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.

Brown carbon (BrC) is a fraction of organic aerosol (OA) that absorbs radiation in the ultraviolet and short visible wavelengths. Its contribution to radiative forcing is uncertain due to limited knowledge of its imaginary refractive index (k). This study investigates the variability of k for OA from wildfires, residential, shipping, and traffic emission sources over Europe. The Multiscale Online Nonhydrostatic Atmosphere Chemistry (MONARCH) model simulated OA concentrations and source contributions, feeding an offline optical tool to constrain k values at 370 nm. The model was evaluated against OA mass concentrations from aerosol chemical speciation monitors (ACSMs) and filter sample measurements, as well as aerosol light absorption measurements at 370 nm derived from an Aethalometer™ from 12 sites across Europe. Results show that MONARCH captures the OA temporal variability across environments (regional, suburban, and urban background). Residential emissions are a major OA source in colder months, while secondary organic aerosol (SOA) dominates in warmer periods. Traffic is a minor primary OA contributor. Biomass and coal combustion significantly influence OA absorption, with shipping emissions also notable near harbors. Optimizing k values at 370 nm revealed significant variability in OA light absorption, influenced by emission sources and environmental conditions. Derived k values for biomass burning (0.03 to 0.13), residential (0.008 to 0.13), shipping (0.005 to 0.08), and traffic (0.005 to 0.07) sources improved model representation of OA absorption compared to a constant k. Introducing such emission source-specific constraints is an innovative approach to enhance OA absorption in atmospheric models.

The apportionment of equivalent black carbon (eBC) to combustion sources from liquid fuels (mainly fossil; eBC(LF)) and solid fuels (mainly non-fossil; eBC(SF)) is commonly performed using data from Aethalometer instruments (AE approach). This study evaluates the feasibility of using AE data to determine the absorption Angstrom exponents (AAEs) for liquid fuels (AAE(LF)) and solid fuels (AAE(SF)), which are fundamental parameters in the AE approach. AAEs were derived from Aethalometer data as the fit in a logarithmic space of the six absorption coefficients (470-950 nm) versus the corresponding wavelengths. The findings indicate that AAE(LF) can be robustly determined as the 1st percentile (PC1) of AAE values from fits with R-2 > 0.99. This R-2-filtering was necessary to remove extremely low and noisy-driven AAE values commonly observed under clean atmospheric conditions (i.e., low absorption coefficients). Conversely, AAE(SF) can be obtained from the 99th percentile (PC99) of unfiltered AAE values. To optimize the signal from solid fuel sources, winter data should be used to calculate PC99, whereas summer data should be employed for calculating PC1 to maximize the signal from liquid fuel sources. The derived PC1 (AAE(LF)) and PC99 (AAE(SF)) values ranged from 0.79 to 1.08, and 1.45 to 1.84, respectively. The AAE(SF) values were further compared with those constrained using the signal at mass-to-charge 60 (m/z 60), a tracer for fresh biomass combustion, measured using aerosol chemical speciation monitor (ACSM) and aerosol mass spectrometry (AMS) instruments deployed at 16 sites. Overall, the AAE(SF) values obtained from the two methods showed strong agreement, with a coefficient of determination (R-2) of 0.78. However, uncertainties in both approaches may vary due to site-specific sources, and in certain environments, such as traffic-dominated sites, neither approach may be fully applicable.

This study applied Positive Matrix Factorization (PMF) to PM10 speciation datasets from 24 urban sites across six European countries (France, Greece, Italy, Portugal, Spain, and Switzerland) to perform a detailed source apportionment (SA) analysis. By using a consistent source apportionment tool for all datasets, the study enhances the comparability of PM10 SA results across urban Europe. The results identified seven major PM10 sources including road traffic, biomass burning, crustal/mineral sources, secondary aerosols, industrial emissions, sea salt, and heavy oil combustion (HOC). Road traffic emerged as the predominant source of PM10 in urban areas, with contributions varying by location, but representing as much as 41

Ultrafine particle (UFP) emissions from aircraft engines are associated with increased UFP concentrations near airports, raising concerns for air quality and public health. This study investigated the impact of airport operations at Brussels Airport (Belgium) on local air quality during a two-month monitoring campaign in autumn 2015. Four monitoring sites, strategically located from the airport and along a transect parallel to the main runway, were used to measure particle number size distributions (PNSD), black carbon (BC), particulate matter (PM10), and nitrogen oxides (NOx). This novel approach, using high-resolution PNSD data, provided a detailed analysis of aircraft-related UFPs under varying meteorological conditions and operational phases. Results showed strong correlations between UFP concentrations, meteorological conditions, and aircraft landing and takeoff (LTO) operations, with the smallest measured particles (10-20 nm) dominating the particle number concentrations (PNC) near the airport, contributing up to 65 % of total PNC. Aircraft emissions were responsible for 15000-20000 # cm-3 of 10-20 nm particles near the airport, with peak concentrations (30-min) reaching 65000-80000 # cm-3 during high-activity periods. While airport-related UFP concentrations decreased with distance, they persisted up to 7 km. Source apportionment analysis identified taxiing and takeoff as major contributors to UFPs near the airport (50-64 % of PNC), with road traffic, biomass burning, and regional background more significant at greater distances. Despite attempts to distinguish between landing and takeoff emissions, similar particle profiles were observed for both operations. These findings provide insight into the spatial persistence, dominant operational phases, and size distribution of aircraft-related UFP emissions, and underscore the need for targeted mitigation strategies to improve air quality near airports.

This study investigates the geochemical composition of sands and sandy soils collected from 10 playgrounds and 13 parks across the Barcelona Metropolitan Area (BMA), a region characterised by high population density, extensive industrial activity, and heavy traffic. By examining the variability in metal loads and the deposition of contaminants, the research sheds light on the impact of urban pollution sources on the spatial distribution of harmful elements. The findings reveal that playgrounds tend to exhibit coarser sand textures compared to parks, likely reflecting both the progressive milling of sand and the accumulation of deposited particulate matter (DPM) over time. Several parks exhibited significant enrichment factors (EF) (5 ≤ EF ≤ 20) for a variety of elements, which can be attributed to vehicle wear, while others showed even higher enrichments, particularly related to braking systems. Distinct anomalies in metal concentrations were found in several parks, indicating a diversity of pollution sources, including industrial emissions and port activities. Particularly noteworthy are the elevated Ce/La ratios (1.79–2.95) found in some parks, which point to vehicular catalytic emissions. Additionally, exceptional anomalies in Pb (EF: 62.3–84.1) and Sb (EF: 26.8–44.4) concentrations were identified, most likely due to industrial and port activities near certain parks. Another significant finding of this study is the higher concentrations of traffic-related metals found beneath tree canopies compared to open spaces. This suggests that the presence of vegetation, particularly trees, plays a crucial role in modifying the deposition and distribution of PM in urban green spaces. This research provides critical insights into urban pollution dynamics, particularly the distribution of metals in relation to sand composition, environmental interactions, and the role of urban greenery in mitigating the negative effects of airborne pollutants. By examining both the geochemical impacts of pollution and the physical processes that contribute to contamination in urban environments, this study underscores the importance of thoughtful urban green infrastructure in reducing the environmental and health risks associated with urban pollution.

Advanced receptor models can leverage the information derived from optical and chemical variables as input by a variety of instruments at different time resolutions to extract the source specific absorption Ångström exponent (AAE) from aerosol absorption. The multilinear engine (ME-2), a Positive Matrix Factorization (PMF) solver, serves as a proficient tool for performing such analyses, thereby overcoming the constraints imposed by the assumptions in current optical source apportionment methods such as the Aethalometer approach since the use of a-priori AAE values introduces additional uncertainty into the results of optical methods. Comprehensive PM2.5 chemical speciation datasets, and aerosol absorption coefficients (babs, λ) at seven wavelengths measured by an Aethalometer (AE33), were used in multi-time source apportionment (MT-PMF). The study focused on two locations in the Los Angeles (LA) Basin: Central LA (CELA, Main St.), an urban area surrounded by major freeways, and Rubidoux (RIVR), a residential area surrounded by local roads. Factor profiles and temporal variations of their contributions were obtained. Additionally, factor displacements (DISP) and profile constraints were applied. Five-factor solutions were obtained at both sites. At CELA, the resolved factors included traffic + crustal matter (traffic+ Cr_M), secondary sulfate + nitrate (SSN), biomass burning (BB), diesel emissions (DIE) and aged sea salt (ASS). Moreover, source-dependent AAE values at CELA were obtained without a-priori assumption, with values of 1.46 for traffic+ Cr_M, 1.45 for DIE and 2.37 for BB. At RIVR, the resolved factors were traffic+ Cr_M (AAE = 1.24), particulate sulfate, particulate nitrate, BB (AAE = 3.00) and aged sea salt. PM2.5 composition differed at both locations. SSN accounted for the largest fraction of the ambient PM2.5 mass concentration, their sum at the CELA site averaged 40 % of the PM2.5 mass while the same species represented 77 % at RIVR.

Atmospheric PM10 and benzo(a)pyrene (BaP) concentrations in Manlleu (NE Spain) have remained high from 2008 to 2023, frequently exceeding EU limit/target values, and reaching BaP levels up to six times higher than urban averages in Spain. Furthermore, PM speciation campaigns were carried out in 2013, 2014-2015, 2016-2017 and 2021-2022. Chemical mass closure for autumn-winter showed a consistent PM10 composition for the different PM speciation campaigns, comprising 46-53% organic matter (OM), 18-26% secondary inorganic aerosol (SIA), 13-23% mineral matter (MM), and 5-9% elemental carbon (EC). Trend analysis revealed very light decrease and constant concentrations for PM10 and BaP, respectively over the study period, emphasizing the need for compliance with current and forthcoming EU air quality directives, the last aiming to halve PM10 limit values. Source apportionment of samples of the sporadic campaigns identified biomass burning (BB, 17.5 μg m-3, 48%) and MM and industry (16.3 μg m-3, 44%) as the main autumn-winter PM10 contributors, with high SIA concentrations attributed to several factors, including high ammonia (NH3) emissions. Local topography and meteorological conditions contribute to aggravate PM10 pollution. While metal concentrations have decreased since 2013, suggesting reduced industrial emissions, persistently high OM and EC concentrations indicate ongoing issues with BB emissions from domestic, commercial, and agricultural sources. Online analysis of black carbon (BC) and non-refractory PM1 components during winter 2016-2017 confirmed domestic and commercial BB as the primary sources of the BB contributions. These findings highlight the need of the implementation of more effective measures in reducing BB and agricultural/farming NH3 emissions. This study highlights the relevance of these issues for similar towns, the probable unremitting problem over the last decade, and the necessity of enhanced monitoring in small cities and policy actions to meet air quality standards under the new EU directive.

Air quality impairment has a massive impact on human health, with atmospheric particulate matter (PM) playing a major role. The People's Republic of China experienced a trend of increasing PM2.5 concentrations from 2000 to 2013. However, after the application of the Air Pollution Prevention and Control Action Plan and other related control measures, sharp decreases in air pollutant concentrations were particularly evident in the city of Wuhan (central China). This study analysed major changes in PM2.5 concentrations, composition and source apportionment (using receptor modelling) based on Wuhan's PM2.5 chemical speciation datasets from 2006 to 2007, 2019-2021 and contemporaneous gaseous pollutant values. Average SO2 concentrations decreased by 88%, from the first to the second period, mostly due to measures that reduced coal combustion. However, NO2 only declined by 25%, with policy measures likely being undermined by an increased number of vehicles. PM2.5 concentrations decreased by 65%, with the PM constituents each being affected differently. Coal combustion-related element concentrations, OC, SO42- , NH4+, EC, Cl-, Al, Ca, Cu, Fe, Co and NO3- decreased by 22-90%. Secondary inorganic aerosol (SIA) was initially dominated by (NH4)2SO4 (73%) in 2006, but later dominated by NH4NO3 (52%) in 2021. Receptor modelling identified major sources contributing to PM2.5: Mineral, road and desert dust (MRDD), Secondary sulphate (SECS), Secondary nitrate (SECN), Tungsten industry (W), Toxic Elements of Coal (TEC), Iron and Steel (IRONS), Coal Combustion (CC), Residential Heating (RH), Refinery (REF) and Traffic (TRF). In relative proportions, TEC (-83%), SECS (-64%) and SECN (-48%) reduced their contributions to PM2.5 whilst MRDD increased (+62.5%). Thus, the results indicate not only a drastic abatement of PM pollution in Wuhan but also a change in the sources of pollution, which requires further actions to reduce PM2.5 concentrations to health protective values. Secondary PM and fugitive emissions are key components to abate.

New particle formation (NPF) events play a significant role in altering aerosol concentrations and dispersion within the atmosphere, making them vital for both climate and air quality research. The primary objective of investigating NPF events is to precisely determine their occurrence dates. In this study, we introduced the ConvNeXt model for the first time to identify NPF events, and compared its performance with two other deep learning models, EfficientNet and Swin Transformer. Our main aim was to automate an objective identification and classification of NPF events accurately. All three models employed transfer learning to effectively capture critical features associated with NPF. Our results demonstrated that the ConvNeXt model significantly outperformed the other models, achieving an impressive accuracy rate of 95.3% on event days, surpassing EfficientNet (92.8%) and Swin Transformer (94.9%). Furthermore, we performed tests using different ConvNeXt variants (ConvNeXt-T/S/B/L/XL) and different pre-training weights, revealing that different configurations of ConvNeXt models exhibited improved NPF event recognition capabilities. Finally, we conducted generalizability experiments using the ConvNeXt-XL model, achieving the highest accuracy of 96.4% on event days. This study not only underscores the recognition prowess of ConvNeXt models but also highlights their practical utility in accurately detecting NPF events in real-world scenarios. This contribution aids in advancing our comprehension of aerosol dynamics in atmospheric environments, providing valuable insights for climate and air quality research.

Characterising the physico-chemical properties of dust-emitting sediments in arid regions is fundamental to understanding the effects of dust on climate and ecosystems. However, knowledge regarding high-latitude dust (HLD) remains limited. This study focuses on analysing the particle size distribution (PSD), mineralogy, cohesion, iron (Fe) mode of occurrence, and visible–near infrared (VNIR) reflectance spectra of dust-emitting sediments from dust hotspots in Iceland (HLD region). Extensive analysis was conducted on samples of top sediments, sediments, and aeolian ripples collected from seven dust sources, with particular emphasis on the Jökulsá basin, encompassing the desert of Dyngjunsandur. Both fully and minimally dispersed PSDs and their respective mass median particle diameters revealed remarkable similarities (56 ± 69 and 55 ± 62 µm, respectively). Mineralogical analyses indicated the prevalence of amorphous phases (68 ± 26 %), feldspars (17 ± 13 %), and pyroxenes (9.3 ± 7.2 %), consistent with thorough analyses of VNIR reflectance spectra. The Fe content reached 9.5 ± 0.40 wt %, predominantly within silicate structures (80 ± 6.3 %), complemented by magnetite (16 ± 5.5 %), hematite/goethite (4.5 ± 2.7 %), and readily exchangeable Fe ions or Fe nano-oxides (1.6 ± 0.63 %). Icelandic top sediments exhibited coarser PSDs compared to the high dust-emitting crusts from mid-latitude arid regions, distinctive mineralogy, and a 3-fold bulk Fe content, with a significant presence of magnetite. The congruence between fully and minimally dispersed PSDs underscores reduced particle aggregation and cohesion of Icelandic top sediments, suggesting that aerodynamic entrainment of dust could also play a role upon emission in this region, alongside saltation bombardment. The extensive analysis in Dyngjusandur enabled the development of a conceptual model to encapsulate Iceland's rapidly evolving high dust-emitting environments.

Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.

Iron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long-term, near-source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub-sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under- or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting-related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long-term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates. Human activities, such as smelting and oil combustion, release smoke and particles into the atmosphere. These particles often contain iron, which not only absorbs sunlight, contributing to atmospheric warming, but also serves as a nutrient for phytoplankton in various ocean regions. However, the precise extent of human-induced iron emissions remains uncertain due to a lack of comprehensive monitoring data. In this study, we leverage a global data set of iron observations to refine our estimates of iron emissions attributed to human activities. Additionally, we examine other co-released substances, such as carbon and nickel, to identify specific emission sources of iron. We employ statistical techniques to distinguish human-caused iron emissions from those originating from natural sources like dust and wildfires. Moreover, we utilize iron oxide observations to constrain emissions originating from East Asia and Norway, which are estimated to originate largely from smelting emissions. Through the analysis of long-term data sets, we provide lower and upper bounds to human-caused iron emissions. Furthermore, we investigate the impact of reduced observation numbers and a sparse network on the range of estimated iron emissions. Our findings highlight the critical role of observation quality in accurately assessing iron emissions from human activities. Anthropogenic total iron emissions are constrained to a factor of 3 in most global regions using long-term aerosol observations The number of sites, interannual variability, and site selection filter can affect the model-observation comparison uncertainty by 15%-50% Smelting-related emissions are constrained to a factor of 1.5 using iron oxide observations from East Asia

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73-0.85) and nitrogen dioxide (NO2, r = 0.68-0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80-0.86; mean absolute error MAE = 3.90-4.73 %) and at the urban background site in Dresden (R2 = 0.79-0.84; MAE = 4.23-4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.

Exposure to particulate matter (PM) pollution is a significant health risk, driving the search for innovative metrics that more accurately reflect the potential harm to human health. Among these, oxidative potential (OP) has emerged as a promising health-based metric, yet its application and relevance across different environments remain to be further explored. This study, set in two high-altitude Bolivian cities, aims to identify the most significant sources of PM-induced oxidation in the lungs and assess the utility of OP in assessing PM health impacts. Utilizing two distinct assays, OPDTT and OPDCFH, we measured the OP of PM samples, while also examining the associations between PM mass, OP, and black carbon (BC) concentrations with hospital visits for acute respiratory infections (ARI) and pneumonia over a range of exposure lags (0-2 weeks) using a Poisson regression model adjusted for meteorological conditions. The analysis also leveraged Positive Matrix Factorization (PMF) to link these health outcomes to specific PM sources, building on a prior source apportionment study utilizing the same dataset. Our findings highlight anthropogenic combustion, particularly from traffic and biomass burning, as the primary contributors to OP in these urban sites. Significant correlations were observed between both OPDTT and PM2.5 concentration exposure and ARI hospital visits, alongside a notable association with pneumonia cases and OPDTT levels. Furthermore, PMF analysis demonstrated a clear link between traffic-related pollution and increased hospital admissions for respiratory issues, affirming the health impact of these sources. These results underscore the potential of OPDTT as a valuable metric for assessing the health risks associated with acute PM exposure, showcasing its broader application in environmental health studies.

A reliable determination of equivalent black carbon (eBC) mass concentrations derived from filter absorption photometers (FAPs) measurements depends on the appropriate quantification of the mass absorption cross-section (MAC) for converting the absorption coefficient (babs) to eBC. This study investigates the spatial–temporal variability of the MAC obtained from simultaneous elemental carbon (EC) and babs measurements performed at 22 sites. We compared different methodologies for retrieving eBC integrating different options for calculating MAC including: locally derived, median value calculated from 22 sites, and site-specific rolling MAC. The eBC concentrations that underwent correction using these methods were identified as LeBC (local MAC), MeBC (median MAC), and ReBC (Rolling MAC) respectively. Pronounced differences (up to more than 50 %) were observed between eBC as directly provided by FAPs (NeBC; Nominal instrumental MAC) and ReBC due to the differences observed between the experimental and nominal MAC values. The median MAC was 7.8 ± 3.4 m2 g-1 from 12 aethalometers at 880 nm, and 10.6 ± 4.7 m2 g-1 from 10 MAAPs at 637 nm. The experimental MAC showed significant site and seasonal dependencies, with heterogeneous patterns between summer and winter in different regions. In addition, long-term trend analysis revealed statistically significant (s.s.) decreasing trends in EC. Interestingly, we showed that the corresponding corrected eBC trends are not independent of the way eBC is calculated due to the variability of MAC. NeBC and EC decreasing trends were consistent at sites with no significant trend in experimental MAC. Conversely, where MAC showed s.s. trend, the NeBC and EC trends were not consistent while ReBC concentration followed the same pattern as EC. These results underscore the importance of accounting for MAC variations when deriving eBC measurements from FAPs and emphasize the necessity of incorporating EC observations to constrain the uncertainty associated with eBC.

Constraining dust models to understand and quantify the effect of dust upon climate and ecosystems requires comprehensive analyses of the physiochemical properties of dust-emitting sediments in arid regions. Building upon previous studies in the Moroccan Sahara and Iceland, we analyse a diverse set of crusts and aeolian ripples (n=55) from various potential dust-emitting basins within the Mojave Desert, California, USA. Our focus is on characterizing the particle size distribution (PSD), mineralogy, aggregation/cohesion state, and Fe mode of occurrence. Our results show differences in fully and minimally dispersed PSDs, with crusts exhibiting average median diameters of 92 and 37 µm, respectively, compared to aeolian ripples with 226 and 213 µm, respectively. Mineralogical analyses unveiled strong variations between crusts and ripples, with crusts being enriched in phyllosilicates (24 % vs. 7.8 %), carbonates (6.6 % vs. 1.1 %), Na salts (7.3 % vs. 1.1 %), and zeolites (1.2 % and 0.12 %) and ripples being enriched in feldspars (48 % vs. 37 %), quartz (32 % vs. 16 %), and gypsum (4.7 % vs. 3.1 %). The size fractions from crust sediments display a homogeneous mineralogy, whereas those of aeolian ripples display more heterogeneity, mostly due to different particle aggregation. Bulk Fe content analyses indicate higher concentrations in crusts (3.0 ± 1.3 wt %) compared to ripples (1.9 ± 1.1 wt %), with similar proportions in their Fe mode of occurrence: nano-sized Fe oxides and readily exchangeable Fe represent ∼1.6 %, hematite and goethite ∼15 %, magnetite/maghemite ∼2.0 %, and structural Fe in silicates ∼80 % of the total Fe. We identified segregation patterns in the PSD and mineralogy differences in Na salt content within the Mojave basins, which can be explained by sediment transportation dynamics and precipitates due to groundwater table fluctuations described in previous studies in the region. Mojave Desert crusts show similarities with previously sampled crusts in the Moroccan Sahara in terms of the PSD and readily exchangeable Fe yet exhibit substantial differences in mineralogical composition, which should significantly influence the characteristic of the emitted dust particles.

Although previous studies report on the effect of street washing on ambient particulate matter levels, there is a lack of studies investigating the results of street washing on the emission strength of road dust. A sampling campaign was conducted in Madrid urban area during July 2009 where road dust samples were collected in two sites, namely Reference site (where the road surface was not washed) and Pelayo site (where street washing was performed daily during night). Following the chemical characterization of the road dust particles the emission sources were resolved by means of Positive Matrix Factorization, PMF (Multilinear Engine scripting) and the mass contribution of each source was calculated for the two sites. Mineral dust, brake wear, tire wear, carbonaceous emissions and construction dust were the main sources of road dust with mineral and construction dust being the major contributors to inhalable road dust load. To evaluate the effectiveness of street washing on the emission sources, the sources mass contributions between the two sites were compared. Although brake wear and tire wear had lower concentrations at the site where street washing was performed, these mass differences were not statistically significant and the temporal variation did not show the expected build-up after dust removal. It was concluded that the washing activities resulted merely in a road dust moistening, without effective removal and that mobilization of particles took place in a few hours between washing and sampling. The results also indicated that it is worth paying attention to the dust dispersed from the construction sites as they affect the emission strength in nearby streets.

Abstract. Understanding the effect of dust upon climate and ecosystems needs comprehensive analyses of the physiochemical properties of dust-emitting sediments in arid regions. Here, we analyse a diverse set of crusts and aeolian ripples (n=55) from various dust-hotspots within the Mojave Desert, California, USA, with focus on their particle size distribution (PSD), mineralogy, aggregation/cohesion state and iron mode of occurrence characterization. Our results showed differences in fully and minimally dispersed PSDs, with crusts average median diameters (92 and 37 µm, respectively) compared to aeolian ripples (226 and 213 µm, respectively). Mineralogical analyses unveiled variations between crusts and ripples, with crusts enriched in phyllosilicates (24 vs 7.8 %), carbonates (6.6 vs 1.1 %), Na-salts (7.3 vs 1.1 %) and zeolites (1.2 and 0.12 %), while ripples enriched in feldspars (48 vs 37 %), quartz (32 vs 16 %), and gypsum (4.7 vs 3.1 %). Bulk Fe content analyses indicate higher concentrations in crusts (3.0±1.3 wt %) compared to ripples (1.9±1.1 wt %), with similar Fe speciation proportions; nano Fe-oxides/readily exchangeable Fe represent ~1.6 %, hematite/goethite ~15 %, magnetite/maghemite ~2.0 % and structural Fe in silicates ~80 % of the total Fe. We identified segregation patterns in PSD and mineralogy differences within the Mojave basins, influenced by sediment transportation dynamics and precipitates due to groundwater table fluctuations. Mojave Desert crusts show similarities with previously sampled crusts in the Moroccan Sahara for PSD and readily exchangeable Fe, yet exhibit differences in mineralogical composition, which could influence the emitted dust particles characteristics.