Exposure to ambient air pollution is a major risk factor for human health yet, the physiological effects of particulate matter (PM) remain poorly understood. Oxidative stress due to excess formation of reactive oxygen species (ROS) is a leading hypothesis for the molecular mechanism behind the adverse health effects of PM. Thus, measurements of ROS production and antioxidant depletion are widely used to assess the oxidative potential (OP) of PM. Here we introduce a chemical kinetic model of oxidative potential (KM-OP) to elucidate and quantify the effects of PM on the production of ROS and the consumption of ascorbic acid (AA) and dithiothreitol (DTT). The chemical mechanism of the model is based on literature rate coefficients and a large compilation of laboratory data on the effects of transition metal ions, quinones, and organic aerosol (OA). We apply the model to field measurement data of PM composition and OP from three European cities (Grenoble, Paris, London), obtaining good correlations (R2>0.75) and low model bias (<15 %) for 4 out of 6 data sets. Previous studies found that PM may inflict damage to biomolecules in the lungs mainly via the production of hydroxyl (⚫OH) radicals. The antioxidant-based OP assays investigated in this study show a good correlation with modeled ⚫OH production. We identify OA as the strongest contributor to antioxidant-based OP assays, with minor contributions from Cu and Fe ions. Cu dominates the production of hydrogen peroxide (H2O2), but does not substantially affect ⚫OH production. Our model and results provide a basis for further investigation and comparison of different metrics of the potential toxicity of PM.

Exposure to particulate matter (PM10) poses a significant threat to human health. We investigated the effects of short-term exposures to PM10 sources and oxidative potential (OP), an indicator of PM related to its toxicity, on lung function in early childhood. The study is based on 435 children from the SEPAGES cohort in Grenoble, with lung function measured at 6-8 weeks (N2 multiple breath washout and tidal breathing flow-volume loop) and 3 years (oscillometry, including resistance and reactance). PM10 chemical composition and OP (ascorbic acid - AA and dithiothreitol - DTT assays) were measured on filters collected at an urban background station. PM10 concentrations were attributed to 10 different sources using a source apportionment method. Associations of short-term exposure (1-, 3-, 7- and 14-day) to each source and OP with lung function were estimated by regression models. Increased acute exposure to PM10 from traffic was associated with parameters indicating impaired lung function at 6-8 weeks and 3 years. Increased exposure to PM10 from biomass burning and primary biogenic sources was associated with lower intra-breath reactance at 3 years. Total PM10 mass and other sources showed no association, or were associated with higher lung function. Increased OPAA was consistently associated to lower child lung function. The greatest magnitude was found between 2-week exposure to mass-normalized OPAA and reduced 7-Hz-reactance at 3 years (β = -0.52 hPa×s/L 95%CI: -0.88, -0.15 per 0.07 nmol/min/μg increase). This study suggests that short-term exposure to PM10 from primary biogenic and local anthropogenic sources, mainly traffic and biomass burning, and to OP may have detrimental effects on early life lung function.

Recent air quality studies point towards the importance of distinguishing aerosol sources and their chemical composition in relation to the toxicity of particulate matter (PM). While aerosol source apportionment datasets are becoming increasingly available, model evaluations remain scarce. In this study, results from the regional-scale European Monitoring and Evaluation Programme (EMEP) Meteorological Synthesizing Centre - West (MSC-W) and coupled urban EMEP (uEMEP) Gaussian plume downscaling system are evaluated against three European positive-matrix-factorization (PMF) source apportionment datasets. These datasets are based on 28 predominantly urban measurement sites, spanning the years 2013 to 2018. In our analysis, special attention is paid to the impact of urban downscaling to 250 m resolution as well as to the role of primary and secondary organic aerosol. Results show that the model performance varies considerably between PMF factors, which may be explained in part by the ambiguity involved in the matching to modelled species and to uncertainties in the PMF analysis itself. Nevertheless, common model strengths and weaknesses can be identified. For example, model strengths relate to the ability to describe temporal variations of individual PMF factor concentrations while weaknesses relate to the apparent discrepancies in some of the underlying emission distributions. While downscaling generally improves results for road traffic and residential heating, it can also enhance existing biases, with overall model performance for these components remaining poor. Downscaling of residential heating is further found to be sensitive to the treatment of condensable wood burning emissions.

This study presents the first nationwide phenomenological analysis of submicron organic aerosol (OA) sources in France, based on highly time‑resolved Aerosol Chemical Speciation Monitor (ACSM) measurements collected at 12 (sub)urban sites over one to seven years. A harmonized rolling Positive Matrix Factorization (PMF) approach was applied, ensuring consistent constraints and criteria across all datasets. The methodology enabled the identification of major OA sources, including primary OA (POA) factors such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and cooking-like OA (COA), which together accounted for approximately 33 % of the OA mass. Secondary or aged OA was represented by oxygenated OA (OOA), which dominated OA at all sites and was further separated into Less Oxidized (LO-OOA) and More Oxidized OOA (MO-OOA), except for Poitiers and Strasbourg. Additional site-specific OA factors were also resolved, including a mixed shipping/industrial OA factor in Marseille Longchamp and an amine-related OA factor in Strasbourg and Creil. Strong seasonality was observed for POA, particularly BBOA, which increased substantially during winter due to residential heating. LO-OOA correlated with BBOA in winter, highlighting the important contribution of biomass combustion to wintertime air quality degradation across France, while summer LO-OOA was mainly associated with biogenic precursors. Comparison with the CHIMERE chemical transport model revealed systematic biases in simulated OA components, underscoring the value of this unique long-term dataset for improving OA representation. These findings are highly relevant for various applications, including epidemiological studies, -in which source-specific exposure indicators can reveal stronger links to health effects-, and near-real-time source apportionment, -by using these identified profiles to constrain the factors in real-time analysis- facilitating the rapid identification of pollution episodes and enable the implementation of air quality management measures. They also offer observational insights for improving air quality, providing valuable information to policymakers for proposing effective mitigation strategies.

Organic aerosol (OA) is a major component of atmospheric particulate matter (PM), affecting both human health and climate. However, high-resolution estimates of OA exposure needed for exposure analysis remain scarce. Here, we integrate a chemical transport model (CAMx) with a random forest (RF) machine learning approach to bias-correct and downscale daily OA concentrations across Europe. CAMx OA simulations at ∼15 km resolution show moderate agreement with observations (r = 0.55). By combining these outputs with high-resolution land-use data and training the RF model on ∼48,000 daily OA measurements from 137 sites, prediction accuracy improved (r = 0.65), with ∼l5% reduction in root mean square error. The resulting maps provide European daily OA concentrations at ∼250 m resolution for alternate years from 2011 to 2019. The model captures key spatial features, including elevated OA in the Po Valley, Southeastern, and Central Europe, as well as intracity variations due to local hotspots. Seasonal analysis reveals higher concentrations in winter, while long-term trends indicate a general decline in OA levels. Exposure estimates show that half of the European population experiences OA levels above 3 µg/m3, and ∼50 million people are exposed to more than 5 µg/m3, which is the current guideline level recommended by the world health organization for total PM2.5. These high-resolution OA maps offer vital critical support for epidemiological research and air quality policy.

Organic aerosol particles (OA) can absorb solar radiation with varying efficiencies depending on their chemical composition and physical properties. This light-absorbing fraction of OA, commonly referred to as brown carbon (BrC), is difficult to accurately represent in climate models due to the inherent diversity of its optical properties. This variability arises from differences in emission sources and atmospheric processing, as well as from variations in experimental design and the analytical methods used to quantify BrC absorption. As a result, the climate effect of BrC remains uncertain. Here, we studied the light absorption properties of surface ambient OA using measurements from 17 sites across Europe. Combining multi-wavelength absorption measurements from filter-based photometers with OA mass concentrations and source apportionment derived from ACSM/AMS data, we derive empirical estimates of the OA mass absorption cross section (MACOA), its wavelength dependence (AAEOA), the OA density (⍴OA), and the MAC associated with different primary and secondary OA sources. We further develop parameterizations that relate MACOA, AAEOA and ⍴OA to the ambient black carbon-to-organic aerosol ratio (eBC/OA) and propose a corresponding parameterization for the imaginary refractive index (kOA). Given the widespread availability of eBC and OA measurements in global monitoring networks, the framework presented here provides a practical approach for estimating the absorptive properties of surface OA particles under real-world conditions.

An interlaboratory comparison (ILC) was conducted for levoglucosan, mannosan, and galactosan, as widely used organic tracers for assessing biomass burning aerosol in ambient air. Organized as part of the European research infrastructure ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) activities the OrGanic Tracers and Aerosol Constituents-Calibration Centre (OGTAC-CC) distributed aliquots from three ambient PM2.5 filter samples and two prepared aqueous standard solutions to ten research laboratories across Europe, each using its own analytical protocol. Overall agreement was good for the ambient filter samples, with relative standard deviations relative to the general mean of 14% for levoglucosan, 22% for mannosan, and 33% for galactosan. Individual measurement accuracy, expressed as mean percentage error, ranged from-33% to 13% for levoglucosan,-51% to 15% for mannosan, and-54% to 42% for galactosan. Laboratory performance was also assessed using z-scores, showing that despite methodological diversity, nearly all results were classified as acceptable. This ILC provides a timely snapshot of current European laboratory capability for key biomass burning tracers. The joint intercomparison study demonstrates the readiness of European laboratories to provide harmonized levoglucosan measurements at a continental scale, meeting the comparability needs arising from the inclusion of levoglucosan in the revised EU Ambient Air Quality Directive (AAQD), and supporting requirements across European (Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP), ACTRIS) and national monitoring networks.

Organic aerosols are an important and highly dynamic component of fine particulate matter, yet their long-term response to emission controls is poorly constrained. We analyze a decade (2013-2023) of wintertime aerosol mass spectrometry data from urban Nanjing, eastern China, using a developed machine learning framework that disentangles anthropogenic emission-driven changes from meteorology-driven changes. The mean organic aerosol concentrations decreased from 24.6 to 16.5 mu g m-3 during the period of 2013-2017. After accounting for meteorological influences, emission controls account for similar to 94% of the observed decrease. However, the effectiveness of anthropogenic emission controls on the total organic aerosol was weakened by a factor of approximately 2-8 times in the subsequent emission control phases, particularly with more oxidized secondary organic aerosol showing a minimal further decrease. Machine learning-based attribution analysis reveals that reductions in fossil fuel combustion and traffic-related aromatic precursors explain, on average, similar to 50-60% of the long-term variability in secondary organic aerosol, while the meteorological influence plays a minor role. These results provide observationally source-resolved evidence that current measures are reaching diminishing returns and that effective future controls must target overlooked precursors and secondary formation pathways.

Since organic aerosols (OA) account for a significant fraction of PM worldwide, source apportionment is essential for effective air quality mitigation and policymaking. In the present study, we developed a novel method based on a chemical mass balance and an elastic net regressor (EN-CMB), using positive matrix factorization (PMF) as prior knowledge for near real-time source apportionment of OA. EN-CMB has been integrated into a so-called continuous aerosol source apportionment (CASA) software package, which has been evaluated against state-of-the-art rolling-PMF data at three contrasted urban sites in Europe. CASA exhibits very satisfactory performance for primary OA components, with, at all sites, R 2 values of 0.87-0.97 and mean bias error (MBE) of between -0.15 and 0.14 μg/m3. Secondary OA (SOA) fractions showed similar R 2 values (0.81-0.97), but slightly higher MBE values (ranging from -0.71 to 0.04 μg/m3), which can be related to the complex nature of SOA and is still acceptable regarding bulk trends. CASA allows near real-time operation and, as it is open source, represents a promising example of timely and efficient air pollution management with applications in real-time air quality monitoring. The next steps will enable community-driven initiatives to improve and expand the application of such open-source methodologies across diverse regions and emission sources.

Black carbon is a global climate forcer due to its strong radiative absorption, which is highly sensitive to coating formation regulated by anthropogenic and biogenic emissions. However, how cross-regional biogenic sources modulate urban black carbon coating and radiative effects remains poorly understood. Here we integrate observations and model simulations to investigate such biogenic-anthropogenic interactions in eastern China. The results show that biogenic volatile organic compounds from vegetation-rich regions undergo atmospheric oxidation to produce oxygenated organic compounds, which are subsequently advected into downwind urban areas. These products enhance regional atmospheric oxidation capacity and supply additional precursors, thereby promoting secondary organic aerosol production. This biogenic-induced strengthening of regional photochemistry drives the formation of highly oxidized secondary organic aerosol coatings on black carbon and increases its fraction within the total particle population. Consequently, black carbon absorption efficiency increases more steeply with the coating carbon oxidation state under biogenic-rich conditions, yielding an average similar to 20% enhancement in radiative absorption from the lensing effect relative to biogenic-poor periods. Our findings reveal that cross-regional biogenic-anthropogenic interactions enhance both the formation and particle population fraction of secondary organic aerosol coatings on urban black carbon, potentially further amplifying its radiative effects as biogenic emissions increase under future warming scenarios.

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 similar to 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.

Mineral desert dust is a major contributor to total atmospheric particulate matter1. Desert dust outbreaks degrade air quality and can pose adverse health effects2, including asthma exacerbation3 and increased mortality4. At some European locations, there has been a rise in the intensity and frequency of transported dust outbreaks from deserts in recent decades5-9. However, it remains unclear whether this increase is consistent across Europe and whether desertification and aridity or shifts in atmospheric circulation are the main drivers behind this rise. Here we compile a database of daily dust metal concentrations from European sites, establishing robust elemental ratios for transported dust. Using this database, we develop a machine learning model to estimate daily PM10 (particulate matter smaller than 10 μm) dust concentrations from 2012 to 2021, ranging from 2.09 ± 1.05 μg m-3 across northern and central Europe to 5.28 ± 2.65 μg m-3 across the south. In southern Europe, residents are exposed to transported dust events averaging 9.68 ± 4.85 μg m-3, linked to a 0.67 ± 0.02% rise in daily mortality. Intensified dust intrusions over the past decade are linked to shifts in atmospheric circulation. Data from an Alpine ice core record shows a 110% increase in dust concentrations since pre-industrial times, mostly associated with North African desertification. As climate change accelerates land degradation and affects weather patterns, worsening dust pollution may pose increasing risks to public health and air quality goals.

The identification of particulate matter (PM) sources and the quantification of their contribution to the urban environment is a necessary input for policymakers to reduce the air pollution impacts. The association between the PM sources and the oxidative potential (OP) is also a key indicator for evaluating the ability of PM sources to induce in-vivo oxidative stress and lead to adverse health effects, which becomes an emerging metric in the Directive on ambient air quality (22024/2881/EU). Most studies in Europe have focused on PM and OP sources in the short term, for only 1 or 2 years. However, the efficiency of reduction policies, trends, and epidemiological impacts cannot be properly evaluated with such short-term studies due to a lack of statistical robustness. Here, long-term PM10 filter sampling at the Grenoble (France) urban background supersite and detailed chemical analyses were used to investigate decadal trends of the main PM sources and related OP metrics. Positive matrix factorization (PMF) analyses were conducted on the corresponding 11-year dataset (January 2013 to May 2023, n=1570), enlightening the contributions of 10 PM sources: mineral dust, sulfate-rich, primary traffic, biomass burning, primary biogenic, nitrate-rich, MSA-rich, aged sea salt, industrial and chloride-rich. The stability of the chemical profile of these sources was validated by comparison with the profiles retrieved from shorter-term (3 years) successive PMF analyses. A Seasonal-Trend using LOESS decomposition was then applied to evaluate the trends of these PM10 sources, which revealed a substantial decrease in PM10 (-0.73 mu gm-3yr-1) as well as that of many of the PM10 sources. Specifically, negative trends for primary traffic and biomass burning sources are detected, with a reduction of 0.30 and 0.11 mu gm-3yr-1, respectively. The OP PM10 source apportionment in 11 years confirmed the high redox activity of the anthropogenic sources, including biomass burning, industrial, and primary traffic. Eventually, downward trends were also observed for OPAA and OPDTT, mainly driven by the reduction of residential heating and transport emissions, respectively.

In today's rapidly evolving society, the sources of atmospheric particulate matter (PM) emissions are shifting significantly. Stringent regulations on vehicle tailpipe emissions, in combination with a lack of control of non-exhaust vehicular emissions, have led to an increase in the relative contribution of non-exhaust PM in Europe. This study analyzes the spatial distribution, temporal trends, and impacts of brake wear PM pollution across Europe by modeling copper (Cu) concentrations at a high spatial resolution of ∼250 m which is a key tracer of brake-wear emissions. We integrated coarse-resolution brake-wear Cu from CAMx chemical transport model and high-resolution land use data into a random forest (RF) model to predict Cu concentrations at ∼250 m over whole of continental Europe. The RF model was trained using an unprecedented dataset of over 50,000 daily Cu measurements from 152 sites. It corrected CAMx underestimation and downscaled Cu to a higher spatial resolution. In validation, the model showed robust spatial and temporal prediction with good Pearson's correlation coefficients of 0.6 and 0.7, respectively. We generated 10 years (2010-2019) of daily Cu concentrations over Europe, revealing spatial patterns aligned with urbanization and road networks, with peaks in cities and lower values in rural areas. Temporal trends reveal that Cu concentrations generally peak on weekdays and in winter. Despite a decline in PM across Europe over decades, Cu concentrations showed no decrease in many cities from 2010 to 2019. Cu levels are strongly correlated with population density with more than 12 million Europeans exposed to levels exceeding 40 ng/m3, equivalent to around 1 μg/m3 of total PM10 from brake wear. Our findings highlight the need for expanded metal measurement for non-exhaust tracers for a better understanding of the health relevance of PM composition including Cu, and more effective regulations of non-exhaust PM emissions as included in EURO 7 vehicles.

Hygroscopicity strongly influences aerosol properties and multiphase chemistry, which is essential in several atmospheric processes. Although CCN (cloud condensation nuclei) properties are commonly measured, sub-saturated hygroscopicity measurements remain rare. During the ACROSS campaign (Atmospheric ChemistRy Of the Suburban foreSt, conducted in Paris in summer 2022), particles' hygroscopic growth rates at 90 % relative humidity (RH) and chemical composition were measured at the sub-urban site using a Hygroscopicity Tandem Differential Mobility Analyser (HTDMA, scanning at 100, 150, 200, and 250 nm) and an Aerodyne High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS). Growth factor probability density functions (GF-PDFs) revealed two distinct modes, namely hydrophobic and hygroscopic, suggesting a combination of internal and external particle mixing, with the split at GF 1.2. The prevalence of the hygroscopic mode increased with particle size, with mean hygroscopicity (κ) values of 0.23 and 0.38 for 100 and 200 nm particles, respectively. Using the Zdanovskii–Stokes–Robinson (ZSR) mixing rule, the agreement between measured and chemically derived hygroscopicity was approximately 51% for 100 nm particles, which declined for 200 and 250 nm. These emphasise the large effect of external particle mixing and its influence on predicting hygroscopicity. The ZSR approach proves to be unreliable in predicting the wide growth distribution of externally mixed particles. In this measurement, 80 %–90 % of the particles were externally mixed and influenced by fresh emission, which affected the hygroscopicity prediction by a factor of 2. A cluster analysis based on backward trajectories and meteorological conditions gives valuable insights into the chemical composition and variations in the hygroscopicity of different air masses.

Gaseous and particulate organic compounds are key components of atmospheric chemistry and better understanding their composition, sources and processes essential to limit their impacts. Source apportionment using positive matrix factorization (PMF) is customarily performed for such studies. Combining organic aerosol data with their gaseous precursors was shown to be a promising approach, however very rarely attempted so far, to refine their origins using PMF. In this study, co located continuous proton-transfer-reaction mass spectrometer (PTR-MS) and aerosol chemical speciation monitor (ACSM) measurements were performed at the suburban SIRTA station located in the Paris region. A combined dataset using both instruments during summer (June-August 2020) was then used in an exploratory PMF analysis to investigate the sources and processes of organic compounds, particularly the influences of biogenic emissions and important photochemical reactions on the formation of secondary organic aerosol (SOA) in this period of the year. Specific parameters and evaluation procedures were needed to ensure an equivalent representation of both instruments in the PMF model. A weighing factor was applied to the PTR-MS uncertainties which was controlled and optimized based on the analysis of the modelled scaled residuals. Seven main factors were obtained, describing anthropogenic sources (hydrocarbon-like organics, cooking-like organics), primary biogenic volatile organics, nighttime VOC, oxidized organics, aged organics and a specific isoprene oxidation factor which contributed 11 % to VOC and 4 % to OA. Compared to single-instrument PMFs, more factors were obtained, notably including the cooking-like factor which is not usually resolved using only ACSM data. This method also showed a better mathematical performance for the PTR-MS variables (mean absolute scaled residuals lower for the combined PMF (11.2) than for the PTR-MS-only PMF (38.2)) and a better separation of the factors for the ACSM variables in the combined PMF.

Carbonaceous aerosols (CA), composed of black carbon (BC) and organic matter (OM), significantly impact the climate. Light absorption properties of CA, particularly of BC and brown carbon (BrC), are crucial due to their contribution to global and regional warming. We present the absorption properties of BC (b(Abs,BC)) and BrC (b(Abs,BrC)) inferred using Aethalometer data from 44 European sites covering different environments (traffic (TR), urban (UB), suburban (SUB), regional background (RB) and mountain (M)). Absorption coefficients showed a clear relationship with station setting decreasing as follows: TR > UB > SUB > RB > M, with exceptions. The contribution of b(Abs,BrC) to total absorption (b(Abs)), i.e. %Abs(BrC), was lower at traffic sites (11-20 %), exceeding 30 % at some SUB and RB sites. Low AAE values were observed at TR sites, due to the dominance of internal combustion emissions, and at some remote RB/M sites, likely due to the lack of proximity to BrC sources, insufficient secondary processes generating BrC or the effect of photobleaching during transport. Higher b(Abs) and AAE were observed in Central/Eastern Europe compared to Western/Northern Europe, due to higher coal and biomass burning emissions in the east. Seasonal analysis showed increased b(Abs), b(Abs,BC), b(Abs,BrC) in winter, with stronger %Abs(BrC), leading to higher AAE. Diel cycles of b(Abs,BC) peaked during morning and evening rush hours, whereas b(Abs,BrC), %Abs(BrC), AAE, and AAE(BrC) peaked at night when emissions from household activities accumulated. Decade-long trends analyses demonstrated a decrease in b(Abs), due to reduction of BC emissions, while b(Abs,BrC) and AAE increased, suggesting a shift in CA composition, with a relative increase in BrC over BC. This study provides a unique dataset to assess the BrC effects on climate and confirms that BrC can contribute significantly to UV-VIS radiation presenting highly variable absorption properties in Europe.

The complex refractive index (CRI; n−ik) and the single scattering albedo (SSA) are key parameters driving the aerosol direct radiative effect. Their spatial, temporal, and spectral variabilities in anthropogenic–biogenic mixed environments are poorly understood. In this study, we retrieve the spectral CRI and SSA (370–950 nm wavelength range) from in situ surface optical measurements and the number size distribution of submicron aerosols at three sites in the greater Paris area, representative of the urban city, as well as its peri-urban and forested rural environments. Measurements were taken as part of the ACROSS (Atmospheric Chemistry of the Suburban Forest) campaign in June–July 2022 under diversified conditions: (1) two heatwaves leading to high aerosol levels, (2) an intermediate period with low aerosol concentrations, and (3) an episode of long-range-transported fire emissions. The retrieved CRI and SSA exhibit an urban-to-rural gradient, whose intensity is modulated by the weather conditions. A full campaign average CRI of 1.41−0.037i (urban), 1.52−0.038i (peri-urban), and 1.50−0.025i (rural) is retrieved. The imaginary part of the CRI (k) increases and the SSA decreases at the peri-urban and forest sites when exposed to the influence of the Paris urban plume. Values of k > 0.1 and SSA < 0.6 at 520 nm are related to a black carbon mass fraction larger than 10 %. Organic aerosols are found to contribute to more than 50 % of the aerosol mass and up to 10 % (urban), 17 % (peri-urban), and 22 % (forest) of the aerosol absorption coefficient at 370 nm. A k value of 0.022 (370 nm) was measured at the urban site for the long-range-transported fire episode.