Air quality assessment, required by the European Union (EU) Air Quality Directive, Directive 2008/50/EC, is part of the functions attributed to Environmental Management authorities. Based on the cost and time consumption associated with the experimental works required for the air quality assessment in relation to the EU-regulated metal and metalloids, other methods such as modelling or objective estimation arise as competitive alternatives when, in accordance with the Air Quality Directive, the levels of pollutants permit their use at a specific location. This work investigates the possibility of using statistical models based on Partial Least Squares Regression (PLSR) and Artificial Neural Networks (ANNs) to estimate the levels of arsenic (As), cadmium (Cd), nickel (Ni) and lead (Pb) in ambient air and their application for policy purposes. A methodology comprising the main steps that should be taken into consideration to prepare the input database, develop the model and evaluate their performance is proposed and applied to a case of study in Santander (Spain). It was observed that even though these approaches present some difficulties in estimating the individual sample concentrations, having an equivalent performance they can be considered valid for the estimation of the mean values - those to be compared with the limit/target values - fulfilling the uncertainty requirements in the context of the Air Quality Directive. Additionally, the influence of the consideration of input variables related to atmospheric stability on the performance of the studied statistical models has been determined. Although the consideration of these variables as additional inputs had no effect on As and Cd models, they did yield an improvement for Pb and Ni, especially with regard to ANN models.
13This work aims to estimate the levels of lead (Pb), nickel (Ni), manganese (Mn), vanadium (V) and 14 chromium (Cr) corresponding to a three-month PM10 sampling campaign conducted in 2008 in the city of 15 Dunkerque (Northern France) by means of statistical models based on Partial Least Squares Regression 16 (PLSR), Artificial Neural Networks (ANN) and Principal Component Analysis (PCA) coupled with ANN. 17According to the European Air Quality Directives, because the levels of these pollutants are sufficiently 18 below the European Union (EU) limit/target values and other air quality guidelines, they may be used for 19 air quality assessment purposes as an alternative to experimental measurements. An external validation of 20 the models has been conducted, and the results indicate that PLSR and ANNs, with comparable 21 performance, provide adequate mean concentration estimations for Pb, Ni, Mn and V, fulfilling the EU 22 uncertainty requirements for objective estimation techniques, although ANNs seem to present better 23 generalization ability. However, in accordance with the European regulation, both techniques can be 24 considered acceptable air quality assessment tools for heavy metals in the studied area. Furthermore, the 25 application of factor analysis prior to ANNs did not yield any improvements in the performance of the 26 ANNs. 27 28
Air quality assessment regarding metals and metalloids using experimental measurements is expensive and time consuming due to the cost and time required for the analytical determination of the levels of these pollutants. According to the European Union (EU) Air Quality Framework Directive (Directive 2008/50/EC), other alternatives, such as objective estimation techniques, can be considered for ambient air quality assessment in zones and agglomerations where the level of pollutants is below a certain concentration value known as the lower assessment threshold. These conditions occur in urban areas in Cantabria (northern Spain). This work aims to estimate the levels of As, Cd, Ni and Pb in airborne PM 10 at two urban sites in the Cantabria region (Castro Urdiales and Reinosa) using statistical models as objective estimation techniques. These models were developed based on three different approaches: partial least squares regression (PLSR), artificial neural networks (ANNs) and an alternative approach consisting of principal component analysis (PCA) coupled with ANNs (PCA-ANN). Additionally, these models were externally validated using previously unseen data. The results show that the models developed in this work based on PLSR and ANNs fulfil the EU uncertainty requirements for objective estimation techniques and provide an acceptable estimation of the mean values. As a consequence, they could be considered as an alternative to experimental measurements for air quality assessment regarding the aforementioned pollutants in the study areas while saving time and resources.
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