Abstract. The Aerosol Chemical Speciation Monitor (ACSM) is an instrument for identifying and quantifying the influence of air quality mitigations. For this purpose, a European ACSM network has been developed within the research infrastructure project ACTRIS (European Research Infrastructure for the observation of Aerosol, Clouds and Trace Gases). To ensure the uniformity of the dataset, as well as instrumental performance and variability, regular intercomparisons are organized at the Aerosol Chemical Monitoring Calibration Center (ACMCC, part of the European Center for Aerosol Calibration, Paris, France). However, in-situ quality assurance remains a fundamental tracking point of the instrument’s stability. In order to check the robustness of the ACSM over the years and to characterize the seasonality effect, nitrate, sulfate, ammonium, organic, and particle mass concentrations were systematically compared with collocated measurements including daily off-line high-volume PM1 and PM2.5 filter samples. 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 weighting, as well as to the derived mass concentration of particle number size distribution measurements (PNSD). A combination of PM1 and PM2.5 filter samples helps identify the critical importance of the upper size cut-off of the ACSM during such exercises. The ACSM-MAAP-derived mass concentrations systematically deviate from the PM1 samples when the mass concentration of the latter represents less than 60 % of PM2.5, which is 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 does not exhibit a seasonal dependency, total PM mass concentration shows a small seasonal effect associated with an 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 to OC by using a similar approach as for the AMS. The resulting estimated OC-ACSM was compared with the measured OC-PM1 (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 cutting 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 artefact 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 slightly overestimation of the MPSS derived mass concentration in winter. This seasonal variability was related to a change on the PNSD and a larger contribution of the super-µm 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 way of both warranting the quality assurance of the ACSM measurements as well as identifying possible deviations in one of the two instruments.
Infraestrutura de Investigação para a Ciência e Tecnologia da Linguagem - PORTULAN CLARIN