Publications
102 results found
Tsai C-Y, Liu W-T, Hsu W-H, et al., 2022, Screening the Risk of Obstructive Sleep Apnea by Utilizing Supervised Learning Techniques Based on Anthropometric Features and Snoring Events (Preprint)
<sec> <title>BACKGROUND</title> <p>Obstructive sleep apnea (OSA) is sleep-disordered breathing and is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features (e.g., symptoms or risk factors of OSA).</p> </sec> <sec> <title>METHODS</title> <p>This retrospective study collected data on 3629 patients from Taiwan, who had undergone PSG for symptoms of OSA. Their baseline characteristics, anthropometric measures, and PSG data were obtained. The number of snoring events of PSG was further derived, and correlations among the collected variables were investigated. Next, this study utilized six common supervised machine learning techniques to establish OSA risk screening models, including random forest (RF), XGBoost, k-nearest neighbors, support vector machine, logistic regression, and naïve Bayes. First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach which had the highest accuracy in the training and validation phase was employed to perform the classification for the test dataset. Moreover, the feature importance of employed models was determined by calculating the Shapley value of every factor, which represented the impact on OSA risk screening.</p> </sec> <sec> <title>RESULTS</title> <p>RF models manifested the
Ye Q, Feng Y, Qiu J, et al., 2022, Approximate optimum curbside utilisation for pick-up and drop-off (PUDO) and parking demands using reinforcement learning, 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Publisher: IEEE, Pages: 2628-2633
With the uptake of automated transport, especially Pick-Up and Drop-Off (PUDO) operations of Shared Autonomous Vehicles (SAVs), the valet parking of passenger vehicles and delivery vans are envisaged to saturate our future streets. These emerging behaviours would join conventional on-street parking activities in an intensive competition for scarce curb resources. Existing curbside management approaches principally focus on those long-term parking demands, neglecting those short-term PUDO or docking events. Feasible solutions that coordinate diverse parking requests given limited curb space are still absent. We propose a Reinforcement Learning (RL) method to dynamically dispatch parking areas to accommodate a hybrid stream of parking behaviours. A partially-learning Deep Deterministic Policy Gradient (DDPG) algorithm is trained to approximate optimum dispatching strategies. Modelling results reveal satisfying convergence guarantees and robust learning patterns. Namely, the proposed model successfully discriminates parking demands of distinctive sorts and prioritises PUDOs and docking requests. Results also identify that when the demand-supply ratio situates at 2:1 to 4:1, the service rate approximates an optimal (83\%), and curbside occupancy surges to 80%. This work provides a novel intelligent dispatching model for diverse and fine-grained parking demands. Furthermore, it sheds light on deploying distinctive administrative strategies to the curbside in different contexts.
Dray L, Schafer AW, Grobler C, et al., 2022, Cost and emissions pathways towards net-zero climate impacts in aviation, NATURE CLIMATE CHANGE, Vol: 12, Pages: 956-+, ISSN: 1758-678X
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McCarthy LP, Knapp P, Walker JS, et al., 2022, Dynamics and outcomes of binary collisions of equi-diameter picolitre droplets with identical viscosities, PHYSICAL CHEMISTRY CHEMICAL PHYSICS, Vol: 24, Pages: 21242-21249, ISSN: 1463-9076
Tiernan H, Friedman S, Clube RKM, et al., 2022, Implementation of a structured decision-making framework to evaluate and advance understanding of airborne microplastics, Environmental Science and Policy, Vol: 135, Pages: 169-181, ISSN: 1462-9011
Microplastic pollution is increasingly recognised as a global environmental challenge which stems from the rapid growth of the use of petrochemical-derived plastic. As researchers and practitioners face a myriad of environmental challenges, oceanic microplastic pollution has so far dominated interest. However, airborne microplastics present an increasing environmental and public health concern. There is currently a need for research addressing this emerging challenge, and at the same time, the lack of knowledge and consensus regarding airborne microplastics presents an obstacle to action. The purpose of this study is to utilise a participatory Structured Decision-Making (SDM) approach to understand the perspectives of a range of stakeholders involved in the microplastics landscape, and subsequently refine common research priorities and knowledge gaps to advance the field. Through two participatory workshops, we first defined shared objectives of stakeholders and then negotiated best courses of action to achieve these objectives based on discussion between stakeholders and facilitators. The qualitative approach taken has enabled the full, complex and multidisciplinary aspects of the research into airborne microplastic pollution to be considered. Our findings highlight some important potential consequences of airborne microplastic pollution, including impacts on human health, and the need for more interdisciplinary research, and collaborative, integrated approaches in this area. As a result of the first workshop, five fundamental objectives on the theme of airborne microplastics were identified. As a direct consequence of this, participants identified 84 actions split across eight themes, which are outlined later in this paper.
Woo M, Schriefl MA, Knoll M, et al., 2022, Open-source modelling of aerosol dynamics and computational fluid dynamics: Bipolar and unipolar diffusion charging and photoelectric charging, COMPUTER PHYSICS COMMUNICATIONS, Vol: 278, ISSN: 0010-4655
Teoh R, Schumann U, Gryspeerdt E, et al., 2022, Aviation contrail climate effects in the North Atlantic from 2016 to 2021, Atmospheric Chemistry and Physics, Vol: 22, Pages: 10919-10935, ISSN: 1680-7316
Around 5 % of anthropogenic radiative forcing (RF) is attributed to aviation CO2 and non-CO2 impacts. This paper quantifies aviation emissions and contrail climate forcing in the North Atlantic, one of the world's busiest air traffic corridors, over 5 years. Between 2016 and 2019, growth in CO2 (+3.13 % yr−1) and nitrogen oxide emissions (+4.5 % yr−1) outpaced increases in flight distance (+3.05 % yr−1). Over the same period, the annual mean contrail cirrus net RF (204–280 mW m−2) showed significant inter-annual variability caused by variations in meteorology. Responses to COVID-19 caused significant reductions in flight distance travelled (−66 %), CO2 emissions (−71 %) and the contrail net RF (−66 %) compared with the prior 1-year period. Around 12 % of all flights in this region cause 80 % of the annual contrail energy forcing, and the factors associated with strongly warming/cooling contrails include seasonal changes in meteorology and radiation, time of day, background cloud fields, and engine-specific non-volatile particulate matter (nvPM) emissions. Strongly warming contrails in this region are generally formed in wintertime, close to the tropopause, between 15:00 and 04:00 UTC, and above low-level clouds. The most strongly cooling contrails occur in the spring, in the upper troposphere, between 06:00 and 15:00 UTC, and without lower-level clouds. Uncertainty in the contrail cirrus net RF (216–238 mW m−2) arising from meteorology in 2019 is smaller than the inter-annual variability. The contrail RF estimates are most sensitive to the humidity fields, followed by nvPM emissions and aircraft mass assumptions. This longitudinal evaluation of aviation contrail impacts contributes a quantified understanding of inter-annual variability and informs strategies for contrail mitigation.
Karamanis R, Anastasiadis E, Stettler M, et al., 2022, Vehicle Redistribution in Ride-Sourcing Markets Using Convex Minimum Cost Flows, IEEE Transactions on Intelligent Transportation Systems, Vol: 23, Pages: 10287-10298, ISSN: 1524-9050
Ride-sourcing platforms often face imbalances in the demand and supply of rides across areas in their operating road-networks. As such, dynamic pricing methods have been used to mediate these demand asymmetries through surge price multipliers, thus incentivising higher driver participation in the market. However, the anticipated commercialisation of autonomous vehicles could transform the current ride-sourcing platforms to fleet operators. The absence of human drivers fosters the need for empty vehicle management to address any vehicle supply deficiencies. Proactive redistribution using integer programming and demand predictive models have been proposed in research to address this problem. A shortcoming of existing models, however, is that they ignore the market structure and underlying customer choice behaviour. As such, current models do not capture the real value of redistribution. To resolve this, we formulate the vehicle redistribution problem as a non-linear minimum cost flow problem which accounts for the relationship of supply and demand of rides, by assuming a customer discrete choice model and a market structure. We demonstrate that this model can have a convex domain, and we introduce an edge splitting algorithm to solve a transformed convex minimum cost flow problem for vehicle redistribution. By testing our model using simulation, we show that our redistribution algorithm can decrease wait times by more than 50%, increase profit up to 10% with less than 20% increase in vehicle mileage. Our findings outline that the value of redistribution is contingent on localised market structure and customer behaviour.
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Woodward H, Schroeder A, Le Cornec C, et al., 2022, High resolution modelling of traffic emissions using the large eddy simulation code Fluidity, Atmosphere, Vol: 13, ISSN: 2073-4433
The large eddy simulation (LES) code Fluidity was used to simulate the dispersion of NOx traffic emissions along a road in London. The traffic emissions were represented by moving volume sources, one for each vehicle, with time-varying emission rates. Traffic modelling software was used to generate the vehicle movement, while an instantaneous emissions model was used to calculate the NOx emissions at 1 s intervals. The traffic emissions were also modelled as a constant volume source along the length of the road for comparison. A validation of Fluidity against wind tunnel measurements is presented before a qualitative comparison of the LES concentrations with measured roadside concentrations. Fluidity showed an acceptable comparison with the wind tunnel data for velocities and turbulence intensities. The in-canyon tracer concentrations were found to be significantly different between the wind tunnel and Fluidity. This difference was explained by the very high sensitivity of the in-canyon tracer concentrations to the precise release location. Despite this, the comparison showed that Fluidity was able to provide a realistic representation of roadside concentration variations at high temporal resolution, which is not achieved when traffic emissions are modelled as a constant volume source or by Gaussian plume models.
Molloy J, Teoh R, Harty S, et al., 2022, Design principles for a contrail-minimizing trial in the North Atlantic, Aerospace, Vol: 9, Pages: 375-375, ISSN: 2226-4310
The aviation industry has committed to decarbonize its CO2 emissions. However, there has been much less industry focus on its non-CO2 emissions, despite recent studies showing that these account for up to two-thirds of aviation’s climate impact. Parts of the industry have begun to explore the feasibility of potential non-CO2 mitigation options, building on the scientific research undertaken in recent years, by establishing demonstrations and operational trials to test parameters of interest. This paper sets out the design principles for a large trial in the North Atlantic. Considerations include the type of stakeholders, location, when to intervene, what flights to target, validation, and other challenges. Four options for safely facilitating a trial are outlined based on existing air-traffic-management processes, with three of these readily deployable. Several issues remain to be refined and resolved as part of any future trial, including those regarding meteorological and contrail forecasting, the decision-making process for stakeholders, and safely integrating these flights into conventional airspace. While this paper is not a formal concept of operations, it provides a stepping stone for policymakers, industry leaders, and other stakeholders with an interest in reducing aviation’s total climate impact, to understand how a large-scale warming-contrail-minimizing trial could work
Phantawesak N, Coyle F, Stettler M, 2022, Long-term in-use NOx emissions from London buses with retrofitted NOx aftertreatment, Environmental Science and Technology (Washington), Vol: 56, Pages: 6968-6977, ISSN: 0013-936X
Buses constitute a significant source of air pollutant emissions in cities. In this study, we present real-world NOx emissions from 97 diesel-hybrid buses measured using on-board diagnostic systems over 44 months and 6.35 million km in London. Each bus had previously been retrofitted with a selective catalytic reduction (SCR) aftertreatment system to reduce emissions of nitrogen oxides (NOx). On average, parallel hybrid (PH) and series hybrid (SH) buses emitted 3.80 g of NOx/km [standard deviation (SD) of 1.02] and 2.37 g of NOx/km (SD of 0.51), respectively. The SCR systems reduced engine-out emissions by 79.8% (SD of 5.0) and 87.2% (SD of 2.9) for the PHs and SHs, respectively. Lower ambient temperatures (0–10 °C) increased NOx emissions of the PHs by 24.2% but decreased NOx emissions of the SHs by 27.9% compared to values found at more moderate temperatures (10–20 °C). To improve emissions inventories, we provide new distance-based NOx emissions factors for different ranges of ambient temperature. During the COVID-19 pandemic, the emissions benefits of reduced congestion were largely offset by more frequent route layovers leading to lower SCR temperatures and effectiveness. This study shows that continuous in-service measurements enable quantification of real-world vehicle emissions over a wide range of operations that complements conventional testing approaches.
Stettler MEJ, Nishida RT, de Oliveira PM, et al., 2022, Source terms for benchmarking models of SARS-CoV-2 transmission via aerosols and droplets, Royal Society Open Science, Vol: 9, ISSN: 2054-5703
There is ongoing and rapid advancement in approaches to modelling the fate of exhaled particles in different environments relevant to disease transmission. It is important that models are verified by comparison with each other using a common set of input parameters to ensure that model differences can be interpreted in terms of model physics rather than unspecified differences in model input parameters. In this paper, we define parameters necessary for such benchmarking of models of airborne particles exhaled by humans and transported in the environment during breathing and speaking.
Cheewinsiriwat P, Duangyiwa C, Sukitpaneenit M, et al., 2022, Influence of Land Use and Meteorological Factors on PM<sub>2.5</sub> and PM<sub>10</sub> Concentrations in Bangkok, Thailand, SUSTAINABILITY, Vol: 14
Teoh R, Schumann U, Gryspeerdt E, et al., 2022, Aviation contrail climate effects in the North Atlantic from 2016–2021
<jats:p>Abstract. Around 5 % of anthropogenic radiative forcing (RF) is attributed to aviation CO2 and non-CO2 impacts. This paper quantifies aviation emissions and contrail climate forcing in the North Atlantic, one of the world’s busiest air traffic corridors, over 5 years. Between 2016 and 2019, growth in CO2 (+3.13 % per annum, p.a.) and nitrogen oxide emissions (+4.5 % p.a.) outpaced increases in flight distance (+3.05 % p.a.). Over the same period, the annual mean contrail cirrus net RF (204–280 mW m-2) showed significant interannual variability caused by variations in meteorology. Responses to COVID-19 caused significant reductions in flight distance travelled (-66 %), CO2 emissions (-71 %), and the contrail net RF (-66 %) compared to the prior one-year period. Around 12 % of all flights in this region cause 80 % of the annual contrail energy forcing, and the factors associated with strongly warming/cooling contrails include seasonal changes in meteorology and radiation, time of day, background cloud fields, and engine-specific non-volatile particulate matter (nvPM) emissions. Strongly warming contrails in this region are generally formed in wintertime, close to the tropopause, between 15:00 and 04:00 UTC, and above low-level clouds. The most strongly cooling contrails occur in the spring, in the upper troposphere, between 06:00 and 15:00 UTC, and without lower-level clouds. Uncertainty in the contrail cirrus net RF (216–238 mW m-2) arising from meteorology in 2019, is smaller than the interannual variability. The contrail RF estimates are most sensitive to the humidity fields, followed by nvPM emissions and aircraft mass assumptions. This longitudinal evaluation of aviation contrail impacts contributes a quantified understanding of inter-annual variability and informs strategies for contrail mitigation. </jats:p>
Teoh R, Schumann U, Gryspeerdt E, et al., 2022, Supplementary material to &quot;Aviation contrail climate effects in the North Atlantic from 2016–2021&quot;
Song J, Stettler MEJ, 2022, A novel multi-pollutant space-time learning network for air pollution inference, Science of the Total Environment, Vol: 811, ISSN: 0048-9697
Detailed information about air pollution in space and time is essential to manage risks to public health. In this paper we propose a multi-pollutant space-time learning network (Multi-AP learning network), which estimates pixel-wise (grid-level) concentrations of multiple air pollutant species based on fixed-station measurements and multi-source urban features, including land use information, traffic data, and meteorological conditions. We infer concentrations of multiple pollutants within one integrated learning network, which is applied to and evaluated on a case study in Chengdu (4900 km2, 26 April - 12 June 2019), where air pollutant (PM2.5, PM10 and O3) measurements from 40 monitoring sites are used to train the network to estimate pollutant concentrations in 4900 grid-cells (1 km2). The Multi-AP learning network allows us to estimate highly-resolved (1 km × 1 km, hourly) air pollution maps based on pollutant measurements which cover less than 1% of the grid-cells with better accuracy compared to other approaches, and with significant computational efficiency improvements. The time-cost is 1/3 of the time-cost of modelling each pollutant individually. Furthermore, we evaluate the relative importance of features and find that the meteorological feature set is the most important followed the land use features. The proposed Multi-AP method could be used to estimate air pollution exposure across a city using a limited set of air pollution monitoring sites.
Stettler MEJ, Nishida RT, de Oliveira PM, et al., 2022, Source terms for benchmarking models of SARS-CoV-2 transmission via aerosols and droplets
<jats:title>Abstract</jats:title><jats:p>There is ongoing and rapid advancement in approaches to modelling the fate of exhaled particles in different environments relevant to disease transmission. It is important that models are verified by comparison with each other using a common set of input parameters to ensure that model differences can be interpreted in terms of model physics rather than unspecified differences in model input parameters. In this paper, we define parameters necessary for such benchmarking of models of airborne particles exhaled by humans and transported in the environment during breathing and speaking.</jats:p>
Ye Q, Feng Y, Candela E, et al., 2022, Spatial-temporal flows-adaptive street layout control using reinforcement learning, Sustainability, Vol: 14, ISSN: 2071-1050
Complete streets scheme makes seminal contributions to securing the basic public right-of-way (ROW), improving road safety, and maintaining high traffic efficiency for all modes of commute. However, such a popular street design paradigm also faces endogenous pressures like the appeal to a more balanced ROW for non-vehicular users. In addition, the deployment of Autonomous Vehicle (AV) mobility is likely to challenge the conventional use of the street space as well as this scheme. Previous studies have invented automated control techniques for specific road management issues, such as traffic light control and lane management. Whereas models and algorithms that dynamically calibrate the ROW of road space corresponding to travel demands and place-making requirements still represent a research gap. This study proposes a novel optimal control method that decides the ROW of road space assigned to driveways and sidewalks in real-time. To solve this optimal control task, a reinforcement learning method is introduced that employs a microscopic traffic simulator, namely SUMO, as its environment. The model was trained for 150 episodes using a four-legged intersection and joint AVs-pedestrian travel demands of a day. Results evidenced the effectiveness of the model in both symmetric and asymmetric road settings. After being trained by 150 episodes, our proposed model significantly increased its comprehensive reward of both pedestrians and vehicular traffic efficiency and sidewalk ratio by 10.39%. Decisions on the balanced ROW are optimised as 90.16% of the edges decrease the driveways supply and raise sidewalk shares by approximately 9%. Moreover, during 18.22% of the tested time slots, a lane-width equivalent space is shifted from driveways to sidewalks, minimising the travel costs for both an AV fleet and pedestrians. Our study primarily contributes to the modelling architecture and algorithms concerning centralised and real-time ROW management. Prospective applications out o
Hu S, Shu S, Bishop J, et al., 2022, Vehicle telematics data for urban freight environmental impact analysis, Transportation Research Part D: Transport and Environment, Vol: 102, Pages: 103121-103121, ISSN: 1361-9209
Road freight transport is one of the major contributors to greenhouse gas and air pollutantemissions. Hence, it is increasingly regulated in urban areas to reduce its impact on theenvironment and human health. The rich data available from telematics has the potentialto provide high-resolution information, yet research has not been conducted to understand,evaluate, and ultimately improve the operation and impacts of urban road freight. This paperdemonstrates the role of vehicle telematics data in enabling quantitative assessment of theimpacts of urban freight transport for the effective management of relevant policies. We presenta comprehensive data-driven approach that provides a robust quantitative evaluation andapplies it to a case study of the London Lorry Control Scheme (LLCS) policy in UK. We showthat, for the studied freight operator, the LLCS policy affects their drivers’ route choice bothinside and outside the restricted hours. The spatio-temporal distributions of different parametersincluding traffic speeds, fuel economy and emissions at different times of the day are comparedand analyzed. The results indicate that the unintended consequences of urban freight transportpolicies can include an extra 15% vehicle-km traveled per trip and 12% liter of fuel consumedper trip
Ye Q, Feng Y, Han J, et al., 2021, A reinforcement learning-based adaptive control model for future street planning an algorithm and a case study, Doha, Qatar, 57th ISOCARP World Planning Congress, Publisher: ISOCARP, Pages: 1-13
With the emerging technologies in Intelligent Transportation System (ITS), the adaptive operation of road space is likely to be realised within decades. An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency. However, there is a lack of effective controlling techniques for these adaptive street infrastructures. To fill this gap in existing studies, we formulate this control problem as a Markov Game and develop a solution based on the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street parking areas in real-time. Integrated with the SUMO traffic simulator, this model was evaluated using the road network of the South Kensington District against three cases of divergent traffic conditions: pedestrian flow rates, AVs traffic flow rates and parking demands. Results reveal that our model can achieve an average reduction of 3.87% and 6.26% in street space assigned for on-street parking and vehicular operations. Combined with space gained by limiting the number of driving lanes, the average proportion of sidewalks to total widths of streets can significantly increase by 10.13%.
Ma L, Graham D, Stettler M, 2021, Has the Ultra Low Emission Zone in London improved air quality?, Environmental Research Letters, Vol: 16, Pages: 1-16, ISSN: 1748-9326
London introduced the world's most stringent emissions zone, the Ultra Low Emission Zone (ULEZ), in April 2019 to reduce air pollutant emissions from road transport and accelerate compliance with the EU air quality standards. Combining meteorological normalisation, change point detection, and a regression discontinuity design with time as the forcing variable, we provide an ex-post causal analysis of air quality improvements attributable to the London ULEZ. We observe that the ULEZ caused only small improvements in air quality in the context of a longer-term downward trend in London's air pollution levels. Structural changes in nitrogen dioxide (NO2) and ozone (O3) concentrations were detected at 70% and 24% of the (roadside and background) monitoring sites and amongst the sites that showed a response, the relative changes in air pollution ranged from −9% to 6% for NO2, −5% to 4% for O3, and −6% to 4% for particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5). Aggregating the responses across London, we find an average reduction of less than 3% for NO2 concentrations, and insignificant effects on O3 and PM2.5 concentrations. As other cities consider implementing similar schemes, this study implies that the ULEZ on its own is not an effective strategy in the sense that the marginal causal effects were small. On the other hand, the ULEZ is one of many policies implemented to tackle air pollution in London, and in combination these have led to improvements in air quality that are clearly observable. Thus, reducing air pollution requires a multi-faceted set of policies that aim to reduce emissions across sectors with coordination among local, regional and national government.
Woo M, Stettler MEJ, 2021, Feasibility study on the use of artificial neural networks to model catalytic oxidation in a metallic foam reactor, Industrial and Engineering Chemistry Research, Vol: 60, Pages: 15416-15427, ISSN: 0888-5885
This study investigates the feasibility of using artificial neural networks (ANNs) to predict catalytic oxidation in diesel after-treatment systems and compares their performance to that of physics-based models. Existing physics models are revisited to generate baseline data for binary reactions of major species (CO, C3H6, and NO) measured in a lab-scale microreactor comprising a metallic foam catalytic substrate. The physics model performs well to predict the measured light-off curves, which are the species conversions with ramping temperature, and the R2 value is above 0.84 across a wide range of operating conditions. However, the model cannot perfectly capture the retarding trends observed in the CO and C3H6 conversion curves after light-off. In contrast, the ANN model is capable of accurately predicting the light-off curves for operating conditions seen during the training process. This might be practically useful but is inherently limited by the availability of experimental data for training. To compensate for the drawbacks of both approaches, this study suggests a hybrid model in which a pretrained ANN is used to calculate reaction rates in the physics models. Despite the more complex data generation process for training ANNs, the hybrid model captures the light-off curves including the retarding trend and is less sensitive to the range of test conditions without renormalization as compared to the pure ANN model. This study investigates the feasibility of ANNs by comparing the pros and cons among the physics models, pure ANN, and hybrid models and suggests a step toward the most appropriate uses of ANNs in modeling exhaust after-treatment in practical applications.
Mijic A, Whyte J, Myers R, et al., 2021, Reply to a discussion of 'a research agenda on systems approaches to infrastructure' by david elms, Civil Engineering and Environmental Systems: decision making and problem solving, Vol: 38, Pages: 295-297, ISSN: 0263-0257
Schumann U, Poll I, Teoh R, et al., 2021, Air traffic and contrail changes over Europe during COVID-19: a model study, Atmospheric Chemistry and Physics, Vol: 21, Pages: 7429-7450, ISSN: 1680-7316
The strong reduction of air traffic during the COVID-19 pandemic provides a unique test case for the relationship between air traffic density, contrails, and their radiative forcing of climate change. Here, air traffic and contrail cirrus changes are quantified for a European domain for March to August 2020 and compared to the same period in 2019. Traffic data show a 72 % reduction in flight distance compared with 2019. This paper investigates the induced contrail changes in a model study. The contrail model results depend on various methodological details as discussed in parameter studies. In the reference case, the reduced traffic caused a reduction in contrail length. The reduction is slightly stronger than expected from the traffic change because the weather conditions in 2020 were less favorable for contrail formation than in 2019. Contrail coverage over Europe with an optical depth larger than 0.1 decreased from 4.6 % in 2019 to 1.4 % in 2020; the total cirrus cover amount changed by 28 % to 25 %. The reduced contrail coverage caused 70 % less longwave and 73 % less shortwave radiative forcing but, because of various nonlinearities, only 54 % less net forcing in this case. The methods include recently developed models for performance parameters and soot emissions. The overall propulsion efficiency of the aircraft is about 20 % smaller than estimated in earlier studies, resulting in 3 % fewer contrails. Considerable sensitivity to soot emissions is found, highlighting fuel and engine importance. The contrail model includes a new approximate method to account for water vapor exchange between contrails and background air and for radiative forcing changes due to contrail–contrail overlap. The water vapor exchange reduces available ice supersaturation in the atmosphere, which is critical for contrail formation. Contrail–contrail overlap changes the computed radiative forcing considera
Song J, Han K, Stettler M, 2021, Deep-MAPS: machine learning based mobile air pollution sensing, IEEE Internet of Things Journal, Vol: 8, Pages: 7649-7660, ISSN: 2327-4662
Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, coined Deep-MAPS, and demonstrates its scientific and financial values in the following aspects. (1) Based on a combination of fixed and mobile air quality sensors, we perform spatial inference of PM2.5 concentrations in Beijing (3,025 km, 19 Jun -16 Jul 2018) for a spatial-temporal resolution of 1 km ×1 km and 1 hour, with under 15% SMAPE. (2) We leverage urban big data to generate insights regarding the potential cause of pollution, which facilitates evidence-based sustainable urban management. (3) To achieve such spatial-temporal coverage and accuracy, Deep-MAPS can save up to 90% hardware investment, compared with ubiquitous sensing that relies primarily on fixed sensors.
Tang C, Hu W, Hu S, et al., 2021, Urban traffic route guidance method with high adaptive learning ability under diverse traffic scenarios, IEEE Transactions on Intelligent Transportation Systems, Vol: 22, Pages: 2956-2968, ISSN: 1524-9050
With the rapid development of urbanization, the problem of urban traffic congestion has become increasingly prominent. Dynamic route guidance promises to improve the capacity of urban traffic management and mitigate traffic congestion in big cities. In the design of simulation-based experiments for most dynamic route guidance methods, the simulation data is generally estimated from a specific traffic scenario in the real-world. However, highly dynamic traffic in the city implies that traffic scenarios in real systems are diverse. Therefore, if a route guidance method cannot adjust its strategy according to the spatial and temporal characteristics of different traffic scenarios, then it cannot guarantee optimal results under all traffic scenarios. Thus, ideal dynamic route guidance methods should have a highly adaptive learning ability under diverse traffic scenarios so as to have extensive improvement capabilities for different traffic scenarios. In this study, an A* trajectory rejection method based on multi-agent reinforcement learning (A*R²) is proposed; the method integrates both system and user perspectives to mitigate traffic congestion and reduce travel time (TT) and travel distance (TD). First, owing to its adaptive learning ability, the A*R² can comprehensively analyze the traffic conditions for different traffic scenarios and intelligently evaluate the road congestion index from a system perspective. Then, the A*R² determines the routes for all vehicles from user perspective according to the road network congestion index. An extensive set of simulation experiments reveal that, under various traffic scenarios, the A*R² can rely on its adaptive learning ability to achieve better traffic efficiency. Moreover, even in cases where many drivers are not fully compliant with the route guidance, the traffic efficiency can still be improved significantly by A*R².
Woo M, Giannopoulos G, Rahman MM, et al., 2021, Multiscale numerical modeling of solid particle penetration and hydrocarbons removal in a catalytic stripper, Aerosol Science and Technology, Vol: 55, Pages: 987-1000, ISSN: 0278-6826
The catalytic stripper has emerged as a technology for removal of semivolatile material from aerosol streams for automotive and aerospace emissions measurements, including portable solid particle emissions measurements governed by the Real Driving Emissions regulations. This study employs coupled energy and mass transfer models to predict solid particle penetration and hydrocarbon removal for various configurations of a catalytic stripper. The continuum-scale macromodel applies mass, momentum and energy conservation for the inlet heating region of a catalytic stripper whereby the catalyst monolith is represented by a porous medium. The particle and species dynamics inside the catalytic monolith were computed by coupled microsimulations of the monolith channel using boundary conditions from the macromodel. The results from the numerical simulations were validated with corresponding experimental data and employed using a parametric study of flow rate and catalyst length with a view to optimizing the operating condition. Results of the simulation and experiment show that solid particle penetration through the catalytic stripper can exceed approximately 60% for particles at 10 nm mobility diameter and hydrocarbons removal of >95% for an optimized catalytic stripper device.
Woo M, Nishida RT, Schriefl MA, et al., 2021, Open-source modelling of aerosol dynamics and computational fluid dynamics: nodal method for nucleation, coagulation, and surface growth, Computer Physics Communications, Vol: 261, ISSN: 0010-4655
Understanding formation, growth and transport of aerosols is critical to processes ranging from cloud formation to disease transmission. In this work, a numerical algorithm of aerosol dynamics including nucleation, coagulation, and surface growth was coupled with flow and heat transfer equations enabling the solution of three-dimensional multi-physics aerosol processes in an open-source platform. The general dynamic equation was solved by a nodal method where the particle size distribution was represented by a finite number of nodes. The models were verified by comparing four test cases, (1) pure coagulation, (2) nucleation and coagulation, (3) pure surface growth, and (4) a general dynamic equation that includes the three mechanisms provided in literature. A high temperature aerosol flow in a cooled pipe is chosen as a tutorial case of coupled computational aerosol and fluid dynamics. The aerosolGDEFoam code is available at https://openaerosol.sourceforge.io and can be further modified under GNU general public licence.
Burridge HC, Bhagat RK, Stettler MEJ, et al., 2021, The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 477, Pages: 1-31, ISSN: 1364-5021
The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation.
Mijic A, Whyte J, Fisk D, et al., 2021, The Centre for Systems Engineering and Innovation – 2030 vision and 10-year celebration
The 2030 vision of the Centre is to bring Systems Engineering and Innovation to Civil Infrastructure by changing how cross-sector infrastructure challenges are addressedin an integrated way using principles of systems engineering to maximise resilience, safety and sustainability in an increasingly complex world.We want to better understand the environmental and societal impacts of infrastructure interventions under uncertainty. This requires a change in current approaches to infrastructure systems engineering: starting from the natural environmentand its resources, encompassing societaluse of infrastructure and the supporting infrastructure assets and services.We argue for modelling that brings natural as well as built environments within the system boundaries to better understand infrastructure and to better assess sustainability. We seethe work as relevant to both the academic community and to a wide range of industry and policy applications that are working on infrastructure transition pathways towards fair, safe and sustainable society.This vision was developed through discussions between academics in preparation for the Centre for Systems Engineering and Innovation (CSEI) 10 years celebration. These rich discussions about the future of the Centre were inspired by developing themes for a celebration event, through which we have summarised the first 10 years of the Centre’s work and our vision for the future and identified six emerging research areas.
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