Methane Mitigation:Methods to Reduce Emissions, on the Path to the Paris Agreement

The atmospheric methane burden is increasing rapidly, contrary to pathways compatible with the goals of the 2015 United Nations Framework Convention on Climate Change Paris Agreement. Urgent action is required to bring methane back to a pathway more in line with the Paris goals. Emission reduction from “tractable” (easier to mitigate) anthropogenic sources such as the fossil fuel industries and landfills is being much facilitated by technical advances in the past decade, which have radically improved our ability to locate, identify, quantify, and reduce emissions. Measures to reduce emissions from “intractable” (harder to mitigate) anthropogenic sources such as agriculture and biomass burning have received less attention and are also becoming more feasible, including removal from elevated-methane ambient air near to sources. The wider effort to use microbiological and dietary intervention to reduce emissions from cattle (and humans) is not addressed in detail in this essentially geophysical review. Though they cannot replace the need to reach “net-zero” emissions of CO2, significant reductions in the methane burden will ease the timescales needed to reach required CO2 reduction targets for any particular future temperature limit. There is no single magic bullet, but implementation of a wide array of mitigation and emission reduction strategies could substantially cut the global methane burden, at a cost that is relatively low compared to the parallel and necessary measures to reduce CO2, and thereby reduce the atmospheric methane burden back toward pathways consistent with the goals of the Paris Agreement

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

How much does a man cost? A dirty, dull, and dangerous application

Thesis (M.A.) University of Alaska Fairbanks, 2017This study illuminates the many abilities of Unmanned Aerial Vehicles (UAVs). One area of importance includes the UAV's capability to assist in the development, implementation, and execution of crisis management. This research focuses on UAV uses in pre and post crisis planning and accomplishments. The accompaniment of unmanned vehicles with base teams can make crisis management plans more reliable for the general public and teams faced with tasks such as search and rescue and firefighting. In the fight for mass acceptance of UAV integration, knowledge and attitude inventories were collected and analyzed. Methodology includes mixed method research collected by interviews and questionnaires available to experts and ground teams in the UAV fields, mining industry, firefighting and police force career field, and general city planning crisis management members. This information was compiled to assist professionals in creation of general guidelines and recommendations for how to utilize UAVs in crisis management planning and implementation as well as integration of UAVs into the educational system. The results from this study show the benefits and disadvantages of strategically giving UAVs a role in the construction and implementation of crisis management plans and other areas of interest. The results also show that the general public is lacking information and education on the abilities of UAVs. This education gap shows a correlation with negative attitudes towards UAVs. Educational programs to teach the public benefits of UAV integration should be implemented

The fourth-revolution in the water sector encounters the digital revolution

The so-called fourth revolution in the water sector will encounter the Big data and Artificial Intelligence (AI) revolution. The current data surplus stemming from all types of devices together with the relentless increase in computer capacity is revolutionizing almost all existing sectors, and the water sector will not be an exception. Combining the power of Big data analytics (including AI) with existing and future urban water infrastructure represents a significant untapped opportunity for the operation, maintenance, and rehabilitation of urban water infrastructure to achieve economic and environmental sustainability. However, such progress may catalyze socio-economic changes and cross sector boundaries (e.g., water service, health, business) as the appearance of new needs and business models will influence the job market. Such progress will impact the academic sector as new forms of research based on large amounts of data will be possible, and new research needs will be requested by the technology industrial sector. Research and development enabling new technological approaches and more effective management strategies are needed to ensure that the emerging framework for the water sector will meet future societal needs. The feature further elucidates the complexities and possibilities associated with such collaborations.Manel Garrido-Baserba and Diego Rosso acknowledge the United States Department of Energy (CERC-WET US Project 525 2.5). Lluís Corominas acknowledges the Ministry of Economy and competitiveness for the Ramon and Cajal grant (RYC2013-465 14595) and the following I3. We thank Generalitat de Catalunya through Consolidated Research Group 2017 SGR 1318. ICRA researchers acknowledge funding from the CERCA program.Peer ReviewedPostprint (author's final draft

Water demand management in Mediterranean regions

Water sustainability needs a balance between demand and availability: 1) Water demand management: demand may be managed by suppliers and regulations responsible persons, using measures like invoicing, consumptions measurement and users education in water conservation measures; 2) Augmentation of water supply: availibility may be augmented by infrastructural measures, waste water reuse, non-conventional resources and losses reduction. Water Demand Management is about achieving a reduction in the use of water resources, normally through increased efficiency of water application. The main objective of this paper is the application of these concepts to Mediterranean regions.

Hydrolink 2021/2. Artificial Intelligence

Topic: Artificial Intelligenc

CyPhERS: A cyber-physical event reasoning system providing real-time situational awareness for attack and fault response

Cyber-physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged by the complex interdependency of numerous physical and digital components, requiring to take cyber attacks and physical failures equally into account. The online event identification problem is further complicated through the lack of historical observations of critical but rare events, and the continuous evolution of cyber attack strategies. This work introduces and demonstrates CyPhERS, a Cyber-Physical Event Reasoning System. CyPhERS provides real-time information pertaining the occurrence, location, physical impact, and root cause of potentially critical events in CPSs, without the need for historical event observations. Key novelty of CyPhERS is the capability to generate informative and interpretable event signatures of known and unknown types of both cyber attacks and physical failures. The concept is evaluated and benchmarked on a demonstration case that comprises a multitude of attack and fault events targeting various components of a CPS. The results demonstrate that the event signatures provide relevant and inferable information on both known and unknown event types

Contributions to time series data mining towards the detection of outliers/anomalies

148 p.Los recientes avances tecnológicos han supuesto un gran progreso en la recogida de datos, permitiendo recopilar una gran cantidad de datos a lo largo del tiempo. Estos datos se presentan comúnmente en forma de series temporales, donde las observaciones se han registrado de forma cronológica y están correlacionadas en el tiempo. A menudo, estas dependencias temporales contienen información significativa y útil, por lo que, en los últimos años, ha surgido un gran interés por extraer dicha información. En particular, el área de investigación que se centra en esta tarea se denomina minería de datos de series temporales.La comunidad de investigadores de esta área se ha dedicado a resolver diferentes tareas como por ejemplo la clasificación, la predicción, el clustering o agrupamiento y la detección de valores atípicos/anomalías. Los valores atípicos o anomalías son aquellas observaciones que no siguen el comportamiento esperado en una serie temporal. Estos valores atípicos o anómalos suelen representar mediciones no deseadas o eventos de interés, y, por lo tanto, detectarlos suele ser relevante ya que pueden empeorar la calidad de los datos o reflejar fenómenos interesantes para el analista.Esta tesis presenta varias contribuciones en el campo de la minería de datos de series temporales, más específicamente sobre la detección de valores atípicos o anomalías. Estas contribuciones se pueden dividir en dos partes o bloques. Por una parte, la tesis presenta contribuciones en el campo de la detección de valores atípicos o anomalías en series temporales. Para ello, se ofrece una revisión de las técnicas en la literatura, y se presenta una nueva técnica de detección de anomalías en series temporales univariantes para la detección de fugas de agua, basada en el aprendizaje autosupervisado. Por otra parte, la tesis también introduce contribuciones relacionadas con el tratamiento de las series temporales con valores perdidos y demuestra su aplicabilidad en el campo de la detección de anomalías