Comparing novel small-angle x-ray scattering approaches for absolute size and number concentration measurements of spherical SiO2particles to established methods.

Biomedical analytical applications, as well as the industrial production of high-quality nano- and sub-micrometre particles, require accurate methods to quantify the absolute number concentration of particles. In this context, small-angle x-ray scattering (SAXS) is a powerful tool to determine the particle size and concentration traceable to the Système international d'unités (SI). Therefore, absolute measurements of the scattering cross-section must be performed, which require precise knowledge of all experimental parameters, such as the electron density of solvent and particles, whereas the latter is often unknown. Within the present study, novel SAXS-based approaches to determine the size distribution, density and number concentrations of sub-micron spherical silica particles with narrow size distributions and mean diameters between 160 nm and 430 nm are presented. For the first-time traceable density and number concentration measurements of silica particles are presented and current challenges in SAXS measurements such as beam-smearing, poorly known electron densities and moderately polydisperse samples are addressed. In addition, and for comparison purpose, atomic force microscopy has been used for traceable measurements of the size distribution and single particle inductively coupled plasma mass spectrometry with the dynamic mass flow approach for the accurate quantification of the number concentrations of silica particles. The possibilities and limitations of the current approaches are critically discussed in this study.

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.

Towards a reference material for microplastics' number concentration-case study of PET in water using Raman microspectroscopy.

Increasing demand for size-resolved identification and quantification of microplastic particles in drinking water and environmental samples requires the adequate validation of methods and techniques that can be used for this purpose. In turn, the feasibility of such validation depends on the existence of suitable certified reference materials (CRM). A new candidate reference material (RM), consisting of polyethylene terephthalate (PET) particles and a water matrix, has been developed. Here, we examine its suitability with respect to a homogeneous and stable microplastic particle number concentration across its individual units. A measurement series employing tailor-made software for automated counting and analysis of particles (TUM-ParticleTyper 2) coupled with Raman microspectroscopy showed evidence of the candidate RM homogeneity with a relative standard deviation of 12% of PET particle counts involving particle sizes >30 µm. Both the total particle count and the respective sums within distinct size classes were comparable in all selected candidate RM units. We demonstrate the feasibility of production of a reference material that is sufficiently homogeneous and stable with respect to the particle number concentration.

Use of a sample injection loop for an accurate measurement of particle number concentration by flow cytometry.

Flow cytometry plays a pivotal role in biotechnology by providing quantitative measurements for a wide range of applications. Nonetheless, achieving precise particle quantification, particularly without relying on counting beads, remains a challenge. In this study, we introduce a novel exhaustive counting method featuring a sample loop-based injection system that delivers a defined sample volume to a detection system to enhance quantification in flow cytometry. We systematically assess the performance characteristics of this system with micron-sized polystyrene beads, addressing issues related to sample introduction, adsorption, and volume measurement. Results underscore the excellent analytical performance of the proposed method, characterized by high linearity and repeatability. We compare our approach to counting bead-based measurements, and while an approximate bias value was observed, the measured values were found to be similar between the methods, demonstrating its comparability and reliability. This method holds great promise for improving the accuracy and precision of particle quantification in flow cytometry, with implications for various fields including healthcare and environmental monitoring.

Characteristics of airborne particles in stone quarrying areas: Human exposure assessment and mitigation.

This investigation aims to assess the levels of human exposure to airborne particulate matter (PM) in various locations of a natural stone quarry for the first time based on simultaneous measurements of both PM mass and number concentrations (PMC and PNC). A quarry located in Danang city, Vietnam, considered to be a "hotspot" of air pollution in the city, was selected for detailed investigations. Both PMC and PNC were found to be significantly higher (1.2-6.0 times) within the quarry compared to surrounding areas. Mechanical activities during mining, notably crushing, screening, hauling, and loading stones, contributed to increased emissions of PM in the coarser mode (1-10 µm) compared to the accumulation mode (0.1-1 µm) and thus increased deposition of PM1-10 in the human upper respiratory tract. In contrast, combustion activities, especially the diesel engine exhaust from various machines and vehicles used in the quarry, resulted in increased emissions of small particles in the accumulation mode that dominated the PNC and in their deposition in the lower respiratory tract. Simultaneous measurements of PNC and PMC revealed that the PM counts were strongly associated with PM deposition in the alveolar region (accounting for ≈ 76% of total PNC of particles less than 10 µm, N10), while the PM mass concentration was a better indicator of the deposition of PM in the head airway region (≈92% of total PMC of PM10). Overall, this study demonstrates the significance of measuring both PNC and PMC to assess PM exposure levels, regional respiratory doses, and potential health effects associated with human exposure to PM generated from stone quarrying activities. The novelty of this work is the integration of real-time mass and number concentrations of PM over the size range from 20 nm to 10 µm to provide insights into respiratory deposited doses of size-fractionated PM among quarry workers.

Los Angeles Basin's air quality transformation: a long-term investigation on the impacts of PM regulations on the trends of ultrafine particles and co-pollutants.

This study investigates the long-term trends of ambient ultrafine particles (UFPs) and associated airborne pollutants in the Los Angeles Basin from 2007 to 2022, focusing on the indirect effects of regulations on UFP levels. The particle number concentration (PNC) of UFPs was compiled from previous studies in the area, and associated co-pollutant data, including nitrogen oxides (NOx), carbon monoxide (CO), elemental carbon (EC), organic carbon (OC), and ozone (O3), were obtained from the chemical speciation network (CSN) database. Over the study period, a general decrease was noted in the PNC of UFPs, NOx, EC, and OC, except for CO, the concentration trends of which did not exhibit a consistent pattern. UFPs, NOx, EC, and OC were positively correlated, while O3 had a negative correlation, especially with NOx. Our analysis discerned two distinct subperiods in pollutant trends: 2007-2015 and 2016-2022. For example, there was an overall decrease in the PNC of UFPs at an annual rate of -850.09 particles/cm3/year. This rate was more pronounced during the first sub-period (2007-2015) at -1814.9 particles/cm3/year and then slowed to -227.21 particles/cm3/year in the second sub-period (2016-2023). The first sub-period (2007-2015) significantly influenced pollutant level changes, exhibiting more pronounced and statistically significant changes than the second sub-period (2016-2022). Since 2016, almost all primary pollutants have stabilized, indicating a reduced impact of current regulations, and emphasizing the need for stricter standards. In addition, the study included an analysis of Vehicle Miles Traveled (VMT) trends from 2007 to 2022 within the Los Angeles Basin. Despite the general increase in VMT, current regulations and cleaner technologies seem to have successfully mitigated the potential increase in increase in PNC. Overall, while a decline in UFPs and co-pollutant levels was observed, the apparent stabilization of these levels underscores the need for more stringent regulatory measures and advanced emission standards.

Multi-instrument assessment of fine and ultrafine titanium dioxide aerosols.

The measurement of fine (diameter: 100 nanometers-2.5 micrometers) and ultrafine (UF: < 100 nanometers) titanium dioxide (TiO2) particles is instrument dependent. Differences in measurements exist between toxicological and field investigations for the same exposure metric such as mass, number, or surface area because of variations in instruments used, operating parameters, or particle-size measurement ranges. Without appropriate comparison, instrument measurements create a disconnect between toxicological and field investigations for a given exposure metric. Our objective was to compare a variety of instruments including multiple metrics including mass, number, and surface area (SA) concentrations for assessing different concentrations of separately aerosolized fine and UF TiO2 particles. The instruments studied were (1) DustTrak™ DRX, (2) personal DataRAMs™ (PDR), (3) GRIMMTM, and (4) diffusion charger (DC). Two devices of each field-study instrument (DRX, PDR, GRIMM, and DC) were used to measure various metrics while adjusting for gravimetric mass concentrations of fine and UF TiO2 particles in controlled chamber tests. An analysis of variance (ANOVA) was used to apportion the variance to inter-instrument (between different instrument-types), inter-device (within instrument), and intra-device components. Performance of each instrument-device was calculated using root mean squared error compared to reference methods: close-faced cassette and gravimetric analysis for mass and scanning mobility particle sizer (SMPS) real-time monitoring for number and SA concentrations. Generally, inter-instrument variability accounted for the greatest (62.6% or more) source of variance for mass, and SA-based concentrations of fine and UF TiO2 particles. However, higher intra-device variability (53.7%) was observed for number concentrations measurements with fine particles compared to inter-instrument variability (40.8%). Inter-device variance range(0.5-5.5%) was similar for all exposure metrics. DRX performed better in measuring mass closer to gravimetric than PDRs for fine and UF TiO2. Number concentrations measured by GRIMMs and SA measurements by DCs were considerably (40.8-86.9%) different from the reference (SMPS) method for comparable size ranges of fine and UF TiO2. This information may serve to aid in interpreting assessments in risk models, epidemiologic studies, and development of occupational exposure limits, relating to health effect endpoints identified in toxicological studies considering similar instruments evaluated in this study.

The surface tension of surfactant-containing, finite volume droplets.

Surface tension influences the fraction of atmospheric particles that become cloud droplets. Although surfactants are an important component of aerosol mass, the surface tension of activating aerosol particles is still unresolved, with most climate models assuming activating particles have a surface tension equal to that of water. By studying picoliter droplet coalescence, we demonstrate that surfactants can significantly reduce the surface tension of finite-sized droplets below the value for water, consistent with recent field measurements. Significantly, this surface tension reduction is droplet size-dependent and does not correspond exactly to the macroscopic solution value. A fully independent monolayer partitioning model confirms the observed finite-size-dependent surface tension arises from the high surface-to-volume ratio in finite-sized droplets and enables predictions of aerosol hygroscopic growth. This model, constrained by the laboratory measurements, is consistent with a reduction in critical supersaturation for activation, potentially substantially increasing cloud droplet number concentration and modifying radiative cooling relative to current estimates assuming a water surface tension. The results highlight the need for improved constraints on the identities, properties, and concentrations of atmospheric aerosol surfactants in multiple environments and are broadly applicable to any discipline where finite volume effects are operative, such as studies of the competition between reaction rates within the bulk and at the surface of confined volumes and explorations of the influence of surfactants on dried particle morphology from spray driers.

The hemispheric contrast in cloud microphysical properties constrains aerosol forcing.

The change in planetary albedo due to aerosol-cloud interactions during the industrial era is the leading source of uncertainty in inferring Earth's climate sensitivity to increased greenhouse gases from the historical record. The variable that controls aerosol-cloud interactions in warm clouds is droplet number concentration. Global climate models demonstrate that the present-day hemispheric contrast in cloud droplet number concentration between the pristine Southern Hemisphere and the polluted Northern Hemisphere oceans can be used as a proxy for anthropogenically driven change in cloud droplet number concentration. Remotely sensed estimates constrain this change in droplet number concentration to be between 8 cm-3 and 24 cm-3 By extension, the radiative forcing since 1850 from aerosol-cloud interactions is constrained to be -1.2 W⋅m-2 to -0.6 W⋅m-2 The robustness of this constraint depends upon the assumption that pristine Southern Ocean droplet number concentration is a suitable proxy for preindustrial concentrations. Droplet number concentrations calculated from satellite data over the Southern Ocean are high in austral summer. Near Antarctica, they reach values typical of Northern Hemisphere polluted outflows. These concentrations are found to agree with several in situ datasets. In contrast, climate models show systematic underpredictions of cloud droplet number concentration across the Southern Ocean. Near Antarctica, where precipitation sinks of aerosol are small, the underestimation by climate models is particularly large. This motivates the need for detailed process studies of aerosol production and aerosol-cloud interactions in pristine environments. The hemispheric difference in satellite estimated cloud droplet number concentration implies preindustrial aerosol concentrations were higher than estimated by most models.

Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain.

Low-cost sensors (LCSs) for particulate matter (PM) concentrations have attracted the interest of researchers, supplementing their efforts to quantify PM in higher spatiotemporal resolution. The precision of PM mass concentration measurements from PMS 5003 sensors has been widely documented, though limited information is available regarding their size selectivity and number concentration measurement accuracy. In this work, PMS 5003 sensors, along with a Federal Referral Methods (FRM) sampler (Grimm spectrometer), were deployed across three sites with different atmospheric profiles, an urban (Germanou) and a background (UPat) site in Patras (Greece), and a semi-arid site in Almería (Spain, PSA). The LCSs particle number concentration measurements were investigated for different size bins. Findings for particles with diameter between 0.3 and 10 µm suggest that particle size significantly affected the LCSs' response. The LCSs could accurately detect number concentrations for particles smaller than 1 µm in the urban (R2 = 0.9) and background sites (R2 = 0.92), while a modest correlation was found with the reference instrument in the semi-arid area (R2 = 0.69). However, their performance was rather poor (R2 < 0.31) for coarser aerosol fractions at all sites. Moreover, during periods when coarse particles were dominant, i.e., dust events, PMS 5003 sensors were unable to report accurate number distributions (R2 values < 0.47) and systematically underestimated particle number concentrations. The results indicate that several questions arise concerning the sensors' capabilities to estimate PM2.5 and PM10 concentrations, since their size distribution did not agree with the reference instruments.

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution-Part B-Particle Number Concentrations.

Low-cost Particulate Matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors, but some models of sensors also report Particle Number Concentrations (PNCs) segregated into different PM size ranges. In this study, eight units each of Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors' to different particle size distributions to quantify and better understand the low-cost sensors performance across a range of relevant environmental ranges. The PNCs reported by the sensors were analysed to characterise sensor-reported particle size distribution, to determine whether sensor-reported PNCs can follow the transient variations of PM observed by the reference instruments and to determine the relative impact of different variables on the performances of the sensors. This study shows that the Alphasense OPC-R1 reported at least five size ranges independently from each other, that the Sensirion SPS30 reported two size ranges independently from each other and that all the size ranges reported by the Plantower PMS5003 were not independent of each other. It demonstrates that all sensors tested here could track the fine temporal variation of PNCs, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it shows that particle size distribution and composition are more impactful on sensor measurements than relative humidity.

Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning.

Atmospheric aerosols are important drivers of Arctic climate change through aerosol-cloud-climate interactions. However, large uncertainties remain on the sources and processes controlling particle numbers in both fine and coarse modes. Here, we applied a receptor model and an explainable machine learning technique to understand the sources and drivers of particle numbers from 10 nm to 20 µm in Svalbard. Nucleation, biogenic, secondary, anthropogenic, mineral dust, sea salt and blowing snow aerosols and their major environmental drivers were identified. Our results show that the monthly variations in particles are highly size/source dependent and regulated by meteorology. Secondary and nucleation aerosols are the largest contributors to potential cloud condensation nuclei (CCN, particle number with a diameter larger than 40 nm as a proxy) in the Arctic. Nonlinear responses to temperature were found for biogenic, local dust particles and potential CCN, highlighting the importance of melting sea ice and snow. These results indicate that the aerosol factors will respond to rapid Arctic warming differently and in a nonlinear fashion.

Indoor air pollution effects on pediatric asthma are submicron aerosol particle-dependent.

The school environment is crucial for the child's health and well-being. On the other hand, the data about the role of school's aerosol pollution on the etiology of chronic non-communicable diseases remain scarce. This study aims to evaluate the level of indoor aerosol pollution in primary schools and its relation to the incidence of doctor's diagnosed asthma among younger school-age children. The cross-sectional study was carried out in 11 primary schools of Vilnius during 1 year of education from autumn 2017 to spring 2018. Particle number (PNC) and mass (PMC) concentrations in the size range of 0.3-10 µm were measured using an Optical Particle Sizer (OPS, TSI model 3330). The annual incidence of doctor's diagnosed asthma in each school was calculated retrospectively from the data of medical records. The total number of 6-11 years old children who participated in the study was 3638. The incidence of asthma per school ranged from 1.8 to 6.0%. Mean indoor air pollution based on measurements in classrooms during the lessons was calculated for each school. Levels of PNC and PMC in schools ranged between 33.0 and 168.0 particles/cm3 and 1.7-6.8 µg/m3, respectively. There was a statistically significant correlation between the incidence of asthma and PNC as well as asthma and PMC in the particle size range of 0.3-1 µm (r = 0.66, p = 0.028) and (r = 0.71, p = 0.017) respectively. No significant correlation was found between asthma incidence and indoor air pollution in the particle size range of 0.3-2.5 and 0.3-10 µm.   Conclusion: We concluded that the number and mass concentrations of indoor air aerosol pollution in primary schools in the particle size range of 0.3-1 µm are primarily associated with the incidence of doctor's diagnosed asthma among younger school-age children. What is Known: • Both indoor and outdoor aerosol pollution is associated with bronchial asthma in children. What is New: • The incidence of bronchial asthma among younger school age children is related to indoor air quality in primary schools. • Aerosol pollutants in the size range of 0.3-1 µm in contrast to larger size range particles can play major role in the etiology of bronchial asthma in children.

Seasonal variations of particle number concentration and its relationship with PM2.5 mass concentration in industrial-residential airshed.

The smaller particles that dominate the particle number concentration (PNC) in the ambient air only contribute to a small percentage of particulate matter (PM) mass concentration although present in high particle number concentration. These small particles may be neglected upon assessing the health impacts of the PM. Hence, the knowledge on the particle number concentration size distribution deserves greater attention than the particulate mass concentration. This study investigates the measurement of the particle mass concentrations (PM2.5) and PNC of 0.27 µm < Dp < 4.50 µm during the southwest (SW), inter-monsoon (IM) and northeast (NE) monsoons in the industrial-residential airshed of Skudai, Johor Bahru, Malaysia. The PM2.5 mass concentrations and PNC were measured using a multi-channel GRIMM Environmental Dust Monitor (GRIMM EDM-SVC 365) equipped with a global positioning system. Diurnal variations, statistical analysis and regression plots were utilised from a six-month hourly data set to examine the patterns of the PNC size distributions and its relationships with the PM2.5 mass concentration. The overall mean PM2.5 mass concentration was 21.85 µg m-3, with the 24 h mean values of 26.80 µg m-3, 26.08 µg m-3 and 13.76 µg m-3 for the SW, IM and NE monsoons, respectively. It was found that the hourly mean of PNC was recorded at the highest concentration during the SW monsoon (373.20 # cm-3). The particles in the accumulation mode (Dp < 1.0 µm) were the prevalent form of the particle number concentration (94-98%). The scatter plots between the PM2.5 mass concentration and particle number size distribution showed that the PNC mode of 0.27 < Dp < 1.0 µm has the highest correlation value of r2 = 0.87 due to the emission from the anthropogenic activities. The results of this study highlight the importance of the PNC measurement in the seasonal variations of the PM2.5 pollution, indicating the significance of the regional-scale emission control actions in the local air quality management.

Counting Nanoplastics in Environmental Waters by Single Particle Inductively Coupled Plasma Mass Spectroscopy after Cloud-Point Extraction and In Situ Labeling of Gold Nanoparticles.

The globally raising concern for nanoplastics (NPs) pollution calls for analytical methods for investigating their occurrence, fates, and effects. Counting NPs with sizes down to 50 nm in real environmental waters remains a great challenge. Herein, we developed a full method from sample pretreatment to quantitative detection for NPs in environmental waters. Various NPs of common plastic types and sizes (50-1200 nm) were successfully labeled by in situ growth of gold nanoparticles and counted by single particle inductively coupled plasma mass spectrometry. Sucrose density gradient centrifugation enables the isolation of gold-labeled NPs from homogeneously nucleated Au nanoparticles, enhancing the particle number detection limit to 4.6 × 108 NPs/L for 269 nm spherical polystyrene NPs. For real environmental water samples, the pretreatment of acid digestion with a mixture of 5 mM HNO3 and 40 mM HF eliminates the coexisting inorganic nanoparticles, while the following dual cloud-point extraction efficiently isolates NPs from various matrices and thus improves the Au-labeling efficiency. The high spiked recoveries (72.9%-92.8%) of NPs in different waters demonstrated the applicability of this method in different scenarios.

Quantification of Nanoparticles in Dispersions Using Transmission Electron Microscopy.

The quantification of the particle size and the number concentration (PNC) of nanoparticles (NPs) is key for the characterization of nanomaterials. Transmission electron microscopy (TEM) is often considered as the gold standard for assessing the size of NPs; however, the TEM sample preparation suitable for estimating the PNC based on deposited NPs is challenging. Here, we use an ultrasonic nebulizer (USN) to transfer NPs from aqueous suspensions into dried aerosols which are deposited on TEM grids in an electrostatic precipitator of an aerosol monitor. The deposition efficiency of the electrostatic precipitator was ≈2%, and the transport efficiency of the USN was ≈7%. Experiments using SiO2 NPs (50­200 nm) confirmed an even deposition of the nebulized particles in the center of the TEM grids. PNCs of the SiO2 NPs derived from TEM images underestimated the expected PNCs of the suspensions by a factor of up to three, most likely resulting from droplet coagulation and NP aggregation in the USN. Nevertheless, single particles still dominated the PNC. Our approach results in reproducible and even deposition of particles on TEM grids suitable for morphological analysis and allows an estimation of the PNC in the suspensions based on the number of particles detected by TEM.

Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters.

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R 2 = 0.77 ) and TDNN for hourly averaged data (with R 2 = 0.66 ) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters.

Fast and Purification-Free Characterization of Bio-Nanoparticles in Biological Media by Electrical Asymmetrical Flow Field-Flow Fractionation Hyphenated with Multi-Angle Light Scattering and Nanoparticle Tracking Analysis Detection.

Accurate physico-chemical characterization of exosomes and liposomes in biological media is challenging due to the inherent complexity of the sample matrix. An appropriate purification step can significantly reduce matrix interferences, and thus facilitate analysis of such demanding samples. Electrical Asymmetrical Flow Field-Flow Fractionation (EAF4) provides online sample purification while simultaneously enabling access to size and Zeta potential of sample constituents in the size range of approx. 1-1000 nm. Hyphenation of EAF4 with Multi-Angle Light Scattering (MALS) and Nanoparticle Tracking Analysis (NTA) detection adds high resolution size and number concentration information turning this setup into a powerful analytical platform for the comprehensive physico-chemical characterization of such challenging samples. We here present EAF4-MALS hyphenated with NTA for the analysis of liposomes and exosomes in complex, biological media. Coupling of the two systems was realized using a flow splitter to deliver the sample at an appropriate flow speed for the NTA measurement. After a proof-of-concept study using polystyrene nanoparticles, the combined setup was successfully applied to analyze liposomes and exosomes spiked into cell culture medium and rabbit serum, respectively. Obtained results highlight the benefits of the EAF4-MALS-NTA platform to study the behavior of these promising drug delivery vesicles under in vivo like conditions.

Cyclists' exposure to air pollution: in situ evaluation with a cargo bike platform.

Cyclists' exposure to air pollutants near roadways has been associated with numerous health effects. While the adverse health effects concerning aerosols have traditionally been assessed with data of particle mass concentrations, it appears that the number concentration is also another important indicator of toxicity. Thus, to holistically evaluate one's exposure to aerosol particles, assessments should be based on mass concentrations and number concentrations. In order to assess individual cyclists' exposure as they move through space and time, spatiotemporal high-resolution approaches are needed. Therefore, a mobile, fast-response monitoring platform was developed that uses a cargo bicycle as a base. Data of particle mass concentrations (PM1, PM2.5, PM10) and particle number concentrations (PN10) were collected along two different routes, one characterized by high-intensity vehicle traffic and one by low-intensity vehicle traffic. While high spatiotemporal heterogeneity was observed for all measured quantities, the PN10 concentrations fluctuated the most. High concentrations of PN10 could be clearly associated with vehicle traffic. For PM2.5, this relation was less pronounced. Mean particle concentrations of all measures were significantly higher along the high-traffic route. Comparing route exposures, the inhalation of PM2.5 was similar between both routes, whereas along the high-traffic route, cyclists were exposed to twice the particle number. We conclude that the cargo bike, featuring high-frequency mobile measurements, was useful to characterize the spatial distribution of mass concentrations and number concentrations across an urban environment. Overall, our results suggest that the choice of route is a key factor in reducing cyclists' exposure to air pollution.

Comparison of methods for converting Dylos particle number concentrations to PM2.5 mass concentrations.

The aim of this study was to (a) develop a method for converting particle number concentrations (PNC) obtained by Dylos to PM2.5 mass concentrations, (b) compare this conversion with similar methods available in the literature, and (c) compare Dylos PM2.5 obtained using all available conversion methods with gravimetric samples. Data were collected in multiple residences in three European countries using the Dylos and an Aerodynamic Particle Sizer (APS, TSI) in the Netherlands or an optical particle counter (OPC, GRIMM) in Greece. Two statistical fitted curves were developed based on Dylos PNC and either an APS or an OPC particle mass concentrations (PMC). In addition, at the homes of 16 volunteers (UK and Netherlands), Dylos measurements were collected along with gravimetric samples. The Dylos PNC were transformed to PMC using all the fitted curves obtained during this study (and three found in the literature) and were compared with gravimetric samples. The method developed in the present study using an OPC showed the highest correlation (Pearson (R) = 0.63, Concordance (ρc ) = 0.61) with gravimetric data. The other methods resulted in an underestimation of PMC compared to gravimetric measurements (R = 0.65-0.55, ρc  = 0.51-0.24). In conclusion, estimation of PM2.5 concentrations using the Dylos is acceptable for indicative purposes.