Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils

When considering complex scenarios involving several attributes, such as in environmental characterization, a clearer picture of reality can be achieved through the dimensional reduction of data. In this context, maps facilitate the visualization of spatial patterns of contaminant distribution and the identification of enriched areas. A set, of 15 Potentially Toxic Elements (PTEs) – (As, Ba, Cd, Co, Cr, Cu, Hg,Mo, Ni, Pb, Sb, Se, Tl, V, and Zn), was measured in soil, collected in Langreo's municipality (80 km2), Spain. Relative enrichment (RE) is introduced here to refer to the proportion of elements present in a given context. Indeed, a novel approach is provided for research into PTE fate. This method involves studying the variability of PTE proportions throughout the study area, thereby allowing the identification of dissemination trends. Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the elements analyzedmake up the entirety of the soil. However, in geochemical studies the analyzed elements are just a fraction of the total soil composition. Therefore, considering compositional data is pivotal. The spatial characterization of PTEs considering raw and compositional data together allowed a broad discussion about, not only the PTEs concentration's distribution but also to reckon possible trends of relative enrichment (RE). Transformations to open closed data are widely used for this purpose. Spatial patterns have an indubitable interest. In this study, the Centered Log-ratio transformation (clr) was used, followed by its back-transformation, to build a set of compositional data that, combined with raw data, allowed to establish the sources of the PTEs and trends of spatial dissemination.info:eu-repo/semantics/publishedVersio

A Functional Data Analysis Approach for the Detection of Air Pollution Episodes and Outliers: A Case Study in Dublin, Ireland

Ground level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. Therefore, detecting small deviations in air quality and deriving methods of controlling air pollution are challenging. This study presents different data analytical methods which can be used to monitor and effectively evaluate policies or measures to reduce nitrogen oxide (NOx) emissions through the detection of pollution episodes and the removal of outliers. This method helps to identify the sources of pollution more effectively, and enhances the value of monitoring data and exceedances of limit values. It will detect outliers, changes and trend deviations in NO2 concentrations at ground level, and consists of four main steps: classical statistical description techniques, statistical process control techniques, functional analysis and a functional control process. To demonstrate the effectiveness of the outlier detection methodology proposed, it was applied to a complete one-year NO2 dataset for a sub-urban site in Dublin, Ireland in 2013. The findings demonstrate how the functional data approach improves the classical techniques for detecting outliers, and in addition, how this new methodology can facilitate a more thorough approach to defining effect air pollution control measures

A functional data analysis approach for the detection of air pollution episodes and outliers: a case study in Dublin, Ireland

Ground level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. Therefore, detecting small deviations in air quality and deriving methods of controlling air pollution are challenging. This study presents different data analytical methods which can be used to monitor and effectively evaluate policies or measures to reduce nitrogen oxide (NOx) emissions through the detection of pollution episodes and the removal of outliers. This method helps to identify the sources of pollution more effectively, and enhances the value of monitoring data and exceedances of limit values. It will detect outliers, changes and trend deviations in NO2 concentrations at ground level, and consists of four main steps: classical statistical description techniques, statistical process control techniques, functional analysis and a functional control process. To demonstrate the effectiveness of the outlier detection methodology proposed, it was applied to a complete one-year NO2 dataset for a sub-urban site in Dublin, Ireland in 2013. The findings demonstrate how the functional data approach improves the classical techniques for detecting outliers, and in addition, how this new methodology can facilitate a more thorough approach to defining effect air pollution control measures.Ministerio de Industria y Competitividad | Ref. RTI2018-096296-B-C2

A functional data analysis for assessing the impact of a retrofitting in the energy performance of a building

There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2

A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building

There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.This paper was funded by the Spanish Government (Science, Innovation and Universities Ministry) under the project RTI2018-096296-B-C21

Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

Predicting anomalous emission of pollutants into the atmosphere well in advance is crucial for industries emitting such elements, since it allows them to take corrective measures aimed to avoid such emissions and their consequences. In this work, we propose a functional location-scale model to predict in advance pollution episodes where two pollutants are involved. Functional generalized additive models (FGAMs) are used to estimate the means and variances of the model, as well as the correlation between both pollutants. The method not only forecasts the concentrations of both pollutants, it also estimates an uncertainty region where the concentrations of both pollutants should be located, given a specific level of uncertainty. The performance of the model was evaluated using real data of SO 2 and NO x emissions from a coal-fired power station, obtaining good resultsThe authors acknowledge financial support from: (1) UO-Proyecto Uni-Ovi (PAPI-18-GR-2014-0014), (2) Project MTM2016-76969-P from Ministerio de Economía y Competitividad—Agencia Estatal de Investigación and European Regional Development Fund (ERDF) and IAP network StUDyS from Belgian Science Policy, (3) Nuevos avances metodológicos y computacionales en estadística no-paramétrica y semiparamétrica—Ministerio de Ciencia e Investigación (MTM2017-89422-P)S

Trend analysis and outlier distribution of CO2 and CH4: A case study at a rural site in northern Spain

Producción CientíficaCO2 and CH4 outliers may have a noticeable impact on the trend of both gases. Nine years of measurements since 2010 recorded at a rural site in northern Spain were used to investigate these outliers. Their influence on the trend was presented and two limits were established. No more than 23.5% of outliers should be excluded from the measurement series in order to obtain representative trends, which were 2.349 ± 0.012 ppm year−1 for CO2 and 0.00879 ± 0.00004 ppm year−1 for CH4. Two types of outliers were distinguished. Those above the trend line and the rest below the trend line. Outliers were described by skewed distributions where the Weibull distribution figures prominently in most cases. A qualitative procedure was presented to exclude the worst fits, although five statistics were considered to select the best fit. In this case, the modified Nash-Sutcliffe efficiency is prominent. Finally, three symmetrical distributions were added to fit the observations when outliers are excluded, with the Gaussian and beta distributions providing the best fits. As a result, certain skewed functions, such as the lognormal distribution, whose use is frequent for air pollutants, could be questioned in certain applications.Ministerio de Economía y Competitividad y fondos FEDER, (project numbers CGL-2009-11979 and CGL2014-53948-P

Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

This research was funded by project PID2020-113961GB-I00 of the Spanish Ministry of Science and Innovation (also supported by the FEDER program), project FQM-307 of the Government of Andalusia (Spain) and the PhD grant (FPU18/01779) awarded to Christian Acal. The authors also thank the support of the University of Granada, Spain, under project for young researchers PPJIB2020-01.Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of NO2, PM10, PM2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the PM10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.Spanish Ministry of Science and Innovation - FEDER program PID2020-113961GB-I00Government of Andalusia (Spain) FQM-307University of Granada, Spain PPJIB2020-01- FPU18/0177

Deteccion de Valores Extremos e Imputación de Valores Faltantes para la Calidad de Agua en Series de Tiempo de Absorbancia UV-VIS

Context: The UV-Vis absorbance collection using online optical captors for water quality detection may yield outliers and/or missing values. Therefore, data pre-processing is a necessary pre-requisite to monitoring data processing. Thus, the aim of this study is to propose a method that detects and removes outliers as well as fills gaps in time series.Method: Outliers are detected using Winsorising procedure and the application of the Discrete Fourier Transform (DFT) and the Inverse of Fast Fourier Transform (IFFT) to complete the time series. Together, these tools were used to analyse a case study comprising three sites in Colombia ((i) Bogotá D.C. Salitre-WWTP (Waste Water Treatment Plant), influent; (ii) Bogotá D.C. Gibraltar Pumping Station (GPS); and, (iii) Itagüí, San Fernando-WWTP, influent (Medellín metropolitan area)) analysed via UV-Vis (Ultraviolet and Visible) spectra.Results: Outlier detection with the proposed method obtained promising results when window parameter values are small and self-similar, despite that the three time series exhibited different sizes and behaviours. The DFT allowed to process different length gaps having missing values. To assess the validity of the proposed method, continuous subsets (a section) of the absorbance time series without outlier or missing values were removed from the original time series obtaining an average 12% error rate in the three testing time series.Conclusions: The application of the DFT and the IFFT, using the 10% most important harmonics of useful values, can be useful for its later use in different applications, specifically for time series of water quality and quantity in urban sewer systems. One potential application would be the analysis of dry weather interesting to rain events, a feat achieved by detecting values that correspond to unusual behaviour in a time series. Additionally, the result hints at the potential of the method in correcting other hydrologic time series.Contexto: El registro de la absorbancia UV-Vis mediante captores ópticos en línea para la detección de la calidad del agua, en donde se pueden presentar valores atípicos o valores faltantes. Por lo tanto, el pre-procesamiento para corregir dichas anomalías es necesario para un mejor análisis de los datos de monitoreo. El objetivo de este estudio es proponer un método para detectar e imputar valores extremos  como también completar valores faltantes en series de tiempo.Método: La detección de valores atípicos utiliza el procedimiento de enventaneo y la aplicación de la Transformada Discreta de Fourier (DFT –Discrete Fourier Transform) y la inversa de la Transformada Rápida de Fourier (IFFT–Inverse of Fast Fourier Transform) para completar las series de tiempo. Estas herramientas fueron utilizadas para un caso de estudio compuesto por tres sitios en Colombia (i) PTAR-Salitre (Planta de Tratamiento de Aguas Residuales) Bogotá D.C., afluente; (ii) Estación´ Elevadora de Gibraltar Bogotá D.C.; y (iii) PTAR-San Fernando, área metropolitana de Medellín, afluente) analizados mediante espectros UV-Vis (Ultravioleta y Visible).Resultados: La detección de valores atípicos con el método propuesto obtiene resultados prometedores cuando los valores de los parámetros de la ventana son pequeños y auto-similares, esto  a pesar de que las tres series de tiempo utilizadas presentan diferentes tamaños y comportamientos. Para validar la metodología propuesta, sub-conjuntos continuos (una sección) de las series de tiempo de absorbancia sin valores ausentes o atípicos, fueron removidos de las series original obteniéndose  tasas de error de 12 % en promedio para todos los tres sitios de estudio.Conclusiones: La aplicación de la DFT y la IIFT, utilizando el 10 % de los harmónicos más importantes de los valores útiles es crucial para su posterior uso en diferentes aplicaciones, específicamente para series de tiempo de calidad y cantidad de agua en sistema de saneamiento urbano. Una posible aplicación podría ser la comparación de los efectos de clima seco respecto a temporadas de lluvia, mediante la detección de valores que corresponden a comportamiento inusual  en una serie de tiempo. Además, los resultados indican potencial aplicación  futura en la corrección de otras series de tiempo hidrológicas

Missing value imputation and outlier detection for functional data: an application for PM10 data

The data collected in the air pollution monitoring such as PM10 is obtained at automated stations that generally contained missing values due to machine failures, routine maintenance, or human errors. Incomplete data sets may cause information bias. Therefore, it is important to find the best way to estimate these missing values to ensure the quality of the analyzed data. In this paper PM10 particulate data considered in time as a functional object were evaluated, for this case the database of the environmental monitoring network of the Environmental Corporation of La Guajira (Corpoguajira) was used. In this study we have implemented the methodology by Jeng-Min Chiou (2014) to impute functional data. The detection of outliers of pollutants is very important for monitoring and control of air quality. Additionally, we have implemented the method of imputation of missing data and detection of outliers for functional data. We considered PM10 particle concentrations in the environmental monitoring stations over the area of influence of the open pit mining during 2012. To impute functional missing data, it was based on applying tools such as functional principal component analysis (ACPF) and graphic procedures to detect outlier curves such as the bagplot and functional highest density region (HDR) boxplot. The results indicate that Barranca station is an atypical curve and it was observed that the imputed intervals capture the dynamics that are shared with the other trajectories of the different stations.Los datos recopilados en el monitoreo de la contaminación del aire, como PM10, se obtienen en estaciones automatizadas que generalmente contenían valores faltantes debido a fallas de la máquina, mantenimiento de rutina o errores humanos. Los conjuntos de datos incompletos pueden causar sesgo de información, por lo tanto, es importante encontrar la mejor manera de estimar estos valores faltantes para garantizar la calidad de los datos analizados. En este trabajo se evaluaron los datos de partículas PM10 consideradas en el tiempo como un objeto funcional, para este caso se utilizó la base de datos de la red de monitoreo ambiental de la Corporación Ambiental de La Guajira (Corpoguajira). En este estudio hemos implementado la metodología de Jeng-Min Chiou, (2014) para imputar datos funcionales. La detección de valores atípicos de contaminantes es muy importante para el monitoreo y control de la calidad del aire. Además, hemos implementado el método de imputación de datos faltantes y detección de valores atípicos para datos funcionales. Consideramos las concentraciones de partículas PM10 en las estaciones de monitoreo ambiental sobre el área de influencia de la mina de carbón a cielo abierto durante 2012. Para imputar datos faltantes funcionales, se basó en la aplicación de herramientas como el análisis de componentes principales funcional (ACPF) y los procedimientos gráficos para detectar curvas de valores atípicos como el bagplot funcional y el diagrama de caja funcional de la región de mayor densidad (HDR) por sus siglas en ingles. Los resultados indican que la estación de Barranca es una curva atípica y se observó que los intervalos imputados capturan la dinámica que se comparte con las otras trayectorias de las diferentes estaciones