Regional and urban air quality modelling under a climate change scenario

The better understanding of the interactions between climate change and air quality is an emerging priority for research and policy. Climate change will bring changes in the climate system, which will affect the concentration and dispersion of air pollutants. The main objective of the current study is to assess the impacts of climate change on air quality in 2050 over Portugal and Porto urban area. First, an evaluation and characterization of the air quality over mainland Portugal was performed for the period between 2002 and 2012. The results show that NO2, PM10 and O3 are the critical pollutants in Portugal. Also, the influence of meteorology on O3, NO2 and PM10 levels was investigate in the national main urban areas (Porto and Lisboa) and was verified that O3 has a statistically significant relationship with temperature in most of the components. The results also indicate that emission control strategies are primary regulators for NO2 and PM10 levels. After, understanding the national air quality problems and the influence that meteorology had in the historical air quality levels, the air quality modelling system WRF-CAMx was tested and the required inputs for the simulations were prepared to fulfil the main goal of this work. For the required air quality modelling inputs, an Emission Projections under RCP scenarios (EmiPro-RCP) model was developed to assist the estimation of future emission inventories for GHG and common air pollutants. Also, the current emissions were estimated for Portugal with a higher detailed disaggregation to improve the performance of the air quality simulations. The air quality modelling system WRF/CAMx was tested and evaluated over Portugal and Porto urban area and the results point out that is an adequate tool for the analysis of air quality under climate change. For this purpose, regional simulations of air quality during historical period and future (2045-2050) were conducted with CAMx version 6.0 to evaluate the impacts of simulated future climate and anthropogenic emission projections on air quality over the study area. The climate and the emission projections were produced under the RCP8.5 scenario. The results from the simulations point out, that if the anthropogenic emissions keep the same in 2050, the concentrations of NO2, PM10 and O3 will increase in Portugal. When, besides the climate change effects, is consider the projected anthropogenic emissions the annual mean concentrations of NO2 decrease significantly in Portugal and Porto urban area, and on the contrary the annual mean PM10 concentrations increases in Portugal and decrease in Porto urban area. The O3 results are mainly caused by the reduction of ozone precursors, getting the higher reductions in urban areas and increases in the surrounding areas. All the analysis performed for both simulations for Porto urban area support that, for PM10 and O3, there will be an increase in the occurrence of extreme values, surpassing the annual legislated parameters and having more daily exceedances. This study constitutes an innovative scientific tool to help in future air quality management in order to mitigate future climate change impacts on air quality.

Soft-computing techniques applied to artificial tissue temperature estimation

Tese dout., Engenharia electrónica e computação - Processamento de sinal, Universidade do Algarve, 2008

A produção de bioetanol e a mitigação dos gases de efeito de estufa: o contributo das culturas energéticas no regadio de Alqueva

Dissertação mest., Gestão Sustentável dos Espaços Rurais, Universidade do Algarve, 2008

Bottom-up regulation of phytoplankton in the Guadiana estuary

Tese dout., Ciências do Mar (Ecologia Marinha), Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2010

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.

A greenhouse climate multivariable predictive controller

A multivariable predictive controller was implemented to regulate the air temperature, humidity and CO2 concentration for a greenhouse located in the north of Portugal. The controller outputs are computed in order to optimise the future behaviour of the greenhouse environment, concerning the set-point accuracy and the minimization of energy inputs.

Identification of greenhouse climate dynamic models

In this paper climate discrete-time dynamic models for the inside air temperature of two different greenhouses are identified, using data acquired during two different periods of the year. These models employ data from air temperature and relative humidity (both outside and inside the greenhouse), solar radiation, wind speed, and control inputs (ventialtion, etc.).

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

In this study, Artificial Neural Networks are applied to multistep long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiationmodels are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods.

Modelos e estratégias de controlo ambientais para estufas agrícolas

For a greenhouse with a double polyethylene cover, it will be presented a dynamic climate transfer function and an adaptive controller for the air temperature. The model employ data acquired from the outside weather and from the heating and cooling inputs.

O uso do data envelopment analysis para avaliação e melhoria de desempenho no setor bancário: estudo de caso

O presente estudo aborda o uso do Data Envelopment Analysis (DEA) para avaliar e melhorar o desempenho no setor bancário. Consiste num estudo de caso aplicado a um dos maiores bancos portugueses. O seu objetivo, é avaliar a eficiência relativa das 333 agências bancárias que formam um dos dois departamentos comerciais do banco. Pretende-se identificar boas práticas e verificar a sua aplicabilidade nas unidades menos eficientes por forma a contribuir para a melhoria do desempenho global da instituição. Ao mesmo tempo, comparam-se os resultados de eficiência obtidos com o desempenho das unidades de negócio no cumprimento dos seus objetivos comerciais. Procura-se, assim, analisar a eventual existência de correlação entre eficiência e eficácia, ou seja, se as agências bancárias mais eficientes são também as mais eficazes. Para o efeito, é construído um modelo DEA que, considera em simultâneo, vários inputs e outputs. No modelo, as variáveis de input agregam os custos das unidades de negócio e as variáveis de output, os seus principais proveitos e alguns dos aspetos estratégicos que o banco pretende maximizar. Os resultados obtidos, identificam não só as agências bancárias menos eficientes, como assinalam aquelas que sendo semelhantes e eficientes, lhes podem servir de referência para a melhoria do seu desempenho. Tendo em conta, o caráter formativo que se pretende para o estudo, e de modo a facilitar a aceitação pelos decisores, procura-se verificar a exequibilidade das propostas apresentadas. O documento conclui pela existência, nas unidades de negócio analisadas, de uma correlação positiva fraca entre eficiência e eficácia, ainda assim estatisticamente significante. Por último, assinala a importância da metodologia DEA, enquanto medida de complementaridade de outras técnicas de controlo de gestão, em particular nas organizações que adotam a gestão por objetivos.

Modelfest and contrast-interrelation-function data predicted by a retinal model

The automatic implementation of decoders for a visual perception is achieved as follows. The action described by a production rule is realized by means of the decoder in which a pattern of connections coreesponds to that of stimuli. According to "S.Karasawa,(Proc. of CCCT, Vol.5, pp.194-1999, Austin, Texas, August, 2004)", each program mable controllable connection among inputs is realized by a floating gate avalanche injection MOS FET, where inverted signals are used at writing, and the detection of matching between inputs and connections is carried out by using the signal source in which low level signal is provided via comparatively smaller resistance than high level.

Habitat use and diel vertical migration of bigeye thresher shark: overlap with pelagic longline fishing gear

Pelagic longliners targeting swordfish and tunas in oceanic waters regularly capture sharks as bycatch, including currently protected species as the bigeye thresher, Alopias superciliosus. Fifteen bigeye threshers were tagged with pop-up satellite archival tags (PSATs) in 2012-2014 in the tropical northeast Atlantic, with successful transmissions received from 12 tags for a total of 907 tracking days. Marked diel vertical movements were recorded on all specimens, with most of the daytime spent in deeper colder water (mean depth = 353 m, SD = 73; mean temperature = 10.7 °C, SD = 1.8) and nighttime spent in warmer water closer to the surface (mean depth = 72 m, SD = 54; mean temperature = 21.9 °C, SD = 3.7). The operating depth of the pelagic longline gear was measured with Minilog Temperature and Depth Recorders (TDRs), and the overlap with habitat utilization was calculated. Overlap is taking place mainly during the night and is higher for juveniles. The results presented herein can be used as inputs for Ecological Risk Assessments for bigeye threshers captured in oceanic tuna fisheries, and serve as a basis for efficient management and conservation of this vulnerable shark species.

Catchment influences on the hydrological flows to lake terra alta (Linhares, ES, Brazil) and ecohydrology perspectives

Dissertação de mestrado, Ecohidrologia, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Hotel Valuation Software, Version 4.0

[Updated August 2016] The Hotel Valuation Software, freely available from Cornell’s Center for Hospitality Research, has been updated to reflect the many changes in the 11th Edition of the Uniform System of Accounts for the Lodging Industry (USALI). Version 4.0 of the Hotel Valuation Software provides numerous enhancements over the original tool from 2011. In addition to a significant increase in functionality and an update to reflect the 11th edition of the USALI, Version 4.0 takes advantage of the power of the latest release of Microsoft Excel®. Note that Version 4.0 works only on a PC running Microsoft Windows, it does not work on a Mac running OS X. Users desiring an OS X compatible version should click here (Labeled as Version 2.5). 酒店评估软件手册和三个程序（点击这里 ) Users desiring a Mandarin version of the Hotel Valuation Software should click here The Hotel Valuation Software remains the only non-proprietary computer software designed specifically to assist in the preparation of market studies, forecasts of income and expense, and valuations for lodging property. The software provides an accurate, consistent, and cost-effective way for hospitality professionals to forecast occupancy, revenues and expenses and to perform hotel valuations. Version 4.0 of the Hotel Valuation Software includes the following upgrades – a complete update to reflect the 11th edition of the USALI – the most significant change to the chart of accounts in a generation, an average daily rate forecasting tool, a much more sophisticated valuation module, and an optional valuation tool useful in periods of limited capital liquidity. Using established methodology, the Hotel Valuation Software is a sophisticated tool for lodging professionals. The tool consists of three separate software programs written as Microsoft Excel files and a software users' guide. The tool is provided through the generosity of HVS and the School of Hotel Administration. The three software modules are: Room Night Analysis and Average Daily Rate: Enables the analyst to evaluate the various competitive factors such as occupancy, average room rate, and market segmentation for competitive hotels in a local market. Calculates the area-wide occupancy and average room rate, as well as the competitive market mix. Produce a forecast of occupancy and average daily rate for existing and proposed hotels in a local market. The program incorporates such factors as competitive occupancies, market segmentation, unaccommodated demand, latent demand, growth of demand, and the relative competitiveness of each property in the local market. The program outputs include ten-year projections of occupancy and average daily rate. Fixed and Variable Revenue and Expense Analysis: The key to any market study and valuation is a supportable forecast of revenues and expenses. Hotel revenue and expenses are comprised of many different components that display certain fixed and variable relationships to each other. This program enables the analyst to input comparable financial operating data and forecast a complete 11-year income and expense statement by defining a small set of inputs: The expected future occupancy levels for the subject hotel Base year operating data for the subject hotel Fixed and variable relationships for revenues and expenses Expected inflation rates for revenues and expenses Hotel Capitalization Software: A discounted cash flow valuation model utilizing the mortgage-equity technique forms the basis for this program. Values are produced using three distinct underwriting criteria: A loan-to-value ratio, in which the size of the mortgage is based on property value. A debt coverage ratio (also known as a debt-service coverage ratio), in which the size of the mortgage is based on property level cash flow, mortgage interest rate, and mortgage amortization. A debt yield, in which the size of the mortgage is based on property level cash flow. By entering the terms of typical lodging financing, along with a forecast of revenue and expense, the program determines the value that provides the stated returns to the mortgage and equity components. The program allows for a variable holding period from four to ten years The program includes an optional model useful during periods of capital market illiquidity that assumes a property refinancing during the holding period

HOTVal Toolkit

**************************************************************************** Scroll down to "Additional Files" to access the HOTVal Toolkit. **************************************************************************** HOTVal is a hotel valuation spreadsheet based on a regression model discussed in the Center for Real Estate and Finance at Cornell called Cornell Hotel Indices: Second Quarter 2012: The Trend is Our Friend by Crocker H. Liu, Adam D. Nowak, and Robert M. White, Jr. The model which will be continually updated, provides a rough estimation of the value of a hotel property once the user inputs information on whether the hotel is a large or small hotel, the year and quarter of the valuation, the state where the property is located, the number of rooms, the number of floors, the land area of the hotel property, the actual age of the hotel and whether the hotel is located in a Gateway city. For the first three inputs as well as the last input, if the user clicks on a cell highlighted in yellow, a pull down menu will appear to expedite inputting. The model is provided as a free public service by The Center for Real Estate and Finance at the School of Hotel Administration at Cornell University to academics and practitioners on an as-is, best-effort basis with no warranties or claims regarding its usefulness or implications. The estimates should be considered preliminary and subject to revision. *This October 2016 version updates the previous Hotel Valuation model, published in 2012 , provides valuation estimates up to and including the third quarter of 2016.