Multi-year high time resolution measurements of fine PM at 13 sites of the French Operational Network (CARA program): Data processing and chemical composition

Abstract. This paper presents a first comprehensive analysis of long-term measurements of atmospheric aerosol components from Aerosol Chemical Speciation Monitor (ACSM) and multi-wavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA program. The datasets contain the mass concentrations of major chemical species within PM₁, namely organic aerosols (OA), nitrate (NO₃^-), ammonium (NH₄⁺), sulfate (SO₄^2-), non-sea-salt chloride (Cl^-), and equivalent black carbon (eBC). Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both the guidance from the French reference laboratory for air quality monitoring and the Aerosol, Clouds, and Trace gases Research Infrastructure (ACTRIS) standard operating procedures. Key findings include geographical differences in aerosol chemical composition, seasonal variations, and diel patterns, which are influenced by meteorological conditions, anthropogenic activities, and proximity to emission sources. Overall, OA dominates PM₁ at each site (43–60 %), showing distinct seasonality with higher concentrations (i) in winter, due to enhanced residential heating emissions, and (ii) in summer, due to increased photochemistry favoring secondary aerosol formation. NO₃ is the second most important contributor to PM₁ (15–30 %), peaking in late winter and early spring, especially in northern France, and playing a significant role during pollution episodes. SO₄ (8–14 %) and eBC (5–11 %) complement the major fine aerosol species, with their relative contributions strongly influenced by the origin of air masses and the stability of meteorological conditions, respectively.

Such chemically-speciated multi-year datasets have significant value for the scientific community, offering opportunities for future research, including source apportionment studies, trend analyses, and epidemiological investigations. They are also vital for evaluating and validating regional air quality models. In this regard, a comparison with the CHIMERE Chemical Transport Model shows high correlations between simulations and measurements, albeit underestimating OA concentrations by 46–76 %. Regional discrepancies in NO₃ concentration levels emphasize the importance of these datasets in validating air quality models and tailoring air pollution mitigation strategies.