Evaluation methods for air pollution levels considering the meteorological parameters in the studied area

The aim of the present thesis is the development of a set of methods in order to evaluate the air pollution levels taking into account the climate characteristics and meteorological parameters of the area under study. Specific tools are suggested for determining the air quality levels in urban scale in relation to available air quality concentrations and meteorological data using statistical methods. Furthermore, a data assimilation technique is developed, in order to investigate any improvement that may arise from its application in the results of atmospheric dispersion models.Σκοπός της διατριβής είναι η ανάπτυξη μεθοδολογίας αποτίμησης της ατμοσφαιρικής ρύπανσης με συνεκτίμηση των κλιματικών χαρακτηριστικών και μετεωρολογικών παραμέτρων της περιοχής ενδιαφέροντος. Προτείνονται συγκεκριμένα εργαλεία πρόλεξης της ποιότητας αέρα σε αστική κλίμακα λαμβάνοντας υπ’ όψιν τη δυνατότητα διασύνδεσης μετεωρολογικών δεδομένων και στοιχείων ποιότητας αέρα με τη χρήση στατιστικών μεθόδων. Παράλληλα, αναπτύσσεται και εφαρμόζεται η μέθοδος της αφομοίωσης δεδομένων με σκοπό τη διερεύνηση της καταλληλότητάς της στην προσπάθεια βελτίωσης των αποτελεσμάτων των μοντέλων διασποράς που επιτυγχάνεται με την αξιοποίηση εκ των υστέρων διαθέσιμων δεδομένων

Previsão de demanda: uma aplicação dos modelos Box-Jenkins na área de assistência técnica de computadores pessoais Demand forecasting: an application of the Box-Jenkins models in the technical assistance of personal computer

A previsão de demanda é uma atividade importante para auxiliar na determinação dos recursos necessários para a empresa. Neste artigo, a metodologia de Box-Jenkins foi utilizada para analisar dados históricos de uma empresa de assistência técnica de computadores pessoais e obter previsões do número de atendimentos. A empresa estudada apresenta três tipos de clientes diferenciados: contratos, garantia e avulsos. Como cada segmento de clientes tem suas peculiaridades, a previsão de demanda foi direcionada a cada tipo, buscando representar o comportamento de tendência e a sazonalidade por meio dos modelos de Box-Jenkins. A obtenção dos modelos mais adequados foi baseada na análise de gráficos e em testes estatísticos próprios da metodologia, os quais subsidiaram a decisão de adotar o modelo AR(1) para prever o número de atendimentos dos clientes tipo contrato, o modelo ARIMA(2,1,0) para os clientes tipo garantia e um modelo sazonal SARIMA(0,1,0)(0,1,1)12 para os clientes tipo avulsos.<br>Demand forecasting is an important tool to aid on the determination of necessary resources of a given company. In this paper, the Box-Jenkins methodology was applied to analyze historical data of a personal computer repair company and provide a forecast for the number of service calls. The company studied presents three segments of clients: contracts, warranty, and on-call. As each client has it own characteristics, in order to better represent tendency and seasonality behavior through the Box-Jenkins models, a specific forecasting model was developed for each segment. The choice of the optimum models were based into graphic analysis and statistical tests, which lead to the decision of adopting the AR(1) model to foresee the number of contract clients, the ARIMA(2,1,0) model for warranty clients and the SARIMA(0,1,0)(0,1,1)12 seasonal model for on-call clients