Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts
Source apportionment studies use prior exploratory methods that are not purpose-oriented and receptor modelling is based on chemical speciation, requiring costly, time-consuming analyses. Hidden Markov Models (HMMs) are proposed as a routine, exploratory tool to estimate PM 10 source contributions....
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universidad Loyola Andalucía |
| Repositorio: | Brújula |
| OAI Identifier: | oai:repositorio.uloyola.es:20.500.12412/5452 |
| Acceso en línea: | https://hdl.handle.net/20.500.12412/5452 |
| Access Level: | acceso abierto |
| Palabra clave: | Apportionments Hidden Markov Model PM10 Sahara |
| Sumario: | Source apportionment studies use prior exploratory methods that are not purpose-oriented and receptor modelling is based on chemical speciation, requiring costly, time-consuming analyses. Hidden Markov Models (HMMs) are proposed as a routine, exploratory tool to estimate PM 10 source contributions. These models were used on annual time series (TS) data from 33 background sites in Spain and Portugal. HMMs enable the creation of groups of PM 10 TS observations with similar concentration values, defining the pollutant's regimes of concentration. The results include estimations of source contributions from these regimes, the probability of change among them and their contribution to annual average PM10 con- centrations. The annual average Saharan PM 10 contribution in the Canary Islands was estimated and compared to other studies. A new procedure for quantifying the wind-blown desert contributions to daily average PM 10 concentrations from monitoring sites is proposed. This new procedure seems to correct the net load estimation from deserts achieved with the most frequently used method. |
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