Abstract

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.

Highlights

  • Aerosol particles strongly influence our environment, having especially an impact on the ecosystem and human health

  • Mass closure analysis was made by comparing the total particle mass (PM) concentration obtained by adding the mass concentration of equivalent black carbon from the multi-angle absorption photometer (MAAP) to the aerosol chemical speciation monitor (ACSM) chemical composition, to that of PM1 and PM2.5 during filter weighing, as well as to the derived mass concentration of particle number size distribution (PNSD)

  • A systematic comparison between the ACSM and collocated measurements over a period of more than 5 years was performed to investigate the robustness of the ACSM as well as to identify the limits of such an exercise and the possible sources of uncertainties and artifacts

Read more

Summary

Introduction

Aerosol particles strongly influence our environment, having especially an impact on the ecosystem and human health. Quantifying the impact of the regulations to the air quality and changes on aerosol chemical composition needs to perform continuous and long-term measurements of aerosol particle properties such as, e.g., the particle number size distribution (PNSD) and the chemical composition For this purpose, a European distributed facility of the ground-based aerosol chemical species monitor (ACSM; Ng et al, 2011) is operated within ACTRIS (European Research Infrastructure for the observation of Aerosol, Clouds and Trace Gases; http://www.actris.eu, last access: 20 September 2019). One of the main objectives of these coordinated programs is to investigate and understand the spatial variability in aerosol chemical composition on a continental scale, including temporal variability over days, seasons, and years With such an instrumental network, it is essential to keep a strong focus on the data quality as well as to assure that the results provided by each instrument are comparable to each other. Data quality is ensured by determining instrumental variability between ACSMs (total mass 9 %, organics 19 %, nitrate 15 %, sulfate 28 %, ammonium 36 %; Crenn et al, 2015; Fröhlich et al, 2015a; Freney et al, 2019)

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call