PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study.

Atmospheric particulate matter (PM) is one of the pollutants that may have a significant impact on human health. Data collected over seven years in a city of the north of Spain is analyzed using four different mathematical models: vector autoregressive moving-average (VARMA), autoregressive integrated moving-average (ARIMA), multilayer perceptron (MLP) neural networks and support vector machines (SVMs) with regression. Measured monthly average pollutants and PM10 (particles with a diameter less than 10µm) concentration are used as input to forecast the monthly averaged concentration of PM10 from one to seven months ahead. Simulations showed that the SVM model performs better than the other models when forecasting one month ahead and also for the following seven months.

Predictive modelling of eutrophication in the Pozón de la Dolores lake (Northern Spain) by using an evolutionary support vector machines approach.

Eutrophication is a water enrichment in nutrients (mainly phosphorus) that generally leads to symptomatic changes and deterioration of water quality and all its uses in general, when the production of algae and other aquatic vegetations are increased. In this sense, eutrophication has caused a variety of impacts, such as high levels of Chlorophyll a (Chl-a). Consequently, anticipate its presence is a matter of importance to prevent future risks. The aim of this study was to obtain a predictive model able to perform an early detection of the eutrophication in water bodies such as lakes. This study presents a novel hybrid algorithm, based on support vector machines (SVM) approach in combination with the particle swarm optimization (PSO) technique, for predicting the eutrophication from biological and physical-chemical input parameters determined experimentally through sampling and subsequent analysis in a certificate laboratory. This optimization technique involves hyperparameter setting in the SVM training procedure, which significantly influences the regression accuracy. The results of the present study are twofold. In the first place, the significance of each biological and physical-chemical variables on the eutrophication is presented through the model. Secondly, a model for forecasting eutrophication is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.90 for the Total phosphorus estimation and 0.92 for the Chlorophyll concentration were obtained when this hybrid PSO-SVM-based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter.

Analysis and detection of functional outliers in water quality parameters from different automated monitoring stations in the Nalón river basin (Northern Spain).

The purposes and intent of the authorities in establishing water quality standards are to provide enhancement of water quality and prevention of pollution to protect the public health or welfare in accordance with the public interest for drinking water supplies, conservation of fish, wildlife and other beneficial aquatic life, and agricultural, industrial, recreational, and other reasonable and necessary uses as well as to maintain and improve the biological integrity of the waters. In this way, water quality controls involve a large number of variables and observations, often subject to some outliers. An outlier is an observation that is numerically distant from the rest of the data or that appears to deviate markedly from other members of the sample in which it occurs. An interesting analysis is to find those observations that produce measurements that are different from the pattern established in the sample. Therefore, identification of atypical observations is an important concern in water quality monitoring and a difficult task because of the multivariate nature of water quality data. Our study provides a new method for detecting outliers in water quality monitoring parameters, using turbidity, conductivity and ammonium ion as indicator variables. Until now, methods were based on considering the different parameters as a vector whose components were their concentration values. This innovative approach lies in considering water quality monitoring over time as continuous curves instead of discrete points, that is to say, the dataset of the problem are considered as a time-dependent function and not as a set of discrete values in different time instants. This new methodology, which is based on the concept of functional depth, was applied to the detection of outliers in water quality monitoring samples in the Nalón river basin with success. Results of this study were discussed here in terms of origin, causes, etc. Finally, the conclusions as well as advantages of the functional method are exposed.

Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain).

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational waters. As a result, anticipate its presence is a matter of importance to prevent risks. The aim of this study is to use a hybrid approach based on support vector regression (SVR) in combination with genetic algorithms (GAs), known as a genetic algorithm support vector regression (GA-SVR) model, in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). The GA-SVR approach is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out proved its high performance. Some physical-chemical parameters have been considered along with the biological ones. The results obtained are two-fold. In the first place, the significance of each biological and physical-chemical variable on the cyanotoxins presence in the reservoir is determined with success. Finally, a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained.

Detection of outliers in water quality monitoring samples using functional data analysis in San Esteban estuary (Northern Spain).

Water quality controls involve large number of variables and observations, often subject to some outliers. An outlier is an observation that is numerically distant from the rest of the data or that appears to deviate markedly from other members of the sample in which it occurs. An interesting analysis is to find those observations that produce measurements that are different from the pattern established in the sample. Therefore, identification of atypical observations is an important concern in water quality monitoring and a difficult task because of the multivariate nature of water quality data. Our study provides a new method for detecting outliers in water quality monitoring parameters, using oxygen and turbidity as indicator variables. Until now, methods were based on considering the different parameters as a vector whose components were their concentration values. Our approach lies in considering water quality monitoring through time as curves instead of vectors, that is to say, the data set of the problem is considered as a time-dependent function and not as a set of discrete values in different time instants. The methodology, which is based on the concept of functional depth, was applied to the detection of outliers in water quality monitoring samples in San Esteban estuary. Results were discussed in terms of origin, causes, etc., and compared with those obtained using the conventional method based on vector comparison. Finally, the advantages of the functional method are exposed.

A new improved study of cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain) using the MARS technique.

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational water uses. The aim of this study is to improve our previous and successful work about cyanotoxins prediction from some experimental cyanobacteria concentrations in the Trasona reservoir (Asturias, Northern Spain) using the multivariate adaptive regression splines (MARS) technique at a local scale. In fact, this new improvement consists of using not only biological variables, but also the physical-chemical ones. As a result, the coefficient of determination has improved from 0.84 to 0.94, that is to say, more accurate predictive calculations and a better approximation to the real problem were obtained. Finally the agreement of the MARS model with experimental data confirmed the good performance.

Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain).

There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins, termed cyanotoxins. These latter can be toxic and dangerous to humans as well as other animals and life in general. It must be remarked that the cyanobacteria are reproduced explosively under certain conditions. This results in algae blooms, which can become harmful to other species if the cyanobacteria involved produce cyanotoxins. In this research work, the evolution of cyanotoxins in Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. The results of the present study are two-fold. On one hand, the importance of the different kind of cyanobacteria over the presence of cyanotoxins in the reservoir is presented through the MARS model and on the other hand a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. The agreement of the MARS model with experimental data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed.

Detection of outliers in gas emissions from urban areas using functional data analysis.

In this work a solution for the problem of the detection of outliers in gas emissions in urban areas that uses functional data analysis is described. Different methodologies for outlier identification have been applied in air pollution studies, with gas emissions considered as vectors whose components are gas concentration values for each observation made. In our methodology we consider gas emissions over time as curves, with outliers obtained by a comparison of curves instead of vectors. The methodology, which is based on the concept of functional depth, was applied to the detection of outliers in gas omissions in the city of Oviedo and results were compared with those obtained using a conventional method based on a comparison of vectors. Finally, the advantages of the functional method are reported.

A mathematical approach to selective scavenging of the different classes of typical atmospheric aerosols by rainout and health impact.

In this work, initially a general mathematical framework for wet deposition processes is developed and followed by an in-depth discussion of the scavenging of material below cloud due to rain. These processes are then integrated into an overall framework focussing mainly on precipitation scavenging by rain. This work studies the scavenging efficiencies of aerosol particles within a given rain regime as a function of time by below-cloud scavenging. The health impact of aerosol before and after rain is also considered by comparing the respirable dust fractions. The well-known equations of below-cloud scavenging are applied to eight different classes of atmospheric aerosols (marine background (MB), clean continental background (CCB), average background (AB), background and aged urban plume (BAUP), background and local sources (BLS), urban average (UA), urban and freeway (UF) and central power plant (CCP)) in two precipitation regimes (drizzle and heavy rain) with one drop size distribution (DSD). From this study it is inferred that respirable dust is scavenged with relative ease by rainout. Compared with the volume of respirable aerosol average urban environment (UA) before rain roughly 5.2% remains after 18 hours of drizzle and 4% after 18 hours of heavy rain. Over a long timescale, the results show that heavy rain is more efficient than drizzle in particle scavenging.

Study of the evolution of aerosol emissions from coal-fired power plants due to coagulation, condensation, and gravtitational settling and health impact.

This paper studies the scavenging efficiencies of aerosol emissions from coal-fired power plants under different removal mechanisms (coagulation, heterogeneous nucleation and gravitational settling) as a function of time. It also analyses the 'health impact' of the aerosol before and after the above dynamic mechanisms by comparing the respirable dust fractions. The well-known equations of evolution are applied to an average PSD that represents the exhaust particulate emissions from coal-fired power plants (i.e. Aboño power plant in Asturias that belongs to Hidrocantábrico Group, S.A.). From this study it is inferred that respirable dust is scavenged with the greatest difficulty and when compared with the initial volume of respirable dust, roughly 20% remains after 18 h of gravitational settling. Therefore, gravitational settling is the main removal mechanism of respirable dust compared to condensation and coagulation.

Parametric study of selective removal of atmospheric aerosol by coagulation, condensation and gravitational settling.

This work studies the scavenging efficiencies of aerosol particles after a given mechanism of removal (coagulation, heterogeneous nucleation and gravitational settling) as a function of time. It also analyses the health impact of the aerosol before and after the above dynamic mechanisms by comparing the respirable dust fractions. The well-known equations of scavenging are applied to three atmospheric environments (clear, hazy and urban) that represent the aerosol PSDs in the countryside, industry and the city, respectively. From this study it is inferred that respirable dust is hardly scavenged and when compared with the initial volume of respirable aerosol, roughly 10% remains after 18 h of gravitational settling. Therefore, gravitational settling is the main removal mechanism of respirable aerosol compared to condensation and coagulation and is almost six times better than rainout.