Road transport impact on PM2.5 pollution over Delhi during the post-monsoon season

We use the WRF-Chem atmospheric chemical transport model, driven by local emission inventories, to quantify the contribution of on-road transport emissions to surface PM2.5 over Delhi during the post-monsoon season. We compare this contribution to other local (within Delhi) and regional (within the broader National Capital Region, NCR) anthropogenic sectors during the post-monsoon period when seasonal burning and stagnating meteorological conditions exacerbate baseline pollution levels. We find that local on-road transport contributes approximately 10% to daily mean PM2.5 over Delhi, rising to 17% if regional on-road transport sources in the NCR are included. The largest individual contributions to Delhi daily mean PM2.5 are from regional power and industry (14%) and domestic (11%) sectors, dominating nighttime and almost all daytime concentrations. Long range transport contribution from sources beyond the NCR is found to account for approximately 40%. The contribution from the local on-road transport sector to diurnal mean PM2.5 is largest (18%) during the evening traffic peak. It is dominated by contributions from two- and three-wheelers (50%) followed by heavy-duty vehicles (30%), which also collectively represent 60–70% of the total on-road transport sector at any hour of the day. The combined contribution from passenger cars and light duty vehicles and from resuspended road dust to daily mean PM2.5 is small (20%). Our work highlights two important factors which need to be considered in developing effective policies to meet PM2.5 air quality standards in Delhi during post-monsoon. First, a multi-sector and multi-scale approach is needed, which prioritise the reduction in local transport emissions within Delhi, and, in the order, regional industries, domestic and transport emissions from NCR. Second, two-and three-wheelers and heavy-duty vehicles dominate on-road transport impact to PM2.5, thus reductions from these vehicles should be given priority, both within Delhi and in the NCR

2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

Teoria delle grandi deviazioni e meccanica statistica

Teoria delle grandi deviazioni e Meccanica Statistica sono intimamente collegate. Partendo dalla connessione tra Teoria delle Fluttuazioni di Einstein e il Principio di Frande Deviazione, molti dei risultati interni alla meccanica statistica trovano una formulazione naturale e rigorosa in un contesto di grande deviazione. Tale linguaggio di grande deviazione è molto utile anche nelle applicazioni: un esempio ne verrà dato con lo studio del modello di Ising

Quantifying the impact of on-road transport on fine particulate matter over Delhi megacity

Outdoor air pollution is an increasing public health burden. Fine particulate matter (PM2.5) is a pollutant of major concern for human health, and it also affects the climate and ecosystems. Understanding and quantifying emission sources and their impact on particulate air pollution is critical for improving global health and for informing climate action. Poor air quality across the globe disproportionately affects middle- and lower-income countries. Delhi, the capital of India, is one of the most populated and polluted megacities in the world, where in 2017 almost 12,000 premature deaths were attributed to outdoor air pollution. My thesis aims to advance our understanding of outdoor air pollution in Delhi megacity, with a focus on the impact of on-road transport emissions on surface levels of PM2.5 and its implications for air quality policymaking. To do this, I use a combination of a state of the art regional atmospheric chemistry transport model, recently developed local emissions inventories, and sensitivity analysis techniques. In the first research chapter I use the WRF-Chem atmospheric chemical transport model to understand the regional influence on air quality over Delhi. As part of this work, I characterise seasonal anthropogenic, pyrogenic, and biogenic influences on fine particulate matter and one of its main constituents, organic aerosol (OA), over the Indo-Gangetic Plain (IGP). My results show that anthropogenic emissions influence the magnitude and distribution of PM2.5 and OA throughout the year, especially over cities including Delhi, while pyrogenic emissions from crop residues burning result in localized contributions over the central and upper parts of IGP in all non-monsoonal seasons, with the highest impact during the post-monsoon season that correspond to the post-harvest season in the agricultural calendar. Biogenic emissions play an important role in the magnitude and distribution of PM2.5 and OA during the monsoon season, particularly over the lower IGP. In all seasons mean values of PM2.5 still exceed the recommended levels, indicating that air pollution is a year-round problem. In the second research chapter I develop the WRF-Chem model used in my first chapter to include local emission inventories, in order to quantify the contribution of the on-road transport sector to surface PM2.5 over Delhi during the highly polluted post-monsoon season. This contribution is compared to the contributions of other local (within Delhi) and regional (within the National Capital Region, NCR) anthropogenic sectors. My results show that emissions from the local transport sector contribute typically less than 10% to daily mean PM2.5 values over Delhi, rising to 17% when regional transport sources are included. The contribution from the local transport sector is largest (18%) during the evening traffic peak. The total transport impact is dominated by contributions from twoand three-wheelers (50%) and heavy-duty vehicles (30%). The largest individual contributions to daily mean PM2.5 values are found to be from regional power and industry (14%) and domestic (11%) sectors. In the third research chapter I drive the WRF-Chem model with future transport emissions scenarios to investigate the potential impact of electric and clean-fuel vehicles on surface PM2.5 and ozone (O3) over Delhi for two contrasting seasons, pre-monsoon and post-monsoon. My results show that the conversion of diesel vehicles to compressed natural gas (CNG) brings a greater reduction in PM2.5 concentrations than the full electrification of two- and three-wheelers. However, the maximum reduction of daily mean PM2.5 concentrations for all scenarios is within 5% compared to baseline values for both seasons. Electrification of two- and three-wheelers increases average 8-hour daily maximum (MDA8) ozone (1.3-3.5% in pre-monsoon 5-13% in post-monsoon) compared to baseline values. On the other hand, conversion of all diesel vehicles to CNG reduces MDA8 O3 in both seasons (by 2.3-5.3% in pre-monsoon and by 1-1.5% in post-monsoon) compared to baseline values. In conclusion, the findings of my thesis highlight different factors that can be relevant for designing effective policies to meet PM2.5 air quality standards over Delhi megacity, with a focus on mitigating the impact from the on-road transport sector. First, air quality over Delhi is strongly influenced by regional and seasonal pollution sources from the IGP. As such, effective mitigation of PM2.5 pollution over Delhi will require a range of regional and state-level policies. In particular, cooperative mitigation strategies between the Delhi megacity and the broader NCR is needed if PM2.5 pollution is to be reduced. Second, two-and three-wheelers and heavy-duty vehicles dominate on-road transport impact on PM2.5, thus emissions reductions from these vehicles should be given priority, both within Delhi and in the NCR. Third, cleaner mobility plans of electrification of two- and threewheelers should be accompanied by diesel vehicles conversion to compressed natural gas, to limit ozone pollution increase and further reduce PM2.5 concentrations. This also highlights the importance of coordinated control of PM2.5 and other pollutants such as O3 when considering emission control strategies for transport over Delhi

Modeling pedestrian dynamics by means of Discrete Thermostatted Kinetic Theory methods

A novel discrete thermostatted kinetic framework is derived for the modeling of complex adaptive systems subjected to external force field (non-equilibrium system). In order to model the non-equilibrium stationary states of the system, the external force field is coupled to a dissipative term (thermostat). The well-posedness of the new framework is mathematically investigated (local and global existence and uniqueness of solution of the related Cauchy problem) thus allowing the discrete thermostatted framework to be suitable for the derivation of specific models and the related computational analysis. This framework is employed for the modeling of the pedestrians dynamics at the entrance of a metro station. Specifically a model is proposed for analysing the time distribution of the pedestrians approaching at different gates (turnistiles) according to a choice dynamics which depends on the microscopic interactions among the pedestrians (internal dynamics). The microscopic interactions, assumed binary, depend on the local pedestrians density (nonlinear interactions) and follow a game theory approach based on the leader-follower dynamics. The external force field mimics different events that can affect significantly pedestrian internal dynamics (collective hurry, preferential gates recommended, periodic sound signals or evacuation alarms), and the thermostat term allows the conservation of the total number of pedestrians. Numerical simulations are addressed to analyse the system behaviour, and in particular a sensitivity analysis on the parameters and the initial conditions is performed. The results show that the model is able to reproduce qualitatively some known emerging behaviours in the metro station, e.g. flow imposed by leader dynamics, concentration of pedestrians at the central gates, and pedestrians tendency to choose progressively with time all the gates available. Moreover the simulations highlight the capability of the new model to capture non-equilibrium stationary states. Perspectives include the possibility to introduce the spatial and velocity dynamics for taking into account the geometry of the domain of interactions

The impact of automation and connectivity on traffic flow and CO2 emissions. A detailed microsimulation study

The interest on the impact of vehicle automation and connectivity in the future road transport networks is very high, both from a research and a policy perspective. Results in the literature show that many of the anticipated advantages of connected and automated vehicles or automated vehicles without connectivity (CAVs and AVs respectively) on congestion and energy consumption are questionable. Some studies provide quantitative answers to the above questions through microsimulation but they systematically ignore the realistic simulation of vehicle dynamics, driver behaviour or instantaneous emissions estimates, mostly due to the overall increased complexity of the transport systems and the need for low computational demand on large-scale simulations. However, recent studies question the capability of common car-following models to produce realistic vehicle dynamics or driving behaviour, which directly impacts emissions estimations as well. This work presents a microsimulation study that contributes on the topic, using a scenario-based approach to give insights regarding the impact of CAVs and AVs on the evolution of emissions over a highway network. The motivation here is to answer whether the different driving behaviours produce significant differences in emissions during rush hours, and how significant is the impact of detailed vehicle dynamics simulation and instantaneous emissions in the outcome. The status of the network is assessed in terms of flow and speed. Furthermore, emissions are estimated using both the average-speed EMEP/EEA fuel consumption factors and a generic version of the European Commission's CO2MPAS model that provides instantaneous fuel consumption estimates. The simulation results of this work show that AVs can deteriorate the status of the network, and that connectivity is the key for improved traffic flow. Emissions-wise, the AVs have the highest fuel consumption per km travelled among other types, while CAVs only marginally lower the overall consumption of human-driven vehicles. For the same traffic demand, the total emissions for different vehicle types remain at comparable levels.JRC.C.4-Sustainable Transpor

Tools for Customized Consumer Information on Vehicle Energy Consumption and Costs - A European Case Study

The European Commission’s Joint Research Centre, in line with the European Strategy for Low-Emission Mobility, has launched in 2016 the Green Driving Tool, an interactive web-based tool aiming at estimating fuel costs and CO2 emissions of individual car journeys. In parallel, it has developed U-SAVE, a routing system for fuel-efficient trip planning aiming at fuel consumption minimization and vehicle specific calibration. This paper provides a first assessment of the performance of the two tools in predicting fuel consumption and CO2 emissions over real-world trips. The analysis focused on the accuracy and uncertainty of the two tools when varying the detail of vehicle input data and of the velocity profile used in the calculation. These elements are particularly important in case of future integration of the tools with traffic simulation models where the level of detail regarding the vehicle input or the speed profile may vary. Results show that U-SAVE prediction is positively affected by the detail of vehicle specifications, while is not significantly sensitive to the detail of the velocity profile. Contrary, Green Driving didn’t show any remarkable change when varying both parameters. Overall, U-SAVE demonstrates a good performance in predicting CO2 emissions over on-road tests reaching an average prediction accuracy over an entire test trip of -4.6% and a standard deviation of 5.2%, while Green Driving exhibit higher uncertainty (on average 12%) but lower bias which ranged in the order of 0 to +3% depending on the vehicle and the test trip considered.JRC.C.4-Sustainable Transpor