On the key role of nutrient data to constrain a coupled physical–biogeochemical assimilative model of the North Atlantic Ocean

A sequential assimilative system has been implemented into a coupled physical–biogeochemical model (CPBM) of the North Atlantic basin at eddy-permitting resolution (1/4°), with the long-term goal of estimating the basin scale patterns of the oceanic primary production and their seasonal variability. The assimilation system, which is based on the SEEK filter [Brasseur, P., Verron, J., 2006. The SEEK filter method for data assimilation in oceanography: a synthesis. Ocean Dynamics. doi: 10.1007/s10236-006-0080-3], has been adapted to this CPBM in order to control the physical and biogeochemical components of the coupled model separately or in combination. The assimilated data are the satellite Topex/Poseidon and ERS altimetric data, the AVHRR Sea Surface Temperature observations, and the Levitus climatology for salinity, temperature and nitrate.In the present study, different assimilation experiments are conducted to assess the relative usefulness of the assimilated data to improve the representation of the primary production by the CPBM. Consistently with the results obtained by Berline et al. [Berline, L., Brankart, J-M., Brasseur, P., Ourmières, Y., Verron, J., 2007. Improving the physics of a coupled physical–biogeochemical model of the North Atlantic through data assimilation: impact on the ecosystem. J. Mar. Syst. 64 (1–4), 153–172] with a comparable assimilative model, it is shown that the assimilation of physical data alone can improve the representation of the mixed layer depth, but the impact on the ecosystem is rather weak. In some situations, the physical data assimilation can even worsen the ecosystem response for areas where the prior nutrient distribution is significantly incorrect. However, these experiments also show that the combined assimilation of physical and nutrient data has a positive impact on the phytoplankton patterns by comparison with SeaWiFS ocean colour data, demonstrating the good complementarity between SST, altimetry and in situ nutrient data. These results suggest that more intensive in situ measurements of biogeochemical nutrients are urgently needed at basin scale to initiate a permanent monitoring of oceanic ecosystems.     

Measurement of hydrocarbon and carbon monoxide emissions during the starting of automotive DI Diesel engines

Most of hydrocarbon (HC) and carbon monoxide (CO) emissions from automotive DI Diesel engines are produced during the engine warm-up period and are primarily caused by difficulties in obtaining stable and efficient combustion under these conditions. Furthermore, the contribution of engine starting to these emissions is not negligible; since this operating condition is highly unfavorable for the combustion progress. Additionally, the catalytic converter is ineffective due to the low engine temperature. In conjunction with adequate engine settings (fuel injection and fresh air control), either the glow plugs or the intake air heater are activated during a portion of the engine warm-up period, so that a nominal engine temperatures is reached faster, and the impact of these difficulties is minimized. Measurement of gaseous pollutants during engine warm-up is currently possible with detectors used in standard exhaust gas analyzers (EGA), which have response times well-suited for sampling at such transient conditions. However, these devices are not suitable for the measurement of exhaust emissions produced during extremely short time intervals, such as engine starting. Herein, we present a methodology for the measurement of the cumulative pollutant emissions during the starting phase of passenger car DI Diesel engines, with the goal of overcoming this limitation by taking advantage of standard detectors. In the proposed method, a warm canister is filled with an exhaust gas sample at constant volumetric flow, during a time period that depends on the engine starting time; the gas concentration in the canister is later evaluated with a standard EGA. When compared with direct pollutant measurements performed with a state-of-art EGA, the proposed procedure was found to be more sensitive to combustion changes and provided more reliable data.     

Public transport reliability across preferences,modes, and space

Transportation - Reliability is understood in public transport as the certainty travellers have regarding the level of service they will experience when travelling. The travel time, waiting time,...     

A new era for airport regulators through capacity investments

Private and public airports’ optimal actions may not coincide. While private airports usually pursue profit maximization, publicly owned airports look for maximum social welfare. Thus, the prices charged by private airports may differ from the socially optimal charges and public intervention may be needed. In this paper, we analyze airport charges when an increase in frequency produces positive or negative externalities and carriers have market power. We use the methodology of game theory to show that there may exist a level of capacity for which private and social objectives coincide, so no price regulation is needed. Thus, the usual role of regulators and planners could be modified in order to decide the appropriate capacity investments for which airport regulation is no longer necessary.     

GPS-based speed collection method for road traffic noise mapping

One of the most difficult and expensive tasks in making noise pollution maps is the collection and processing of the data needed to create acoustic models. In the case of road traffic noise maps, obtaining speed data for light and heavy vehicles a problem that has usually been avoided by using a road’s speed limit or by making assumptions based on experience from similar road types. Here global positioning systems-based techniques are applied for acquiring vehicle speed data and adapted to fulfill the requirements of noise prediction models.     

An analysis of the Spanish high capacity road network criticality

The analysis of complex networks has been carried out in different fields using an ample variety of method and concepts. Recently, in the general literature of regional economics, the concepts of resilience, connectivity, vulnerability and criticality have been gaining their momentum. The aim of this paper is to provide an analytical framework, using well-known accessibility indicators, in order to calculate the critical links or road sections of the Spanish high-capacity road network. Our analysis will be based on approximately four hundred sections that will be classified in five different groups according to their criticality degree in the whole network. Our analysis will be complemented with the comparison of the results obtained in five different scenarios, namely the average criticality using the effects on the whole country, Madrid, Barcelona, Valencia and Pontevedra. Furthermore, the paper will also analyze what kind of intrinsic characteristics of the sections favor or not the links’ criticality using a method based on a classification and regression tree. This analysis is crucial to understand other important concepts that are recently being studied in network and spatial economics, like, for example, resilience and vulnerability. It is concluded that the number of relations or routes, being a trunk or not, the road density and the time to Madrid capital play an important role in the criticality of the roads section in the high capacity road network.     

Understanding suburban travel demand: Flexible modelling with revealed and stated choice data

We analyse the choice of mode in suburban corridors using nested logit specifications with revealed and stated preference data. The latter were obtained from a choice experiment between car and bus, which allowed for interactions among the main policy variables: travel cost, travel time and frequency. The experiment also included parking cost and comfort attributes. The attribute levels in the experiment were adapted to travellers’ experience using their revealed preference information. Different model specifications were tested accounting for the presence of income effect, systematic taste variation, and incorporating the effect of latent variables. We also derived willingness-to-pay measures, such as the subjective value of time, that vary among individuals as well as elasticity values. Finally, we analysed the demand response to various policy scenarios that favour public transport use by considering improvements in level-of-service, fare reductions and/or increases in parking costs. In general, demand was shown to be more sensitive to policies that penalise the private car than those improving public transport.