The CARA program has been running since 2008 by the French reference laboratory for air quality monitoring (LCSQA) and the regional monitoring networks, to gain better knowledge—at a national level—on particulate matter (PM) chemistry and its diverse origins in urban environments. It results in strong collaborations with international-level academic partners for state-of-the-art, straightforward, and robust results and methodologies within operational air quality stakeholders (and subsequently, decision makers). Here, we illustrate some of the main outputs obtained over the last decade, thanks to this program, regarding methodological aspects (both in terms of measurement techniques and data treatment procedures) as well as acquired knowledge on the predominant PM sources. Offline and online methods are used following well-suited quality assurance and quality control procedures, notably including inter-laboratory comparison exercises. Source apportionment studies are conducted using various receptor modeling approaches. Overall, the results presented herewith underline the major influences of residential wood burning (during the cold period) and road transport emissions (exhaust and non-exhaust ones, all throughout the year), as well as substantial contributions of mineral dust and primary biogenic particles (mostly during the warm period). Long-range transport phenomena, e.g., advection of secondary inorganic aerosols from the European continental sector and of Saharan dust into the French West Indies, are also discussed in this paper. Finally, we briefly address the use of stable isotope measurements (δ¹⁵N) and of various organic molecular markers for a better understanding of the origins of ammonium and of the different organic aerosol fractions, respectively.

The adverse health impact of particles and ultrafine particles (UFP) is proven, highlighting the need of measuring the particle number concentration (PNC) dominated by UFP. So far, PNC had never been measured in the Strasbourg urban area (France). The present study on particle size distribution and PNC measurements by an UFP-3031 analyzer was conducted during winter 2019 on a background and a roadside multi-instrumented sites (Black Carbon, chemical speciation, particulate matter 10 μm or less in diameter—PM₁₀ mass). This paper shows significantly higher particle number concentrations of particles below 100 nm at the traffic site compared to the background site. The presence of a road axis thus mainly influences UFP, contrary to larger particles whose levels are more homogeneous over the agglomeration. During the measurement period, the nature of the particles (particle size contribution and chemical composition) was different between periods of high PM₁₀ mass concentrations and periods of high PNC. High PM₁₀ mass concentrations were associated with a high contribution of particles larger than 100 nm but they did not show specific chemical signature. On the other hand, during the periods with high PNC, the chemical composition was modified with an increase of the primary carbonaceous fraction compared to the periods with low PNC, but there was then no clear change in size distribution. Overall, this study illustrates that PM₁₀ mass concentrations were barely representative of UFP and PNC variations, confirming that the monitoring of the latter metrics is necessary to better evaluate the particles toxicity, knowing that this toxicity also depends on the particle’s chemical composition.

Ammonia (NH₃) is an unregulated atmospheric gaseous pollutant in ambient air, involved in the formation of fine particles. Ammonia is therefore a major precursor of particulate matter (PM), the health effects of which have been widely demonstrated. NH₃ emissions are clearly dominated by the agricultural sector (livestock and fertilizers), but other sources may also be important and less studied, such as road traffic with the increased use of catalytic converters in vehicles. This study is based on a long-term real-time measurements campaign (December 2019–September 2021) on two urban sites: a background site and a roadside site in the same agglomeration in France. The study of historical measurements at the background site clearly demonstrated the dominance of agriculture on the ammonia concentrations. This influence was also observed at both sites during the measurement campaign. The annual and monthly averages obtained in the study were similar to previous ones, with concentrations between 1–10 µg/m³ at both sites, indicating lower levels than previous studies for the roadside site. The ammonia levels measured during the campaign at the traffic site were significantly higher than those measured at the background site, highlighting the road traffic influence on ammonia in urban area. The biomass burning influence also seemed to be observed during this long measurement campaign at the agglomeration scale. The influences of road traffic and biomass burning on ammonia concentration remain small compared to agriculture.

Source apportionment analyses of carbonaceous aerosol were conducted at two neighboring urban sites in Strasbourg, France, during the winter of 2019/2020 using ACSMs (Aerosol Chemical Speciation Monitors; for non-refractory submicron aerosols), aethalometers (AE33; for equivalent Black Carbon – eBC) and filter-based offline chemical speciation. Positive Matrix Factorization (PMF) was applied to organic aerosols (OA) following two strategies: (i) analyzing each site individually, (ii) combining both sites into a single dataset. Both methods resolved five OA factors: hydrocarbon-like (HOA), biomass burning (BBOA), cooking-like (COA-like), oxygenated (OOA), and an amine-related OA (58-OA) factor. The latter factor, accounting for ∼ 4 % of the total OA mass at each site, showed clear diel profiles and a distinct origin marked by specific wind directions, suggesting a unique local source, potentially linked to industrial emissions. The present study also highlights the challenge of attributing a cooking-only origin to the COA-like factor, which exhibited a diel cycle similar to biomass burning OA at the background site. The combined PMF analysis improved the apportionment of cooking emissions at nighttime, especially for the traffic site, compared to individual PMF analyses, but it did not enhance the other OA factors due to instrumental specificities (i.e., different fragmentation patterns) leading to differences in OA mass spectra between the two instruments. Overall, this study argues for careful inspection of instrumental peculiarities in ACSM and AE33 data treatment and provides hints to benefit from their use at various locations at the city scale. It also allows comparison between different types of PMF analyses, showing that combined PMF may not be appropriate for improving the consistency of OA factors in some cases such as the one presented here.

Source apportionment analyses of carbonaceous aerosol were conducted at two neighboring urban sites in Strasbourg, France, during the winter of 2019/2020 using ACSMs (Aerosol Chemical Speciation Monitors; for non-refractory submicron aerosols), aethalometers (AE33; for equivalent Black Carbon – eBC) and filter-based offline chemical speciation. Positive Matrix Factorization (PMF) was applied to organic aerosols (OA) following two strategies: i) analyzing each site individually, ii) combining both sites into a single dataset. Both methods resolved five OA factors: hydrocarbon-like (HOA), biomass burning (BBOA), cooking-like (COA-like), oxygenated (OOA), and an amine-related OA (58-OA) factor. The latter factor, accounting for ~4 % of the total OA mass at each site, showed clear diel profiles and a distinct origin marked by specific wind directions, suggesting a unique local source, potentially linked to industrial emissions. The present study also highlights the challenge of attributing a cooking-only origin to the COA-like factor, which exhibited a diel cycle similar to biomass burning OA at the background site. The combined PMF analysis improved the apportionment of cooking emissions at nighttime, especially for the traffic site, compared to individual PMF analyses, but it did not enhance the other OA factors due to instrumental specificities (i.e., different fragmentation patterns) leading to differences in OA mass spectra between the two instruments. Overall, this study argues for careful inspection of instrumental peculiarities in ACSM and AE33 data treatment and provides hints to benefit from their use at various locations at the city scale. It also allows comparison between different types of PMF analyses, showing that combined PMF may not be appropriate for improving the consistency of OA factors in some cases such as the one presented here.

Source apportionment analyses of carbonaceous aerosol were conducted at two neighboring urban sites in Strasbourg, France, during the winter of 2019/2020 using ACSMs (Aerosol Chemical Speciation Monitors; for non-refractory submicron aerosols), aethalometers (AE33; for equivalent Black Carbon – eBC) and filter-based offline chemical speciation. Positive Matrix Factorization (PMF) was applied to organic aerosols (OA) following two strategies: i) analyzing each site individually, ii) combining both sites into a single dataset. Both methods resolved five OA factors: hydrocarbon-like (HOA), biomass burning (BBOA), cooking-like (COA-like), oxygenated (OOA), and an amine-related OA (58-OA) factor. The latter factor, accounting for ~4 % of the total OA mass at each site, showed clear diel profiles and a distinct origin marked by specific wind directions, suggesting a unique local source, potentially linked to industrial emissions. The present study also highlights the challenge of attributing a cooking-only origin to the COA-like factor, which exhibited a diel cycle similar to biomass burning OA at the background site. The combined PMF analysis improved the apportionment of cooking emissions at nighttime, especially for the traffic site, compared to individual PMF analyses, but it did not enhance the other OA factors due to instrumental specificities (i.e., different fragmentation patterns) leading to differences in OA mass spectra between the two instruments. Overall, this study argues for careful inspection of instrumental peculiarities in ACSM and AE33 data treatment and provides hints to benefit from their use at various locations at the city scale. It also allows comparison between different types of PMF analyses, showing that combined PMF may not be appropriate for improving the consistency of OA factors in some cases such as the one presented here.