Estimation of PM10-bound As, Cd, Ni and Pb levels by means of statistical modelling: PLSR and ANN approaches

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 PM10 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.The authors gratefully acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through the Project CMT2010-16068. The authors also thank the Regional Environment Ministry of the Cantabria Government for providing the PM10 samples at the Castro Urdiales and Reinosa sites

A novel model for hourly PM2.5 concentration prediction based on CART and EELM

Hourly PM2.5 concentrations have multiple change patterns. For hourly PM2.5 concentration prediction, it is beneficial to split the whole dataset into several subsets with similar properties and to train a local prediction model for each subset. However, the methods based on local models need to solve the global-local duality. In this study, a novel prediction model based on classification and regression tree (CART) and ensemble extreme learning machine (EELM) methods is developed to split the dataset into subsets in a hierarchical fashion and build a prediction model for each leaf. Firstly, CART is used to split the dataset by constructing a shallow hierarchical regression tree. Then at each node of the tree, EELM models are built using the training samples of the node, and hidden neuron numbers are selected to minimize validation errors respectively on the leaves of a sub-tree that takes the node as the root. Finally, for each leaf of the tree, a global and several local EELMs on the path from the root to the leaf are compared, and the one with the smallest validation error on the leaf is chosen. The meteorological data of Yancheng urban area and the air pollutant concentration data from City Monitoring Centre are used to evaluate the method developed. The experimental results demonstrate that the method developed addresses the global-local duality, having better performance than global models including random forest (RF), v-support vector regression (v-SVR) and EELM, and other local models based on season and k-means clustering. The new model has improved the capability of treating multiple change patterns

Environmental informatics with computational intelligence methods in mechanical engineering problems

In this thesis, we identify appropriate tools in modern data technologies, such as Computational Intelligence, and develop methodologies for real world engineering problem solving. The main application domain of this thesis is the atmospheric environment. In particular, we address the problem of assessing the air quality of urban environments, taking into account the current legislation (2008/50/EC). We develop a harmonized framework consisting of computational intelligence methods, and apply this methodology in order to analyze air quality data of two European cities (Thessaloniki, Greece and Helsinki, Finland). Furthermore, we expand our approach by forecasting airborne pollen concentrations, whereas we demonstrate the development of innovative quality of life information services, by utilizing medical databases, thus turning numeric forecasts of pollen concentrations into personalized symptoms forecast. Furthermore, we develop and apply a methodology for creating electricity use profiles, utilizing electricity consumption data. Finally, we utilize data monitored during wear experiments in order to develop a model to estimate the friction coefficient. The primary conclusion of this thesis is the ability of computational intelligence methods to support new innovative services.Στα πλαίσια της παρούσας διατριβής, αναπτύσσουμε και εφαρμόζουμε μεθοδολογίες υπολογιστικής νοημοσύνης για την επίλυση προβλημάτων μηχανολόγου μηχανικού. Κύρια θεματική περιοχή είναι το ατμοσφαιρικό περιβάλλον, ενώ παράλληλα παρουσιάζονται και δύο νέες εφαρμογές από την θεματικές περιοχές της ενέργειας και της τριβολογίας. Ξεκινώντας από το πρόβλημα της αποτίμησης της ποιότητας αέρα αστικών κέντρων, αναπτύσσουμε μία εναρμονισμένη μεθοδολογία που απαντά στις απαιτήσεις της υφιστάμενης νομοθεσίας (2008/50/EC), και την εφαρμόζουμε στην περίπτωση δύο Ευρωπαϊκών πόλεων (Θεσσαλονίκη, Ελλάδα και Ελσίνκι, Φινλανδία). Επεκτείνοντας την αρχική προσέγγιση προβλέπουμε συγκεντρώσεις βιολογικών ατμοσφαιρικών ρύπων, ενώ παράλληλα επιδεικνύουμε την ανάπτυξη καινοτόμων υπηρεσιών ενημέρωσης, αξιοποιώντας νέα δεδομένα αλλεργικών συμπτωμάτων (βιολογικών ρύπων), μετατρέποντας την πρόβλεψη αριθμητικής συγκέντρωσης σε εξατομικευμένη πρόβλεψη συμπτωμάτων. Η διατριβή ολοκληρώνεται με την ανάπτυξη και την εφαρμογή μεθοδολογίας δημιουργίας και απόδοσης προφίλ κατανάλωσης ηλεκτρικής ενέργειας, αξιοποιώντας πραγματικές καταναλώσεις. Τέλος, χρησιμοποιούμε τριβολογικά δεδομένα από πειράματα φθοράς, με στόχο την εκτίμηση του συντελεστή τριβής από μία σειρά μεταβλητών που καταγράφονται κατά τη διάρκεια του πειράματος. Βασικό, συμπέρασμα της διατριβής είναι η αναγνώριση της καταλληλότητας των μεθοδολογιών υπολογιστικής νοημοσύνης για την ανάπτυξη νέων καινοτόμων υπηρεσιών

Investigation of Medication Dosage Influences from Biological Weather

Part 20: Informatics and Intelligent Systems Applications for Quality of Life information Services (ISQLIS) WorkshopInternational audienceAirborne pollen has been associated with allergic symptoms in sensitized individuals, whereas atmospheric pollution indisputably aggravates the impact on the overall quality of life. Therefore, it is of major importance to correlate, forecast and disseminate information concerning high concentration levels of allergic pollen types and air pollutants to the public, in order to safeguard the quality of life of the population. In this study, we investigate the relationship between the Defined Daily Dose (DDD) given to patients in a triggered allergy reaction and the different levels of air pollutants and pollen types. By profiling specific atmospheric conditions, specialists may define the need for medication to individuals suffering from pollen allergy, not only according to their personal medical record but also to the existing air quality observations. Paper results indicate some interesting interrelationships between the use of medication and atmospheric quality conditions and shows that the forecasting of daily medication is possible with the aid of proper algorithms

Personalized Information Services for Quality of Life: The Case of Airborne Pollen Induced Symptoms

Part 20: Informatics and Intelligent Systems Applications for Quality of Life information Services (ISQLIS) WorkshopInternational audienceAllergies due to airborne pollen affect approximately 15-20% of European citizens; therefore, the provision of health related services concerning pollen-induced symptoms can improve the overall quality of life. In this paper, we demonstrate the development of personalized quality of life services by adopting a data-driven approach. The data we use consist of allergic symptoms reported by citizens as well as detailed pollen concentrations of the most allergenic taxa. We apply computational intelligence methods in order to develop models that associate pollen concentration levels with allergic symptoms on a personal level. The results for the case of Austria, show that this approach can result to accurate and reliable models; we report a correlation coefficient up to r=0.70 (average of 102 citizens). We conclude that some of these models could serve as the basis for personalized health services

Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data

The recent technological developments monitoring the electricity use of small customers provides with a whole new view to develop electricity distribution systems, customer-specific services and to increase energy efficiency. The analysis of customer load profile and load estimation is an important and popular area of electricity distribution technology and management. In this paper, we present an efficient methodology, based on self-organizing maps (SOM) and clustering methods (K-means and hierarchical clustering), capable of handling large amounts of time-series data in the context of electricity load management research. The proposed methodology was applied on a dataset consisting of hourly measured electricity use data, for 3989 small customers located in Northern-Savo, Finland. Information for the hourly electricity use, for a large numbers of small customers, has been made available only recently. Therefore, this paper presents the first results of making use of these data. The individual customers were classified into user groups based on their electricity use profile. On this basis, new, data-based load curves were calculated for each of these user groups. The new user groups as well as the new-estimated load curves were compared with the existing ones, which were calculated by the electricity company, on the basis of a customer classification scheme and their annual demand for electricity. The index of agreement statistics were used to quantify the agreement between the estimated and observed electricity use. The results indicate that there is a clear improvement when using data-based estimations, while the new-estimated load curves can be utilized directly by existing electricity power systems for more accurate load estimates.Electricity use Load curves Load profiling Time-series clustering Self-organizing map Energy efficiency