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.

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.

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.

Investigação de aquífero fraturado para entendimento de fluxo e transporte de contaminantes clorados: estudo de caso em Valinhos, SP

Casos de contaminação de aquíferos fraturados são bastante complexos, tendo em vista a heterogeneidade das redes de fraturas, e no geral, sua investigação demanda a utilização de técnicas pouco usuais, como por exemplo o imageamento acústico e a perfilagem de velocidade de fluxo de água. Na área de estudo, localizada em Valinhos/SP, o uso inadequado de solventes organoclorados no passado ocasionou a contaminação do aquífero raso em duas áreas, e o aparecimento de concentrações no aquifero profundo levaram a condução do atual trabalho, que teve como principal objetivo a elaboração de um modelo conceitual de fluxo de água e transporte de contaminantes no aquífero cristalino. Previamente à investigação do aquífero fraturado, foi realizada uma análise de trabalhos existentes, incluindo a interpretação de lineamentos, levantamentos geológicos além de perfilagens geofísicas de superfície. Em cada área investigada, foi realizada a perfuração de um poço profundo e aplicadas as técnicas de perfilagens de raios gama, cáliper, flowmeter, imageamento acústico, além da filmagem do poço e realização de ensaios hidráulicos nos dois pontos perfurados. Para caracterização química do aquífero fraturado, foram realizadas coletas de água subterrânea em intervalos selecionados com a utilização de obturadores pneumáticos. As cargas hidráulicas medidas durante a amostragem também auxiliaram no entendimento da direção do fluxo de água. O aquífero cristalino é formado por rochas gnáissicas e se encontra bastante fraturado e intemperizado, principalmente na porção superficial da rocha (até aproximadamente 65,0 m) onde as maiores velocidades de fluxo de água também foram observadas. A rocha sã possui uma menor densidade de fraturas e predominância de minerais mais claros. As fraturas de baixo a médio angulo de mergulho (Grupo 1) são as mais frequentes em ambas as perfurações e possuem direção principal N-S a NE-SW. São observadas, no geral, exercendo grande influência sobre o fluxo de água, principalmente na porção alterada do gnaisse. Fraturas com ângulo elevado de mergulho, classificadas como Grupo 2 (paralelas à foliação) e Grupo 3 (direção NW à W), são também observadas ao longo de toda a perfuração estabelecendo a conexão hidráulica entre as fraturas do Grupo 1. Em menor proporção, são ainda verificadas fraturas com ângulos de mergulho >40 ° pertencente aos Grupos 4 (NE-SW), 5 (E-W), 6 (NW-SE) e 7 (E-W). O fluxo de água subterrânea se mostrou descendente na porção superior da rocha alterada e ascendente na porção mais profunda, possivelmente direcionando a água subterrânea para a região de transição da rocha mais alterada para a rocha sã (entre 61 a 65 m de profundidade). Apesar do fluxo ascendente em profundidade, o bombeamento de poços tubulares existentes no entorno ao longo dos anos, favoreceu a migração dos contaminantes para porções mais profundas. Os contaminantes observados no poço tubular P6 possuem maior semelhança com os contaminantes observados na Área 2, e ambos estão localizados entre lineamentos NW-SE, indicando uma possível influência dos lineamentos no controle sobre o fluxo de água. No entanto, para entendimento do transporte dos contaminantes em área, é necessário um adensamento da rede de monitoramento, levando em consideração a heterogeneidade do meio e as incertezas relacionadas à extrapolação dos dados para áreas não investigadas.

Evolution of Posidonia oceanica seagrass meadows and its implications for management

Results of the monitoring network of the Posidonia oceanica meadows in the Valencia region in Spain are analysed. For spatial comparison the whole data set has been analysed, however, for temporal trends we only selected stations that have been monitored at least 6 years in the period of 2002–2011 (26 stations in 13 localities). At the south of the studied area, meadows are larger, and they have higher density and covering than that in the Valencia Gulf, excluding Oropesa meadow. Monitoring of P. oceanica meadows in the Valencia region in Spain indicates that most of them are stationary or they are increasing their density and covering while no decline was observed in the studied meadows. These results indicate that there is not a general decline of P. oceanica meadows and that the decline of P. oceanica, when it has been observed in other studies, is produced by local causes that may be managed at the local level. This study also reflects the importance of long series of direct data to analyse trends in the population dynamics for slow-growing species.

Variación espacial de la exposición a contaminación atmosférica en la ciudad de Valencia y su relación con un índice de privación

Objetivo: Evaluar la variación espacial de la exposición a dióxido de nitrógeno (NO2) en la ciudad de Valencia y su relación con la privación socioeconómica y la edad. Métodos: La población por sección censal (SC) procede del Instituto Nacional de Estadística. Los niveles de NO2 se midieron en 100 puntos del área de estudio, mediante captadores pasivos, en tres campañas entre 2002 y 2004. Se utilizó regresión por usos del suelo (LUR) para obtener el mapa de los niveles de NO2. Las predicciones del LUR se compararon con las proporcionadas por: a) el captador más cercano de la red de vigilancia, b) el captador pasivo más cercano, c) el conjunto de captadores en un entorno y d) kriging. Se asignaron niveles de contaminación para cada SC. Se analizó la relación entre los niveles de NO2, un índice de privación con cinco categorías y la edad (≥65 años). Resultados: El modelo LUR resultó el método más preciso. Más del 99% de la población superó los niveles de seguridad propuestos por la Organización Mundial de la Salud. Se encontró una relación inversa entre los niveles de NO2 y el índice de privación (β = –2,01 μg/m3 en el quintil de mayor privación respecto al de menor, IC95%: –3,07 a –0,95), y una relación directa con la edad (β = 0,12 μg/m3 por incremento en unidad porcentual de población ≥65 años, IC95%: 0,08 a 0,16). Conclusiones: El método permitió obtener mapas de contaminación y describir la relación entre niveles de NO2 y características sociodemográficas.

Management of social networks in the educational process

The research developed in this work consists in proposing a set of techniques for management of social networks and their integration into the educational process. The proposals made are based on assumptions that have been proven with simple examples in a real scenario of university teaching. The results show that social networks have more capacity to spread information than educational web platforms. Moreover, educational social networks are developed in a context of freedom of expression intrinsically linked to Internet freedom. In that context, users can write opinions or comments which are not liked by the staff of schools. However, this feature can be exploited to enrich the educational process and improve the quality of their achievement. The network has covered needs and created new ones. So, the figure of the Community Manager is proposed as agent in educational context for monitoring network and aims to channel the opinions and to provide a rapid response to an academic problem.

Continuous CO2/CH4/CO measurements (2012–2014) at Beromünster tall tower station in Switzerland

The understanding of the continental carbon budget is essential to predict future climate change. In order to quantify CO₂ and CH₄ fluxes at the regional scale, a measurement system was installed at the former radio tower in Beromünster as part of the Swiss greenhouse gas monitoring network (CarboCount CH). We have been measuring the mixing ratios of CO₂, CH₄ and CO on this tower with sample inlets at 12.5, 44.6, 71.5, 131.6 and 212.5 m above ground level using a cavity ring down spectroscopy (CRDS) analyzer. The first 2-year (December 2012–December 2014) continuous atmospheric record was analyzed for seasonal and diurnal variations and interspecies correlations. In addition, storage fluxes were calculated from the hourly profiles along the tower. The atmospheric growth rates from 2013 to 2014 determined from this 2-year data set were 1.78 ppm yr⁻¹, 9.66 ppb yr⁻¹ and and -1.27 ppb yr⁻¹ for CO₂, CH₄ and CO, respectively. After detrending, clear seasonal cycles were detected for CO₂ and CO, whereas CH₄ showed a stable baseline suggesting a net balance between sources and sinks over the course of the year. CO and CO₂ were strongly correlated (r² > 0.75) in winter (DJF), but almost uncorrelated in summer. In winter, anthropogenic emissions dominate the biospheric CO₂ fluxes and the variations in mixing ratios are large due to reduced vertical mixing. The diurnal variations of all species showed distinct cycles in spring and summer, with the lowest sampling level showing the most pronounced diurnal amplitudes. The storage flux estimates exhibited reasonable diurnal shapes for CO₂, but underestimated the strength of the surface sinks during daytime. This seems plausible, keeping in mind that we were only able to calculate the storage fluxes along the profile of the tower but not the flux into or out of this profile, since no Eddy covariance flux measurements were taken at the top of the tower.

Supporting interoperable interpolation: the INTAMAP approach

In many Environmental Information Systems the actual observations arise from a discrete monitoring network which might be rather heterogeneous in both location and types of measurements made. In this paper we describe the architecture and infrastructure for a system, developed as part of the EU FP6 funded INTAMAP project, to provide a service oriented solution that allows the construction of an interoperable, automatic, interpolation system. This system will be based on the Open Geospatial Consortium’s Web Feature Service (WFS) standard. The essence of our approach is to extend the GML3.1 observation feature to include information about the sensor using SensorML, and to further extend this to incorporate observation error characteristics. Our extended WFS will accept observations, and will store them in a database. The observations will be passed to our R-based interpolation server, which will use a range of methods, including a novel sparse, sequential kriging method (only briefly described here) to produce an internal representation of the interpolated field resulting from the observations currently uploaded to the system. The extended WFS will then accept queries, such as ‘What is the probability distribution of the desired variable at a given point’, ‘What is the mean value over a given region’, or ‘What is the probability of exceeding a certain threshold at a given location’. To support information-rich transfer of complex and uncertain predictions we are developing schema to represent probabilistic results in a GML3.1 (object-property) style. The system will also offer more easily accessible Web Map Service and Web Coverage Service interfaces to allow users to access the system at the level of complexity they require for their specific application. Such a system will offer a very valuable contribution to the next generation of Environmental Information Systems in the context of real time mapping for monitoring and security, particularly for systems that employ a service oriented architecture.

Automated mapping of environmental variables from a SEIS or SISE perspective

The INTAMAP FP6 project has developed an interoperable framework for real-time automatic mapping of critical environmental variables by extending spatial statistical methods and employing open, web-based, data exchange protocols and visualisation tools. This paper will give an overview of the underlying problem, of the project, and discuss which problems it has solved and which open problems seem to be most relevant to deal with next. The interpolation problem that INTAMAP solves is the generic problem of spatial interpolation of environmental variables without user interaction, based on measurements of e.g. PM10, rainfall or gamma dose rate, at arbitrary locations or over a regular grid covering the area of interest. It deals with problems of varying spatial resolution of measurements, the interpolation of averages over larger areas, and with providing information on the interpolation error to the end-user. In addition, monitoring network optimisation is addressed in a non-automatic context.

A projected process kriging algorithm for sensor networks with heterogeneous error characteristics

Large monitoring networks are becoming increasingly common and can generate large datasets from thousands to millions of observations in size, often with high temporal resolution. Processing large datasets using traditional geostatistical methods is prohibitively slow and in real world applications different types of sensor can be found across a monitoring network. Heterogeneities in the error characteristics of different sensors, both in terms of distribution and magnitude, presents problems for generating coherent maps. An assumption in traditional geostatistics is that observations are made directly of the underlying process being studied and that the observations are contaminated with Gaussian errors. Under this assumption, sub–optimal predictions will be obtained if the error characteristics of the sensor are effectively non–Gaussian. One method, model based geostatistics, assumes that a Gaussian process prior is imposed over the (latent) process being studied and that the sensor model forms part of the likelihood term. One problem with this type of approach is that the corresponding posterior distribution will be non–Gaussian and computationally demanding as Monte Carlo methods have to be used. An extension of a sequential, approximate Bayesian inference method enables observations with arbitrary likelihoods to be treated, in a projected process kriging framework which is less computationally intensive. The approach is illustrated using a simulated dataset with a range of sensor models and error characteristics.

Multi-year weather data in the remote semi-arid to arid mountain region of the Saharan flank of the Atlas Mountains

In 2001, a weather and climate monitoring network was established along the temperature and aridity gradient between the sub-humid Moroccan High Atlas Mountains and the former end lake of the Middle Drâa in a pre-Saharan environment. The highest Automated Weather Stations (AWS) was installed just below the M'Goun summit at 3850 m, the lowest station Lac Iriki was at 450 m. This network of 13 AWS stations was funded and maintained by the German IMPETUS (BMBF Grant 01LW06001A, North Rhine-Westphalia Grant 313-21200200) project and since 2011 five stations were further maintained by the GERMAN DFG Fennec project (FI 786/3-1), this way some stations of the AWS network provided data for almost 12 years from 2001-2012. Standard meteorological variables such as temperature, humidity, and wind were measured at an altitude of 2 m above ground. Other meteorological variables comprise precipitation, station pressure, solar irradiance, soil temperature at different depths and for high mountain station snow water equivalent. The stations produced data summaries for 5-minute-precipitation-data, 10- or 15-minute-data and a daily summary of all other variables. This network is a unique resource of multi-year weather data in the remote semi-arid to arid mountain region of the Saharan flank of the Atlas Mountains. The network is described in Schulz et al. (2010) and its further continuation until 2012 is briefly discussed in Redl et al. (2015, doi:10.1175/MWR-D-15-0223.1) and Redl et al. (2016, doi:10.1002/2015JD024443).

Estudo da distribuição de metais em água, sedimento e organismos aquáticos de rios e reservatórios pertencentes à rede de monitoramento da qualidade dos sedimentos do estado de São Paulo, Brasil

Dissertação (Mestrado)