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Parking has been a common problem over several years in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, the aim of the study is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect videos during varying environmental conditions and frames from these videos were extracted to prepare the dataset. The frames in the dataset were manually labelled as there were no pre-labelled thermal images available. Vehicle detection with deep learning algorithms was implemented to perform multi-object detection. Multiple deep learning networks such as Yolo, Yolo-conv, GoogleNet, ReNet18 and ResNet50 with varying layers and architectures were evaluated on vehicle detection. ResNet18 performed better than other detectors which had an average precision of 96.16 and log-average miss rate of 19.40. The detected results were compared with a template of parking spaces to identify vehicle occupancy information. Yolo, Yolo-conv, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time and are suitable for real-time detection while Resnet50 was computationally expensive.
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.
The combustion of fossil fuels primarily provides the energy supply in Sri Lanka. The energy generation in Sri Lanka is primarily realized by the combustion of thermal energy such as diesel and coal. The second energy generation source is Hydroelectricity. In 2016, Sri Lanka supplied an average of 67% of the total energy demand using fossil fuels such as thermal oil and thermal coal (100% imported); and 33% with renewable energy (30% hydro and 3% non-conventional, renewable energy), even though there are enough available renewable resources such as solar, wind and biomass, to supply all the energy required. Sri Lanka should begin to utilize the above renewable energy resources more effectively and abundantly as fossil fuel prices increase and deplete; and their effects of global warming worsen. Sri Lanka aims to achieve 100% electricity generation from high-quality renewable energy resources (100RE) by 2050. When meeting this target, the use of solar, biomass, wind, ocean energy and energy recovery from waste technologies are expected to play a significant role. The increasing use of renewable energy technologies will be of help with the solution of energy-related current environmental and socioeconomic problems and will assist in sustainable development. This paper reviews the available potential resources, the status of renewable energy technologies and market status in Sri Lanka by considering Sri Lanka's present energy status and the future energy mix towards 100RE.
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 (PM2.5). 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.
A visual analytics system is proposed to reveal the lead/lag correlation when air pollution is detected. In this system, an Overview + Detail approach is utilized for analyzing the correlation of air quality under both the spatial and temporal dimensions and diffierent spatial-temporal scales. An annular container is proposed to preserve the context spatial information while the zoom level of the map changes. Based on the annular container, several analysis techniques such as STL decomposition view and correlation algorithm are integrated.
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.
Haze occurs frequently in many cities, and becomes the authors' great common concern. A distributed real-time monitoring system for atmospheric particles has been designed, implemented and tested. The proposed system consists of a front-end data wireless acquisition network, an embedded web server system, a central server and remote monitoring terminals. The front-end data-acquisition network is made up of mobile data-acquisition nodes distributed in atmospheric particle monitoring areas which collect atmospheric particle data periodically. The data is transmitted by Jennet wireless network and sent to the Internet through the embedded web servers attached to the coordinator nodes via the serial ports. Only a common Internet browser is required for a remote user to check the atmospheric particle data. The central server is responsible for storing the pollution data for further usage of pollution analysis, evaluation and early warning. In this study, the PM1.0, PM2.5 and PM10 particle data were successfully collected, transmitted and checked by the monitoring system in Lanzhou, China. The results show that the designed system can meet the needs of functional requirements of atmospheric particle monitoring.
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.
Most European countries have concerns about the integration of large amounts of renewable energy sources (RES) into electric power systems, and this is currently a topic of growing interest. In January 2008, the European Commission published the 2020 package, which proposes committing the European Union to a 20% reduction in greenhouse gas emissions, to achieve a target of deriving 20% of the European Union's final energy consumption from renewable sources, and to achieve 20% improvement in energy efficiency both by the year 2020 [1]. Member states have different individual goals to meet these overall objectives, and they each need to provide a detailed roadmap describing how they will meet these legally binding targets [2]. At this time, RES are an indispensable part of the global energy mix, which has been partially motivated by the continuous increases in hydropower as well as the rapid expansion of wind and solar photovoltaic (PV). The International Energy Agency's 2012 edition of the World Energy Outlook stated that the rapid increases in RES integration are underpinned by falling technology costs as well as rising fossilfuel prices and carbon pricing, but RES integration is also encouraged by continued subsidies: from $88 billion globally in 2011 (compared to $523 billion in fossil-fuel subsidies in 2012 [3], with a share of $131 billion for electricity generation) to an estimated $240 billion in 2035 [4]. According to [3], in 2015 RES accounted for 22% of electricity generation, which was approximately the same level as gas and about one-half the level of coal.
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.
Transportation and electricity industries are considered as major sources of greenhouse gases (GHGs) emission. Different methods have been proposed to deal with the increasing rate of the emission, such as employing plug-in electric vehicles (PEVs) and integrating renewable energy sources (RESs). However, it is important to scrutinise different scenarios of incorporating the mentioned elements to decrease the concerning emission rate while considering the economic constraints. In this study, a combined economic emission dispatch (CEED) is employed to investigate the effectiveness of using PEVs and RESs from different aspects. A sensitivity analysis is executed to survey the influence of emission and cost coefficients. Two test cases each including different scenarios are simulated to determine the efficacy of different types of integration in the proposed model. To have a more accurate and realistic survey, an extended model of the wind farm's cost function is employed in economic dispatch calculations. The particle swarm optimisation algorithm is applied to solve the CEED non-linear problem. The obtained results indicate that the integration of PEVs cannot necessarily reduce the net emission of two industries. In fact, the optimum solution should include the incorporation of PEVs along with RESs to return the desired results.
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.
The growth in emissions of carbon dioxide, implicated as a prime contributor to global warming, is a problem that can no longer be swept under the rug, but perhaps it can be buried deep underground or beneath the ocean. This article presents a discussion on the carbon capture and storage technology and its implications on atmospheric carbon dioxide mitigation.
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.
This paper presents evidence that there are no catastrophic changes leading to climate change.
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.
Clean air technology management is one of the key requirements in research. Maintaining the technology is a major challenge in hot and humid climate like The Gambia. Situated at the mouth of the River Gambia, Medical Research Council Unit, The Gambia has been constantly improving its clean air systems that support and ensure safety around the medical research facilities. The containment laboratories at the MRC Unit, The Gambia are now appropriately redesigned to prevent and control the exposure of laboratory workers, those around the labs and the environment to the biological agent used. PuriCore International in collaboration with local technologist at the MRC Unit, The Gambia were able to overcome the challenges by designing an efficient system and provided training for users as well as technologist to maintain the facility. (5 pages)
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.