Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Álvaro Gómez-Losada (Author) Other; José Carlos M. Pires (Author) FEUP; Rafael Pino-Mejías (Author) Other

Journal

Title: Atmospheric Environment

Vol. 117

Pages: 271-281

ISSN: 1352-2310

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 9 Citations

Scientific classification

CORDIS: Natural sciences > Environmental science > Global change ; Natural sciences > Environmental science > Earth science

FOS: Natural sciences > Earth and related Environmental sciences ; Engineering and technology > Environmental engineering

Associated Projects

LEPABE - Laboratório de Engenharia de Processos, Ambiente, Biotecnologia e Energia

Other information

Authenticus ID: P-00G-FS9

DOI: 10.1016/j.atmosenv.2015.07.027

Abstract (EN): 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 PM10 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 PM10 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 concentrations. The annual average Saharan PM10 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 PM10 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11