The Volunteer Functions Inventory (VFI) is an instrument widely used to assess volunteers’ motivation based on the Functionalist Model of Omoto and Snyder. It assesses six factors that reflect several motivational functions. The VFI has been translated into various languages and validated in different cultural contexts, but some studies have reported different factor structures (e.g., five or four factors). In the Portuguese context, previous studies have also shown inconsistent results. The aim of this study was to adapt and validate the VFI for Portuguese volunteers, testing several alternative models (nine models) using confirmatory factor analysis. The sample comprised 468 volunteers (76.3% women), aged from 13 to 81 years (M = 36.66, SD = 14.93). The results support the original interrelated six-factor model as the best-fitting one. The VFI showed good internal consistency and convergent validity. Significant correlations were found between the VFI factors, organizational commitment, and volunteers’ satisfaction. Overall, the six-factor VFI is a valid and reliable tool for measuring the motivational functions of Portuguese volunteers, with implications for practice and research in the volunteering field.

O artigo apresenta a análise espacial da sustentabilidade nos municípios cearenses a partir da construção de um índice de desenvolvimento sustentável, embasado em dimensões social, ambiental, econômica e institucional. Os índices de sustentabilidade forçam instituições e governos a questionarem seus padrões e atuam como “forças motrizes” para a elaboração de políticas públicas. A validade dos índices de sustentabilidade depende da definição do conjunto de indicadores e das técnicas adotadas para definição dos ponderadores e agregação dos indicadores. A pesquisa é de natureza quantitativa, desenvolvida por meio de dados secundários, e adotou análise fatorial confirmatória para a construção do índice de desenvolvimento sustentável (IDS) e modelagem econométrica espacial para representação das desigualdades no mapa dos municípios cearenses. Os resultados da pesquisa revelam um ajuste regular do índice de desenvolvimento sustentável. O IDS permite uma visualização geográfica e identificação de associação espacial dos indicadores de desenvolvimento sustentável, e apresenta-se como uma ferramenta de suporte à definição de políticas públicas. A pesquisa revelou que áreas mais urbanas do estado do Ceará apresentam um melhor índice de desenvolvimento sustentável, e confirmou a fragilidade das políticas públicas em promover o equilíbrio regional.

The main goal of this work is to obtain reliable predictions of pollutant concentrations related to maritime traffic (SO₂, PM₁₀, NO₂, NO_X, and NO) in the Bay of Algeciras, located in Andalusia, the south of Spain. Furthermore, the objective is to predict future air quality levels of the principal maritime traffic-related pollutants in the Bay of Algeciras as a function of the rest of the pollutants, the meteorological variables, and vessel data. In this sense, three scenarios were analysed for comparison, namely Alcornocales Park and the cities of La Línea and Algeciras. A database of hourly records of air pollution immissions, meteorological measurements in the Bay of Algeciras region and a database of maritime traffic in the port of Algeciras during the years 2017 to 2019 were used. A resampling procedure using a five-fold cross-validation procedure to assure the generalisation capabilities of the tested models was designed to compute the pollutant predictions with different classification models and also with artificial neural networks using different numbers of hidden layers and units. This procedure enabled appropriate and reliable multiple comparisons among the tested models and facilitated the selection of a set of top-performing prediction models. The models have been compared using several quality classification indexes such as sensitivity, specificity, accuracy, and precision. The distance (d₁) to the perfect classifier (1, 1, 1, 1) was also used as a discriminant feature, which allowed for the selection of the best models. Concerning the number of variables, an analysis was conducted to identify the most relevant ones for each pollutant. This approach aimed to obtain models with fewer inputs, facilitating the design of an optimised monitoring network. These more compact models have proven to be the optimal choice in many cases. The obtained sensitivities in the best models were 0.98 for SO₂, 0.97 for PM₁₀, 0.82 for NO₂ and NO_X, and 0.83 for NO. These results demonstrate the potential of the models to forecast air pollution in a port city or a complex scenario and to be used by citizens and authorities to prevent exposure to pollutants and to make decisions concerning air quality.

Predicting the levels of a pollutant in a given area is an open problem, mainly because historical data are typically available at certain locations, where monitoring stations are located, but not at all locations in the area. This work presents an approach based on developing predictions at each of the points where an immission station is available; in this case, based on shallow Artificial Neural Networks, ANNs, and then using a simple geostatistical interpolation algorithm (Inverse Distance Weighted, IDW), a pollutant map is constructed over the entire study area, thus providing predictions at each point in the plane. The ANN models are designed to make 1 h ahead and 4 h ahead predictions, using an autoregressive scheme as inputs (in the case of 4 h ahead as a jumping strategy). The results are then compared using the Friedman and Bonferroni tests to select the best model at each location, and predictions are made with all the best models. In general, to the 1 h ahead prediction models, the optimal models typically have fewer neurons and require minimal historical data. For instance, the best model in Algeciras has an R of almost 0.89 and consists of 1 hidden neuron and 3 to 5 lags, similar to Colegio Los Barrios. In the case of 4h ahead prediction, Colegio Carteya station shows the best model, with an R of almost 0.89 and a MSE of less than 240, including 5 hidden neurons and different lags from the past. The results are sufficiently adequate, especially in the case of predictions 4 h into the future. The aim is to integrate the models into a tool for citizens and administrations to make decisions.

This study aims to evaluate and compare the performance of Autoencoders (AEs) and Sparse Autoencoders (SAEs) in forecasting the next-hour concentration levels of various air pollutants—specifically NO₂(t + 1), PM₁₀(t + 1), and SO₂(t + 1)—in the Bay of Algeciras, a highly complex region located in southern Spain. Hourly data related to air quality, meteorological conditions, and maritime traffic were collected from 2017 to 2019 across multiple monitoring stations distributed throughout the bay, enabling the analysis of diverse forecasting scenarios. The output variable was segmented into four distinct, non-overlapping quartiles (Q1–Q4) to capture different concentration ranges. AE models demonstrated greater accuracy in predicting moderate pollution levels (Q2 and Q3), whereas SAE models achieved comparable performance at the lower and upper extremes (Q1 and Q4). The results suggest that stacking AE layers with varying degrees of sparsity—culminating in a supervised output layer—can enhance the model’s ability to forecast pollutant concentration indices across all quartiles. Notably, Q4 predictions, representing peak concentrations, benefited from more complex SAE architectures, likely due to the increased difficulty associated with modelling extreme values.