A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts
Abstract 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.
Highlights The impact of wind-blown desert to annual average PM10 concentrations was estimated. Hidden Markov models used to define and estimate regimes of PM10 concentrations. New methodology for calculating daily net PM10 loads from deserts. The modelling used complements other source apportionments techniques.
Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts
Abstract 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.
Highlights The impact of wind-blown desert to annual average PM10 concentrations was estimated. Hidden Markov models used to define and estimate regimes of PM10 concentrations. New methodology for calculating daily net PM10 loads from deserts. The modelling used complements other source apportionments techniques.
Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts
Gómez-Losada, Álvaro (author) / Pires, José Carlos M. (author) / Pino-Mejías, Rafael (author)
Atmospheric Environment ; 117 ; 271-281
2015-07-17
11 pages
Article (Journal)
Electronic Resource
English
| Taylor & Francis Verlag | 2023