Source apportionment and dynamic changes of carbonaceous aerosols during the haze bloom-decay process in China based on radiocarbon and organic molecular tracers

Fine carbonaceous aerosols (CAs) is the key factor influencing the currently filthy air in megacities in China, yet few studies simultaneously focus on the origins of different CAs species using specific and powerful source tracers. Here, we present a detailed source apportionment for various CAs fractions, including organic carbon (OC), water-soluble OC (WSOC), water-insoluble OC (WIOC), elemental carbon (EC) and secondary OC (SOC) in the largest cities of North (Beijing, BJ) and South China (Guangzhou, GZ), using the measurements of radiocarbon and anhydrosugars. Results show that non-fossil fuel sources such as biomass burning and biogenic emission make a significant contribution to the total CAs in Chinese megacities: 56±4 in BJ and 46±5% in GZ, respectively. The relative contributions of primary fossil carbon from coal and liquid petroleum combustions, primary non-fossil carbon and secondary organic carbon (SOC) to total carbon are 19, 28 and 54% in BJ, and 40, 15 and 46% in GZ, respectively. Non-fossil fuel sources account for 52 in BJ and 71% in GZ of SOC, respectively. These results suggest that biomass burning has a greater influence on regional particulate air pollution in North China than in South China. We observed an unabridged haze bloom-decay process in South China, which illustrates that both primary and secondary matter from fossil sources played a key role in the blooming phase of the pollution episode, while haze phase is predominantly driven by fossil-derived secondary organic matter and nitrate.

Potential Role of Adenosine Signaling in Acetic Acid Activation of Murine Sensory Neurons

Chronic respiratory illnesses are a significant cause of morbidity and mortality, and acute changes in respiratory function often lead to hospitalization. Air pollution is known to exacerbate asthma, but the molecular mechanisms of this are poorly understood. The current studies were aimed at clarifying the roles of nerve subtypes and purinergic receptors in respiratory reflex responses following exposure to irritants. In C57Bl/6J female mice, inspired adenosine produced sensory irritation, shown to be mediated mostly by A-delta fibers. Secondly, the response to inhaled acetic acid was discovered to be dually influenced by C and A-delta fibers, as indicated by the observed effects of capsaicin pretreatment, which selectively destroys TRPV1-expressing fibers (mostly C fibers) and pretreatment with theophylline, a nonselective adenosine receptor antagonist. The responses to both adenosine and acetic acid were enhanced in the ovalbumin-allergic airway disease model, although the particular pathway altered is still unknown.

Associations between ambient levels of combustion pollutants and small for gestational age infants in Texas

There is scant evidence regarding the associations between ambient levels of combustion pollutants and small for gestational age (SGA) infants. No studies of this type have been completed in the Southern United States. The main objective of the project presented was to determine associations between combustion pollutants and SGA infants in Texas using three different exposure assessments. ^ Birth certificate data that contained information on maternal and infant characteristics were obtained from the Texas Department of State Health Services (TX DSHS). Exposure assessment data for the three aims came from: (1) U.S. Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA), (2) U.S. EPA Air Quality System (AQS), and (3) TX Department of Transportation (DOT), respectively. Multiple logistic regression models were used to determine the associations between combustion pollutants and SGA. ^ For the first study looked at annual estimates of four air toxics at the census tract level in the Greater Houston Area. After controlling for maternal race, maternal education, tobacco use, maternal age, number of prenatal visits, marital status, maternal weight gain, and median census tract income level, adjusted ORs and 95% confidence intervals (CI) for exposure to PAHs (per 10 ng/m3), naphthalene (per 10 ng/m3), benzene (per 1 µg/m3), and diesel engine emissions (per 10 µg/m3) were 1.01 (0.97–1.05), 1.00 (0.99–1.01), 1.01 (0.97–1.05), and 1.08 (0.95–1.23) respectively. For the second study looking at Hispanics in El Paso County, AORs and 95% confidence intervals (CI) for increases of 5 ng/m3 for the sum of carcinogenic PAHs (Σ c-PAHs), 1 ng/m3 of benzo[a]pyrene, and 100 ng/m3 in naphthalene during the third trimester of pregnancy were 1.02 (0.97–1.07), 1.03 (0.96–1.11), and 1.01 (0.97–1.06), respectively. For the third study using maternal proximity to major roadways as the exposure metric, there was a negative association with increasing distance from a maternal residence to the nearest major roadway (Odds Ratio (OR) = 0.96; 95% CI = 0.94–0.97) per 1000 m); however, once adjusted for covariates this effect was no longer significant (AOR = 0.98; 95% CI = 0.96–1.00). There was no association with distance weighted traffic density (DWTD). ^ This project is the first to look at SGA and combustion pollutants in the Southern United States with three different exposure metrics. Although there was no evidence of associations found between SGA and the air pollutants mentioned in these studies, the results contribute to the body of literature assessing maternal exposure to ambient air pollution and adverse birth outcomes. ^

Evaluación del proceso de compostaje de dos mezclas de residuos avícolas

Los residuos del sector avícola, principalmente guano (aves ponedoras) y cama de parrilleros (aves de engorde), pueden generar un impacto negativo en el ambiente contribuyendo a la contaminación de suelo, agua y aire. La estabilización aeróbica a través del compostaje es una alternativa de tratamiento para reducir la contaminación. El objetivo de este trabajo fue evaluar el proceso de compostaje en dos mezclas con diferentes porcentajes de residuos avícolas (guano de aves ponedoras y cama de pollos parrilleros). Se compostaron dos mezclas que contenían 81% y 70% de residuos avícolas durante 16 semanas. Las variables analizadas fueron: temperatura (T°), pH, conductividad eléctrica (CE), humedad (H), capacidad de intercambio catiónico (CIC), carbono orgánico total (COT), amonio (NH4+), nitrato (NO3 - ), nitrógeno total (NT ) y carbono soluble (CS). Las características finales de los compost A y B fueron: pH 7,1 - 6,8, CE 3,3 - 2,9 (mS. cm- 1), COT 14,8 - 17,9 %, NT 0,97 - 0,88 %, NH4 + 501 - 144,9 mg kg-1, NO3-552,3 - 543,0 mg kg-1 respectivamente. El proceso de compostaje podría ser una herramienta para estabilizar los residuos avícolas minimizando su impacto en el ambiente.

Urban forest health survey on Roznik in Ljubljana in 2013 by two methods

Urban forest health was surveyed on Roznik in Ljubljana (46.05141 N, 14.47797 E) in 2013 by two methods: ICP Forests and UFMO. ICP Forests is most commonly used monitoring programme in Europe - the International Co-operative Programme on the Assessment and Monitoring of Air Pollution Effects on Forests, which is based on systematic grid. UFMO method - Urban Forests Management Oriented method was developed in the frame of EMoNFUr Project - Establishing a monitoring network to assess lowland forest and urban plantations in Lombardy and urban forest in Slovenia (LIFE10 ENV/IT/000399). UFMO is based on non-linear transects (GPS tracks). ICP forests monitoring plots were established in July 2013 in the urban forest Roznik in Ljubljana .The 32 plots are located on sampling grid 500 × 500 m. The grid was down-scaled from the National Forest Monitoring survey, which bases on national sample grid 4 × 4 km. With the ICP forests method the following parameters for each tree within the 15 plots were gathered according to the ICP forests manual for Visual assessment of crown condition and damaging agents: tree species, percentage of defoliation, affected part of the tree, specification of affected part, location in crown, symptom, symptom specification, causal agents / factors, age of damage, damage extent, and damage extent on the trunk. With the UFMO method, the following parameters for each tree that needed sylviculture measure (felling, pruning, sanitary felling, thinning, etc.) were recorded: tree species, breast diameter, causal agent / damaging factor, GPS waypoint and GPS track. For overall picture in the urban forest health problems, also other biotic and abiotic damaging factors that did not require management action were recorded.

Does Quality Matter in the Iron and Scrap Trade?

This paper sheds light on the iron and steel (IS) scrap trade to examine how economic development affects the quality demanded of recyclable resource. A simple model is presented that show a mechanism of how scrap quality impacts the direction of trade due to comparative advantage. We find that economic development in both importing and exporting countries has a positive effect on the quality of traded recyclables. Developed countries that intend to improve the domestic recovery of recyclables should raise the quality of separating recyclables while developing countries should tighten environmental regulations to help decrease the import of recyclables that cause pollution.

Tracing CO2 emissions in global value chains

This paper integrates two lines of research into a unified conceptual framework: trade in global value chains and embodied emissions. This allows both value added and emissions to be systematically traced at the country, sector, and bilateral levels through various production network routes. By combining value-added and emissions accounting in a consistent way, the potential environmental cost (amount of emissions per unit of value added) along global value chains can be estimated. Using this unified accounting method, we trace CO2 emissions in the global production and trade network among 41 economies in 35 sectors from 1995 to 2009, basing our calculations on the World Input–Output Database, and show how they help us to better understand the impact of cross-country production sharing on the environment.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Comparison and Assessment of Two Emission inventories for the Madrid Region

Emission inventories are databases that aim to describe the polluting activities that occur across a certain geographic domain. According to the spatial scale, the availability of information will vary as well as the applied assumptions, which will strongly influence its quality, accuracy and representativeness. This study compared and contrasted two emission inventories describing the Greater Madrid Region (GMR) under an air quality simulation approach. The chosen inventories were the National Emissions Inventory (NEI) and the Regional Emissions Inventory of the Greater Madrid Region (REI). Both of them were used to feed air quality simulations with the CMAQ modelling system, and the results were compared with observations from the air quality monitoring network in the modelled domain. Through the application of statistical tools, the analysis of emissions at cell level and cell – expansion procedures, it was observed that the National Inventory showed better results for describing on – road traffic activities and agriculture, SNAP07 and SNAP10. The accurate description of activities, the good characterization of the vehicle fleet and the correct use of traffic emission factors were the main causes of such a good correlation. On the other hand, the Regional Inventory showed better descriptions for non – industrial combustion (SNAP02) and industrial activities (SNAP03). It incorporated realistic emission factors, a reasonable fuel mix and it drew upon local information sources to describe these activities, while NEI relied on surrogation and national datasets which leaded to a poorer representation. Off – road transportation (SNAP08) was similarly described by both inventories, while the rest of the SNAP activities showed a marginal contribution to the overall emissions.

Influence of driving style on fuel consumption and Emissions in diesel-powered passenger car

This paper presents the main results of a study on the influence of driving style on fuel consumption and pollutant emissions of diesel passenger car in urban traffic. Driving styles (eco, normal or aggressive) patterns were based on the “eco-driving” criteria. The methodology is based on on-board emission measurements in real urban traffic in the city of Madrid. Five diesel passenger cars, have been tested. Through a statistical analysis, a Dynamic Performance Index was defined for diesel passenger cars. Likewise, the CO, NOX and HC emissions were compared for each driving style for the tested vehicles. Eco-driving reduces by 14% fuel consumption and CO2 emissions, but aggressive driving increase consumption by 40%. Aggressive driving increases NOX emission by more than 40%. CO and HC, show different trends, but being increased in eco-driving style.

Optimization of urban mobility measures to achieve win-win strategies

European cities are essential in the development of Europe as they constitute the living environment of more than 60% of the population in the European Union and are drivers of the European economy – just under 85% of the EU’s gross domestic product is produced in urban areas (EC, 2007a). The car has been one of the main factors of development during the 20th century, but it is at the same time the origin of the key problems cities have to face: traffic increase. This has resulted in chronic congestion with many adverse consequences such as air pollution and noise. This loss of environmental quality is one of the reasons for urban sprawl in European cities during recent decades. But this urban sprawl at the same time worsens the environmental conditions. We must return to the dense city, but clean and competitive, and this implies reducing car use yet provides quality transport alternatives sufficient to recover and maintain the competitiveness of cities (EC, 2007a). Consequently, European cities need to establish an urban transport strategy which helps reduce their environmental problems –mainly emissions and noise – but without decreasing their trip attraction. This aspect is very important because a loss of trip attraction would result in an increase of people moving to more disperse areas, contributing towards worsening the current situation. This thesis is an attempt to contribute solutions to this problem in two ways: 1) The first is to analyze the complementarity and possible synergies of several urban transport measures aimed at improving a modal split to a more sustainable means of transport. This analysis will focus on the three aspects already mentioned: emissions, noise and attractiveness or competitiveness. 2) Once possible synergies and complementarities have been analyzed, the second objective is to propose the best combination of these measures, in terms of level of implementation, to achieve the maximum benefit with respect to the three aspects previously established: emissions, noise and attractiveness or competitiveness. Therefore, within the wide range of measures enhancing sustainable urban transport, three of them have been be selected in this thesis to establish a methodology for achieving these objectives. The analysis will be based on the region of Madrid, which is also the case study selected for this research. Las ciudades europeas son piezas fundamentales para el desarrollo europeo, ya que son el lugar de residencia de más del 60% de la población de la unión europea así como los motores de su economía – casi el 85% del PIB europeo se produce en áreas urbanas (EC, 2007a). El coche ha sido uno de los principales motores de desarrollo de las ciudades durante el siglo XX, pero se ha terminado por convertir a su vez en uno de los principales problemas con los que tiene que lidiar las ciudades: el aumento del tráfico. Esto ha derivado en unos niveles crónicos de congestión, con multitud de efectos adversos, entre los que cabe destacar la contaminación del aire y el ruido. Esta pérdida de calidad ambiental es una de las razones que ha propiciado la dispersión urbana que han experimentado las ciudades europeas en las últimas décadas. Pero esta dispersión urbana a su vez contribuye a empeorar las condiciones ambientales de las ciudades. Debemos retornar a la ciudad densa, pero limpia y competitiva, y esto implica reducir el uso del coche, pero proporcionando alternativas de transporte que permitan recuperar y mantener la competitividad de las ciudades (EC, 2007a). Por lo tanto, las ciudades europeas necesitan encontrar una estrategia de transporte urbano que ayude a reducir sus problemas medio ambientales – principalmente ruido y emisiones – pero sin hacerlas perder atractividad o competitividad. Este aspecto tiene gran importancia porque una pérdida de la misma se traduciría en un aumento de dispersión de la población hacia áreas periféricas, contribuyendo a empeorar la situación actual. Esta tesis contribuye a solucionar este problema de dos maneras: 1) La primera, analizando la complementariedad y posibles sinergias de diferentes medidas de transporte urbano orientadas a promover un reparto modal hacia modos más sostenibles. Este análisis se centrará en los tres aspectos anteriormente citados: emisiones, ruido y atractividad o competitividad. 2) Una vez las posibles sinergias y complementariedades se han analizado, el segundo objetivo es proponer la mejor combinación de estas medidas – en términos de grado de aplicación - para lograr el máximo beneficio en lo que respecta a los tres objetivos previamente establecidos. Para ello, en esta tesis se han seleccionado una serie de medidas que permitan establecer una metodología para alcanzar estos objetivos previamente definidos. El análisis se centra en la ciudad de Madrid y su área metropolitana, la cual se ha escogido como caso de estudio para realizar esta investigación.

Comparasion of road traffic emission models in Madrid (Spain)

Many cities in Europe have difficulties to meet the air quality standards set by the European legislation, most particularly the annual mean Limit Value for NO2. Road transport is often the main source of air pollution in urban areas and therefore, there is an increasing need to estimate current and future traffic emissions as accurately as possible. As a consequence, a number of specific emission models and emission factors databases have been developed recently. They present important methodological differences and may result in largely diverging emission figures and thus may lead to alternative policy recommendations. This study compares two approaches to estimate road traffic emissions in Madrid (Spain): the COmputer Programme to calculate Emissions from Road Transport (COPERT4 v.8.1) and the Handbook Emission Factors for Road Transport (HBEFA v.3.1), representative of the ‘average-speed’ and ‘traffic situation’ model types respectively. The input information (e.g. fleet composition, vehicle kilometres travelled, traffic intensity, road type, etc.) was provided by the traffic model developed by the Madrid City Council along with observations from field campaigns. Hourly emissions were computed for nearly 15 000 road segments distributed in 9 management areas covering the Madrid city and surroundings. Total annual NOX emissions predicted by HBEFA were a 21% higher than those of COPERT. The discrepancies for NO2 were lower (13%) since resulting average NO2/NOX ratios are lower for HBEFA. The larger differences are related to diesel vehicle emissions under “stop & go” traffic conditions, very common in distributor/secondary roads of the Madrid metropolitan area. In order to understand the representativeness of these results, the resulting emissions were integrated in an urban scale inventory used to drive mesoscale air quality simulations with the Community Multiscale Air Quality (CMAQ) modelling system (1 km2 resolution). Modelled NO2 concentrations were compared with observations through a series of statistics. Although there are no remarkable differences between both model runs, the results suggest that HBEFA may overestimate traffic emissions. However, the results are strongly influenced by methodological issues and limitations of the traffic model. This study was useful to provide a first alternative estimate to the official emission inventory in Madrid and to identify the main features of the traffic model that should be improved to support the application of an emission system based on “real world” emission factors.

Changes in the external costs of freight surface transport in Spain

This paper studies the external costs of surface freight transport in Spain and finds that a reduction occurred over the past 15 years. The analysis yields two conclusions: trucks have experienced a reduction in external costs, and rail has lower externalities. The external costs of road freight transport decrease between 1993 and 2007 (44%). The external costs of rail freight increase by 12%. During this period, the external costs of road freight related to climate increase by 16%, oppositely than those from air pollution and accidents (51 and 44%). The external costs of rail related to pollutant emissions and climate increase by 4% and 43%. Oppositely, the external costs related to accidents decrease by 27%. Road freight generates eight times the external costs of rail, 2.35 Euro cents per tonne kilometre in 2005 (5.6% accidents, 74.7% air pollution and 19.7% climate) vs. 0.28 (13.4% accidents, 53.9% air pollution and 32.7% climate).

Assessing urban mobility through participatory scenario building and combined utility-regret assessment

La planificación de la movilidad sostenible urbana es una tarea compleja que implica un alto grado de incertidumbre debido al horizonte de planificación a largo plazo, la amplia gama de paquetes de políticas posibles, la necesidad de una aplicación efectiva y eficiente, la gran escala geográfica, la necesidad de considerar objetivos económicos, sociales y ambientales, y la respuesta del viajero a los diferentes cursos de acción y su aceptabilidad política (Shiftan et al., 2003). Además, con las tendencias inevitables en motorización y urbanización, la demanda de terrenos y recursos de movilidad en las ciudades está aumentando dramáticamente. Como consecuencia de ello, los problemas de congestión de tráfico, deterioro ambiental, contaminación del aire, consumo de energía, desigualdades en la comunidad, etc. se hacen más y más críticos para la sociedad. Esta situación no es estable a largo plazo. Para enfrentarse a estos desafíos y conseguir un desarrollo sostenible, es necesario considerar una estrategia de planificación urbana a largo plazo, que aborde las necesarias implicaciones potencialmente importantes. Esta tesis contribuye a las herramientas de evaluación a largo plazo de la movilidad urbana estableciendo una metodología innovadora para el análisis y optimización de dos tipos de medidas de gestión de la demanda del transporte (TDM). La metodología nueva realizado se basa en la flexibilización de la toma de decisiones basadas en utilidad, integrando diversos mecanismos de decisión contrariedad‐anticipada y combinados utilidad‐contrariedad en un marco integral de planificación del transporte. La metodología propuesta incluye dos aspectos principales: 1) La construcción de escenarios con una o varias medidas TDM usando el método de encuesta que incorpora la teoría “regret”. La construcción de escenarios para este trabajo se hace para considerar específicamente la implementación de cada medida TDM en el marco temporal y marco espacial. Al final, se construyen 13 escenarios TDM en términos del más deseable, el más posible y el de menor grado de “regret” como resultado de una encuesta en dos rondas a expertos en el tema. 2) A continuación se procede al desarrollo de un marco de evaluación estratégica, basado en un Análisis Multicriterio de Toma de Decisiones (Multicriteria Decision Analysis, MCDA) y en un modelo “regret”. Este marco de evaluación se utiliza para comparar la contribución de los distintos escenarios TDM a la movilidad sostenible y para determinar el mejor escenario utilizando no sólo el valor objetivo de utilidad objetivo obtenido en el análisis orientado a utilidad MCDA, sino también el valor de “regret” que se calcula por medio del modelo “regret” MCDA. La función objetivo del MCDA se integra en un modelo de interacción de uso del suelo y transporte que se usa para optimizar y evaluar los impactos a largo plazo de los escenarios TDM previamente construidos. Un modelo de “regret”, llamado “referencedependent regret model (RDRM)” (modelo de contrariedad dependiente de referencias), se ha adaptado para analizar la contribución de cada escenario TDM desde un punto de vista subjetivo. La validación de la metodología se realiza mediante su aplicación a un caso de estudio en la provincia de Madrid. La metodología propuesta define pues un procedimiento técnico detallado para la evaluación de los impactos estratégicos de la aplicación de medidas de gestión de la demanda en el transporte, que se considera que constituye una herramienta de planificación útil, transparente y flexible, tanto para los planificadores como para los responsables de la gestión del transporte. Planning sustainable urban mobility is a complex task involving a high degree of uncertainty due to the long‐term planning horizon, the wide spectrum of potential policy packages, the need for effective and efficient implementation, the large geographical scale, the necessity to consider economic, social, and environmental goals, and the traveller’s response to the various action courses and their political acceptability (Shiftan et al., 2003). Moreover, with the inevitable trends on motorisation and urbanisation, the demand for land and mobility in cities is growing dramatically. Consequently, the problems of traffic congestion, environmental deterioration, air pollution, energy consumption, and community inequity etc., are becoming more and more critical for the society (EU, 2011). Certainly, this course is not sustainable in the long term. To address this challenge and achieve sustainable development, a long‐term perspective strategic urban plan, with its potentially important implications, should be established. This thesis contributes on assessing long‐term urban mobility by establishing an innovative methodology for optimizing and evaluating two types of transport demand management measures (TDM). The new methodology aims at relaxing the utility‐based decision‐making assumption by embedding anticipated‐regret and combined utilityregret decision mechanisms in an integrated transport planning framework. The proposed methodology includes two major aspects: 1) Construction of policy scenarios within a single measure or combined TDM policy‐packages using the survey method incorporating the regret theory. The purpose of building the TDM scenarios in this work is to address the specific implementation in terms of time frame and geographic scale for each TDM measure. Finally, 13 TDM scenarios are built in terms of the most desirable, the most expected and the least regret choice by means of the two‐round Delphi based survey. 2) Development of the combined utility‐regret analysis framework based on multicriteria decision analysis (MCDA). This assessment framework is used to compare the contribution of the TDM scenario towards sustainable mobility and to determine the best scenario considering not only the objective utility value obtained from the utilitybased MCDA, but also a regret value that is calculated via a regret‐based MCDA. The objective function of the utility‐based MCDA is integrated in a land use and transport interaction model and is used for optimizing and assessing the long term impacts of the constructed TDM scenarios. A regret based model, called referente dependent regret model (RDRM) is adapted to analyse the contribution of each TDM scenario in terms of a subjective point of view. The suggested methodology is implemented and validated in the case of Madrid. It defines a comprehensive technical procedure for assessing strategic effects of transport demand management measures, which can be useful, transparent and flexible planning tool both for planners and decision‐makers.