Procesos de innovación agroalimentaria y demanda internacional de alimentos frescos. El sector empaque de cítricos en Entre Ríos

Las firmas agroindustriales exportadoras argentinas de productos en fresco están transitando por un proceso progresivo de modernización e implementación de normativas internacionales sobre calidad agroalimentaria. Desde esta perspectiva, el presente trabajo aporta algunos elementos de análisis que permiten profundizar en el conocimiento del eslabón de acondicionamiento/ empaque del complejo agroindustrial citrícola del noreste de la provincia de Entre Ríos, departamentos de Concordia y Federación, a partir de la segunda mitad de la década de los noventa, centrando la mirada en las empresas exportadoras y en las innovaciones que han implementado para ajustar los estándares de calidad.

Surface in situ measurements of atmospheric CO, CH4, and CO2 combined with selected meteorological measurements at the University of Wollongong, Australia

Wollongong, Australia is an urban site at the intersection of anthropogenic, biomass burning, biogenic and marine sources of atmospheric trace gases. The location offers a valuable opportunity to study drivers of atmospheric composition in the Southern Hemisphere. Here, a record of surface carbon monoxide (CO), methane (CH4) and carbon dioxide (CO2) was measured with an in situ Fourier transform infrared trace gas analyser between April 2011 and August 2014. Clean air was found to arrive at Wollongong in approximately 10% of air masses. Biomass burning influence was evident in the average annual cycle of clean air CO during austral spring. A significant negative short-term trend was found in clean air CO (-1.5 nmol/mol/a), driven by a reduction in northern Australian biomass burning. Significant short-term positive trends in clean air CH4 (5.4 nmol/mol/a) and CO2 (1.9 ?mol/mol/a) were consistent with the long-term global average trends. Polluted Wollongong air was investigated using wind-direction/wind-speed clustering, which revealed major influence from local urban and industrial sources from the south. High values of CH4, with anthropogenic DCH4/DCO2 enhancement ratio signatures, originated from the northwest, in the direction of local coal mining. A pollution climatology was developed for the region using back trajectory analysis and DO3/DCO enhancement ratios. Ozone production environments in austral spring and summer were associated with anticyclonic meteorology on the east coast of Australia, while ozone depletion environments in autumn and winter were associated with continental transport, or fast moving trajectories from southern latitudes. This implies the need to consider meteorological conditions when developing policies for controlling air quality.

Procesos de innovación agroalimentaria y demanda internacional de alimentos frescos. El sector empaque de cítricos en Entre Ríos

Las firmas agroindustriales exportadoras argentinas de productos en fresco están transitando por un proceso progresivo de modernización e implementación de normativas internacionales sobre calidad agroalimentaria. Desde esta perspectiva, el presente trabajo aporta algunos elementos de análisis que permiten profundizar en el conocimiento del eslabón de acondicionamiento/ empaque del complejo agroindustrial citrícola del noreste de la provincia de Entre Ríos, departamentos de Concordia y Federación, a partir de la segunda mitad de la década de los noventa, centrando la mirada en las empresas exportadoras y en las innovaciones que han implementado para ajustar los estándares de calidad.

Status and constraints of costly port rejection : a case from the Vietnamese frozen seafood export industry

While the rising exports have been the source of growth for many developing countries in recent years, the rate of commodities rejected at the ports of developed countries has also been high. Yet why it has remained so despite the costs involved is mostly unknown. This paper takes a case of the frozen seafood export industry in Vietnam and examines the current status of port rejection, roles played by various stakeholders along the value chains, and the constraints faced by the Vietnamese producers and exporters. It concludes with some policy implications, including strengthening the enforcement mechanism of standards compliance particularly at the upstream of the value chain and providing public testing labs for small-scale producers.

Emergence of Asian GAPs and its relationship to global G.A.P.

Global G.A.P. is a one of the most influential private standards in the area of food safety and sustainability. With increasing impacts of Global G.A.P., many Asian countries have introduced the country versions of GAPs; China GAP, Japan GAP, Viet GAP, Thai GAP and ASEAN GAP. Each has been influenced by Global G.A.P. but ways of implementation, implementation bodies as well as focus differ from each other. This paper examines the development and motivation behind how the Asian GAPs have been introduced both from current situation and from historical perspectives. Then we compare current situation of different Asian GAPs.

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.

Do PGI integrated farms perform better? The case of the beef farms in Spain

European rural development policy is gaining in importance through one of its key instruments, the Protected Geographical Indications (PGI) system, which is designed to improve quality standards. Previous research has shown that PGI-certified beef farms tend to be more extensively managed operations that are better adapted to mountainous areas. This paper describes a comparative study of two production systems, one with PGI certification and one without, focusing on a number of economic variables. The results show a positive association between PGI production and profitability. In efficiency terms, non-certified farms show better pure technical efficiency scores, while PGI-certified holdings score higher on scale efficiency.

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.

Estudio de la digestión anaerobia en dos fases para el tratamiento de las aguas residuales de despulpe del beneficiado húmedo del café

Debido al aumento de los estándares de calidad exigidos internacionalmente, así como por una mayor presión sobre la industria mediante legislaciones ambientales más rigurosas, el sector cafetalero está obligado a buscar, a través de la investigación, un sistema adecuado de tratamiento para las aguas residuales generadas en el beneficiado húmedo del café. En este trabajo se evaluó el funcionamiento de la digestión anaerobia para el tratamiento de las aguas residuales de despulpe. Para ello, se utilizaron dos sistemas anaerobios, uno en una etapa (UASB), y otro con separación de fases (2PUASB). Se investigó el efecto en la digestión anaerobia de tres cargas orgánicas volumétricas (OLR) y de las dos configuraciones de reactor usadas. Los valores de OLR de operación en el sistema UASB variaron en un intervalo de 3.6-4.1 kgCOD m-3 d-1, con una tasa de recirculación del efluente de 1.0. El sistema 2PUASB fue alimentado con OLR similares a las que se emplearon en el sistema en una etapa. El reactor de acidificación fue cargado a 11.0 kgCOD m-3 d-1, mientras que en el reactor metanogénico varió en el intervalo de 2.6-4.67 kgCOD m-3 d-1. El uso de reactores UASB en una etapa y en dos fases, bajo las mismas condiciones de operación ya descritas, propiciaron el logro de una eficiencia de degradación de COD total superior al 75% y al 85% para la COD soluble, respectivamente. Sin embargo, el sistema en dos fases mostró mejores resultados en el tratamiento de este tipo de agua residual, no solo en cuanto a eficiencia de eliminación de la carga orgánica contaminante así como una menor concentración de ácidos grasos volátiles (VFA) en el efluente. Obtenidas las mejores condiciones de trabajo, fue evaluada la separación de fases bajo el efecto de la recirculación. Los grupos de fermentaciones producidos fueron similares a los obtenidos en el experimento sin recirculación, indicando que está última no afectó la composición relativa de los VFA del reactor anaerobio, por lo que no cambió el patrón de degradación del residuo. Una tasa de recirculación de 1.0 del efluente del reactor metanogénico al reactor acidogénico mejoró significativamente el proceso, ya que se incrementó la conversión de los VFA (31%), la eliminación de la fracción total y soluble del residuo tratado (6.5%) y la reducción del consumo de alcalinizante (39%); manteniendo similares producciones de metano. El uso de la digestión anaerobia en dos fases demostró una mejora en la estabilidad del proceso y un incremento de la eficiencia de operación y de la producción de metano, respectivamente.Tesis Doctoral Yans Guardia Puebla Abstract ix ABSTRACT Due to the increase of quality standards internationally demanded, as well as for a greater pressure on the industry by means of more rigorous environmental legislations, the coffee sector is forced to search, through the research, an appropriated treatment system for coffee wet wastewaters generated. In this work the performance of the anaerobic digestion for the coffee wet wastewater treatment was evaluated. For it, two anaerobic systems, one in single-stage (UASB), and another with two-phase (2PUASB) were used. The effect in the anaerobic digestion of three organic loading rates (OLR) and of two reactor configurations used was investigated. OLR operation values in UASB system varied in an interval of 3.6-4.1 kgCOD m-3 d-1, with a recycle rate of the effluent of 1.0. 2PUASB system was fed with OLR similar to those that were used in the reactor in a stage. The acidification reactor was loaded to 11.0 kgCOD m-3 d-1, whereas in the methanogenic reactor varied in the interval of 2.6-4.67 kgCOD m-3 d-1. The use of single-stage and two-phase UASB reactors, under the same operation conditions already before described, a total COD removal efficiency of 75% and 85% for the soluble COD removal efficiency, respectively, was achieved. However, two-phase system showed better results in the treatment of this wastewater type, not only as for removal efficiency of loading organic polluting as well as a smaller volatile fatty acid (VFA) concentration in the effluent. Obtained the best work conditions, the two-phase system under the effect of the recycle was evaluated. Fermentations groups produced were similar to those obtained in the experiment without recycle, indicating that it last one do not affect the relative composition of VFA of the anaerobic reactor, for that reason the degradation pattern of the residue does not change. A recycle rate of 1.0 of the effluent of the methanogenic reactor to the acidogenic reactor improved the process significantly, since it was increased the VFA conversion (31%), the removal of total and soluble fraction of the residue treated (6.5%) and the decrease of the alkalinity consumption (39%); maintaining similar methane productions. The use of the two-phase anaerobic digestion demonstrated to an improvement in the stability of the process and an increase of the operation efficiency and methane production, respectively.

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Modelling Saharan dust transport into the Mediterranean basin with CMAQ

The need for a better quantification of the influence of Saharan dust transport processes on the air quality modelling in the Mediterranean basin led to the formulation of a dust emission module (DEM) integrated into the Air Quality Risk Assessment System for the Iberian Peninsula (SERCA). This paper is focused on the formulation of DEM based on the GOCART aerosol model, along with its integration and execution into the air quality model. It also addresses the testing of the module and its evaluation by contrasting results against satellite products such as MODIS and CALIPSO and ground-level observations of aerosol optical thickness (AOT) and concentration levels of PM10 for different periods in July 2007. DEM was found capable of reproducing the spatial (horizontal and vertical) and temporal profiles of Saharan dust outbreaks into the Mediterranean basin and the Atlantic coast of Africa. Moreover, it was observed that its combination with CMAQ increased the correlation degree between observed and modelled PM10 concentrations at the selected monitoring locations. DEM also enhanced CMAQ capabilities to reproduce observed AOT, although significant underestimations remain. The implementation of CMAQ + DEM succeeded in capturing Saharan dust transport into the Iberian Peninsula, with contributions up to 25 and 14 μg m−3 in 1 h and 24 h average PM10 respectively. The general improvement of total PM10 predictions in Spain are however moderate. The analysis of model performance for the main PM components points out that remaining PM10 underestimation is due to dust local sources missing in the inventories and misrepresentation of organic aerosol processes, which constitutes the main areas for future improvement of CMAQ capabilities to simulate particulate matter within SERCA.

Levels, sources and seasonality of coarse particles (PM10-PM2.5) in three European capitals e implications for particulate pollution control

Coarse particles of aerodynamic diameter between 2.5 and 10 mm (PMc) are produced by a range of natural (windblown dust and sea sprays) and anthropogenic processes (non-exhaust vehicle emissions, industrial, agriculture, construction and quarrying activities). Although current ambient air quality regulations focus on PM2.5 and PM10, coarse particles are of interest from a public health point of view as they have been associated with certain mortality and morbidity outcomes. In this paper, an analysis of coarse particle levels in three European capitals (London, Madrid and Athens) is presented and discussed. For all three cities we analysed data from both traffic and urban background monitoring sites. The results showed that the levels of coarse particles present significant seasonal, weekly and daily variability. Their wind driven and non-wind driven resuspension as well as their roadside increment due to traffic were estimated. Both the local meteorological conditions and the air mass history indicating long-range atmospheric transport of particles of natural origin are significant parameters that influence the levels of coarse particles in the three cities especially during episodic events.

Pollutant concentrations and Meteorological data classification by Neural Networks

This paper present an environmental contingency forecasting tool based on Neural Networks (NN). Forecasting tool analyzes every hour and daily Sulphur Dioxide (SO2) concentrations and Meteorological data time series. Pollutant concentrations and meteorological variables are self-organized applying a Self-organizing Map (SOM) NN in different classes. Classes are used in training phase of a General Regression Neural Network (GRNN) classifier to provide an air quality forecast. In this case a time series set obtained from Environmental Monitoring Network (EMN) of the city of Salamanca, Guanajuato, México is used. Results verify the potential of this method versus other statistical classification methods and also variables correlation is solved.

Unsupervised method to classify PM10 pollutant concentrations

In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM10) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Integración de la calidad del aire en la planificación física

En esta tesis se presenta una nueva aproximación para la realización de mapas de calidad del aire, con objeto de que esta variable del medio físico pueda ser tenida en cuenta en los procesos de planificación física o territorial. La calidad del aire no se considera normalmente en estos procesos debido a su composición y a la complejidad de su comportamiento, así como a la dificultad de contar con información fiable y contrastada. Además, la variabilidad espacial y temporal de las medidas de calidad del aire hace que sea difícil su consideración territorial y exige la georeferenciación de la información. Ello implica la predicción de medidas para lugares del territorio donde no existen datos. Esta tesis desarrolla un modelo geoestadístico para la predicción de valores de calidad del aire en un territorio. El modelo propuesto se basa en la interpolación de las medidas de concentración de contaminantes registradas en las estaciones de monitorización, mediante kriging ordinario, previa homogeneización de estos datos para eliminar su carácter local. Con el proceso de eliminación del carácter local, desaparecen las tendencias de las series muestrales de datos debidas a las variaciones temporales y espaciales de la calidad del aire. La transformación de los valores de calidad del aire en cantidades independientes del lugar de muestreo, se realiza a través de parámetros de uso del suelo y de otras variables características de la escala local. Como resultado, se obtienen unos datos de entrada espacialmente homogéneos, que es un requisito fundamental para la utilización de cualquier algoritmo de interpolación, en concreto, del kriging ordinario. Después de la interpolación, se aplica una retransformación de los datos para devolver el carácter local al mapa final. Para el desarrollo del modelo, se ha elegido como área de estudio la Comunidad de Madrid, por la disponibilidad de datos reales. Estos datos, valores de calidad del aire y variables territoriales, se utilizan en dos momentos. Un momento inicial, donde se optimiza la selección de los parámetros más adecuados para la eliminación del carácter local de las medidas y se desarrolla cada una de las etapas del modelo. Y un segundo momento, en el que se aplica en su totalidad el modelo desarrollado y se contrasta su eficacia predictiva. El modelo se aplica para la estimación de los valores medios y máximos de NO2 del territorio de estudio. Con la implementación del modelo propuesto se acomete la territorialización de los datos de calidad del aire con la reducción de tres factores clave para su efectiva integración en la planificación territorial o en el proceso de toma de decisiones asociado: incertidumbre, tiempo empleado para generar la predicción y recursos (datos y costes) asociados. El modelo permite obtener una predicción de valores del contaminante objeto de análisis en unas horas, frente a los periodos de modelización o análisis requeridos por otras metodologías. Los recursos necesarios son mínimos, únicamente contar con los datos de las estaciones de monitorización del territorio que, normalmente, están disponibles en las páginas web viii institucionales de los organismos gestores de las redes de medida de la calidad del aire. Por lo que respecta a las incertidumbres de la predicción, puede decirse que los resultados del modelo propuesto en esta tesis son estadísticamente muy correctos y que los errores medios son, en general, similares o menores que los encontrados con la aplicación de las metodologías existentes. ABSTRACT This thesis presents a new approach for mapping air quality, so that this variable of physical environment can be taken into account in physical or territorial planning. Ambient air quality is not normally considered in territorial planning mainly due to the complexity of its composition and behavior and the difficulty of counting with reliable and contrasted information. In addition, the wide spatial and temporal variability of the measurements of air quality makes his territorial consideration difficult and requires georeferenced information. This involves predicting measurements in the places of the territory where there are no data. This thesis develops a geostatistical model for predicting air quality values in a territory. The proposed model is based on the interpolation of measurements of pollutants from the monitoring stations, using ordinary kriging, after a detrending or removal of the local character of sampling values process. With the detrending process, the local character of the time series of sampling data, due to temporal and spatial variations of air quality, is removed. The transformation of the air quality values into site-independent quantities is performed using land use parameters and other characteristic parameters of local scale. This detrending of the monitoring data process results in a spatial homogeneous input set which is a prerequisite for a correct use of any interpolation algorithm, particularly, ordinary kriging. After the interpolation step, a retrending or retransformation is applied in order to incorporate the local character in the final map at places where no monitoring data is available. For the development of this model, the Community of Madrid is chosen as study area, because of the availability of actual data. These data, air quality values and local parameters, are used in two moments. A starting point, to optimize the selection of the most suitable indicators for the detrending process and to develop each one of the model stages. And a second moment, to fully implement the developed model and to evaluate its predictive power. The model is applied to estimate the average and maximum values of NO2 in the study territory. With the implementation of the proposed model, the territorialization of air quality data is undertaken with the reduction in three key factors for the effective integration of this parameter in territorial planning or in the associated decision making process: uncertainty, time taken to generate the prediction and associated resources (data and costs). This model allows the prediction of pollutant values in hours, compared to the implementation time periods required for other modeling or analysis methodologies. The required resources are also minimal, only having data from monitoring stations in the territory, that are normally available on institutional websites of the authorities responsible for air quality networks control and management. With regard to the prediction uncertainties, it can be concluded that the results of the proposed model are statistically very accurate and the mean errors are generally similar to or lower than those found with the application of existing methodologies.