An uncertainty and sensitivity analysis applied to the prioritisation of pharmaceuticals as surface water contaminants from wastewater treatment plant direct emissions

In this study, the concentration probability distributions of 82 pharmaceutical compounds detected in the effluents of 179 European wastewater treatment plants were computed and inserted into a multimedia fate model. The comparative ecotoxicological impact of the direct emission of these compounds from wastewater treatment plants on freshwater ecosystems, based on a potentially affected fraction (PAF) of species approach, was assessed to rank compounds based on priority. As many pharmaceuticals are acids or bases, the multimedia fate model accounts for regressions to estimate pH-dependent fate parameters. An uncertainty analysis was performed by means of Monte Carlo analysis, which included the uncertainty of fate and ecotoxicity model input variables, as well as the spatial variability of landscape characteristics on the European continental scale. Several pharmaceutical compounds were identified as being of greatest concern, including 7 analgesics/anti-inflammatories, 3 β-blockers, 3 psychiatric drugs, and 1 each of 6 other therapeutic classes. The fate and impact modelling relied extensively on estimated data, given that most of these compounds have little or no experimental fate or ecotoxicity data available, as well as a limited reported occurrence in effluents. The contribution of estimated model input variables to the variance of freshwater ecotoxicity impact, as well as the lack of experimental abiotic degradation data for most compounds, helped in establishing priorities for further testing. Generally, the effluent concentration and the ecotoxicity effect factor were the model input variables with the most significant effect on the uncertainty of output results.

DC Fuzzy Multicriteria Approach to Increase the Probability of Delivering Power in Distribution Networks

This paper proposes a methodology to increase the probability of delivering power to any load point through the identification of new investments. The methodology uses a fuzzy set approach to model the uncertainty of outage parameters, load and generation. A DC fuzzy multicriteria optimization model considering the Pareto front and based on mixed integer non-linear optimization programming is developed in order to identify the adequate investments in distribution networks components which allow increasing the probability of delivering power to all customers in the distribution network at the minimum possible cost for the system operator, while minimizing the non supplied energy cost. To illustrate the application of the proposed methodology, the paper includes a case study which considers an 33 bus distribution network.

Formation Control Driven by Cooperative Object Tracking

In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles.

Utilização de sensores de baixo custo na medição de monóxido de carbono no ar ambiente

Nos últimos anos tem-se assistido à introdução de novos dispositivos de medição da poluição do ar baseados na utilização de sensores de baixo custo. A utilização menos complexa destes sistemas, possibilita a obtenção de dados com elevada resolução temporal e espacial, abrindo novas oportunidades para diferentes metodologias de estudos de monitorização da poluição do ar. Apesar de apresentarem capacidades analíticas distantes dos métodos de referência, a utilização destes sensores tem sido sugerida e incentivada pela União Europeia no âmbito das medições indicativas previstas na Diretiva 2008/50/CE, com uma incerteza expandida máxima de 25%. O trabalho desenvolvido no âmbito da disciplina de Projeto consistiu na escolha, caracterização e utilização em medições reais de um sensor de qualidade do ar, integrado num equipamento protótipo desenvolvido com esse fim, visando obtenção uma estimativa da incerteza de medição associada à utilização deste dispositivo através da aplicação da metodologia de demonstração de equivalência de métodos de medição de qualidade do ar definida pela União Europeia. A pesquisa bibliográfica realizada permitiu constatar que o monóxido de carbono é neste momento o parâmetro de qualidade do ar que permite ser medido de forma mais exata através da utilização de sensores, nomeadamente o sensor eletroquímico da marca Alphasense, modelo COB4, amplamente utilizado em projetos de desenvolvimento neste cotexto de monitorização ambiental. O sensor foi integrado num sistema de medição com o objetivo de poder ser utlizado em condições de autonomia de fornecimento de energia elétrica, aquisição interna dos dados, tendo em consideração ser o mais pequeno possível e de baixo custo. Foi utlizado um sistema baseado na placa Arduino Uno com gravação de dados em cartão de memória SD, baterias e painel solar, permitindo para além do registo das tensões elétricas do sensor, a obtenção dos valores de temperatura, humidade relativa e pressão atmosférica, com um custo global a rondar os 300 euros. Numa primeira fase foram executados um conjunto de testes laboratoriais que permitiram a determinação de várias características de desempenho em dois sensores iguais: tempo de resposta, a equação modelo do sensor, avaliação da repetibilidade, desvio de curto e longo termo, interferência da temperatura e histerese. Os resultados demonstraram um comportamento dos sensores muito linear, com um tempo de resposta inferior a um minuto e com uma equação modelo do sensor dependente da variação da temperatura. A estimativa da incerteza expandida laboratorial ficou, para ambos os sensores, abaixo dos 10%. Após a realização de duas campanhas reais de medição de CO em que os valores foram muito baixos, foi realizada uma campanha de quinze dias num parque de estacionamento subterrâneo que permitiu a obtenção de concentrações suficientemente elevadas e a comparação dos resultados dos sensores com o método de referência em toda a gama de medição (0 a 12 mol.mol-1). Os valores de concentração obtidos pelos dois sensores demonstraram uma excelente correlação com o método de referência (r2≥0,998), obtendo-se resultados para a estimativa da incerteza expandida de campo inferiores aos obtidos para a incerteza laboratorial, cumprindo o objetivo de qualidade de dados definido para as medições indicativas de incerteza expandida máxima de 25%. Os resultados observados durante o trabalho realizado permitiram confirmar o bom desempenho que este tipo de sensor pode ter no âmbito de medições de poluição do ar com um caracter mais indicativo.

Accounting for the dissociating properties of organic chemicals in LCIA: an uncertainty analysis applied to micropollutants in the assessment of freshwater ecotoxicity

In life cycle impact assessment (LCIA) models, the sorption of the ionic fraction of dissociating organic chemicals is not adequately modeled because conventional non-polar partitioning models are applied. Therefore, high uncertainties are expected when modeling the mobility, as well as the bioavailability for uptake by exposed biota and degradation, of dissociating organic chemicals. Alternative regressions that account for the ionized fraction of a molecule to estimate fate parameters were applied to the USEtox model. The most sensitive model parameters in the estimation of ecotoxicological characterization factors (CFs) of micropollutants were evaluated by Monte Carlo analysis in both the default USEtox model and the alternative approach. Negligible differences of CFs values and 95% confidence limits between the two approaches were estimated for direct emissions to the freshwater compartment; however the default USEtox model overestimates CFs and the 95% confidence limits of basic compounds up to three orders and four orders of magnitude, respectively, relatively to the alternative approach for emissions to the agricultural soil compartment. For three emission scenarios, LCIA results show that the default USEtox model overestimates freshwater ecotoxicity impacts for the emission scenarios to agricultural soil by one order of magnitude, and larger confidence limits were estimated, relatively to the alternative approach.

From local to global importance measures of uncertainty propagation in composite structures

The influence of uncertainties of input parameters on output response of composite structures is investigated in this paper. In particular, the effects of deviations in mechanical properties, ply angles, ply thickness and on applied loads are studied. The uncertainty propagation and the importance measure of input parameters are analysed using three different approaches: a first-order local method, a Global Sensitivity Analysis (GSA) supported by a variance-based method and an extension of local variance to estimate the global variance over the domain of inputs. Sample results are shown for a shell composite laminated structure built with different composite systems including multi-materials. The importance measures of input parameters on structural response based on numerical results are established and discussed as a function of the anisotropy of composite materials. Needs for global variance methods are discussed by comparing the results obtained from different proposed methodologies. The objective of this paper is to contribute for the use of GSA techniques together with low expensive local importance measures.

Uncertainty propagation in inverse reliability-based design of composite structures

An approach for the analysis of uncertainty propagation in reliability-based design optimization of composite laminate structures is presented. Using the Uniform Design Method (UDM), a set of design points is generated over a domain centered on the mean reference values of the random variables. A methodology based on inverse optimal design of composite structures to achieve a specified reliability level is proposed, and the corresponding maximum load is outlined as a function of ply angle. Using the generated UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on an evolutionary learning process. Then, a Monte Carlo simulation using ANN development is performed to simulate the behavior of the critical Tsai number, structural reliability index, and their relative sensitivities as a function of the ply angle of laminates. The results are generated for uniformly distributed random variables on a domain centered on mean values. The statistical analysis of the results enables the study of the variability of the reliability index and its sensitivity relative to the ply angle. Numerical examples showing the utility of the approach for robust design of angle-ply laminates are presented.

Privatization and entry of a foreign firm with demand uncertainty

We investigate the effects of trade with a foreign firm and privatization of the domestic pubUc firm on an incentive for the domestic firm to reduce costs by undertaking R&D investment, under demand uncertainty. We suppose that the domestic firm is less efficient than the foreign firm. However, the domestic firm can lower its marginal costs by conducting cost-reducing R&D investment. We examine the impacts of entry of a foreign firm, and the effects of demand uncertainty, on decisions upon cost-reducing R&D investment by the domestic firm and how these affect the domestic welfare.