In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM(10) concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R (2) values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R (2) values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.

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The relative contribution of chemical (air pollution) and physical (temperature and humidity) health stressors to urban hospitalization rates is the objective of the current study. The data used in the study included the daily number of hospital admissions due to cardiorespiratory diseases, hourly mean concentrations of CO, NO 2 , SO 2 , O 3 , and black smoke in several monitoring stations, as well as meteorological data (temperature, relative humidity, wind speed/direction) in Athens, Greece. The relations among the data above were studied using Generalized Linear Models (GLMs) and Artificial Neural Networks (ANNs). Elevated particulate concentrations are the dominant parameter related to hospital admissions (an increase of 10 μg/m 3 leads to an increase of 8.6% in hospital admissions), followed by O 3 and the other atmospheric pollutants (CO, NO 2 , and SO 2 ). Meteorological parameters also play a decisive role in the formation of airpollutant levels affecting public health. Both models performed adequately, however the ANN adaptation in complicate environmental issues results in improved modeling outcomes compared to the GLMs. The major finding of the study lies on the flexibility and the adaptation of the methodological approach for assessing non-linear problems and specifically the effect of non-linear parameters, such as the temperature. Moreover, the importance of temperature is established even when the whole dataset is modeled, reflecting the dual mode effect of temperature on cardiorespiratory admissions. Considering the urgent challenge to predict climate change effects on public health, a mathematical tool that successfully captures the direct impact of the affecting meteorological parameters (temperature and humidity) to health outcomes is of high added value.

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Use of a multi-sensor approach can provide citizens a holistic insight in the air quality in their immediate surroundings and assessment of personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from 7 European cities, discusses data fusion and harmonization on a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants, and offers possible solutions and improvements. Harmonizing the data streams identified issues with the used devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate the process of generating visualizations and reports and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization on a diverse set of multi-sensor data streams considerably improved the quality and quantity of data that a research participant receives. Though automatization accelerated the production of the reports considerably, manual structured double checks are strongly recommended.