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This paper extends a traditional supply chain network design problem through two perspectives, i.e., the multiperiod decision environment and the environmental concerns. A planning horizon contains multiple periods. Different periods have different demands of products and hence should have different supply chain decisions, which mainly include facilities’ locations, the environment protection level in facilities, as well as the transportation flows of products in the supply chain. For each facility in two consecutive periods, the changes of the location and environment protection level related decisions may incur adjustment cost. A bi-objective mixed-integer programming model is proposed to minimize both the total cost and the CO2 emissions of the supply chain during the planning horizon with multiple periods. A solution approach is proposed to solve the model. Numerical experiments are conducted to validate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

In the last years, vehicular networking has grown up in terms of interest and transmission capability, due to the possibility of exploiting the distributed communication paradigm in a mobile scenario, where moving nodes are represented by vehicles. The different existing standards for vehicular ad-hoc networks, such as dedicate short range communication (DSRC), wireless access for vehicular environment (WAVE)/IEEE802.11p, have given to the research community the possibility of developing new medium access control (MAC) and routing schemes, in order to enhance the quality and the comfort of mobile users who are driving their vehicles. In this paper, we focus our attention on the optimization of traffic flowing in a vehicular environment with vehicle-2-roadside capability. As shown later, the proposed idea exploits the information that is gathered by road-side units to redirect traffic flows (in terms of vehicles) to less congested roads, with an overall system optimization, also in terms of carbon dioxide emissions reduction. An analytical model, as well as a set of pseudo-code instructions, have been introduced in the paper. A deep campaign of simulations has been carried out to give more effectiveness to our proposal. [ABSTRACT FROM AUTHOR]

Air pollutants do much harm to human safety. In particular, the fine particulate matter (PM2.5), a complex air pollutant which is composed of the particles beneath the aerodynamic diameters of 2.5 $\mu$ m, very possibly causes severe diseases since it is easy to intrude into the lungs. To that end, in this paper, we design a new picture-based predictor of PM2.5 concentration (PPPC), which employs the pictures acquired using mobile phones or cameras to make a real-time estimation of PM2.5 concentration. First, using a large body of pictures which were captured under the good weather conditions, i.e., very low PM2.5 concentration, naturalness statistics (NS) models are built upon entropy features in spatial and transform domains. Second, for a novel picture, we measure its deviation degree from the above-mentioned NS models, considering the fact that the naturalness of a picture tends to reduce with the PM2.5 concentration increased. Third, a simple nonlinear function is introduced to map the deviation degree to the PM2.5 concentration. In comparison to existing relevant state-of-the-art predictors, sufficient experimental results manifest the superiority of the proposed PPPC model in terms of prediction accuracy and implementation efficiency. [ABSTRACT FROM AUTHOR]

Air-borne micrometer and submicrometer particles produced by anthropogenic sources contaminate atmospheric air, especially in large cities where both population and industrial activities are higher leading to a reduced air quality. Recent research has pointed out particles less than 2.5 \mum in diameter (PM2.5) as a potential health hazard. To address this issue, stricter legislation has been put into force to reduce PM2.5 emissions. This paper is focused on the development of an impulsive microelectrostatic precipitation technology for charging and removal of fine air-borne particles in an economically feasible way. In this paper, a compact coaxial precipitator has been developed for possible indoor air cleaning applications. High-voltage impulses together with dc voltage have been used for energization of the reactor as it has been shown to enhance the precipitation efficiency. This precipitation system has been used for removal of fumes and fine air-borne particles from ambient air. In addition to the experimental part, analytical work has been conducted to optimize the electrostatic precipitation process and to reduce its power consumption. [ABSTRACT FROM PUBLISHER]

Accurate and effective control mode of pollutant emissions is the key to alleviate the environmental impacts of energy supply systems. For this purpose, this paper establishes a precise space-time diffusion (STD) model of air pollutants associated with emission sources and atmosphere environment factors, and proposes a novel STD control of air pollutant emissions. Compared to the traditional emission quantity control, the proposed STD control takes the air pollutant diffusion and space-varying environmental protection demands into account, which can truly reduce the harm of air pollutant emissions. In view of an increasing interdependence between power system and natural gas system, a novel multi-objective optimal power-gas flow (MOPGF) with STD control of air pollutants is constructed. To solve the MOPGF with conflicting objectives and nonconvex gas transmission constraints, a generalized membership optimization (GMO) incorporating penalty convex-concave procedure (PCCP) based convexification is developed. GMO aims to coordinate conflicting objectives and PCCP convexifies the nonconvex gas dynamics, such that a high-quality solution can be obtained. Finally, simulations are carried out to verify the effectiveness of the STD control to reduce the influence of generation emissions, and the superiority of the convex multi-objective optimization for the MOPGF. [ABSTRACT FROM AUTHOR]

Rapid population growth, urbanization and motorization have brought about secondary effects that have gradually damaged the atmosphere, whose importance is vital for both the survival of all living beings and the climate balance. In this sense, air pollution is a problem that affects current society and is much more critical in developing countries. In this context, in the present paper non-parametric statistical inference techniques are used to carry out the analysis of measurements of health concerning fine particulate matter concentration, PM2.5, in an urban park of Quito, Ecuador. In short, the data collected during the measurements were stored in random variables and the Kruskal-Wallis test was used to test if these random variables come from populations with identical distributions. Also, the Wilcoxon signed rank test was used to test if the numerical values collected in the samples of the random variables of interest represent a level of contamination that could be dangerous for human beings. The experimental results show that urban parks and, specifically, trees are a natural filter between the pollution generated in the road and the center of the park. Therefore, the role of trees in the face of vehicular pollution will depend on two variables: the amount and compactness of the vegetation, and the emission levels recorded in the border roads. [ABSTRACT FROM AUTHOR]

A system for monitoring and forecasting urban air pollution is presented in this paper. The system uses low-cost air-quality monitoring motes that are equipped with an array of gaseous and meteorological sensors. These motes wirelessly communicate to an intelligent sensing platform that consists of several modules. The modules are responsible for receiving and storing the data, preprocessing and converting the data into useful information, forecasting the pollutants based on historical information, and finally presenting the acquired information through different channels, such as mobile application, Web portal, and short message service. The focus of this paper is on the monitoring system and its forecasting module. Three machine learning (ML) algorithms are investigated to build accurate forecasting models for one-step and multi-step ahead of concentrations of ground-level ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2). These ML algorithms are support vector machines, M5P model trees, and artificial neural networks (ANN). Two types of modeling are pursued: 1) univariate and 2) multivariate. The performance evaluation measures used are prediction trend accuracy and root mean square error (RMSE). The results show that using different features in multivariate modeling with M5P algorithm yields the best forecasting performances. For example, using M5P, RMSE is at its lowest, reaching 31.4, when hydrogen sulfide (H2S) is used to predict SO2. Contrarily, the worst performance, i.e., RMSE of 62.4, for SO2 is when using ANN in univariate modeling. The outcome of this paper can be significantly useful for alarming applications in areas with high air pollution levels. [ABSTRACT FROM PUBLISHER]

Environmental pollution is a major issue for public health and safety. Measurement of fine-grain spatiotemporal distribution of pollution for a micro-environment is necessary to explore the correlations among environment, health, and behavior. The continuous advancement of miniature environmental sensing devices, microelectronics, and communication technologies propelled the development of wearable devices for environmental monitoring applications. This type of devices are able to acquire high-resolution objective geo-tagged data. The aim of this review is to summarize the contemporary research and developments on wearable devices intended for environmental Internet of Things (IoT) applications. This paper provides comprehensive descriptions of the state-of-the-art wearable environmental monitoring systems (WEMS) and compares them in terms of sensing and communication technologies. The reviewed WEMS are mainly classified into two groups based on whether they used commercial off-the-shelf (COTS) sensors or self-developed sensors. The first group is further categorized based on their wearability. In line with this, a consolidated description is provided by presenting the main features of the available wearable environmental monitoring devices from the industry. In addition, several issues are identified for WEMS and future research directions are provided. Thus, this review paper examines the current research on WEMS and can serve as a reference for the researchers and developers to provide perspectives toward future research improvements. [ABSTRACT FROM AUTHOR]

In this paper, we report the vertical profiles of CO2 and CH4 measured with a cavity ring-down spectrometer (CRDS) on a research aircraft from near-ground level to 8 km above mean sea level. The airborne platform employed in this paper is an Alpha Jet aircraft operated from NASA's Ames Research Center. Flights were undertaken to Railroad Valley, NV, USA, to coincide with overpasses of the Greenhouse Gases Observing Satellite (GOSAT). Ground-based CO2 and CH4 were simultaneously measured using CRDS, at the time and location of the airborne and satellite measurements. Results of three GOSAT coordinated aircraft profiles and ground-based measurements in June 2011 are presented and discussed in this paper. The accuracy of the CO2 and CH4 measurements has been determined based upon laboratory calibrations (World Meteorological Organisation traceable standard) and pressure/temperature flight simulations in a test chamber. The overall uncertainty for the airborne measurements ranged from 0.31 to 0.39 ppm for CO2 and from 3.5 to 5.6 ppb for CH4. Our column-averaged CO2 and CH4 measurements, which include about 61% of the total atmospheric mass, are extrapolated, using different techniques, to include the remainder of the tropospheric and stratospheric CO2 and CH4. The CO2 data are then analyzed using the Atmospheric CO2 Observations from Space 2.9 and 3.3 algorithms. For methane data, the RemoTeC v2.1 algorithm was used in its full physics setup. Column-averaged CO2 and XCO2, measured by GOSAT and analyzed from our data, ranged from 388.1 to 396.4 ppm, and XCH4 ranged from 1.743 to 1.822 ppm. The agreement of the satellite and aircraft CO2 mixing ratios, as well as ground measurements, falls within the uncertainties of the methods employed to acquire these numbers. [ABSTRACT FROM PUBLISHER]

In this paper, we evaluate the changes in energy demand and resulting climate change and air pollutant emissions from the electrification of both the private vehicle fleet and the public transport fleet in the city of Rome, Italy. This paper provides a well-to-wheel analysis and considers two alternative hypotheses for the vehicles fleet renewal up to 2025. A data-driven approach is followed, where real traffic patterns from floating car data are adopted as well as geo-referenced open data published in the General Transit Feed Specification format by Rome’s public transport agency. Specific energy consumption models for electric vehicles have been calibrated, based on real driving cycles. Moreover, the economic benefit resulting from the reduction of externalities has been assessed. [ABSTRACT FROM AUTHOR]

Environmental problems, combined with a finite supply of fossil fuels, have made the use of renewable energy sources necessary. Biomass is a renewable source of energy that has played a very important role in energy production in recent years. Because there are a number of technologies that can be used to convert biomass into energy, it is important to select the best option. The fact that multiple options are available that need to be evaluated based on a set of decision-making criteria makes this a multicriteria decision-making problem. This paper takes the first step in proposing an evaluation framework and identifying the importance of the relevant decision-making criteria in biofuel production technology selection. To determine the importance of the selection criteria, experts were asked to respond to an online questionnaire based on the best–worst method. The results indicate that air pollution, land use change, and human expertise are the three most important criteria for selecting the best biofuel production technology in our case country, Iran. [ABSTRACT FROM AUTHOR]

As the rulemaking compliance dates come further into effect in 2015, U.S. cement plants will take final actions to upgrade compliance programs for the National Emission Standards for Portland Cement Manufacturing [also known as the Portland Cement Maximum Achievable Control Technology (PC MACT)]. For those facilities using alternative fuels and possibly raw materials, past practices will also require altering to address the non-hazardous secondary material (NHSM) and Commercial and Industrial Solid Waste Incineration (CISWI) rules. Meanwhile, there is continued work by the industry and the U.S. Environmental Protection Agency (EPA) to decipher and clarify final implementation steps of each rule and, in particular, to address the question of “what is a waste,” which drives facility implementation strategies. The Portland cement industry has a long and proven track record in historically implementing the PC MACT rules and in using alternative fuels and raw materials (AFR) for a variety of environmentally and economically beneficial reasons. As the final compliance steps are put in place, there are continuing questions on how the new rules will be successfully implemented, while optimizing plant operations and continuing to grow the important AFR programs. Given that this suite of regulations will forever change the playing field on day-to-day operations, and the historic use of AFR in cement plants in the U.S., there will be critical compliance and financial risk implications on how to best manage final decisions. This paper reviews final approaches for cement plant operators to maximize opportunities and reduce unintentional risks posed by the new rules, by exploring the lessons learned on implementing PC MACT, CISWI, and NHSM rules based on the authors' work with the Portland Cement Association, individual facilities, and general industry/EPA discussions. [ABSTRACT FROM PUBLISHER]

This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models.While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration,mathematicalmodels are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities. [ABSTRACT FROM AUTHOR]

Fine-grained city-scale outdoor air pollution maps provide important environmental information for both city managers and residents. Installing portable sensors on vehicles (e.g., taxis, Ubers) provides a low-cost, easy-maintenance, and high-coverage approach to collecting data for air pollution estimation. However, as non-dedicated platforms, vehicles like taxis usually prefer gathering at busy areas of a city where it is more likely to pick up riders. This leaves many parts of the city unsensed or less-sensed. In addition, due to the natural changes in a city and the movements of the vehicles, the sensed and unsensed areas change over time. Consequently, challenges of air pollution estimation with data collected by non-dedicated mobile platforms are twofold: i. data coverage is sparse; ii. data coverage changes over time. Therefore, the major research question is: how can we derive accurate and robust fine-grained field (e.g., air pollution) estimation given dynamic and sparse data collected from uncontrollable mobile sensing platforms? This paper presents adaptive HMSS, an adaptive hybrid model-enabled sensing system for fine-grained air pollution estimation with dynamic and sparse data collected from uncontrollable mobile sensing platforms, which is achieved by combining the advantages of a physics guided model and a data driven model. To address the challenge of sparse coverage, the physical understanding of the spatiotemporal correlation for air pollution distribution in the physics guided model is utilized to infer values at unsensed sparse areas. Meanwhile, the data driven model is adopted to estimate the air pollution influential factors (e.g., buildings) not included in the physics guided model. To address the challenge of time-varying coverage, an adaptive model combination algorithm is designed to enable the system bias to either of the two models according to the amount of data collection and uncertainty of the model. To evaluate the system performance, we deployed 47 air pollution sensing devices on taxis and fixed locations in 2 cities for both controlled and uncontrolled experiments for over two weeks. The results show that with a resolution of $500 \;\mathrm m$ 500 m by $500\;\mathrm m$ 500 m by $1\;\mathrm {hour}$ 1 hour , our system achieves up to $3.2\times$ 3. 2 × error reduction when compared to the baseline approaches. [ABSTRACT FROM AUTHOR]

Research on the broadcasting of signal countdown messages (SCMs) to vehicles via vehicular ad hoc network (VANET) technology has shown that it can reduce \CO2 emissions and energy consumption; however, past studies have lacked consideration of car following and vehicle gliding mode. In this paper, two green driving suggestion models, namely, the Maximize Throughput Model (MaxTM) and the Minimize Acceleration and Deceleration Model (MinADM), are proposed to minimize the \CO2 emissions by considering real-time traffic information nearby the intersection. The two proposed strategies are compared with an open traffic light control model (OTLCM) . The main facts this paper demonstrate are that traffic models lack consideration of car following, which would make the simulation result unrealistic, that the proposed MaxTM can reduce more \CO2 emissions than the MinADM and the OTLCM, and the total travel time in the MaxTM is also better than the other two models so that the general traffic performance can be improved. Simulation results show that the performance of \CO2 emissions of the MaxTM is 5%–102% better than the MinADM and 13%–209% better than the OTLCM in the simulation cases, and the performance of \CO2 emissions of the MaxTM is 8%–14% better than the MinADM and 15%–231% better than the OTLCM in the real traffic cases. [ABSTRACT FROM PUBLISHER]

Road transportation is one of the main sources of greenhouse gas emissions, which lead to global warming and climate change. Promoting the decarbonization of this sector through more efficient and greener mobility is a challenging task that can be achieved by intelligent transportation systems (ITS) enabled by vehicular communications. In this paper, we briefly present how mobility players and enablers (driver, vehicle, and road network) influence energy consumption and pollutant emissions. Furthermore, this survey paper details how different ITS, ranging from eco-routing to intelligent intersection management, can lead to sustainable mobility by promoting a more efficient vehicle usage and enhanced efficiency on road network utilization. Results shown that ITS has the potential to considerably reduce fuel consumption and pollutant emissions, namely, by smoothing the traffic flow, reducing the number of start–stops, and reducing the total travel distance. In addition, we present and analyze two main methods for evaluating green transportation systems: 1) field operational tests; and 2) simulation-based evaluations. We give special emphasis to simulation-based assessment of Green ITS measures by detailing the necessary models and their interactions. Finally, we propose a number of recommendations and future research directions in the area of Green ITS. [ABSTRACT FROM PUBLISHER]

Ever increasing expectations regarding the penetration level of electric vehicles (EVs) are driving several areas of research related to EV charging. One topic of interest treats EVs not only as controllable loads but also as storage systems, which can be used to mitigate the load on the grid during peak times by offering power. This is known as vehicle to grid (V2G). Since returning energy to the grid affects mobility patterns, V2G has an associated environmental cost. In this paper, to investigate this issue, we formulate the problem of returning electrical load to the grid as an optimization whose goal is to return the desired energy in a fashion that minimizes the cost on the environment. We show that this optimization is highly complex, and in some circumstances, the cost of V2G can be prohibitive. [ABSTRACT FROM PUBLISHER]

The spatial time series generated by city sensors allow us to observe urban phenomena like environmental pollution and traffic congestion at an unprecedented scale. However, recovering causal relations from these observations to explain the sources of urban phenomena remains a challenging task because these causal relations tend to be time-varying and demand proper time series partitioning for effective analyses. The prior approaches extract one causal graph given long-time observations, which cannot be directly applied to capturing, interpreting, and validating dynamic urban causality. This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. To develop Compass, we identify and address three challenges: detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. First, multiple causal graphs over time among urban time series are obtained with a causal detection framework extended from the Granger causality test. Then, a dynamic causal graph visualization is designed to reveal the time-varying causal relations across these causal graphs and facilitate the exploration of the graphs along the time. Finally, a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses. The effectiveness of Compass is evaluated with two case studies conducted on the real-world urban datasets, including the air pollution and traffic speed datasets, and positive feedback was received from domain experts. [ABSTRACT FROM AUTHOR]

The growth of industries and other human activities have led to ever increasing amounts of pollutants in both outdoor and indoor spaces. These pollutants have hazardous effects on humans and the wider ecology. Hence, air quality monitoring (AQM) is essential and involves the robust monitoring of various toxic gases and volatile organic compounds (VOCs)—in case, the concentration of any pollutant exceeds the safe limit in a given location. This paper describes the different sources of indoor and outdoor pollutants, reviews the current status of gas sensors, and discusses the role of new two dimensional (2-D) materials in detecting these hazardous gases at low power, i.e., close to the ambient temperature. Here, we review different synthesis techniques of 2-D materials and discuss the sensing performances of pristine and functionalized nanomaterials for some of the important pollutants such as NOx, NH3, SOx, CO, formaldehyde, toluene, and so on. The review concludes with some proposed methods to help in reducing air pollution today. [ABSTRACT FROM AUTHOR]

PM2.5 is an important global air pollution issue. PM2.5 causes various problems on the environment and human health. Even though PM2.5 of the outdoor air is eliminated by particulate air filters, the chemicals adsorbed on the filtered PM2.5 can be outgassed and introduced into the semiconductor manufacturing facilities and processes, thus causing defects on the semiconductor chips by producing particles and haze as airborne molecular contamination. Therefore, estimating the amount of the outgassing from PM2.5 is a very important factor to control gaseous contaminants in the semiconductor industry. In this paper, the PM2.5 samples at different PM2.5 mass loadings were collected in Xi’an, China and evaluated using the soft X-ray radiolysis method for determining the relation between the outgassing and the PM2.5 mass loading. The PM2.5 samples were also analyzed by several methods for quantifying organic and elemental carbons, water soluble ions, and organic compounds in the PM2.5 samples. The outgassing measured by the soft X-ray method increased linearly with the PM2.5 mass loading, and the chemical analyses explained that the organic carbon (OC) was dominantly detected by the soft X-ray method in the current study. Moreover, the sampling location of the PM2.5 affected on the organic compounds of the PM2.5. [ABSTRACT FROM AUTHOR]

The effect of transport-related pollution on human health is fast becoming recognized as a major issue in cities worldwide. Cyclists, in particular, face great health risks, as they typically are most exposed to tail-pipe emissions. Four avenues are being explored to combat urban air pollution: 1) policy interventions such as outright bans on polluting vehicles, 2) embracing zero tailpipe emission vehicles, 3) measuring air-quality as a means to better informing citizens of zones of higher pollution, and 4) developing smart mobility devices that seek to minimize the effect of polluting devices on citizens as they transport goods and individuals in our cities. Following this latter direction, in this paper, we present a new way to protect cyclists from the effect of urban pollution. Namely, by exploiting the actuation possibilities afforded by pedelecs or e-bikes (electric bikes), we design a cyber-physical system that mitigates the effect of urban pollution by indirectly controlling the ventilation rates (volume of air inhaled per minute) of cyclists in polluted areas. Results from a real device are presented to illustrate the efficacy of our system. [ABSTRACT FROM AUTHOR]

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality. [ABSTRACT FROM AUTHOR]

Air quality is currently arousing drastically increasing attention from the governments and populace all over the world. In this paper, we propose a heuristic recurrent air quality predictor (RAQP) to infer air quality. The RAQP exploits some key meteorology- and pollution-related variables to infer air pollutant concentrations (APCs), e.g. the fine particulate matter (PM2.5). It is natural that the meteorological factors and APCs at the current time have strong influences on air quality the next adjacent moment, that is to say, there exist high correlations between them. With this consideration, applying simple machine learners to the current meteorology- and pollution-related factors can reliably predict the air quality indices at a time later. However, owing to the nonlinear and chaotic reasons, the above correlations decline with the time interval enlarged. In such cases, it fails to forecast the air quality after several hours by only using simple machine learners and the current measurements of meteorology- and pollution-related variables. To solve the problem, our RAQP method recurrently applies the 1-h prediction model, which learns the current records of meteorology- and pollution-related factors to predict the air quality 1 h later, to then estimate the air quality after several hours. Via extensive experiments, results confirm that the RAQP predictor is superior to the relevant state-of-the-art techniques and nonrecurrent methods when applied to air quality prediction. [ABSTRACT FROM AUTHOR]

The motivation for the use of hydrogen as fuel is that it can be renewable and can reduce emissions. Hydrogen fuel cell vehicles are still likely to be more of a far-term reality because of their high manufacturing cost. A hybrid electric vehicle (HEY) with a hydrogen-fueled internal combustion (IC) engine has the potential of becoming a low-emission low-cost practical solution in the near future. This paper describes a standard sport utility vehicle (SUV) that has been converted into a hydrogen- powered HEY. The powertrain utilizes compressed gaseous hydrogen as fuel, a boosted hydrogen IC engine, an induction motor, a hydraulic transmission, regenerative braking, advanced nickel-metal hybrid batteries, and a real-time control system. Tests show that the vehicle can deliver higher fuel economy and much lower emissions than those of a traditional SUV without compromises in performance. This paper presents an overview of the prototype vehicle and emphasizes some of the unique features of this energy-saving clean environment solution. [ABSTRACT FROM AUTHOR]

Traffic prediction is one of the most important use cases for smart cities. Accurate traffic information is key to managing traffic issues. Many approaches that use traffic time series data to predict traffic flow have been proposed. In addition to traffic- specific parameters, some other features (called signatures) may be associated with road traffic, i.e., air and noise pollution. In this paper, we show how noise pollution and traffic time-series data were used to train Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNNs), which led to better traffic prediction on major roads in Madrid. This approach has already been used with pollution signatures. This work addresses a new potential investigation path closely related to the use of signature profiles and Artificial Intelligent techniques as a way to reduce the specialization of sensing infrastructure. [ABSTRACT FROM AUTHOR]

The vehicular sensor network (VSN) is emerging as a new solution for monitoring urban environments such as intelligent transportation systems and air pollution. One of the crucial factors that determine the service quality of urban monitoring applications is the delivery delay of sensing data packets in the VSN. In this paper, we study the problem of routing data packets with minimum delay in the VSN by exploiting 1) vehicle traffic statistics, 2) anycast routing, and 3) knowledge of future trajectories of vehicles such as busses. We first introduce a novel road network graph model that incorporates the three factors into the routing metric. We then characterize the packet delay on each edge as a function of the vehicle density, speed, and the length of the edge. Based on the network model and delay function, we formulate the packet routing problem as a Markov decision process (MDP) and develop an optimal routing policy by solving the MDP. Evaluations using real vehicle traces in a city show that our routing policy significantly improves the delay performance compared with existing routing protocols. Specifically, optimal VSN data forwarding (OVDF) yields, on average, 96% better delivery ratio and 72% less delivery delay than existing algorithms in some areas distant from destinations. [ABSTRACT FROM AUTHOR]

Emissions from power plants constitute a major part of air pollution and should be adequately estimated. In this paper, we consider the problem of estimating nitrogen dioxide (NO $_X$) emission of power plants by developing an inverse method to integrate satellite observations of atmospheric pollutant column concentrations with species concentrations and direct sensitivities predicted by a regional air quality model, in order to discern biases in the emissions of the pollutant precursors. Using this method, the emission fields are analyzed using a “bottom-up” approach, with an inversion performed by an unscented Kalman filter (UKF) to improve estimation profiles from emissions inventories data for the Sydney metropolitan area. The idea is to integrate information from the original inventories with tropospheric nitrogen dioxide (NO $_2$) emissions estimated during one month from the air pollution model-chemical transport model, and then, for validation, to compare the resulting model with satellite retrievals from the ozone monitoring instrument (OMI) above the region. The UKF-based estimation of NO $_2$ emissions shows better agreement with OMI observations, implying a significant improvement in accuracy as compared with the original inventories. Therefore, the proposed method is a promising tool for estimation of air emissions in urban areas. [ABSTRACT FROM AUTHOR]

Over the past few years, regional air pollution has frequently occurred in Mid-Eastern China, especially in Beijing. As the primary pollutant in urban air, atmospheric particulate matter (PM) not only leads to the decrease of atmospheric visibility, but also increases the mortality and morbidity of respiratory system diseases. By analyzing aerosol volume size distribution data downloaded from the AERONET official website, we find that the size distribution of aerosol in Beijing appears a bimodal log-normal structures and parameters of fine mode in AERONET data are mainly contributed by PM $_{\text{2.5}}$. In this paper, a physically based model is developed to estimate the concentration of PM $_{\text{2.5}}$ , in which, fine mode aerosol optical depth (AOD) at 440, 550, and 675 nm, Effective Radius of the Fine particles, ground-based fine particulate matter (PM $_{\text{2.5}}$ ) data, relative humidity, and boundary layer height data from 2015 to 2016 are used. Those from 2015 are used for calculating integrated extinction efficiencies (〈Qext〉) based on the model, and those from 2016 are used for PM $_{\text{2.5}}$ validation. Result shows that R 2 of retrieved PM $_{\text{2.5}}$ against ground-based PM $_{\text{2.5}}$ can reach to 0.70 and RMSE is 33.67 μg/m3 at Beijing site at 440 nm. This study concludes that this method has the potential to retrieve PM $_{\text{2.5}}$ by using AERONET AOD in Beijing, which is independent of ground-based PM $_{\text{2.5}}$ measurement. [ABSTRACT FROM AUTHOR]

Operational retrievals of aerosol optical depth (AOD) from Multi-angle Imaging SpectroRadiometer (MISR) data have been shown to have a high bias in pristine oceanic areas. One line of evidence involves comparison with Maritime Aerosol Network (MAN) observations, including the areas of low aerosol loading close to Antarctica. In this paper, a principal reason for the AOD overestimation is identified, which is stray light measured by the MISR cameras in dark regions of high-contrast scenes. A small fraction of the light from surrounding bright areas, such as clouds or sea ice, is redistributed to dark areas, artificially increasing their brightness. Internal reflections and light scattering from optical elements in MISR's pushbroom cameras contribute to this veiling light effect. A simple correction model is developed that relies on the average scene brightness and an empirically determined set of veiling light coefficients for each MISR camera and wavelength. Several independent methods are employed to determine these coefficients. Three sets of coefficients are further implemented and tested in prototype MISR 4.4-km AOD retrievals. The results show dramatic improvements in retrieved AODs compared against MAN observations and the currently operational V22 MISR retrievals. For the best performing set of coefficients, the bias is reduced by 51%, from 0.039 to 0.019, the RMSE is lowered by 19%, from 0.062 to 0.050, and 84% of retrievals fall within the uncertainty envelope compared with 66% of retrievals in V22. The best performing set will be implemented operationally in the next V23 MISR AOD product release. [ABSTRACT FROM AUTHOR]

In the past few years ridesharing has largely reshaped the transportation marketplace. It is envisioned as a promising solution to transportation-related problems in metropolitan cities, such as traffic congestion and air pollution. In the current ridesharing research, social ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing are two active directions with important business implications. Simultaneously optimizing social cohesion and revenue is vital to a commercial ridesharing platform's sustainable development, which, however, has not been previously studied. In this paper, we first present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top-k Matching (PTkM) queries which retrieve the top-k vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment. [ABSTRACT FROM AUTHOR]

The natural contamination components vary in different areas. NaCl is the major soluble salt on the surface of insulators in coastal areas. In contrast, the proportion of CaSO4 in inland areas is greater, which makes pollution flashover voltage of insulators have large scatter. As a result, the relationship between ESDD and pollution flashover voltage is unclear. However, in the artificial contamination pollution flashover experiments, NaCl is often utilized as the single soluble salt in the contamination, and there is a clear negative exponential relationship between the pollution flashover voltage and the SDD. In this paper, the relationship among ESDD, contamination components and surface conductivity of insulators under saturated moisture is analyzed. On this basis, the ratio of surface conductivity to ESDD is proposed to describe the difference of soluble salt components in natural contamination. Experimental results indicate that the ratio of surface conductivity of the polluted insulators under saturated moisture to ESDD is in the range of 14?866. This ratio is inversely proportional to the proportion of CaSO4 in the soluble salts of the natural contamination. If two of the three parameters, namely, the surface conductivity, ESDD, and contamination components, are measured, the remaining one can be calculated. The ratio of surface conductivity to ESDD can be utilized as a parameter to describe the components of natural contamination. [ABSTRACT FROM PUBLISHER]

Among all types of computations, the polynomial function evaluation is a fundamental, yet an important one due to its wide usage in the engineering and scientific problems. In this paper, we investigate publicly verifiable outsourced computation for polynomial evaluation with the support of multiple data sources. Our proposed verification scheme is universally applicable to all types of polynomial computations and allows the clients to outsource new data at any time. While the existing solutions only support the verification for polynomial evaluation over a single data source, i.e., all the inputs of the polynomial function are outsourced and signed by a single entity, our solution supports polynomial evaluations over multiple different data sources, which are more common and have wider applications, e.g., to assess the city air pollution, one needs to evaluate the environmental data uploaded from the multiple environmental monitor sites. In our proposed scheme, the verification cost for the client is independent with either the input size or the polynomial size so that it scales well in practice. We formally prove the correctness and soundness of our scheme and conduct numerical analysis and evaluation study to validate its high efficiency and scalability. The experimental results show that the data contributor signing 1000 new data only takes 2.1 s, and the verification of the delegated polynomial function takes only 22 ms, which is practically efficient for the real-world applications. [ABSTRACT FROM PUBLISHER]

Superior environmental performance has not traditionally been a goal of managers of power-generating facilities, but there is now considerable pressure to go "beyond compliance," even for facilities that have been in existence for decades. In this regard, significant environmental improvement can be achieved by taking a life-cycle perspective. We have conducted a streamlined life-cycle assessment on an existing gas/oil-fired generating facility. With the results of this assessment as a basis, we propose a number of generic steps that can be taken to improve the environmental performance of most existing power-generating facilities. [ABSTRACT FROM PUBLISHER]

Increased levels of particulate matter (PM) in the atmosphere have contributed to an increase in mortality and morbidity in communities and are the main contributing factor for respiratory health problems in the population. Currently, PM concentrations are sparsely monitored; for instance, a region of over 2200 square kilometers surrounding Melbourne in Victoria, Australia, is monitored using ten sensor stations. This paper proposes to improve the estimation of PM concentration by complementing the existing high-precision but expensive PM devices with low-cost lower precision PM sensor nodes. Our evaluation reveals that local PM estimation accuracies improve with higher densities of low-precision sensor nodes. Our analysis examines the impact of the precision of the lost-cost sensors on the overall estimation accuracy. [ABSTRACT FROM PUBLISHER]

Air pollution monitoring has attracted much attention in recent years because of the deterioration of the environment. Standard stations, installed by governments with a high cost, can provide reliable air quality information; whereas a large number of portable air monitoring sensors with low cost are widely used and output less precise results. In this paper, we propose a category-based calibration approach (CCA) using machine learning algorithms for such portable sensors. Compared with traditional methods that often learn a single regression model, CCA includes multiple regression models according to pollutant concentration categories, and builds a more accurate mapping from sensor readings to reference. Furthermore, CCA introduces two fault-tolerance modules: classification tolerance and sample tolerance. The former mitigates the impact of misclassification for concentration category, and the latter improves the robustness of individual regression model. Our approach is evaluated on carbon monoxide (CO) and ozone (O3) from two cities of China. The experiment results show that CCA has a better performance than traditional calibration models in both accuracy and robustness. [ABSTRACT FROM AUTHOR]

Metropolitan air pollution is a growing concern in both developing and developed countries. Fixed-station monitors, typically operated by governments, offer accurate but sparse data, and are increasingly being augmented by lower fidelity but denser measurements taken by mobile sensors carried by concerned citizens and researchers. In this paper, we introduce HazeEst—a machine learning model that combines sparse fixed-station data with dense mobile sensor data to estimate the air pollution surface for any given hour on any given day in Sydney. We assess our system using seven regression models and tenfold cross validation. The results show that estimation accuracy of support vector regression (SVR) is similar to decision tree regression and random forest regression, and higher than extreme gradient boosting, multi-layer perceptrons, linear regression, and adaptive boosting regression. The air pollution estimates from our models are validated via field trials, and results show that SVR not only yields high spatial resolution estimates that correspond well with the pollution surface obtained from fixed and mobile sensor monitoring systems, but also indicates boundaries of polluted area better than other regression models. Our results can be visualized using a Web-based application customized for metropolitan Sydney. We believe that the continuous estimates provided by our system can better inform air pollution exposure and its impact on human health. [ABSTRACT FROM PUBLISHER]

Because the power industry makes a significant contribution to air pollution, a variety of air pollution control technologies have been adopted to reduce emissions of nitrogen oxides, sulfur dioxide, and particulate matters. However, the deployment of these technologies affects the operation of the power plants and of the power system. This paper first discusses the emissions of multiple pollutants by coal- and gas-fired generators equipped with different emission control devices. It then presents the formulation of an environmental power generation scheduling (EnPGS) model, which coordinates the operating cost and the emissions of these pollutants, including the emissions during the startup and shutdown processes. This model considers the air quality index and how this index is affected by the weather, and optimizes the spatial distribution of generation between regions. It also takes into account the operating characteristics of various emission control devices, such as the deactivation of selective catalytic reduction at low output, and the burn mode switching of combined cycle gas turbines. A case study covering several Chinese provinces demonstrates the potential effectiveness of the EnPGS at reducing multiple air pollutants. [ABSTRACT FROM AUTHOR]

Titania-supported gold nanoparticles exhibit excellent low-temperature carbon monoxide (CO) oxidation activity, which is sensitive to the structure of the TiO2 support and the preparation method. In this paper, gold catalysts were prepared on TiO2 with different anatase-to-rutile ratios by the deposition–precipitation method (Au/TiO2-C) and plasma-assisted deposition–precipitation method (Au/TiO2-PC). The highest performance for CO oxidation was obtained over Au/TiO2 for an anatase-to-rutile ratio of 4:6, which may be ascribed to the phase transformation mechanism of TiO2 and the preferential deposition of Au nanoparticles on rutile TiO2 in the deposition–precipitation process. Therefore, enlarged contact surface between the Au nanoparticles and the TiO2 support was obtained, and more oxygen vacancies may be formed, which are beneficial to CO oxidation. Interestingly, compared with Au/TiO2-C, Au/TiO2-PC exhibited enhanced CO oxidation activity. For example, the reaction rate ( $\xi )$ at 30 °C for CO oxidation over Au/TiO2-PC was about 5.8 times greater than that over Au/TiO2-C for an anatase-to-rutile ratio of about 4:6, which was primarily caused by the more active gold species on the surface of TiO2 support in Au/TiO2-PC due to the Coulomb interaction during plasma preparation. [ABSTRACT FROM AUTHOR]

The proliferation of electric vehicles (EVs) offers additional opportunities for improving the ambient air quality and reducing the associated health impacts. However, such opportunities could become unattainable if the electricity for charging EVs is mainly supplied by coal-fired units. This paper aims to enhance the environmental benefits of EVs by differentiating the marginal impacts of coal-fired unit emissions at various times and locations on ambient air pollutant concentration (AAPC) and human health conditions. The proposed cost-effective approach will assign EV battery charging loads and other electricity demands to generation units with low health impacts. The proposed air pollution dispersion model maps the emission into spatial AAPC increments (${\Delta }$ AAPCs), and concentration-response function maps ${\Delta }$ AAPCs into health impacts. As such, we estimate differentiating health impacts of emissions at various times and locations without executing large-scale atmospheric models. We also integrate differentiating emission regulations with the joint optimization of security-constrained unit commitment (SCUC) and EV battery swapping-charging system (BSCS). BSCS refers to the optimal scheduling of an aggregated number of EV batteries which are centrally charged and then dispatched via delivery trucks to battery swapping stations (BSSs) to supply local EVs. BSCS determines optimal schedules for battery charging, swapping, and truck routing. A Lagrangian decomposition method decouples the formulated large-scale mixed-integer linear programming model into SCUC and BSCS subproblems. The case studies demonstrate the effectiveness of the proposed approach for managing ambient air quality and human health conditions. [ABSTRACT FROM AUTHOR]

There is a constant drive within the nuclear power industry to improve upon the characterization capabilities of current ultrasonic inspection techniques in order to improve safety and reduce costs. Particular emphasis has been placed on the ability to characterize very small defects which could result in extended component lifespan and help reduce the frequency of in-service inspections. Super-resolution (SR) algorithms, also known as sampling methods, have been shown to demonstrate the capability to resolve scatterers separated by less than the diffraction limit when deployed in representative inspections and therefore could be used to tackle this issue. In this paper, the factorization method (FM) and the Time Reversal Multiple-Signal-Classification (TR-MUSIC) algorithms are applied to the simulated ultrasonic array inspection of small rough embedded planar defects to establish their characterization capabilities. Their performance was compared to the conventional total focusing method (TFM). A full 2-D finite-element (FE) Monte Carlo modeling study was conducted for defects with a range of sizes, orientations, and magnitude of surface roughness. The results presented show that for subwavelength defects, both the FM and TR-MUSIC algorithms were able to size and estimate defect orientation accurately for smooth cases and, for rough defects, up to a roughness of 100 $\mu \text{m}$. This level of roughness is representative of the thermal fatigue defects encountered in the nuclear power sector. This contrasted with the relatively poor performance of TFM in these cases which consistently oversized these defects and could not be used to estimate the defect orientation, making through-wall sizing with this method impossible. [ABSTRACT FROM AUTHOR]

Air pollution has become a worldwide concerned issue and automatical estimation of air quality can provide a positive guidance to both individual and industrial behaviors. Given that the traditional instrument-based method requires high economic, labor costs on instrument purchase and maintenance, this paper proposes an effective, efficient, and cheap photo-based method for the air quality estimation in the case of particulate matter (PM2.5). The success of the proposed method lies in extracting two categories of features (including the gradient similarity and distribution shape of pixel values in the saturation map) by observing the photo appearances captured under different PM2.5 concentrations. Specifically, the gradient similarity is extracted to measure the structural information loss with the consideration that PM2.5 attenuates the light rays emitted from the objects and accordingly distorts the structures of the formed photo. Meanwhile, the saturation map is fit by the Weibull distribution to quantify the color information loss. By combining two features, a primary PM2.5 concentration estimator is obtained. Next, a nonlinear function is adopted to map the primary one to the real PM2.5 concentration. Sufficient experiments on real data captured by professional PM2.5 instrument demonstrate the effectiveness and efficiency of the proposed method. Specifically, it is highly consistent with real sensor’s measures and requires low implementation time. [ABSTRACT FROM AUTHOR]