Lightning is a widely recognized source of damage and disruption to electrical power systems worldwide. The climate is changing, with both natural and anthropogenic origins. This chapter is concerned with the response of lightning to changes in temperature and aerosol loading of the atmosphere that are expected to accompany climate change. In the present climate, lightning is shown to increase with both temperature and with the boundary layer populations of cloud condensation nuclei (CCN). In a future climate characterized by the continued consumption of fossil fuels, the threat from lightning is expected to increase.

Nowadays, during the colder season, a number of cities in central and southern Chile are affected by a problem of air pollution. This problem is associated to the topography of the central valley, high emissions of domestic wood-burning stoves, and other meteorological characteristics such as the lack of rain or when the prevailing winds are calmed. Although, numerous studies have been undertaken for predicting the air quality in Santiago city, other medium-sized cities have seldom been studied. The present work focuses on the problem of air quality in Talca city (35°26'S; 71°44'W), a medium sized city located in Central Chile. The objective of the study is to predict, with a day in advance, the particulate matter with a diameter less than or equal to 2.5 micrometers (PM_2.5). For this purpose we have used a deep learning neural network. The algorithm learns from historical records of air quality pollution as well as meteorological information at three monitoring stations. Unlike state-of-the-art methods that require intensive computational power to simulate the weather conditions, our proposed solution uses only pollutants measures of the stations in the city. We use exactly 24 records for a particular day, one for each hour. Our study focuses on the autumn-winter season for 3 years, including data for 612 days, i.e., 151 days per year (from April 1st, until August 31st). Our results prove the high capacity of RNN as a predictor algorithm for environmental emergency episodes in the three monitoring stations of the city. As a result, the model is an alternative for local authorities because it would improve the current forecast system of the city.

MIMOZA is a simulation tool developed by CITEPA at the request of the French Ministry for Ecological and Solidary Transition as part of the implementation of Low Emission Zones (LEZ) to reduce air emissions from vehicles in cities and improve their air quality. It is intended to facilitate the assessments carried out by local authorities responsible for the implementation of LEZ. This innovative tool simulates the impact of different LEZ scenarios on NOx, NO₂, PM₁₀ and CO₂ emissions from the vehicle fleet concerned; i.e. between a scenario without LEZ (baseline scenario) and a scenario with LEZ for a given year (test scenario). This last scenario corresponds to the year of the LEZ implementation. After selecting the year of the test scenario, national default or local emission factors are identified accordingly. The vehicle fleet composition and the associated traffic (i.e. vehicle x kilometers traveled) are also proposed by the tool. As for emission factors, national default or local data can be selected. Emissions from the baseline scenario are then calculated on the basis of the traffic per vehicle fleet category and the corresponding emission factors. Finally, assumptions on traffic restrictions per vehicle categories and renewal rates of vehicles have to be selected. This leads to modified vehicle fleets (and traffic), which combined with emission factors, provides the new emissions for the test scenario.

The dielectric barrier discharge-based non-thermal plasma technique is one of the most prominent techniques which give peerless results in controlling the concentration of NO _X . However, when it comes to the automobile diesel engine, availability of high-voltage pulse power supply is the major constraint. In this study, battery-powered high-voltage pulse power supply for NO _X treatment has been proposed. Two types of electrodes: rod type and rod with helical spring type are studied for the treatment of exhaust. Cascaded plasma-adsorbent technique has also been used to enhance NO _X removal efficiency. Experiments have been conducted with two different gas flow rates, i.e. 4 l and 6 l/min at laboratory level and have got significant results toward removal of NO _X . When the exhaust has been treated with plasma alone, the reactor with rod-type electrode has shown 85% NO _X removal efficiency at a specific energy (SE) of 283 J/l with a flow rate of 4 l/min. When the plasma reactor is cascaded with the adsorbent reactor, both adsorbents: 13x molecular sieve (MS13x) and activated alumina are able to remove 100% of NO _X with the proposed power supply at a lesser SE.

In the last decades, the application of LiDAR/DIAL measurements to remote sensing and atmospheric physics has been consolidated from both the experimental and the interpretation point of view. The laser and optic technologies involved have become very sophisticated and the quality of the results have reflects this fact. These techniques are therefore seriously considered also for defence applications, for example for the survey of large areas to detect the release of chemical agents. On the other hand, for a reliable deployment of these techniques in real life applications, robust data analysis tools are required, an aspect to which not enough consideration is typically accorded during the design phase of the instrumentation. In this paper, it is shown how the absorption signals generated by various chemical substances can be processed to maximise the success rate of their identification. The developed classification methods are based on state of the art classification trees. The quality of the proposed technique is well supported by simulations based on the HITRAN database. Significant efforts have been devoted to the issue of providing an estimate of the robustness against noise of the classification provided by the machine learning tools.

International Maritime Organization (IMO) Marine Environment Protection Committee (MEPC) Circular 684 (Circ. 684) is a detailed explanation of the Energy Efficiency Operational Indicator (EEOI). So MEPC Circ. 684 is cited as it is in this section, including the annex, because IMO official documents must be transmitted unchanged. MEPC Circ. 684 is an invaluable reference for developing a real-time EEOI on board. The MEPC of the IMO, at its 59th session (13-17 July 2009), agreed to circulate the guidelines for voluntary use of the ship EEOI as set out in the annex. As a result member governments are invited to bring the Guidelines (MEPC.1/Circ. 684) to the attention of all parties concerned and to recommend that they use the Guidelines on a voluntary basis. IMO Assembly resolution A.963 (23) is related to the reduction of greenhouse gas (GHG) emissions from ships and it urges the MEPC to identify and develop such a mechanism or mechanisms as are needed to achieve first, the limitation or reduction of GHG emissions from international shipping, giving priority, in doing so, to the establishment of a GHG baseline; and secondly, the development of a methodology to describe the GHG efficiency of a ship in terms of a GHG emission indicator for that ship.

Employing an extensive system dynamics market agent model of the EU light-duty vehicle sector, this paper investigates the interaction between the provision of charging infrastructure and the uptake of electric vehicles. Focusing on subsidies for both infrastructure deployment and vehicle purchase, and set within the context of the EU fleet CO2 emission regulation for new cars and light commercial vehicles, our findings suggest that infrastructure provision does improve the utility of a plug-in electric vehicle, but may have a weaker correlation with uptake than other policy options.

Light vehicles are responsible for around 16% of UK CO₂ emissions and, amongst other issues, contribute to poor air quality and urban congestion. Vehicle technology has a critical role in UK CO₂ reduction; especially increasing efficiency and the use of alternative fuels (bio-fuels, electricity and hydrogen). Vehicle manufacturers and fuel suppliers are addressing these issues for their global markets but there has been little investment in future energy infrastructure in the UK. The ETI, working with its Government and Industry Members (BP, Caterpillar, EDF, E.ON, Rolls-Royce and Shell), has made significant investments to understand the range of issues and determine the most affordable roadmap for developing the future energy infrastructure for sustainable and secure energy for transport.

The Intelligent Transportation Systems (ITS) concept has been recently introduced to define modern embedded systems with enhanced digital connectivity, combining people, vehicles and public infrastructure. The smart transducer concept has been also established by the IEEE 1451 Standard to simplify the scalability of networked electronic equipments. Both of them, smart transducers and ITS, have become a reality in the last decade. This study describes the design and implementation of an environmental wireless sensor network that characterises air quality in Asuncion, Paraguay. Mobile sensor devices in public transport vehicles provide an effective mechanism to develop an efficient solution for this characterisation. The development of the sensor network is presented and experimental results obtained for the characterisation of the proposed environmental monitoring system are provided.

Late in November 2010, United Nations Secretary-General Ban Ki-moon gathered world leaders in New York for a day-long pre-Copenhagen meeting to mobilise political will for a deal on climate change because he felt that negotiations were becoming worryingly bogged down.