Air pollution relevance analysis in the bay of Algeciras (Spain).

The aim of this work is to accomplish an in-depth analysis of the air pollution in the two main cities of the Bay of Algeciras (Spain). A large database of air pollutant concentrations and weather measurements were collected using a monitoring network installed throughout the region from the period of 2010-2015. The concentration parameters contain nitrogen dioxide (NO2), sulphur dioxide (SO2) and particulate matter (PM10). The analysis was developed in two monitoring stations (Algeciras and La Línea). The higher average concentration values were obtained in Algeciras for NO2 (28.850 µg/m3) and SO2 (11.966 µg/m3), and in La Línea for PM10 (30.745 µg/m3). The analysis shows patterns that coincide with human activity. One of the goals of this work is to develop a useful virtual sensor capable of achieving a more robust monitoring network, which can be used, for instance, in the case of missing data. By means of trends analysis, groups of equivalent stations were determined, implying that the values of one station could be substituted for those in the equivalent station in case of failure (e.g., SO2 weekly trends in Algeciras and Los Barrios show equivalence). On the other hand, a calculation of relative risks was developed showing that relative humidity, wind speed and wind direction produce an increase in the risk of higher pollutant concentrations. Besides, obtained results showed that wind speed and wind direction are the most important variables in the distribution of particles. The results obtained may allow administrations or citizens to support decisions.

An artificial neural network ensemble approach to generate air pollution maps.

The objective of this research is to propose an artificial neural network (ANN) ensemble in order to estimate the hourly NO2 concentration at unsampled locations. Spatial interpolation methods and linear regression models with regularization have been compared to perform the ensemble. The study case is based on the region of the Bay of Algeciras (Spain). This area is very industrialized and presents high concentrations of traffic. Air pollution data has been collected from the monitoring network maintained by the Andalusian Government in the region. On one hand, two totally different methods have been used and compared such as inverse distance weight (IDW) and least absolute shrinkage and selection operator (LASSO) in order to generate maps of pollutant concentration values. On the other hand, an ensemble approach has been developed using the outputs of the previous models. The ensemble is based on an ANN with backpropagation learning. An experimental procedure using cross-validation has been applied in order to compare the different models based on several performance indexes (R correlation coefficient, MSE, MAE and d index of fitness) and together to Friedman test and Bonferroni correction. The results reveal that the proposed ensemble approach presents better performance than single models in general terms. A validation procedure has been conducted using a leave-one-out strategy using each monitoring station. IDW method presents an average value of R equals 0.72 and a maximum R equals 0.87, a minimum MSE equals 78.00, a minimum MAE equals 5.841 and a maximum d equals 0.913. LASSO presents an average value of R equals 0.76 and a maximum R equals 0.86, a minimum MSE equals 59.13, a minimum MAE equals 5.490 and a maximum d equals 0.900. Finally, the ANN ensemble shows an average value of R equals 0.77 and a maximum R equals 0.87, a minimum MSE equals 54.05, a minimum MAE equals 4.972 and a maximum d equals 0.915. The main objective has been to produce adequate atmospheric pollutant concentration maps and, therefore, to obtain estimations for locations that are distinct to the monitoring stations. Another objective has been to have in hand a system to produce robust measurements. This kind of system could be useful for missing data imputation and to find out reading errors (i.e. unexpected deviations or calibration problems) in some of the nodes of a network.

Prediction of CO maximum ground level concentrations in the Bay of Algeciras, Spain using artificial neural networks.

The region of the Bay of Algeciras is a very industrialized area where very few air pollution studies have been carried out. The main objective of this work has been the use of artificial neural networks (ANNs) as a predictive tool of high levels of ambient carbon monoxide (CO). Two approaches have been used: multilayer perceptron models (MLPs) with backpropagation learning rule and k-Nearest Neighbours (k-nn) classifiers, in order to predict future peaks of carbon monoxide. A resampling strategy with twofold cross-validation allowed the statistical comparison of the different topologies and models considered in the study. The procedure of random resampling permits an adequate and robust multiple comparisons of the tested models and allow us to select a group of best models.