Abstract. The aerosol chemical speciation monitor (ACSM) is
nowadays widely used to identify and quantify the main components of fine
particles in ambient air. As such, its deployment at observatory platforms
is fully incorporated within the European Aerosol, Clouds and Trace Gases
Research Infrastructure (ACTRIS). Regular intercomparisons are organized at
the Aerosol Chemical Monitoring Calibration Center (ACMCC; part of the
European Center for Aerosol Calibration, Paris, France) to ensure the
consistency of the dataset, as well as instrumental performance and
variability. However, in situ quality assurance remains a fundamental aspect
of the instrument's stability. Here, we present and discuss the main outputs
of long-term quality assurance efforts achieved for ACSM measurements at the
research station Melpitz (Germany) since 2012 onwards. In order to validate
the ACSM measurements over the years and to characterize seasonal
variations, nitrate, sulfate, ammonium, organic, and particle mass
concentrations were systematically compared against the collocated
measurements of daily offline high-volume PM1 and PM2.5 filter
samples and particle number size distribution (PNSD) measurements. Mass
closure analysis was made by comparing the total particle mass (PM)
concentration obtained by adding the mass concentration of equivalent black
carbon (eBC) from the multi-angle absorption photometer (MAAP) to the ACSM
chemical composition, to that of PM1 and PM2.5 during filter
weighing, as well as to the derived mass concentration of PNSD. A
combination of PM1 and PM2.5 filter samples helped identifying the
critical importance of the upper size cutoff of the ACSM during such
exercises. The ACSM–MAAP-derived mass concentrations systematically deviated
from the PM1 mass when the mass concentration of the latter represented
less than 60 % of PM2.5, which was linked to the transmission
efficiency of the aerodynamic lenses of the ACSM. The best correlations are
obtained for sulfate (slope =0.96; R2=0.77) and total PM
(slope =1.02; R2=0.90). Although, sulfate did not exhibit
a seasonal dependency, total PM mass concentration revealed a small seasonal
variability linked to the increase in non-water-soluble fractions. The
nitrate suffers from a loss of ammonium nitrate during filter collection,
and the contribution of organo-nitrate compounds to the ACSM nitrate signal
make it difficult to directly compare the two methods. The contribution of
m∕z 44 (f44) to the total organic mass concentration was used to
convert the ACSM organic mass (OM) to organic carbon (OC) by using a similar approach as for the
aerosol mass spectrometer (AMS). The resulting estimated OCACSM was compared with the measured
OCPM1 (slope =0.74; R2=0.77), indicating that the
f44 signal was relatively free of interferences during this period. The
PM2.5 filter samples use for the ACSM data quality might suffer from a
systematic bias due to a size truncation effect as well as to the presence
of chemical species that cannot be detected by the ACSM in coarse mode (e.g.,
sodium nitrate and sodium sulfate). This may lead to a systematic
underestimation of the ACSM particle mass concentration and/or a positive
artifact that artificially decreases the discrepancies between the two
methods. Consequently, ACSM data validation using PM2.5 filters has to
be interpreted with extreme care. The particle mass closure with the PNSD
was satisfying (slope =0.77; R2=0.90 over the entire
period), with a slight overestimation of the mobility particle size
spectrometer (MPSS)-derived mass concentration in winter. This seasonal
variability was related to a change on the PNSD and a larger contribution of
the supermicrometer particles in winter. This long-term analysis between the ACSM and other collocated instruments
confirms the robustness of the ACSM and its suitability for long-term
measurements. Particle mass closure with the PNSD is strongly recommended to
ensure the stability of the ACSM. A near-real-time mass closure procedure
within the entire ACTRIS–ACSM network certainly represents an optimal
quality control and assurance of both warranting the quality assurance of
the ACSM measurements as well as identifying cross-instrumental biases.
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