Atmospheric Particles Research Articles

Effect of the Addition of Diblock Copolymer Nanoparticles on the Evaporation Kinetics and Final Particle Morphology for Drying Aqueous Aerosol Droplets.

A deeper understanding of the key processes that determine the particle morphologies generated during aerosol droplet drying is highly desirable for spray-drying of powdered pharmaceuticals and foods, predicting the properties of atmospheric particles, and monitoring disease transmission. Particle morphologies are affected by the drying kinetics of the evaporating droplets, which are in turn influenced by the composition of the initial droplet as well as the drying conditions. Herein, we use polymerization-induced self-assembly (PISA) to prepare three types of sterically stabilized diblock copolymer nanoparticles comprising the same steric stabilizer block and differing core blocks with z-average diameters ranging from 32 to 238 nm. These well-defined nanoparticles enable a systematic investigation of the effect of the nanoparticle size and composition on the drying kinetics of aqueous aerosol droplets (20-28 μm radius) and the final morphology of the resulting microparticles. A comparative kinetics electrodynamic balance was used to obtain evaporation profiles for 10 examples of nanoparticles at a relative humidity (RH) of 0, 45, or 65%. Nanoparticles comprising the same core block with mean diameters of 32, 79, and 214 nm were used to produce microparticles, which were dried under different RH conditions in a falling droplet column. Scanning electron microscopy was used to examine how the drying kinetics influenced the final microparticle morphology. For dilute droplets, the chemical composition of the nanoparticles had no effect on the evaporation rate. However, employing smaller nanoparticles led to the formation of dried microparticles with a greater degree of buckling.

Distribution and Fate of Polyethylene Microplastics Released by a Portable Toilet Manufacturer into a Freshwater Wetland and Lake.

A portable toilet manufacturer in northwest Indiana (USA) released polyethylene microplastic (MP) pollution into a protected wetland for at least three years. To assess the loads, movement, and fate of the MPs in the wetland from this point source, water and sediment samples were collected in the fall and spring of 2021-2023. Additional samples, including sediment cores and atmospheric particulates, were collected during the summer of 2023 from select areas of the wetland. The MPs were isolated from the field samples using density separation, filtration, and chemical oxidation. Infrared and Raman spectroscopy analyses identified the MPs as polyethylene, which were quantified visually using a stereomicroscope. The numbers of MPs in 100 mL of the marsh water closest to the source ranged from several hundred to over 400,000, while the open water samples contained few microplastics. Marsh surface sediments were highly contaminated with MPs, up to 18,800 per 30.0 g dry mass (dm), compared to core samples in the lower depths (>15 cm) that contained only smaller MPs (<200 µm), numbering 0-480 per 30.0 g (dm). The wide variations in loads of MP contaminants indicate the influence of numerous factors, such as proximity to the point source pollution, weather conditions, natural matter, and pollution sinks, namely sediment deposition. As proof of concept, we demonstrated a novel remediation method using these real-world samples to effectively agglomerate and remove MPs from contaminated waters.

New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach.

Atmospheric particles have profound implications for the global climate and human health. Among them, ultrafine particles dominate in terms of the number concentration and exhibit enhanced toxic effects as a result of their large total surface area. Therefore, understanding the driving factors behind ultrafine particle behavior is crucial. Machine learning (ML) provides a promising approach for handling complex relationships. In this study, three ML models were constructed on the basis of field observations to simulate the particle number concentration of nucleation mode (PNCN). All three models exhibited robust PNCN reproduction (R2 > 0.80), with the random forest (RF) model excelling on the test data (R2 = 0.89). Multiple methods of feature importance analysis revealed that ultraviolet (UV), H2SO4, low-volatility oxygenated organic molecules (LOOMs), temperature, and O3 were the primary factors influencing PNCN. Bivariate partial dependency plots (PDPs) indicated that during nighttime and overcast conditions, the presence of H2SO4 and LOOMs may play a crucial role in influencing PNCN. Additionally, integrating additional detailed information related to emissions or meteorology would further enhance the model performance. This pilot study shows that ML can be a novel approach for simulating atmospheric pollutants and contributes to a better understanding of the formation and growth mechanisms of nucleation mode particles.

A searchable database and mass spectral comparison tool for the Aerosol Mass Spectrometer (AMS) and the Aerosol Chemical Speciation Monitor (ACSM)

Abstract. The Aerodyne Aerosol Mass Spectrometer (AMS) and Aerosol Chemical Speciation Monitor (ACSM) are the most widely applied tools for in situ chemical analysis of the non-refractory bulk composition of fine atmospheric particles. The mass spectra (MS) of many AMS and ACSM observations from field and laboratory studies have been reported in peer-reviewed literature and many of these MS have been submitted to an open-access website. With the increased reporting of such datasets, the database interface requires revisions to meet new demands and applications. One major limitation of the web-based database is the inability to automatically search the database and compare previous MS with the researcher's own data. In this study, a searchable database tool for the AMS and ACSM mass spectral dataset was built to improve the efficiency of data analysis using Igor Pro, consistent with existing AMS and ACSM software. The database tool incorporates the published MS and sample information uploaded on the website. This tool allows the comparison of a target mass spectrum with the reference MS in the database, calculating cosine similarity, and provides a range of MS comparison plots, reweighting, and mass spectrum filtering options. The aim of this work is to help AMS and ACSM users efficiently analyze their own data for possible source or atmospheric processing features by comparison to previous studies, enhancing information gained from past and current global research on atmospheric aerosol.

Characteristics, source, and health risk assessment of aerosol polyaromatic hydrocarbons in the rural and urban regions of western Saudi Arabia

Abstract Air quality represents one of the most important parameters determining indoor microclimate and human comfort. Thus, the current study reports a comprehensive study on the dominant sources, organic compositions, and potential health impacts of the polyaromatic hydrocarbons (PAHs) in the atmospheric particle matters (PMs) ranging from 2.5 µm (PM2.5) to 10 µm (PM10) size in the rural and urban regions of western (Jeddah city) Saudi Arabia collected over 1 year between 2014 and 2015. The levels of PAHs in two locations namely Obhur (Urban) and Hada Alsham (Rural) were monitored over 1 year (2014 and 2015) using the gas chromatography coupled mass spectrometry. The level of ƩPAHs in Obhur (819.25 ng/m3) has a significantly high concentration of PAHs compared to Hada Alsham (Rural) (675.26 ng/m3). Indeno(1,2,3-CD)pyrene was the major contributor with an average value of 215.66 ng/m3 followed by benzo[k]fluranthene with a concentration of 150.68 ng/m3, respectively. The major contributors were indeno[1,2,3-cd]pyrene, benzo[k]fluranthene, dibenzo[a,h]anthracene, benzo[g,h]perylene, and benzo[b]fluranthene are the major contributors with contributing percentages of 26.32, 18.39, 9.07, and 8.29%, respectively. The rest of all compounds were below 4%. The highest concentrations of PAHs in Obhur (1836.99 ng/m3) and in Hada Alsham (1107.40 ng/m3) were observed in winter in January 2014. PAHs with 4–6 aromatic ring components are primarily emitted by high temperature combustion. The average values for the BaA/(BaA + Chr) and Flt/(Flt + Pyr) ratios at Obhur were found 0.58 and 0.43 and at Hada Alsham were found 0.63 and 0.38, respectively, indicating that coal/biomass burning is the major source of PAHs. Hada Alsham (rural area), the transportation system, is a significant contributor to the observed PAHs. These results reflect Saudi Arabia’s traffic load in both rural and urban areas. On road sites, the impact of petroleum combustion and vehicular emissions was also identified. The sum of the incremental lifetime cancer risk (ILCR) for all congeners for infants along the Obhur location was 2.13 × 10−6 and 1.38 × 10−6, respectively. ILCR values were less than 1.0 × 10−4, implying that PAH exposure posed an acceptable potential cancer risk in this study. Various local emission sources contributed more PAHs in many Saudi urban areas, increasing the risk of lung cancer, and the health risk. PAHs have an associated large surface area and are capable of deposition in the respiratory system with high efficiency. The total health risk assessment study also helps in alarming the toxicity at both the locations.

Characteristics, source, and health risk assessment of aerosol polyaromatic hydrocarbons in the rural and urban regions of western Saudi Arabia

Abstract Air quality represents one of the most important parameters determining indoor microclimate and human comfort. Thus, the current study reports a comprehensive study on the dominant sources, organic compositions, and potential health impacts of the polyaromatic hydrocarbons (PAHs) in the atmospheric particle matters (PMs) ranging from 2.5 µm (PM2.5) to 10 µm (PM10) size in the rural and urban regions of western (Jeddah city) Saudi Arabia collected over 1 year between 2014 and 2015. The levels of PAHs in two locations namely Obhur (Urban) and Hada Alsham (Rural) were monitored over 1 year (2014 and 2015) using the gas chromatography coupled mass spectrometry. The level of ƩPAHs in Obhur (819.25 ng/m3) has a significantly high concentration of PAHs compared to Hada Alsham (Rural) (675.26 ng/m3). Indeno(1,2,3-CD)pyrene was the major contributor with an average value of 215.66 ng/m3 followed by benzo[k]fluranthene with a concentration of 150.68 ng/m3, respectively. The major contributors were indeno[1,2,3-cd]pyrene, benzo[k]fluranthene, dibenzo[a,h]anthracene, benzo[g,h]perylene, and benzo[b]fluranthene are the major contributors with contributing percentages of 26.32, 18.39, 9.07, and 8.29%, respectively. The rest of all compounds were below 4%. The highest concentrations of PAHs in Obhur (1836.99 ng/m3) and in Hada Alsham (1107.40 ng/m3) were observed in winter in January 2014. PAHs with 4–6 aromatic ring components are primarily emitted by high temperature combustion. The average values for the BaA/(BaA + Chr) and Flt/(Flt + Pyr) ratios at Obhur were found 0.58 and 0.43 and at Hada Alsham were found 0.63 and 0.38, respectively, indicating that coal/biomass burning is the major source of PAHs. Hada Alsham (rural area), the transportation system, is a significant contributor to the observed PAHs. These results reflect Saudi Arabia’s traffic load in both rural and urban areas. On road sites, the impact of petroleum combustion and vehicular emissions was also identified. The sum of the incremental lifetime cancer risk (ILCR) for all congeners for infants along the Obhur location was 2.13 × 10−6 and 1.38 × 10−6, respectively. ILCR values were less than 1.0 × 10−4, implying that PAH exposure posed an acceptable potential cancer risk in this study. Various local emission sources contributed more PAHs in many Saudi urban areas, increasing the risk of lung cancer, and the health risk. PAHs have an associated large surface area and are capable of deposition in the respiratory system with high efficiency. The total health risk assessment study also helps in alarming the toxicity at both the locations.

Modeling the Formation of Organic Compounds across Full Volatility Ranges and Their Contribution to Nanoparticle Growth in a Polluted Atmosphere.

Nanoparticle growth influences atmospheric particles' climatic effects, and it is largely driven by low-volatility organic vapors. However, the magnitude and mechanism of organics' contribution to nanoparticle growth in polluted environments remain unclear because current observations and models cannot capture organics across full volatility ranges or track their formation chemistry. Here, we develop a mechanistic model that characterizes the full volatility spectrum of organic vapors and their contributions to nanoparticle growth by coupling advanced organic oxidation modeling and kinetic gas-particle partitioning. The model is applied to Nanjing, a typical polluted city, and it effectively captures the volatility distribution of low-volatility organics (with saturation vapor concentrations <0.3 μg/m3), thus accurately reproducing growth rates (GRs), with a 4.91% normalized mean bias. Simulations indicate that as particles grow from 4 to 40 nm, the relative fractions of GRs attributable to organics increase from 59 to 86%, with the remaining contribution from H2SO4 and its clusters. Aromatics contribute much to condensable organic vapors (∼37%), especially low-volatility vapors (∼61%), thus contributing the most to GRs (32-46%) as 4-40 nm particles grow. Alkanes also contribute 19-35% of GRs, while biogenic volatile organic compounds contribute minimally (<13%). Our model helps assess the climatic impacts of particles and predict future changes.

Measurement of ambient particulate matter (PM1.0, PM2.5 and PM10) in Khulna City of Bangladesh and their implications for human health

Atmospheric particles have been significantly affecting urban air quality and urban-oriented living in an increasing share of the population in Bangladesh. This study assessed the concentration of PM1.0, PM2.5, and PM10 in Khulna, one of the largest cities in Bangladesh located near the Bay of Bengal. The maximum average concentrations were recorded 415 ± 184.01 µg/m3 for PM10, 302 ± 109.89 µg/m3 for PM2.5, and 143 ± 45.05 µg/m3 for PM1.0. These values are several times higher than the World Health Organization air quality standard and Bangladesh National Ambient Air Quality Standard. According to the size and fractional distribution of PM, most of the monitoring locations were dominated by fine particles. Carcinogenic and non-carcinogenic risks due to exposure to ambient PM1.0, PM2.5 and PM10 were also quantified to illustrate the relevant potential human health risks. The excess lifetime cancer risk (ELCR) values of PM1.0 ranged from 8.6E0–4 to 6.0E–07 and PM2.5 varied between 8.6E–04 and 6.0E–07 exceeded the allowable limit at every location indicating the potential cancer-developing risk to the urban population. The health quotient (HQ) values also crossed the least permissible value at most of the locations depicting strong non-carcinogenic risks. Average HQ values of PM2.5 varied from 1.07 to 20.13 while PM10 ranged from 0.44 to 8.3. This research revealed children and elderly people as the most vulnerable age groups with the highest carcinogenic risks through exposure to atmospheric PM in Khulna city. Therefore, air pollution reduction plans and risk mitigation strategies should be developed and implemented by the government authorities.Graphical

Monte Carlo-Simulated Annealing and Machine Learning-Based Funneled Approach for Finding the Global Minimum Structure of Molecular Clusters.

Understanding the physical underpinnings and geometry of molecular clusters is of great importance in many fields, ranging from studying the beginning of the universe to the formation of atmospheric particles. To this end, several approaches have been suggested, yet identifying the most stable cluster geometry (i.e., global potential energy minimum) remains a challenge, especially for highly symmetric clusters. Here, we suggest a new funneled Monte Carlo-based simulated annealing (SA) approach, which includes two key steps: generation of symmetrical clusters and classification of the clusters according to their geometry using machine learning (MCSA-ML). We demonstrate the merits of the MCSA-ML method in comparison to other approaches on several Lennard-Jones (LJ) clusters and four molecular clusters-Ser8(Cl-)2, H+(H2O)6, Ag+(CO2)8, and Bet4Cl-. For the latter of these clusters, the correct structure is unknown, and hence, we compare the experimental and simulated fragmentation patterns, and the fragmentation of the proposed global minimum matches experiments closely. Additionally, based on the fragmentation of the predicted betaine cluster, we were able to identify hitherto unknown neutral fragmentation channels. In comparison to results obtained with other methods, we demonstrated a superior ability of MCSA-ML to predict clusters with high symmetry and similar abilities to predict clusters with asymmetrical structures.

Iodine oxoacids enhance nucleation of sulfuric acid particles in the atmosphere

The main nucleating vapor in the atmosphere is thought to be sulfuric acid (H 2 SO 4 ), stabilized by ammonia (NH 3 ). However, in marine and polar regions, NH 3 is generally low, and H 2 SO 4 is frequently found together with iodine oxoacids [HIO x , i.e., iodic acid (HIO 3 ) and iodous acid (HIO 2 )]. In experiments performed with the CERN CLOUD (Cosmics Leaving OUtdoor Droplets) chamber, we investigated the interplay of H 2 SO 4 and HIO x during atmospheric particle nucleation. We found that HIO x greatly enhances H 2 SO 4 (-NH 3 ) nucleation through two different interactions. First, HIO 3 strongly binds with H 2 SO 4 in charged clusters so they drive particle nucleation synergistically. Second, HIO 2 substitutes for NH 3 , forming strongly bound H 2 SO 4 -HIO 2 acid-base pairs in molecular clusters. Global observations imply that HIO x is enhancing H 2 SO 4 (-NH 3 ) nucleation rates 10- to 10,000-fold in marine and polar regions.

Regional air quality study by assessing trace metal atmospheric deposition

This study addresses the atmospheric deposition of trace elements investigated in Albania, Croatia and Macedonia in 2010 as part of the European Moss Study. This study provides data on the concentration of ten metals (Cd, Cr, Cu, Ni, Pb, V, Zn, Fe, Al, and Li) in naturally growing mosses. In general, all concentration data follow a lognormal distribution. Cd, Cr, Ni, and Zn show strong fluctuations in the moss samples from Albania and Macedonia, and Pb, Fe, and Al in the mosses from Croatia. The concentrations of Cd, Cu, and Zn were higher in the samples from Croatia than in those from Albania and Macedonia; the concentrations of Cr, Ni, V, Fe, Al, and Li were higher in the samples from Albania than in those from Croatia and Macedonia; and a higher concentration of Pb was found in the samples from Macedonia. The observed relationship between the concentration of lithogenic elements (Al, Fe, and V) and the air quality index (AQI) confirms that moss species have a high capacity to retain atmospheric deposition particles. The anthropogenic emission sources of these elements from local and long-range transport were considered to be the most important factors affecting air quality in the studied areas.

Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind

Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality, climate, ecosystems and human health. Therefore, measurements, prediction and forecasting of TSP concentrations are necessary to mitigate their negative effects. This study applies the gated recurrent unit (GRU) deep learning model to predict TSP concentrations in Zabol, Iran, during the dust period of the 120-day wind (3 June - 4 October 2014). Three uncertainty quantification (UQ) techniques consisting of the blackbox metamodel, heteroscedastic regression and infinitesimal jackknife were applied to quantify the uncertainty associated with GRU model. Permutation feature importance measure (PFIM), based on the game theory, was employed for the interpretability of the predictive model's outputs. A total of 80 TSP samples were collected and were randomly divided as training (70%) and validation (30%) datasets, while eight variables were used in the TSP prediction model. Our findings showed that GRU performed very well for TSP prediction (with r and Nash Sutcliffe coefficient (NSC) values above 0.99 for both datasets, and RMSE of 57 μg m−3 and 73 μg m−3 for training and validation datasets, respectively). Among the three UQ techniques, the infinitesimal jackknife was the most accurate one, while all the observed and predicted TSP values fell within the continence limitation estimated by the model. PFIM plots showed that wind speed and air humidity were the most and least important variables, respectively, impacting the predictive model's outputs. This is the first attempt of using an interpretable DL model for TSP prediction modelling, recommending that future research should involve aspects of uncertainty and interpretability of the predictive models. Overall, UQ and interpretability techniques have a key role in reducing the impact of uncertainties during optimization and decision making, resulting in better understanding of sophisticated mechanisms related to the predictive model.

Environmental and health implications of Pb-bearing particles in settled urban dust from an arid city affected by Pb–Zn factory emissions

Metal-rich particles originating from non-ferrous metallurgical activities are the primary source of atmospheric metals in urban environments. These particles vary in size, morphology, and elemental compositions and they undergo weathering processes that alter their composition and affect their toxicity. This study focuses on lead (Pb)-rich particles in settled urban dust within an arid and dusty city, Torreón in North Mexico, affected by Met–Mex Peñoles complex, one of the world's largest Ag–Cd–Pb–Zn smelting and refining facilities in operating since 1901. Torreón is characterized by arid conditions, temperature fluctuations, and low humidity. Dry atmospheric particles were collected in 2015 and 2017 from Torreón's urban area within a 3 km radius of the Met–Mex Peñoles complex. We used various analytical techniques, including scanning electron microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and X-ray powder diffraction (XRD) to determine the size, morphology, elemental composition and mineralogy of Pb-bearing particles. Our analysis revealed a range of Pb-bearing particle sizes and morphologies with varying Pb (0.3 to 51–87.2%) and other element contents, such as As (0.04 to 1–3.4%), Cd (0.4 to 3.3–5.1%), Cu (0.51–14.1%), Hg (ND-0.6%), and Zn (1.7 to 79–90.3%). XRD analysis confirmed the presence of Pb and Zn sulfides, Pb carbonates, Pb sulfate, and Pb oxides in urban dust, both as individual particles and agglomerates. Primary Pb minerals were linked to fugitive feed concentrates and smelter flue gas at Met–Mex Peñoles, while secondary Pb minerals, like Pb carbonates, Pb sulfate, and Pb oxides, resulted from direct emissions and weathering processes. Compared to galena, secondary Pb minerals exhibit higher chemical availability in the environment, posing greater risks to the environment and human health. As the particles analyzed are presumed to be resuspended rather than freshly emitted by Met–Mex, the presence of secondary Pb minerals in settled urban dust is predominantly linked to weathering processes. The physical and chemical transformations in Pb-rich particles contribute to increased Pb bioavailability and toxicity in urban dust, with substantial implications for environmental and human health. These findings highlight the potential consequences of weathered Pb-rich particle in urban areas, particularly in the arid and dusty city of Torreón.

Seasonal variations in composition and sources of atmospheric ultrafine particles in urban Beijing based on near-continuous measurements

Abstract. Understanding the composition and sources of atmospheric ultrafine particles (UFPs) is essential in evaluating their exposure risks. It requires long-term measurements with high time resolution, which are scarce to date. We performed near-continuous measurements of UFP composition during four seasons in urban Beijing using a thermal desorption chemical ionization mass spectrometer, accompanied by real-time size distribution measurements. We found that UFPs in urban Beijing are dominated by organic components, varying seasonally from 68 % to 81 %. CHO organics (i.e., molecules containing carbon, hydrogen, and oxygen) are the most abundant in summer, while sulfur-containing organics, some nitrogen-containing organics, nitrate, and chloride are the most abundant in winter. With the increase of particle diameter, the contribution of CHO organics decreases, while that of sulfur-containing and nitrogen-containing organics, nitrate, and chloride increases. Source apportionment analysis of the UFP organics indicates contributions from cooking and vehicle sources, photooxidation sources enriched in CHO organics, and aqueous/heterogeneous sources enriched in nitrogen- and sulfur-containing organics. The increased contributions of cooking, vehicle, and photooxidation components are usually accompanied by simultaneous increases in UFP number concentrations related to cooking emission, vehicle emission, and new particle formation, respectively, while the increased contribution of the aqueous/heterogeneous composition is usually accompanied by the growth of UFP mode diameters. The highest UFP number concentrations in winter are due to the strongest new particle formation, the strongest local primary particle number emissions, and the slowest condensational growth of UFPs to larger sizes. This study provides a comprehensive understanding of urban UFP composition and sources and offers valuable datasets for the evaluation of UFP exposure risks.

Development of a tunable, narrow, and ultraviolet Ce:LiCAF crystal multipass laser amplifier for environmental application

In this paper, the authors present results on the development of both an oscillator and a 4-pass amplifier of ultraviolet and narrow linewidth pulses using a Ce:LiCAF crystal. By using a grating at one end of the laser cavity, the output laser wavelength of the laser can be tuned from 285 nm to 296 nm with a maximum output power of 8 mW at 288.5 nm. Moreover, amplification in a 4-pass amplifier of the Ce:LiCAF laser was investigated theoretically and experimentally. From the results, 49.0 mW amplified pulses can be obtained using a 4-pass amplifier with 7.0 mW power and 3.6 ns pulse duration at 288.5 nm injection, corresponding to an amplifier gain of 7. Furthermore, the angular scattering intensity of some common single-particle aerosols such as black carbon, brown carbon, and polluted water has been studied using the Ce:LiCAF laser. This result can serve as the basis for the multipass laser amplifier’s environmental application, particularly in identifying atmospheric particles.

Evidence of nitrate-based nighttime atmospheric nucleation driven by marine microorganisms in the South Pacific.

Our understanding of ocean-cloud interactions and their effect on climate lacks insight into a key pathway: do biogenic marine emissions form new particles in the open ocean atmosphere? Using measurements collected in ship-borne air-sea interface tanks deployed in the Southwestern Pacific Ocean, we identified new particle formation (NPF) during nighttime that was related to plankton community composition. We show that nitrate ions are the only species for which abundance could support NPF rates in our semicontrolled experiments. Nitrate ions also prevailed in the natural pristine marine atmosphere and were elevated under higher sub-10 nm particle concentrations. We hypothesize that these nucleation events were fueled by complex, short-term biogeochemical cycling involving the microbial loop. These findings suggest a new perspective with a previously unidentified role of nitrate of marine biogeochemical origin in aerosol nucleation.

PH dependence of brown-carbon optical properties in cloud water

Abstract. Light-absorbing organic species present in aerosols, collectively called brown carbon (BrC), have important but highly uncertain effects on climate. Clouds likely represent a significant medium for secondary BrC production and for bleaching reactions, though the relative importance of the formation and loss processes in clouds is unknown at present. The acidity (or pH) of atmospheric particles and clouds affects the optical properties of BrC and bleaching rates. Given the wide variability of pH in the atmosphere (pH in particles and clouds ranges from −1 to 8), the optical properties of BrC and its bleaching behavior are expected to vary significantly, and the link between pH and BrC is yet another uncertainty in attempts to constrain its climate forcing effects. In this work, we characterize the pH dependence of BrC optical properties – including light absorption at 365 nm (Abs365), the mass absorption coefficient (MAC365), and the absorption Ångström exponent (AAE) – in bulk cloud water sampled from the summit of Whiteface Mountain, NY. In all samples (n=17), Abs365 and MAC365 increased linearly with increasing pH, highlighting the importance of reporting pH in studies of BrC in aqueous media. There was strong variability in the sensitivity of Abs365 to pH, with normalized slopes that ranged from 5.1 % to 17.2 % per pH unit. The normalized slope decreased strongly with increasing cloud water [K+], suggesting that the non-biomass-burning BrC has optical properties that are more sensitive to pH than BrC associated with biomass burning. AAE also showed a distinct pH dependence as it was relatively flat between pH 1.5–5 and then decreased significantly above pH 5. The cloud water composition was used to inform thermodynamic predictions of aerosol pH upwind and/or downwind of Whiteface Mountain and the subsequent changes in BrC optical properties. Overall, these results show that, in addition to secondary BrC production, photobleaching, and the altitudinal distribution, the climate forcing of BrC is quite strongly affected by its pH-dependent absorption.

Spatial and temporal patterns of organophosphate Esters flame retardants and plasticizers in airborne particles over the Mediterranean sea

We studied the co-occurrence of OPEs and other constituents in atmospheric particles at the two edges of the Mediterranean Sea, under the influence of the transport of polluted air from Europe and dust from the Sahara. The highest OPE concentrations were observed during the summer period in the East Mediterranean and in spring for the NW Mediterranean. The total average atmospheric concentration of Σ6OPEs in the NW Mediterranean was 2103 ± 2020 pg m−3 (n = 23) with EHDPP and TCPP to be the predominant OPEs, accounting on average for 46% and 37% of the total Ʃ6OPEs concentrations, respectively. The average concentration of Σ6OPEs in East Mediterranean was 156.4 ± 170.3 pg m−3 (n = 67) with TCPP showing the highest concentration (116.1 ± 92.8 pg m−3), followed by TCEP (67.5 ± 55.8 pg m−3). In both areas, OPEs were mostly associated with fossil fuel combustion and road traffic, while the air masses from Saharan desert influenced the concentration of EHDPP, TCEP in NW Mediterranean and the TCEP concentration levels in the East Mediterranean. The total annual deposition of reported OPEs to the Mediterranean basin was estimated to be 584 tonnes, accounting for about 8.5% of the total deposited anthropogenic phosphorus.

Contribution of chemical composition to oxidative potential of atmospheric particles at a rural and an urban site in the Po Valley: Influence of high ammonia agriculture emissions

The present study investigates the chemical composition and oxidative potential (OP) of PM10 particles at two sites in the Po Valley (Norther Italy), with the aim of evidence the impact of high level of atmosphere ammonia (NH3). Field measurements were conducted in a rural location (Bertonico) close to farms strongly impacted by high ammonia emissions from agriculture and livestock husbandry. The urban site was in the metropolitan area of Milan. The NH3 concentrations observed at the rural site were about 5 times higher than those at the urban location, generating higher levels of secondary inorganic aerosol (ΣSIA) at Bertonico (16.6 ± 6.33 μg m−3) than at Milan 12.6 ± 4.93 μg m−3. The oxidative potential measured with the ascorbic acid assay (OPAAV) was almost triple in Milan than at Bertonico, namely 1.48 ± 1.08 nmol min−1 m−3 and 0.44 ± 0.23 nmol min−1 m−3, respectively, while the measured OPDTTV responses were similar at both sites, close to ∼ 0.5 ± 0.3 nmol min−1 m−3. These results suggested that the SIA components don't significantly contribute to the PM10 oxidative reactivity, although they account for most of the PM10 mass. Despite such an OP-inactivity, the SIA components resulted significantly correlated with both OPAAV and OPDTTV responses. This may be likely due to their inter-correlation with other redox-active components, such as the secondary organic species, which are synergically produced along with the SIA formation. In addition, an increase in the metals reactivity towards the OP assays, may be associated with the PM10 acidic environment produced by enhanced HNO3 and H2SO4 levels. Therefore, the contribution of the SIA to PM oxidative toxicity cannot be ignored in assessing health risk associated with PM exposure.

Measurement report: Dust and anthropogenic aerosols' vertical distributions over northern China dense aerosols gathered at the top of the mixing layer

Abstract. Over the past decades, northern China has been suffering from persistent air pollution caused by both fine and coarse atmospheric particles. Although there are plenty of theoretical and observational studies on aerosols in northern China, most of them only consider total aerosol concentrations and focus on heavy pollution episodes; the long-term vertical distributions of dust (coarse) and anthropogenic aerosols (fine) and their relationships with the mixing layer height (MLH) have not been revealed. In this study, the dust and anthropogenic aerosols' mass concentration and the MLH were retrieved by polarization Raman lidar over Beijing from May 2019 to February 2022. We found that large amounts of anthropogenic aerosols accumulate at the top of the mixing layer, which is most noticeable in summer, with monthly mean mass concentration up to 57 µg m−3. It is mainly influenced by the southward transport in the upper air, where the atmosphere is relatively stable and moist, favoring hygroscopic growth of particles. Dust mass concentration is discontinuous in the vertical direction, not only on the ground but also in lofted layers that reach up to several kilometers. The heights of these lofted dust layers exhibited apparent seasonal dependence, with the height of the main dust layer gradually ascending from 1.1 km to about 2.5 km from April to June and below 3 km from October to December. In addition, there is a significant negative correlation between bottom anthropogenic aerosols' mass concentration and the MLH, and an inverse function fit is more suitable to characterize this relationship, while the relationship between bottom dust mass concentration and the MLH is insignificant. These results will enhance our understanding of the sophisticated interactions between dust and anthropogenic aerosols, the MLH, and regional transport in northern China. It will also help to refine atmospheric chemistry models and improve surface prediction capabilities.