Published October 20, 2022
| Version v1
Publication
Time series clustering for estimating particulate matter contributions and its use in quantifying impacts from deserts
Description
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.
Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/138169
- URN
- urn:oai:idus.us.es:11441/138169