Clustering and forecasting of dissolved oxygen concentration on a river basin

The aim of this contribution is to combine statistical methodologies to geographically classify homogeneous groups of water quality monitoring sites based on similarities in the temporal dynamics of the dissolved oxygen (DO) concentration, in order to obtain accurate forecasts of this quality variable. Our methodology intends to classify the water quality monitoring sites into spatial homogeneous groups, based on the DO concentration, which has been selected and considered relevant to characterize the water quality. We apply clustering techniques based on Kullback Information, measures that are obtained in the state space modelling process. For each homogeneous group of water quality monitoring sites we model the DO concentration using linear and state space models, which incorporate tendency and seasonality components in different ways. Both approaches are compared by the mean squared error (MSE) of forecasts

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

Extreme weather and air pollution effects on cardiovascular and respiratory hospital admissions in Cyprus.

In many regions of the world, climatic change is associated with increased extreme temperatures, which can have severe effects on mortality and morbidity. In this study, we examine the effect of extreme weather on hospital admissions in Cyprus, for inland and coastal areas, through the use of synoptic weather classifications (air mass types). In addition, the effect of particulate air pollution (PM10) on morbidity is examined. Our results show that two air mass types, namely (a) warm, rainy days with increased levels of water vapour in the atmosphere and (b) cold, cloudy days with increased levels of precipitation, were associated with increased morbidity in the form of hospital admissions. This was true both for cardiovascular and respiratory conditions, for all age groups, but particularly for the elderly, aged over 65. Particulate air pollution was also associated with increased morbidity in Cyprus, where the effect was more pronounced for cardiovascular diseases

Food security among dryland pastoralists and agropastoralists: The climate, land-use change, and population dynamics nexus

During the last decades, pastoralist, and agropastoralist populations of the world’s drylands have become exceedingly vulnerable to regional and global changes. Specifically, exacerbated stressors imposed on these populations have adversely affected their food security status, causing humanitarian emergencies and catastrophes. Of these stressors, climate variability and change, land-use and management practices, and dynamics of human demography are of a special importance. These factors affect all four pillars of food security, namely, food availability, access to food, food utilization, and food stability. The objective of this study was to critically review relevant literature to assess the complex web of interrelations and feedbacks that affect these factors. The increasing pressures on the world’s drylands necessitate a comprehensive analysis to advise policy makers regarding the complexity and linkages among factors, and to improve global action. The acquired insights may be the basis for alleviating food insecurity of vulnerable dryland populations

Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

In recent years, the application of titanium dioxide (TiO2) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO2 on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO2 solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency. © 2014 Springer-Verlag Berlin Heidelberg