<i>Rolling</i> vs. <i>seasonal</i> PMF: real-world multi-site and synthetic dataset comparison
<p>Particulate matter (PM) has become a major concern in terms of human health and climate impact. In particular, the source apportionment (SA) of organic aerosols (OA) present in submicron particles (PM<span class="inline-formula"><sub>1</sub>)</span> has gai...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-09-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/15/5479/2022/amt-15-5479-2022.pdf |
| Summary: | <p>Particulate matter (PM) has become a major concern in terms of human health
and climate impact. In particular, the source apportionment (SA) of organic
aerosols (OA) present in submicron particles (PM<span class="inline-formula"><sub>1</sub>)</span> has gained relevance
as an atmospheric research field due to the diversity and complexity of its
primary sources and secondary formation processes. Moreover, relatively
simple but robust instruments such as the Aerosol Chemical Speciation
Monitor (ACSM) are now widely available for the near-real-time online
determination of the composition of the non-refractory PM<span class="inline-formula"><sub>1</sub></span>. One of the
most used tools for SA purposes is the source-receptor positive matrix
factorisation (PMF) model. Even though the recently developed <i>rolling PMF</i> technique has
already been used for OA SA on ACSM datasets, no study has assessed its
added value compared to the more common <i>seasonal</i> PMF method using a practical
approach yet. In this paper, both techniques were applied to a synthetic
dataset and to nine European ACSM datasets in order to spot the main output
discrepancies between methods. The main advantage of the synthetic dataset
approach was that the methods' outputs could be compared to the expected
“true” values, i.e. the original synthetic dataset values. This approach
revealed similar apportionment results amongst methods, although the
<i>rolling</i> PMF profile's adaptability feature proved to be advantageous, as it
generated output profiles that moved nearer to the <i>truth</i> points. Nevertheless,
these results highlighted the impact of the profile anchor on the solution,
as the use of a different anchor with respect to the <i>truth</i> led to
significantly different results in both methods. In the multi-site study,
while differences were generally not significant when considering year-long
periods, their importance grew towards shorter time spans, as in intra-month
or intra-day cycles. As far as correlation with external measurements is
concerned, <i>rolling</i> PMF performed better than <i>seasonal </i>PMF globally for the ambient
datasets investigated here, especially in periods between seasons. The results of this
multi-site comparison coincide with the synthetic dataset in terms of
<i>rolling–seasonal</i> similarity and <i>rolling</i> PMF reporting moderate improvements. Altogether, the
results of this study provide solid evidence of the robustness of both
methods and of the overall efficiency of the recently proposed <i>rolling</i> PMF
approach.</p> |
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| ISSN: | 1867-1381 1867-8548 |