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Álvaro Gómez-Losada   Mr.  Research or Laboratory Scientist 
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Álvaro Gómez-Losada published an article in July 2016.
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(2014 - 2016)
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CONFERENCE-ARTICLE 4 Reads 0 Citations Time Series Clustering to Estimate Particulate Matter Contributions from Deserts Álvaro Gómez-Losada, José Carlos M. Pires, Rafael Pino-Mejía... Published: 15 July 2016
The 1st International Electronic Conference on Atmospheric Sciences, doi: 10.3390/ecas2016-A002
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Exploratory analysis of time series (TS) data is an important approach in experimental studies, with a large range of applications in many different fields, including air pollution studies. To identify structures in single (univariate) TS, main clustering analyses are based on general-purpose clustering algorithms (e.g., k-means, hierarchical clustering methods) and made the assumption that the samples (data) of a TS are independent, ignoring the correlations in consecutive sample values in time. This is specially the case of air pollutant studies based on monitoring data. Air pollutants TS can be studied using TS clustering techniques and as a result, pollution profiles or concentration regimes detected as well as the dependency structure between consecutive data is preserved. Once TS clustering applied over the TS data stream, a set of clusters group the data according to their similar concentration values, and therefore, different pollution profiles can be defined and their estimated range of concentration values. Hidden Markov Models (HMMs) are flexible general-purpose models for univariate and multivariate TS. The TS data are assumed to have a Markov property, and may be viewed as the results of a probabilistic walk along a fixed set of (no directly observable) states. This class of approach considers that each TS is generated by a mixture of underlying probability distributions, typically the Gaussian ones. In this study, HMMs were applied to cluster daily average particulate matter with aerodynamic diameter of 10 μm or less (PM10) TS collected at background monitoring stations from the Iberian Peninsula and Canarian Archipelago (Spain). As a result, PM10 concentration regimes were studied and in particular, the contribution to PM10 ambient concentration levels from the regimes associated to transport of air masses from North Africa deserts was estimated. Regarding this last contribution, we later compared to those obtained using the monthly moving 40th percentile (P40) method over the same TS and no significant quantitative differences were detected. However, the results obtained with HMMs seem to correct the net load of PM10 given by the P40 method, and attributes less impact on areas suffering greater influence from African episodes. The method proposed in this work to estimate PM10 from deserts could improve the P40 method in two ways since it avoids: (i) the smoothed effect which is implicit in the P40 methods after applying a mobile procedure in the TS treatment; and (ii) the empirical approach based on a correlation analysis applied in order to select this particular percentile (40th). Moreover, the use of statistical replicative techniques (bootstrap) together with HMMs has let to obtain an interval confidence in the PM10 contribution estimates from North African deserts. This methodology may be used to estimate particulate matter contributions from any desert; however, a consensus among experts is required to give the regimes obtained with HMMs a definition.

Article 1 Read 2 Citations Characterization of background air pollution exposure in urban environments using a metric based on Hidden Markov Models Álvaro Gómez-Losada, José Carlos M. Pires, Rafael Pino-Mejía... Published: 01 February 2016
Atmospheric Environment, doi: 10.1016/j.atmosenv.2015.12.046
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Highlights•Background air pollution is estimated using a metric based on Hidden Markov Models.•The relation between ambient and background air pollution in three cities is studied.•The proposed metric is readily transferable to the study of air pollutant time series. AbstractUrban area air pollution results from local air pollutants (from different sources) and horizontal transport (background pollution). Understanding urban air pollution background (lowest) concentration profiles is key in population exposure assessment and epidemiological studies. To this end, air pollution registered at background monitoring sites is studied, but background pollution levels are given as the average of the air pollutant concentrations measured at these sites over long periods of time. This technical note shows how a metric based on Hidden Markov Models (HMMs) can characterise the air pollutant background concentration profiles. HMMs were applied to daily average concentrations of CO, NO2, PM10 and SO2 at thirteen urban monitoring sites from three cities from 2010 to 2013. Using the proposed metric, the mean values of background and ambient air pollution registered at these sites for these primary pollutants were estimated and the ratio of ambient to background air pollution and the difference between them were studied. The ratio indicator for the studied air pollutants during the four-year study sets the background air pollution at 48% to 69% of the ambient air pollution, while the difference between these values ranges from 101-193 μg/m3, 7-12 μg/m3, 11-13 μg/m3 and 2-3 μg/m3 for CO, NO2, PM10 and SO2, respectively.
Article 1 Read 1 Citation Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts Álvaro Gómez-Losada, José Carlos M. Pires, Rafael Pino-Mejía... Published: 01 September 2015
Atmospheric Environment, doi: 10.1016/j.atmosenv.2015.07.027
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Article 1 Read 5 Citations Finite mixture models to characterize and refine air quality monitoring networks Álvaro Gómez-Losada, Antonio Lozano-García, Rafael Pino-Mejí... Published: 01 July 2014
Science of The Total Environment, doi: 10.1016/j.scitotenv.2014.03.091
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Existing air quality monitoring programs are, on occasion, not updated according to local, varying conditions and as such the monitoring programs become non-informative over time, under-detecting new sources of pollutants or duplicating information. Furthermore, inadequate maintenance may cause the monitoring equipment to be utterly deficient in providing information. To deal with these issues, a combination of formal statistical methods is used to optimize resources for monitoring and to characterize the monitoring networks, introducing new criteria for their refinement.