Fleet Composition Research Articles

Effective utilization of service vessels in fish farming: fleet design considering the characteristics of the locations

Effective utilization of service vessels in sea-based fish farming requires that the vessels are suited to the operating environments at the fish farms. This paper presents a methodology for assessing service vessel fleet performance when serving a network of farms with different metocean conditions. Fleet performance is defined as the ability to perform operations requested by the fish farms, in due time. An optimization for simulation approach is employed, implementing a routing and scheduling heuristic developed for aquaculture service vessels. A case study was performed assessing the performance of two different fleets serving a set of 21 fish farms. The variation in local metocean conditions between the farms, and how weather changes in time, challenges the operability of the aquaculture infrastructure and the effective routing and scheduling of the vessels. Hence, the results show that proper fleet composition in this context improves fleet performance. Fleet performance is substantially higher when fleet composition, routing, and scheduling is based on the specific weather conditions.

Mobile service robots for the operating room wing: balancing cost and performance by optimizing robotic fleet size and composition

PurposeIntegrating fleets of mobile service robots into the operating room wing (OR wing) has the potential to help overcome staff shortages and reduce the amount of dull or unhealthy tasks for humans. However, the OR wing has been little studied in this regard and the requirements for realizing this vision have not yet been fully identified. This includes fundamental aspects such as fleet size and composition, which we have now studied comprehensively for the first time.MethodsUsing simulation, 150 different scenarios with varying fleet compositions, robot speeds and workloads were studied for a setup based on a real-life OR wing. The simulation included battery recharging cycles and queueing due to shared resources.ResultsFor all simulated scenarios we report results regarding total duration of execution, average task response times and fleet utilization. The relationship between these performance measures and global scenario parameters—such as fleet size, fleet composition, robot velocity and the number of operating rooms to be served—is visualized.ConclusionOur simulation-based studies have proven to be a valuable tool for individualized dimensioning of mobile robotic fleets, based on realistic workflows and environmental models. Thereby, important implications for future developments of mobile robots have been identified and a basis of decision-making regarding fleet size, fleet composition, robot capabilities and robot velocities can be provided. Due to costs, space limitations and safety requirements, these aspects must be carefully considered to successfully integrate mobile robotic technology into real-world OR wing environments.

A new closed-form two-stage budgeting-based multiple discrete-continuous model

In this paper, we propose a multiple discrete-continuous (MDC) model approach that (a) does not need the total budget to be observed or predetermined, (b) allows for any finite or not-so-finite budget over the entire set of inside and outside goods, and (c) preserves a strong endogenous utility-theoretic link between inside good consumptions and the budget allocated to the inside goods (that is, to the product group of interest). We show that our proposed model, including a fractional MDC model at the lower level linked up to a Tobit model for the budget allocation to the inside goods, is strictly consistent with a two-stage budgeting utility theoretic structure. As importantly, by using reverse Gumbel distributional assumptions for the stochastic terms in the model system, we derive an incredibly simple closed-form model that, to our knowledge, is a first of its kind in the econometric literature. In doing so, we formally introduce a new distribution, which we label as the minLogistic distribution, to the statistical literature, and derive the properties of the distribution that is then used in the forecasting stage of the proposed model. An application of the proposed model to investigate the household vehicle fleet composition and usage demonstrates its potential relative to an unlinked and exogenously developed budget for the inside goods. The proposed model has the potential to open up a whole new world of MDC applications in general, and particularly for those cases with an unobserved total budget over the inside and outside goods.

An approximate dynamic programming algorithm for short-term electric vehicle fleet operation under uncertainty

This paper considers the dynamic problem of optimally operating a fleet of plug-in hybrid electric vehicles in a market environment. With uncertainty in future electricity prices and driving demands, we formulate a Markov decision process and determine a cost-minimizing policy for using the engine and charging and discharging the battery. As such, the policy is based on the trade-off between the costs of gasoline and electricity and between current and future power prices. To accommodate an inhomogeneous fleet composition and overcome the computational challenges of stochastic and dynamic optimization, including large-scale state and action spaces, we adopt the methodology of approximate dynamic programming. More specifically, using simulation and value function approximation by linear regression, we apply a least squares Monte Carlo method. This methodology allows for scaling with respect to fleet size and we are able to establish convergence of our algorithm for 100 vehicles by using 5000 samples in the simulation. Our results show that the vehicles should generally discharge the battery rather than using the engine unless battery capacity is insufficient to fully cover driving demand, but the timing of battery charging should be according to power prices. When comparing our policy to the simple policy of immediate charging, we demonstrate superiority for small and medium-sized fleets, with 2%–4% cost differences.

On-road vehicle emission inventory and its spatial and temporal distribution in the city of Guayaquil, Ecuador

Emissions from mobile sources have become a major concern for health, environmental sustainability and climate change and high-resolution inventories are needed to support the design and assessment of abatement measures in urban areas. This study addresses the development of a traffic emissions inventory for Guayaquil, the second largest city in Ecuador, using the International Vehicle Emissions Model (IVE). Emissions are allocated with a spatial resolution of 1 km × 1 km and a temporal resolution of 1 h using a top-down methodology. This application combines traffic statistics already available in the city with the data from a field campaign to characterize vehicle fleet composition and activity patterns. The estimated annual emissions for the city were 237.1 kt of CO, 46.4 kt of NOx, 28.5 kt of VOC, 7.7 kt of PM10, 0.70 kt of SO2 and 4549.7 kt of CO2. 92.3 % of CO and 85.4 % of VOC were emitted by light gasoline vehicles, including private passenger vehicles and taxis, which represents 68.6 % and 8.8 %, respectively of the total fleet and contributes 52 % and 22 % of the total vehicle kilometer traveled (VKT), respectively. 48.9 % of NOx and 82 % of PM10 were emitted by the bus fleet although buses only represent 7.5 % of the total fleet and contribute 10.6 % of total VKT in the city. 41.1 % and 36.5 % of CO2 were emitted by buses and private vehicles, respectively. Even though, the average age of the fleet is below 10 years, the fleet in Guayaquil presents outdated emission standards and high emission factors. We found the higher emission rates in dense populated areas are associated to secondary roads. There is not much variability of emissions between months, but the typical daily pattern of emissions shows a peak in the morning and another in the afternoon.

Examining commercial vehicle fleet ownership decisions and the mediating role of freight generation: A structural equation modeling assessment

Commercial Vehicle (CV) ownership is a variable of interest in planning interventions and policy instruments for sustainable transportation, but the decision-making process to own a vehicle fleet is not well understood in the context of freight transportation. The necessity to understand the heterogeneity in CV type ownership arises from the need to mitigate externalities associated with them. We argue that the need to own CVs is a derived demand arising out of the demand to move goods and is influenced by freight handling requirements of businesses. To this end, this paper examines the dual role (mediation) of freight generation variables while modelling CV ownership, which is an underexplored area of research in freight transportation planning. Establishment-based freight survey data from seven cities in Kerala, India, are used to develop structural equation models. The CV ownership is analysed through freight generation variables as mediators, apart from the direct influence of business size indicators and commodity characteristics. The study findings confirm the intermediatory role of freight generation and show the consequences of neglecting such a dual role in the overall CV ownership modelling paradigm. The model estimates offer insights into the causal structure of the decision-making process of establishments to own different levels of fleet ownership and composition. Understanding the business managers’ decisions to own CVs in their fleet can be used for freight traffic management using urban consolidation centre (UCC). The statistical findings also provide the total effects between fleet and employment size and how freight generation mediates this relationship. The path diagrams representing the CV ownership patterns can be useful to policy makers while implementing policies like low-emission zones.

Modelling Electric Vehicle Charge Demand: Implementation for the Greek Power System

The emerging popularity of Plug-in Electric Vehicles (PEVs) is creating new connections between the transportation and electric sectors, and PEV charging will bring new opportunities and challenges to a system of growing complexity. The electrification of transport will increase energy security, reduce carbon emissions, and improve local air quality. The actual expansion of electric vehicles (EVs) will depend on several factors: the evolution of autonomy, the acquisition price, the charging process and infrastructure, etc. This paper provides a guide for simulating the accumulative load profile for EV charging on a national level. The importance of all the parameters and variables involved (deterministic or stochastic) is investigated. Detailed tables and references concerning the distribution of values and the composition of the EV fleet are provided. A multivariate probabilistic model is developed considering the EV classes, weekly and seasonal driving patterns, charging strategies, battery capacities, consumption per EV, etc., leading to an accurate estimation of aggregated EV charging demand. Finally, a net-metering scheme is proposed, in which a photovoltaic (PV) system of a certain size will be able to provide the annual energy needs of the first 10,000 EVs in the Greek market.

Change of community noise and noise impact with changes in air-taxi fleet compostitions over a longer period of time

Generic air vehicles are helping to understand effects specifically related to urban air mobility (UAM) noise. Air corridors were already studied, managing complex air-taxi traffic to reduce population's exposure to higher or critical noise levels in the vicinity of a highly frequented flight paths. The results still depend on fleet compositions, which may change over shorter or longer periods because future UAM carriers can select from a variety of different air-taxi types, in some cases strongly differing in their noise characteristics. Once an air-taxi-fleet is operated at a certain vertiport, its composition may not remain the same after one month or even after one year. This study is evaluating a long-term effect with changing fleets operated on the same flight paths in the same air corridor. Besides usual noise metrics like DNL, the results provide also a first insight how metrics like the dose-response may be affected over a longer period of time. 25 acoustically diverse air-taxis were distributed inside an 8 km wide air corridor, carrying 165 single flight tracks at 5 flight levels. Approximately 60,200 movements were simulated representing short-term effects, ten times more for mid-term scenarios, and finally a large number of 1,505,000 for the long-term study.

Evidence on impacts of automated vehicles on traffic flow efficiency and emissions: Systematic review

Despite high expectations of driving automation improving road traffic, its practical implications on traffic flow and emissions are not yet definite. This study systematically reviewed literature on practical impacts of non-connected automation of passenger cars on motorway traffic efficiency. A conceptual framework showed the importance of understanding interactions between vehicles, both human-driven and automated, but they are not yet sufficiently known and reproduced by traffic models. Field studies have focused on equipped vehicles. Simulation studies have used different models and assumptions, narrow fleet compositions and road layouts, and covered the theoretical potential in ideal conditions rather than likely impacts in practice. Simulations with automated vehicle time gaps below 1.2 s have found throughput increases, but recent field experiments and simulations using commercial ACC vehicles indicate decreased traffic flow efficiency with increasing traffic volumes and penetration rates. Concluding implications for real traffic from available data is challenging. While benefits are possible for equipped vehicles in low traffic, results suggest negative implications for throughput and emissions at higher traffic volumes. Importantly, more differentiated discussion on the impacts of automated vehicles on traffic flow is needed, considering also the practical implications, such as tradeoffs with safety goals, if benefits are to be achieved.

Urban consolidation centers and city toll schemes–Investigating the impact of city tolls on transshipment decisions

Around the world, many cities are struggling with the negative effects of urban freight transport, such as congestion and emissions. To mitigate these negative effects, both innovative solutions, such as urban consolidation centers, and regulations, such as city toll schemes and delivery time windows, can be adopted. In this paper, we investigate the impact of city toll regulations on the vehicle routing and transshipment decisions of logistics service providers. We present a mixed-integer linear programming formulation and an adaptive large neighborhood search heuristic that address the interdependent decisions of vehicle routing and the use of urban consolidation centers, taking into account city toll schemes and time window constraints. In this context, we consider heterogeneous fleets, multiple trips per vehicle, and use a multigraph to address the trade-off between fastest and cheapest paths. We validate the adaptive large neighborhood search by comparing it with the mixed-integer linear programming model on test instances with up to 20 customers. To analyze how city toll schemes affect the cost attractiveness of urban consolidation centers and the fleet composition of logistics service providers, we conduct an extensive computational study featuring real-world instances that provide managerial insights for both local administrations and logistics service providers. Our results show that both per-day and per-entrance tolls can be used by local administrations to support the use of urban consolidation centers and that the number of truck entries into urban areas can be reduced. However, our results also indicate that due to the transshipment fee per unit load, the cost attractiveness of using urban consolidation centers is considerably lower for logistics service providers with larger delivery quantities per stop.

Impact from the evolution of private vehicle fleet composition on traffic related emissions in the small-medium automotive city

Understanding the emission characteristics in the evolution of private vehicle fleet composition has become a key issue to be addressed to develop appropriate emission mitigation strategies in transportation sector. In this study, the influence of such evolution on on-road emissions was investigated based on a comprehensive dataset encompassing vehicle fleet composition, demographic, economic, and energy features from a representative small-medium automotive city in North America. The decoupling analysis was carried out to assess the dynamic linkage between environmental pressure exerted by the transportation sector and economic growth at both city level and national level in North America. We also developed an approach that supports the long-term traffic-related air pollutant prediction and investigated the potential influence on urban air quality. A sharp upward trajectory was observed in the quantity of SUVs from 2001 to 2018, gradually replacing the dominance of the quantity of four-door cars. There was a significant shift in the GHG emissions emitted from vehicle types used for passenger transport: emissions from SUVs and trucks rose by 374.0% and 69.3%, respectively, whereas emissions from four-door cars, two-door cars, station wagons, and vans all decreased. The changes in vehicle composition, along with the steady trend in GHG emissions from private fleet and decrease in on-road air pollutant concentrations found in Regina, were a response to the establishment of federal fuel economy standards and improved fuel economy. Relative decoupling was observed in aggregate for Regina and Canada in most of the years while both experienced economic downturns and increases in environmental pressure in the form of emissions from 2014 to 2015. The predicted results also demonstrate the high capability of XGboost machine learning algorithm in predicting on-road air pollutant concentrations of CO, PM2.5, and NOX.

Test-drives & information might not boost actual battery electric vehicle uptake?

Many scholars have suggested driving experiences and information campaigns to promote battery electric vehicle (BEV) uptake. Especially, when BEVs are powered with green energy, they reduce transport emissions, which is vital for mitigating climate change. Nevertheless, BEVs are still adopted far less than conventional cars, which motivates studying whether information on and experience with BEVs changes household car fleet compositions through the adoption of BEVs in a randomised control trial (RCT). A large random sample (N = 4,149) of conventional vehicle (CV) drivers was randomly assigned to one of three conditions: a comprehensive information sheet on BEVs, the same information plus a multiple day-long test drive with a BEV, and a control group receiving no intervention. For the first time, information and test-driving were assessed side-by-side to measure effects on actual BEV adoption. The results do not point towards substantial effects of these interventions after around 18 months. Larger samples or faster growing BEV markets would be needed to find (possible) significant effects.

Comparison of the Impact of Ship Emissions in Northern Europe and Eastern China

It is well known that ship emissions contribute significantly to atmospheric pollution. However, the impact on air quality can regionally vary, as influenced by parameters such as the composition of the regional shipping fleet, state of background atmospheric pollution, and meteorological aspects. This study compared two regions with high shipping densities in 2015. These include the North and Baltic Seas in Europe and the Yellow and East China Seas in China. Here, a key focal point is an evaluation of differences and similarities of the impacts of ship emissions under different environmental conditions, particularly between regions with medium (Europe) and high air pollution (China). To assess this, two similarly performed chemical transport model runs were carried out with highly resolved bottom-up ship emission inventories for northern Europe and China, calculated with the recently developed MoSES model, publicly available emissions data for nonshipping sources (EDGAR, MEIC). The performance of the model was evaluated against measurement data recorded at coastal stations. Annual averages at affected coastal regions for NO2, SO2, O3 and PM2.5 were modeled in Europe to be 3, below 0.3, 2.5, 1 and in China 3, 2, 2–8, 1.5, respectively, all given in μg/m3. In highly affected regions, such as large harbors, the contributions of ship-related emissions modeled in Europe were 15%, 0.3%, −12.5%, 1.25% and in China were 15%, 6%, −7.5%, 2%, respectively. Absolute pollutant concentrations from ships were modeled slightly higher in China than in Europe, albeit the relative impact was smaller in China due to higher emissions from other sectors. The different climate zones of China and the higher level of atmospheric pollution were found to seasonally alter the chemical transformation processes of ship emissions. Especially in northern China, high PM concentrations during winter were found to regionally inhibit the transformation of ship exhausts to secondary PM, and reduce the impact of ship-related aerosols, compared to Europe.

An emission inventory update for Tehran: The difference between air pollution and greenhouse gas source contributions

Many highly populated cities are still struggling to reach clean air targets, while the zero greenhouse gas emission objectives may accelerate the path toward healthy air for all. Still, there is a fine line between intensive electrification's impact on greenhouse gas emissions and criteria air contaminants depending on the source of the electricity. In this study, the previous version of the emission inventory for Tehran was evaluated and re-calculated in a detailed bottom-up approach to provide the most updated data on the contribution of stationary sources derived by power plants vs. mobile sources. The objectives were to update the emission inventory for improved policymaking, study the impact of changes in emissions in 4 years, provide a detailed methodology for cities highly impacted by transportation emissions, and exercise a simple yet effective modelling task for emission inventory evaluation. The study included all possible sources in the mobile sector with eight categories of fleet composition, industries, power plants, house heating, and point sources related to transportation such as terminals and gas stations. It also included exhaust, non-exhaust, and evaporative emissions for mobile sources. An intensive data collection campaign was launched to collect activity data. The traffic information was obtained from a travel-demand model and validated by traffic counting. The license plate registration database provided detailed fleet composition based on vehicle technology, fuel, age, and state of maintenance.The emission inventory calculated annual emissions of 478 kt for CO, 103 kt for NOx, 91 kt for VOCs, and 19 kt for SOx. TSP was estimated at 10.4 kt. NOx emissions were significantly increased from 87 to 102.6 ktonnes per year (17% increase) and were confirmed by an increase in the annual mean concentration of NO2 from 39.4 to 53.4 ppb (35% increase) between 2013 and 2017. A notable observation was the impact of house heating on CO2,eq emissions. More than 30% of CO2,eq emissions were from house heating (natural gas, predominantly methane) in a city where total air contaminant emissions are highly driven by the transportation sector. The result clearly shows the differences between zero-emission target paths for the two objectives of greenhouse gas emissions and air quality. An integrated approach is needed to develop policies that will lead o zero-emission GHG targets while keeping the air clean.

Traffic and Pollution Modelling for Air Quality Awareness: An Experience in the City of Zaragoza

Air pollution due to the presence of small particles and gases in the atmosphere is a major cause of health problems. In urban areas, where most of the population is concentrated, traffic is a major source of air pollutants (such as nitrogen oxides or hbox {NO}_x and carbon monoxide or CO). Therefore, for smart cities, carrying out an adequate traffic monitoring is a key issue, since it can help citizens to make better decisions and public administrations to define appropriate policies. Thus, citizens could use these data to make appropriate mobility decisions. In the same way, a city council can exploit the collected data for traffic management and for the establishment of suitable traffic policies throughout the city, such as restricting the traffic flow in certain areas. For this purpose, a suitable modelling approach that provides the estimated/predicted values of pollutants at each location is needed. In this paper, an approach followed to model traffic flow and air pollution dispersion in the city of Zaragoza (Spain) is described. Our goal is to estimate the air quality in different areas of the city, to raise awareness and help citizens to make better decisions; for this purpose, traffic data play an important role. In more detail, the proposal presented includes a traffic modelling approach to estimate and predict the amount of traffic at each road segment and hour, by combining historical measurements of real traffic of vehicles and the use of the SUMO traffic simulator on real city roadmaps, along with the application of a trajectory generation strategy that complements the functionalities of SUMO (for example, SUMO’s calibrators). Furthermore, a pollution modelling approach is also provided, to estimate the impact of traffic flows in terms of pollutants in the atmosphere: an R package called Vehicular Emissions INventories (VEIN) is used to estimate the amount of hbox {NO}_x generated by the traffic flows by taking into account the vehicular fleet composition (i.e., the types of vehicles, their size and the type of fuel they use) of the studied area. Finally, considering this estimation of hbox {NO}_x, a service capable of offering maps with the prediction of the dispersion of these atmospheric pollutants in the air has been established, which uses the Graz Lagrangian Model (GRAL) and takes into account the meteorological conditions and morphology of the city. The results obtained in the experimental evaluation of the proposal indicate a good accuracy in the modelling of traffic flows, whereas the comparison of the prediction of air pollutants with real measurements shows a general underestimation, due to some limitations of the input data considered. In any case, the results indicate that this first approach can be used for forecasting the air pollution within the city.

Spatial and temporal catch concentrations for Antarctic krill: Implications for fishing performance and precautionary management in the Southern Ocean

The undergoing rapid climate changes recorded along the Western Antarctic Peninsula (WAP) in combination with the increasing seasonal catches reported by the krill fishery have raised concerns as to whether the management strategy established by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) is effectively avoiding impacts on the krill stock and related ecosystem.Despite the current fixed catch limit being spread across fishing areas to reduce spatial catches concentrations, our 38-year analysis revealed the highest historical spatial (ton/km2) and temporal (ton/day) fishing concentration levels across the WAP and South Orkney Islands. Higher seasonal catches in recent decades removed persistently within the same small fishing areas and during shorter fishing seasons are key factors influencing this situation.We used the catch per unit effort (CPUE) as a measure of fishing performance. CPUE was standardized using a GAMM model taking into account operational factors such as fleet composition, trawling methods, seasonality and daily catches, and we detected negative CPUE trends over time and across the different fishing areas. Our results suggest that the fishing performance have responded to the elevated spatio-temporal fishing concentration and changes in sea-ice. We discuss whether the negative CPUE trends are caused by fishing-induced depletion or driven by other factors such as krill flux, reduction and contraction of krill abundance and increased cetacean foraging (due recovery of whale populations). Finally, we also highlight the need to expand the survey's coverage (by acoustic or net-based methods) to the new, highly fished, and non-monitored areas such as Gerlache Strait, which is an important area for krill and dependent predators.

Assessing vehicle fuel efficiency using a dense network of CO<sub>2</sub> observations

Abstract. Transportation represents the largest sector of anthropogenic CO2 emissions in urban areas in the United States. Timely reductions in urban transportation emissions are critical to reaching climate goals set by international treaties, national policies, and local governments. Transportation emissions also remain one of the largest contributors to both poor air quality (AQ) and to inequities in AQ exposure. As municipal and regional governments create policy targeted at reducing transportation emissions, the ability to evaluate the efficacy of such emission reduction strategies at the spatial and temporal scales of neighborhoods is increasingly important; however, the current state of the art in emissions monitoring does not provide the temporal, sectoral, or spatial resolution necessary to track changes in emissions and provide feedback on the efficacy of such policies at the abovementioned scale. The BErkeley Air Quality and CO2 Network (BEACO2N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a ∼ 1300 km2 multicity spatial domain. Here, we focus on a 5 km, high-volume, stretch of highway in the San Francisco Bay Area. We show that inversion of the BEACO2N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO2 emission rate of the average vehicle (in grams per vehicle kilometer) is shown to vary by as much as 27 % at different times of a typical weekday because of changes in these two factors. The BEACO2N-derived emission estimates are consistent to within ∼ 3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the EMission FACtor model (EMFAC) of vehicle fuel efficiency.

Integrated fleet assignment and scheduling for environmentally friendly electrified regional air mobility

Regional air mobility refers to the transportation of passengers aboard small aircraft over short distances. These operations are challenging due to low passenger volumes, competing transportation modes, and high operating costs. Nevertheless, the recent convergence of new technologies for electric propulsion and autonomy brings the promise of step-changes in both operating efficiency and sustainability. The next challenge is to define concepts of operations that best use these advanced aircraft. We develop a modeling and optimization environment to address this challenge by first estimating the passenger demand for regional air mobility, and then by concurrently optimizing the fleet assignment and scheduling of these operations. This helps identify the optimum fleet composition and the network of routes that best serve this demand. The contribution of this research is the formulation of the optimization as a half-leg half-itinerary mixed-integer linear program using a hierarchical multi-objective approach that provides insights about trade-offs between profitability and emissions. We demonstrate the ability of this environment to solve large fleet assignment and scheduling problems to near optimality by applying it to the United States Northeast Corridor using a fleet of electric and hybrid-electric regional aircraft. Results highlight that the transition to an electrified fleet allows serving twice the number of communities currently served, while reducing the carbon emissions per passenger by fifty percent. With improving battery specific energy density, many more untapped markets can be served profitably and sustainably, and many smaller communities can get connected to the rest of the National Airspace System.

Effect of vehicle fleet composition and mobility on outdoor population exposure: A street resolution analysis in Paris

High concentrations of health-related pollutants are observed in streets, inducing high outdoor population exposure. The impact of reducing traffic emissions on urban air quality is often quantified using regional-scale chemical-transport models (CTMs), which are representative of background concentrations but fail to represent the high street concentrations. Local-scale models may be more suitable to represent them, but their use for outdoor exposure assessments is still limited, because they often neglect secondary species, use simplified methods to estimate background concentrations, and omit/underestimate non-exhaust emissions. Here the impact of realistic and ambitious tendencies of vehicle fleet renewal and mobility on outdoor population exposure is investigated using multi-scale simulations. They are capable of representing secondary species in urban background and streets (including the formation of secondary organic and inorganic aerosols), and use non-exhaust emissions determined with a recently revisited approach. The 10-year vehicle fleet renewal induces large decreases in population exposure to NO2, black carbon, PM10, PM2.5, and organics. These decreases are stronger than estimated using CTMs. Favouring recent diesel, petrol, or electric vehicles, as proposed here, induces similar decreases of outdoor population exposure to PM2.5. Promoting electric vehicles induces the highest decrease of exposure to NO2. Home-office practice enhances the decrease of pollutant concentrations, but it is less effective than vehicle fleet renewal. However, much more ambitious reductions of pollutant emissions are still needed to respect air-quality guidelines.

Тенденции развития флота морских технологических платформ в период 2015–2021 гг.

Object and purpose of research. This paper discusses marine oil and gas production platforms splitted into four types depending on their purpose. The study was intended to analyse the changes in global production platform fleet and outline the main trends in its development. Materials and methods. The study was based on the open-access data available with offshore field developers, oil and gas companies, shipyards and design offices. The methods in this study were acquisition, analysis and comparison of the data about the fleet of marine oil and gas production platforms. Main results. The paper presents the results of fleet composition analysis for floating oil and gas production platforms over the period of 2015–2021 characterized by the decline in global oil and gas prices. The study shows that the most common type of marine production platforms is FPSO (213 vessels). The strength of FPU and FPDSO fleets remains the same: 99 and 2 vessels respectively. Their geography has not changed either. The fleet of FLNGs has increased up to 7 ships since the commissioning of first FLNGs in 2014. Conclusion. FPSO platforms of various designs (mostly ship-type) offer high mobility and operational flexibility, i.e. greater project revenues and zero pipeline construction costs in case of remote field developments. It must be noted that water depths at FPSO locations have reached their record highs: 2900 m for production platforms and 3400 m for drilling ones. In future, floating platforms could be replaced by subsea production system but their massive introduction cannot be expected in at least 30–50 years to come.