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Oligotrophic catchments with short spatey streams, upland lakes and peaty soils characterise northwest European Atlantic coastal regions. These catchments are important biodiversity refuges, particularly for sensitive diadromous fish populations but are subject to changes in land use and land management practices associated with afforestation, agriculture and rural development. Quantification of the degree of catchment degradation resulting from such anthropogenic impacts is often limited by a lack of long-term baseline data in what are generally relatively isolated, poorly studied catchments. This research uses a combination of palaeolimnological (radiometrically-dated variations in sedimentary geochemical elements, pollen, diatoms and remains of cladocera), census, and instrumental data, along with hindcast estimates to quantify environmental changes and their aquatic impacts since the late 19th century. The most likely drivers of any change are also identified. Results confirm an aquatic biotic response (phyto- and zooplankton) to soil erosion and nutrient enrichment associated with the onset of commercial conifer afforestation, effects that were subsequently enhanced as a result of increased overgrazing in the catchment and, possibly, climate warming. The implications for the health of aquatic resources in the catchment are discussed Environmental Protection Agency in Ireland (ILLUMINATE 2005-W-MS-40, P.McGinnity was supported by the Beaufort Marine Research Award in Fish Population Genetics funded by the Irish Government under the Sea Change Programme.

The SmartBay NIAP fund was made available in 2012 through Dublin City University over a two year period to enable researchers to access the SmartBay Ireland National Test and Demonstration Facility in Galway Bay. Research proposals were invited for funding under a number of activity types that are in line with the objectives of the SmartBay PRTLI Cycle 5 programme. This fund provided small awards (typically €2-25K) to research teams through a national competitive process, which was open to all higher education institutions on the island of Ireland. There were both open and biannual calls. The SmartBay NIAP fund was established to enable researchers in academia and industry to access the SmartBay Ireland national test and demonstration infrastructure. Proposals to access the infrastructure were brief and required information on the researcher(s), a description of the proposed research and its potential impact to the research team arising from the access to SmartBay Ireland. Marine Institute

Cholera epidemics have a recorded history in eastern Congo dating to 1971. A study was conducted to find out the linkage between climate variability/change and cholera outbreak and to assess the related economic cost in the management of cholera in Congo.This study integrates historical data (20 years) on temperature and rainfall with the burden of disease from cholera in South-Kivu province, eastern Congo.Analyses of precipitation and temperatures characteristics in South-Kivu provinces showed that cholera epidemics are closely associated with climatic factors variability. Peaks in Cholera new cases were in synchrony with peaks in rainfalls. Cholera infection cases declined significantly (P<0.05) with the rise in the average temperature. The monthly number of new Cholera cases oscillated between 5 and 450. For every rise of the average temperature by 0.35 °C to 0.75 °C degree Celsius, and for every change in the rainfall variability by 10-19%, it is likely cholera infection risks will increase by 17 to 25%. The medical cost of treatment of Cholera case infection was found to be of US$50 to 250 per capita. The total costs of Cholera attributable to climate change were found to fall in the range of 4 to 8% of the per capita in annual income in Bukavu town.It is likely that high rainfall favor multiplication of the bacteria and contamination of water sources by the bacteria (Vibrio cholerae). The consumption of polluted water, promiscuity, population density and lack of hygiene are determinants favoring spread and infection of the bacteria among human beings living in over-crowded environments.

{"references": ["U.S. Energy Information Administration. \"How much energy is\nconsumed in residential and commercial buildings in the United States?\"\nAvailable at: http://www.eia.gov/tools/faqs/faq.cfm?id=86&t=1", "S. Darby, \"The effectiveness of feedback on energy consumption.\"\nEnvironmental Change Institute, University of Oxford, 2006. Available\nat: http://www.globalwarmingisreal.com/energyconsump-feedback.pdf.\nVisited: September 2015", "J. S. John, \"Putting energy disaggregation tech to the test,\" November,\n2013. Greentech Media. Available at:\nhttp://www.greentechmedia.com/articles/read/putting-energydisaggregation-tech-to-the-test.\nVisited: September 2015", "A. Zoha, A. Gluhak, M. A. Imran, S. Rajasegarar, \"Non-intrusive load\nmonitoring approaches for disaggregated energy sensing: a survey,\"\nSensors, vol. 12, no. 12, pp. 16838-16866, December 2012.", "G. W. Hart, \"Nonintrusive appliance load monitoring,\" in Proc. of the\nIEEE, vol. 80, pp. 1870-1891, December 1992.", "M. Baranski, J. Voss, \"Non-intrusive appliance load monitoring based\non Optical Sensor,\" IEEE Bologna PowerTech Conference, Bologna,\nItaly, June 2003. Available at:\nhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1304732", "L. Farinaccio, R. Zmeureanu, \"Using a pattern recognition approach to\ndisaggregate the total electricity consumption in a house into the major\nen-uses,\" Elsevier, Energy and Buildings, vol. 30, no. 3, pp. 245-259,\nAugust 1999.", "J. M. Abreu, F. C. Pereira, P. Ferr\u00e3o, \"Using pattern recognition to\nidentify habitual behavior in residential electricity consumption,\"\nElsevier, Energy and Buildings, vol. 49, pp. 479-487, June 2012.", "C. Beckel, L. Sadamori, S. Santini, \"Automatic socio-economic\nclassification of households using electricity consumption data,\" in\nProc. of the 4th international conference on future energy systems, New\nYork, 2013, pp. 75-86.\n[10] H. Zhao, F. Magoul\u00e8s, \"A review on the prediction of building energy\nconsumption,\" Elsevier, Renewable and Sustainable Energy Reviews,\nvol. 16, no. 6, pp. 3586-3592, August 2012.\n[11] G. K. F. Tso, K. K. W. Yau, \"Predicting electricity energy consumption:\nA comparison of regression analysis, decision tree and neural networks,\"\nElsevier, Energy, vol. 32, no. 9, pp. 1761-1768, September 2007.\n[12] F. Farzan, S. A. Vaghefi, K. Mahani, M. A. Jafari, J. Gong, \"Operational\nplanning for multi-building portfolio in an uncertain energy market,\"\nElsevier, Energy and Buildings, vol. 103, pp. 271-283, September 2015."]} Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.

Climate change is one of the preeminent policy issues of our day, and the social cost of carbon (SCC) is one of the foremost tools for determining the socially optimal policy response. The SCC is estimated using Integrated Assessment Models (IAMs), of which Nordhaus’ DICE is the oldest and one of the best respected. These numerical models capture the various steps in the climate and economic processes that translate a marginal unit of CO2 emissions into economic damage. While accuracy at each of these steps is necessary to precisely estimate the SCC, correct calibrating the climate damage function, which translates a temperature change into a percentage change in GDP, is critical. Calibration of the damage function determines which climate damages are included and excluded from the cost of carbon. Traditionally, Nordhaus calibrated the DICE damage function using a global damage estimate calculated by aggregating a series of region-sector specific damage estimates (Nordhaus and Boyer, 2000; Nordhaus, 2008). However, in DICE-2013, Nordhaus moved to calibrating the DICE damage function using a meta-analysis at the global scale (Nordhaus and Sztorc, 2013). This paper critiques this meta-analysis approach as it is currently applied and re-estimates the DICE-2013 damage function using up-to-date meta-analysis techniques to more accurately reflect climate damages and the uncertainty underlying them. This paper finds that DICE-2013 damage function significantly under-estimates climate damages by a factor of two to three. This is a working paper.

The goal of this study is to determine the difference in CO2 emissions between 2019-2020 and 2020-2021, more specifically during lockdown periods during the COVID-19 pandemic. In the beginning of the pandemic, most countries were forced into lockdowns, and a countless number of people had to continue their daily work from home in isolation. Previously, people would go to an office or to school and leave their houses empty for eight hours, without having lights or any electronics on. Because of this, there should be a direct correlation between electricity usage before and during lockdowns, as a private residence should have higher electricity consumption during 2020-2021, when they are at home. Using machine learning, we will investigate to see if COVID-19 affected CO2 emissions as a result of more electricity usage in private residences. A model will be made to predict what the CO2 emissions would be for 2019-2020, based on electricity usage data from 2020-2021. Then, the real CO2 emissions from 2019-2020 will be compared with the model’s predicted values, and the difference will indicate if COVID-19 caused an inconsistency between actual and predicted CO2 emissions. Factors that were taken into account when making a model were independent variables relating to outdoor conditions, the number of people living in the house, and the temperature that the thermostat is set at, making the response variable CO2 emissions.

Photovoltaic (PV) technologies constitute a leading renewable energy source with a worldwide installed capacity of 135 GW in 2013 that may increase to nearly 4700 GW in 2050. To achieve this production level while minimizing environmental impacts, decision makers must rely at national level on relevant technological, economic and planning aspects which are highly geographically dependent. The access to performance data is a critical issue in the decision-making process and determines the successful development of efficient PV systems. For this reason, a new interactive tool is proposed here to provide the users with easy-to-use data and maps for the solar irradiation and screening level environmental results of representative PV technologies. The calculation procedures account for the geographic location and the PV system layout (installation, orientation and inclination angles). The tool has a worldwide coverage with a multi-criteria scope, both in terms of the numerous technological scenarios and of the wide range of environmental indicators. Moreover, the user is given the possibility to compare the PV environmental performance to the corresponding country electricity mix environmental footprint. 32nd European Photovoltaic Solar Energy Conference and Exhibition; 2869-2873

Power generation from photovoltaic systems is highly variable due to its dependence on meteorological conditions. An efficient use of this fluctuating energy source requires reliable forecast information for management and operation strategies. Due to the strong increase of solar power generation the prediction of solar yields becomes more and more important. As a consequence, in the last years various research organisations and companies have developed different methods to forecast irradiance as a basis for respective power forecasts. For the end-users of these forecasts it is important that standardized methodology is used when presenting results on the accuracy of a prediction model in order to get a clear idea on the advantages of a specific approach. In this paper we introduce a benchmarking procedure to asses the accuracy of irradiance forecasts and compare different approaches of forecasting. The evaluation shows a strong dependence of the forecast accuracy on the climatic conditions. For Central European stations the relative rmse ranges from 40 % to 60 %, for Spanish stations relative rmse values are in the range of 20 % to 35 %. 24th European Photovoltaic Solar Energy Conference, 21-25 September 2009, Hamburg, Germany; 4199-4208

In this paper, we have studied the solar radiation data available at two meteorological stations located in the south of Tunisia. Measurements of global solar radiation on horizontal surface are compared to predictions made by different methods. The first method is based on Angström-Prescott formula which correlates relative global solar radiation H/H0 to corresponding relative duration of bright sunshine SS/SS0. The second method, a model due to Mechlouch et al., uses cloud cover N, the hours of the day t and the quantum of the year q. The third method, an empirical relation due to Sivkov, uses the monthly sunshine duration nm and the noon altitude of the sun h. The models are compared and tested on the basis of statistical error tests (MBE, RMSE, MPE and R2) and the results are presented.