Seismic data clustering management system

This is the abstract of the paper given at the conference. Copyright @ 2011 The Authors.Over the last years, seismic images have increasingly played a vital role to the study of earthquakes. The large volume of seismic data that has been accumulated has created the need to develop sophisticated systems to manage this kind of data. Seismic interpretation can play a much more active role in the evaluation of large volumes of data by providing at an early stage vital information relating to the framework of potential producing levels. [1] This work presents a novel method to manage and analyse seismic data. The data is initially turned into clustering maps using clustering techniques [2] [3] [4] [5] [6], in order to be analysed on the platform. These clustering maps can then be analysed with the friendly-user interface of Seismic 1 which is based on .Net framework architecture [7]. This feature permits the porting of the application in any Windows – based computer as also to many other Linux based environments, using the Mono project functionality [8], so it can run an application using the No-Touch Deployment [7]. The platform supports two ways of processing seismic data. Firstly, a fast multifunctional version of the classical region-growing segmentation algorithm [9], [10] is applied to various areas of interest permitting their precise definition and labelling. Moreover, this algorithm is assigned to automatically allocate new earthquakes to a particular cluster based upon the magnitude of the centre of gravity of the existing clusters; or create a new cluster if all centers of gravity are above a predefined by the user upper threshold point. Secondly, a visual technique is used to record the behaviour of a cluster of earthquakes in a designated area. In this way, the system functions as a dynamic temporal simulator which depicts sequences of earthquakes on a map [11]

Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r = 0.13–0.83), NO (r = 0.24–0.93), O (r = 0.22–0.84), PM10 (r = 0.54–0.83), PM2.5 (r = 0.33–0.40) and SO (r = 0.49–0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements.Peer Reviewe

Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise - Part II

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r2=0.13-0.83), NO2 (r2=0.24-0.93), O3 (r2=0.22-0.84), PM10 (r2=0.54-0.83), PM2.5 (r2=0.33-0.40) and SO2 (r2=0.49-0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements

Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise

The 1st EuNetAir Air Quality Joint Intercomparison Exercise organized in Aveiro (Portugal) from 13th–27th October 2014, focused on the evaluation and assessment of environmental gas, particulate matter (PM) and meteorological microsensors, versus standard air quality reference methods through an experimental urban air quality monitoring campaign. The IDAD-Institute of Environment and Development Air Quality Mobile Laboratory was placed at an urban traffic location in the city centre of Aveiro to conduct continuous measurements with standard equipment and reference analysers for CO, NOx, O3, SO2, PM10, PM2.5, temperature, humidity, wind speed and direction, solar radiation and precipitation. The comparison of the sensor data generated by different microsensor-systems installed side-by-side with reference analysers, contributes to the assessment of the performance and the accuracy of microsensor-systems in a real-world context, and supports their calibration and further development. The overall performance of the sensors in terms of their statistical metrics and measurement profile indicates significant differences in the results depending on the platform and on the sensors considered. In terms of pollutants, some promising results were observed for O3 (r2: 0.12–0.77), CO (r2: 0.53–0.87), and NO2 (r2: 0.02–0.89). For PM (r2: 0.07–0.36) and SO2 (r2: 0.09–0.20) the results show a poor performance with low correlation coefficients between the reference and microsensor measurements. These field observations under specific environmental conditions suggest that the relevant microsensor platforms, if supported by the proper post processing and data modelling tools, have enormous potential for new strategies in air quality control

International Journal of Sustainable Development and Planning.

The International Journal of Sustainable Development and Planning is an interdisciplinary journal covering the subjects of environmental design and planning, environmental management, spatial planning, environmental planning, environmental management and sustainable development in an integrated way as well as in accordance with the principles of sustainability. In the beginning of the 21st century, despite major scientific and technological accomplishments, the struggle for a cleaner environment as well as for rational organization of space is not settled. It is clear to us that environmentalists, planners, policy makers, engineers and economists have to work together in order to ensure that environmental protection, spatial co-ordination and economic development could all be achieved without compromising the ability of future generations to meet their own requirements. In recent years, an increase in spatial and environmental problems in many countries has led to a crisis in environmental planning and management. The increasing urbanization of the world coupled with the issues of environmental pollution, resource shortages and economic restructuring demand that a lot of effort will be required in order to make our cities sustainable. Moreover, problems of sustainable planning and management are not restricted to urban areas, since rural areas face serious environmental challenges. The aim of the International Journal of Sustainable Development and Planning is to inform its readers about all aspects of environmental planning and management. The journal includes subjects ranging from social to technical environmental management issues, having always as an axis the concept of sustainable planning and development