences in terms of result variability and sensitivity. Finally, we applied a machine learning modelling by using decision trees and neural networks on the mobile-generated data and show that humidity and noise are the most important factors influencing the prediction of nitrogen dioxide concentrations of mobile stations

Evaluating the impact of urban mobility policies on the air quality levels of Barcelona by means of an integrated modelling system

Persistent high levels of NO2 have severe health effects on population. These are often found in large urban conurbations with high vehicle densities. In Barcelona, with one of the highest vehicle densities in Europe, the two traffic air quality monitoring stations are continuously exceeding the limit values established by the 2008/50/EC Ambient Air Quality Directive. To reduce traffic emissions and associated air pollution levels, Barcelona is applying a series of traffic restrictions that attempt to renew and reduce the amount of circulating vehicles within the city. These include the reduction of private vehicle space in specific areas or urban corridors of the city (superblocks and tactical urbanism) and the implementation of a Low Emission Zone (LEZ) that restricts the entrance of most polluting vehicles in the city. In order to quantify and evaluate the level of effectiveness of the applied restrictions, air quality modelling is presented as a necessary tool to complement the information provided by the air quality monitoring stations. In this sense, the present thesis evaluates the impact that the different restrictions have on the resulting NOx emissions and NO2 concentration levels in Barcelona. To accomplish that, we developed an integrated air quality system composed by the VISUM traffic simulator, the emission model HERMESv3 and the street-scale dispersion model CALIOPE-Urban, which integrates the mesoscale CALIOPE air quality forecast system and the Gaussian dispersion model R-LINE. The thesis first explains the coupling, calibration and validation process of the traffic-emission system. This is followed by an emission sensitivity analysis of typically high uncertainty emission features such as different approaches in regard of vehicle fleet composition, public bus transport implementation, temperature effect or the application of non-exhaust PM sources. We also explore the limitations of the developed macroscopic system by comparing it with a microscopic -highly detailed- approach composed by the microscopic traffic simulator Aimsun Next and the PHEMLight vehicle emission model. Finally, we explain the coupling of the traffic-emission system with the mesoscale CALIOPE and street-scale CALIOPE-Urban air quality systems. In this study, we apply the traffic restrictions previously mentioned in Barcelona to observe their effects in traffic routing, traffic emissions and resulting air quality levels at a resolution of 20 meters. Our results show that the only measures with an overall reduction on NOx emissions are the ones considering the LEZ or a reduction on the traffic demand of -25%. The combination of all strategies with the demand reduction shows the highest NOx emission decrease (-30%) while if traffic demand is kept constant, the computed NOx reductions are of -13%. The strategies limited to restrict the vehicle space on the city show a negligible impact on the overall traffic emissions (+0.1%), although they generate important street-level emission gradients, up to +/-17% in NOx. The impact on NO2 air quality levels follows the same pattern as for emissions. The scenarios comprising the LEZ and the -25% demand reduction show the highest NO2 reductions (-5 to -10 and -10 to -20 ug/m3 in daily average NO2 concentration values). The unique application of traffic measures limiting the vehicle space show limited impacts of +/-5 ug/m3 due to traffic re-routing, as previously commented. Considering the obtained results, the reductions achieved are insufficient to ensure compliant air quality levels, and are very far from reaching the new WHO air quality guideline values. The applied restrictions must be accompanied by a larger decrease in the total number of circulating vehicles throughout the city which could be achieved, for instance, by the application of a congestion charge, or the implementation of local zero emission zones similar to the ones that are currently being deployed in the city of London.La persistente acumulación de altos valores de NO2 presenta serios problemas de salud. Esto ocurre con frecuencia en grandes zonas urbanas con altas densidades de tráfico. En Barcelona, con una de las mayores densidades de vehículos de Europa, las dos estaciones monitoreo de calidad del aire de tráfico exceden de forma continuada los valores limite establecidos por la 2008/50/EC Ambient Air Quality Directive. Para reducir las emisiones de tráfico Barcelona esta aplicando una serie de restricciones al tráfico con el propósito de renovar y reducir la cantidad de vehículos circulante. Estas medidas incluyen la reducción de espacio al vehículo privado en áreas específicas o en corredores de la ciudad (Supermanzanas o urbanismo táctico) y la implementación de una Zona de Bajas Emisiones (ZBE) que restringe la entrada a los vehículos mas contaminantes. Para cuantificar y evaluar el nivel de eficacia de las restricciones mencionadas, la modelización de calidad del aire se presenta como una herramienta necesaria para complementar la información dada por las estaciones de monitoreo de calidad del aire. Esta tesis evalúa el impacto que las diferentes restricciones tienen en los valores de emisión de NOx y de concentración de NO2 en Barcelona. Para ello, hemos desarrollado un sistema de calidad del aire compuesto por el simulador de tráfico VISUM, el modelo de emisiones HERMESv3 y el modelo de dispersión urbana CALIOPE-Urban, que integra el sistema mesoescalar de calidad del aire CALIOPE y el sistema Gaussiano de dispersión R-LINE. En la tesis se detalla el acoplamiento y el proceso de calibración y validación del sistema de tráfico-emisiones. A continuación, se realiza un estudio de sensibilidad valorando diferentes aproximaciones de variables de alta incertidumbre para la estimación de emisiones tales como la composición vehicular, la implementación del transporte público, el efecto de la temperatura o la consideración de fuentes PM no provenientes del gas de escape. También exploramos las limitaciones del sistema macroscópico desarrollado comparándolo con un sistema de alto detalle compuesto por el simulador micro Aimsun Next y el modelo de emisiones vehiculares PHEMLight. Finalmente, explicamos el acoplamiento del sistema tráfico-emisiones con el sistema de calidad del aire mesoescalar CALIOPE y el urbano CALIOPE-Urban que usamos para evaluar las restricciones de tráfico antes mencionadas en Barcelona y observar sus efectos en las rutas de tráfico, emisiones y concentración a una resolución de 20 metros. Los resultados muestran que las únicas medidas con una reducción global de emisiones NOx son las que consideran la ZBE o una reducción de demanda del -25%. La combinación de todas las estrategias con la reducción de demanda muestra las mayores reducciones en NOx (-30%) mientras que si la demanda se mantiene constante las reducciones observadas son del -13%. Las estrategias que se limitan a restringir el espacio del vehículo muestran reducciones negligibles (+0.1%), aunque generan importantes gradientes a nivel de calle que pueden llegar al +/-17% en NOx. El impacto en los valores de concentración de NO2 sigue los mismos patrones que las emisiones. Los escenarios que comprenden la ZBE y la reducción de demanda del -25% muestran las mayores reducciones (-5 a -10 y -10 a -20 ug/m3 de NO2). La consideración de las medidas que únicamente limitan el espacio al vehículo muestran reducciones de NO2 de +/-5 ug/m3 debido a la redistribución de rutas de tráfico. Concluimos que las reducciones obtenidas son insuficientes para asegurar valores de calidad del aire conforme a los límites de la UE, y están muy lejos de llegar a los nuevos valores guía de la OMS. Las restricciones aplicadas deben ir acompañadas por un mayor descenso del total de vehículos circulantes que podría conseguirse, por ejemplo, mediante la aplicación de un peaje de congestión o la implementación de zonas de cero emisiones, similares a las que se están desplegando actualmente en la ciudad de LondresPostprint (published version

Transportation modelling for environmental impact assessment : Porto metropolitan area case study

Tese de mestrado. Transportes. Faculdade de Engenharia. Universidade do Porto. Departamento de Engenharia Mecânica. Universidade de Aveiro. 200

Uncertainties and Errors in Predicting Vehicle Exhaust Emissions using Traffic Flow Models

Vehicle exhaust emissions predicted based on the outputs of traffic flow models are used directly to calculate traffic-related emissions, but also indirectly as input to 'air quality - human exposure' models. Both of which inform transport and environmental policies aimed at achieving sustainable mobility. To be effective, these must be based on robust modelling approaches that not only provide point-based emission predictions, but also inform these with an interval of confidence that properly accounts for the propagation of uncertainties and errors through the complex chain of models involved. This research develops a data-driven methodological framework to probabilistic average speed-based emission predictions using two widely deployed macroscopic traffic flow models. These are the Cell Transmission Model (CTM), a discretised first-order LWR-type model, and METANET, a discretised second-order Payne-type model. Studying both allows quantitative comparison in their application to predicting emissions. While this research discusses all potential sources of uncertainty in this modelling chain, it focusses on those arising from the traffic flow modelling output. The methodology starts with an ensemble-based optimisation approach to estimate both calibration and validation prediction errors in the traffic flow model, and then proposes a Monte Carlo sampling approach to propagate these to emission predictions. This allows predicting emissions alongside their upper and lower bounds for any time period and road network, at different levels of detail. To ensure transferability of findings, this methodology has been tested on three motorway road networks, one of which operates under Variable Speed Limits (VSL). This permits the quantitative assessment of VSL-modified traffic flow models. In the results of this research, emissions of Oxides of Nitrogen (NOx) and uncertainty associated with their prediction are specifically reported for each road network under study. Finally, this research argues that the methodological framework developed can (and should) be applied to any other (relatively) simple or complex integrated 'traffic flow - emission' modelling chain used as part of policy and decision making process

Internet-of-vehicles network for CO₂ emission estimation and reinforcement learning-based emission reduction

The escalating impact of vehicular Carbon Dioxide (CO2) emissions on air pollution, global warming, and climate change necessitates innovative solutions. This paper proposes a comprehensive Internet-of-Vehicles (IoV) network for real-time CO2 emissions estimation and reduction. We implemented and tested an on-board device that estimates the vehicle’s emissions and transmits the data to the network. The estimated CO2 emissions values are close to the standard emissions values of petrol and diesel vehicles, accounting for expected discrepancies due to vehicles’ age and loading. The network uses the aggregate emissions readings to inform the Reinforcement Learning (RL) algorithm, enabling the prediction of optimal speed limits to minimize vehicular emissions. The results demonstrate that employing the RL algorithm can achieve an average CO2 emissions reduction of 11 kg/h to 150 kg/h

Uncertainty propagation from the cell transmission traffic flow model to emission predictions: a data-driven approach

Road traffic exhaust emission predictions are used to inform transport policy and investment decisions aimed at reducing emissions and achieving sustainable mobility. Emission predictions are also used as inputs when modeling air quality and human exposure to traffic-related air pollutants. To be effective, such policies and/or integration must be based on robust models that not only provide point-based predictions but also inform these with an interval of confidence that properly accounts for the propagation of uncertainties through the complex chain of models involved. This paper develops a data-driven methodological framework that enables calculating the uncertainty in average speed–based emission predictions induced by uncertainty in its traffic data inputs, which are most often predictions (or outputs) of traffic flow models. An ensemble-based optimisation approach is used to estimate both calibration and validation errors arising from uncertainty in the structure and parameterisation of the cell transmission model, a discretised first-order macroscopic traffic flow model that is often integrated with average speed–based emission models. A Monte Carlo sampling approach is proposed to propagate the uncertainty in traffic flow inputs to emission predictions. To ensure transferability of findings, this methodology has been tested using multiple real data sets on three motorway road networks, one of which operates under variable speed limits

Internet-of-Vehicles Network for CO2 Emission Estimation and Reinforcement Learning-Based Emission Reduction

The escalating impact of vehicular Carbon Dioxide (CO 2 ) emissions on air pollution, global warming, and climate change necessitates innovative solutions. This paper proposes a comprehensive Internet-of-Vehicles (IoV) network for real-time CO 2 emissions estimation and reduction. We implemented and tested an on-board device that estimates the vehicle’s emissions and transmits the data to the network. The estimated CO 2 emissions values are close to the standard emissions values of petrol and diesel vehicles, accounting for expected discrepancies due to vehicles’ age and loading. The network uses the aggregate emissions readings to inform the Reinforcement Learning (RL) algorithm, enabling the prediction of optimal speed limits to minimize vehicular emissions. The results demonstrate that employing the RL algorithm can achieve an average CO 2 emissions reduction of 11 kg/h to 150 kg/h

Assessing the Environmental Performances of Urban Roundabouts Using the VSP Methodology and AIMSUN

In line with globally shared environmental sustainability goals, the shift towards citizen-friendly mobility is changing the way people move through cities and road user behaviour. Building a sustainable road transport requires design knowledge to develop increasingly green road infrastructures and monitoring the environmental impacts from mobile crowdsourced data. In this view, the paper presents an empirically based methodology that integrates the vehicle-specific power (VSP) model and microscopic traffic simulation (AIMSUN) to estimate second-by-second vehicle emissions at urban roundabouts. The distributions of time spent in each VSP mode from instantaneous vehicle trajectory data gathered in the field via smartphone were the starting point of the analysis. The versatility of AIMSUN in calibrating the model parameters to better reflect the field-observed speed-time trajectories and to enhance the estimation accuracy was assessed. The conversion of an existing roundabout within the sample into a turbo counterpart was also made as an attempt to confirm the reproducibility of the proposed procedure. The results shed light on new opportunities in the environmental performance evaluation of road units when changes in design or operation should be considered within traffic management strategies and highlighted the potential of the smart approach in collecting big amounts of data through digital communities

micro and macro modelling approaches for the evaluation of the carbon impacts of transportation

Abstract To quantify CO2 emissions from road transport, literature suggests the adoption of several alternative methods, based on transport modelling and carbon modules. Some of these methods are labelled as a micro approach and others as a macro approach. Their distinction is made according to the temporal and spatial horizons, the aim of the study and the degree of accuracy required. This paper presents these methods and discusses their appropriateness, whereby special focus is laid on the potential of the micro approach on ICT, based on a literature review of several European projects. We conclude that the adoption of the micro approach, is quite promising – mostly at the urban level, despite the computational efforts required and the technical difficulties to model driver behaviors. Thus, further research is required to overcome the numerous sources of scientific uncertainties