Chemically Resolved Respiratory Deposition of Ultrafine Particles Characterized by Number Concentration in the Urban Atmosphere.

Ultrafine particles (UFPs) dominate the atmospheric particles in number concentration, impacting human health and climate change. However, existing studies primarily rely on mass-based approaches, leading to a restricted understanding of the number-based and chemically resolved health effects of atmospheric UFPs. In this study, we utilized a high-mass-resolution single-particle aerosol mass spectrometer to investigate the online chemical composition and number size distribution of ultrafine, fine, and coarse particles during the summertime in urban Shenzhen, China. Human respiratory deposition dose assessments of particles with varying chemical compositions were further conducted by a respiratory deposition model. The results showed that during our observation, particles containing elemental carbon (EC) were the dominant components in UFPs (0.05-0.1 µm). Compared to fine and coarse particles, UFPs can deposit more deeply into the respiratory tract with a daily dose of ∼2.08 ± 0.67 billion particles. Among the deposited UFPs, EC-cluster particles constituted ∼85.7% in number fraction, accounting for a daily number dose of ∼1.78 billion particles, which poses a greater impact on human health. Simultaneously, we found discrepancies in the chemically resolved particle depositions among number-, surface area-, and mass-based approaches, emphasizing the importance of an appropriate metric for particle health-risk evaluation.

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.

Aeromicelle-A new form of liquid aerosol for delivering aqueous samples into a single-particle mass spectrometer.

For applying highly sensitive mass spectrometry to chemical analysis of aqueous samples, we have developed a novel technique using a new form of liquid droplets, which we call "aeromicelle" (AM), to deliver aqueous sample solutions directly into the vacuum region of a single-particle mass spectrometer in liquid form and conduct immediate mass analysis. AMs are generated by spraying an aqueous solution containing a surfactant at a concentration significantly lower than its critical micelle concentration (CMC). When the solution is sprayed, liquid droplets containing the surfactant are formed, which gradually dry in an air flow. Upon drying, the surfactant concentration in the droplet exceeds its CMC, and consequently, the surfactant molecules begin to cover the droplet surface. Finally, the surface is expected to be fully covered with surfactant molecules such as reverse micelles. The surface coverage helps suppress the evaporation of water, thereby enhancing the residence time of the liquid droplet. Our experimental results show that the AMs retained a liquid form for at least 100 s in air and survived even under vacuum conditions for further mass analysis: each AM delivered in the vacuum region of a single-particle mass spectrometer is ablated with an intense laser pulse and then, mass analyzed. Individual AMs generated from an aqueous solution containing CsCl were analyzed using a single-particle mass spectrometer. The Cs+ ion peak was observed even in AMs generated from the 10 nM solution. The number of Cs atoms in each AM was estimated to be approximately 7 × 103, which corresponds to 1.2 × 10-20 mol (12 zmol). Meanwhile, in the mass analysis of tyrosine, both positive and negative fragmentation ions from tyrosine in AMs were observed in the mass spectrum and 4.6 × 105 (760 zmol) tyrosine molecules were detected.

Simultaneous profiling of dust aerosol mass concentration and optical properties with polarized high-spectral-resolution lidar.

Dust particles originating from arid desert regions can be transported over long distances, presenting severe risks to climate, environment, social economics, and human health at the source and downwind regions. However, there has been a dearth of continuous diurnal observations of vertically resolved mass concentration and optical properties of dust aerosols, which hinders our understanding of aerosol mixing, stratification, aerosol-cloud interactions, and their impacts on the environment. To fill the gap of the insufficient observations, to the best of our knowledge, this work presents the first high-spectral-resolution lidar (HSRL) observation providing days of continuous profiles of the mass concentration, along with particle linear depolarization ratio (PLDR), backscattering coefficient, extinction coefficient and lidar ratio (LR), simultaneously. We present the results of two strong dust events observed by HSRL over Beijing in 2021. The maximum particle mass concentrations reached (1.52 ± 3.5) x103 µg/m3 and (19.48 ± 0.36) x103 µg/m3 for the two dust events, respectively. The retrieved particle mass concentrations and aerosol optical depth (AOD) agree well with the observation from the surface PM10 concentrations and sun photometer with correlation coefficients of 0.90 and 0.95, respectively. The intensive properties of PLDR and LR of the dust aerosols are 0.31 ± 0.02 and 39 ± 7 sr at 532 nm, respectively, which are generally close to those obtained from observations in the downwind areas. Moreover, inspired by the observations from HSRL, a universal analytical relationship is discovered to evaluate the proportion of dust aerosol backscattering, extinction, AOD, and mass concentration using PLDR. The universal analytical relationship reveals that PLDR can directly quantify dust aerosol contribution, which is expected to further expand the application of polarization technology in dust detection. These valuable observations and findings further our understanding of the contribution of dust aerosol to the environment and help supplement dust aerosol databases.

New insight: particle flow rate from the airways as an indicator of cardiac failure in the intensive care unit.

AIMS: Exhaled breath particles have been explored for diagnosing different lung diseases. We recently showed in an experimental model that different cardiac output results in different particle flow rate (PFR) from the airways. Given the well-known close relationship between impaired cardiac function and respiratory failure, we hypothesized that PFR in exhaled air can be used to detect cardiac failure. METHODS: PFR was analysed using a customized PExA device. Particles in the range of 0.41-4.55 µm were measured. The included patients (n = 20) underwent cardiac surgery and received mechanical ventilation as a part of routine post-operative care. Ten patients with clinical signs of pronounced post-operative haemodynamic instability and need for inotrope or mechanical support had been selected to the cardiac failure group. The control group consisted of 10 patients without signs of cardiac failure. RESULTS: The patients in cardiac failure group required inotropic support in the form of dobutamine (9/10), epinephrine (2/10), or levosimendan (4/10) or use of an intra-aortic balloon pump (4/10). There was no use of inotropes or mechanical support devices among the controls. All patients in the cardiac failure group had pre-operative left ventricular ejection fraction ≤40% compared with the control group, whose pre-operative ejection fraction was ≥50%, P < 0.001. Patients with cardiac failure had significantly longer median total time in mechanical ventilation compared with the patients in the control group: 53.5 h (IQR 6.8-116101.0 h) and 4.5 h (IQR 4.0-5.5 h), respectively, P < 0.001, and the median length of stay in the ICU, 165 h (IQR 28-192 h) and 22 h (IQR 20-23.5 h), respectively, P = 0.007. Median PFR in patients with cardiac failure was higher than PFR in those with normal cardiac function: 80.9 particles/min (interquartile range (IQR) 25.8-336.6 particles/min), vs. 15.3 particles/min (IQR 8.1-17.7 particles/min), respectively, P < 0.001. Median particle mass was 8.95 ng (IQR 1.68-41.73 ng) in the cardiac failure group and 0.75 ng (IQR 0.18-1.45 ng) in the control group, P = 0.002. CONCLUSIONS: Patients with post-operative cardiac failure following cardiac surgery exhibited an increase in exhaled particle mass and PFR compared with the control group, indicating a significant difference between those two groups. The increase in particle mass and PFR in the absence of respiratory pathologies may indicate cardiac failure. In comparison with controls, impaired cardiac function was also associated with different composition of the particles regarding their size distribution.

Distinct diurnal chemical compositions and formation processes of individual organic-containing particles in Beijing winter.

Organic aerosols (OA) are major components of fine particulate matter, yet their formation mechanism remains unclear, especially in polluted environments. In this study, we investigated the diurnal chemical compositions and formation processes of OA in carbonaceous particles during winter in Beijing using aerosol time-of-flight mass spectrometry. We found that 84.5% of the measured carbonaceous particles underwent aging processes, characterized by larger diameter and more secondary species compared to fresh carbonaceous particles, and presented different chemical compositions of OA in the daytime and nighttime. During the day, under high O3 concentrations, organosulfates and oligomers existed in the aged carbonaceous particles, which were mixed with a higher signal of nitrate compared with sulfate. At night, under high relative humidity, distinct spectral signatures of hydroxymethanesulfonate and organic nitrogen compounds, and minor signals of other hydroxyalkylsulfonates and high molecular weight organic compounds were present in the aged carbonaceous particles, which were mixed with a higher signal of sulfate compared with nitrate. Our results indicated that photochemistry contributed to OA formation in the daytime, while aqueous chemistry played an important role in OA formation in the nighttime. The findings can help improve the performance of air quality and climate models on OA simulation.

Relative Contribution of Metal Content and Soil Particle Mass to Health Risk of Chromium-Contaminated Soil.

Three soil samples from a chromium (Cr)-contaminated field were classified into five particle fractions (i.e., 0-50 µm, 50-100 µm, 100-250 µm, 250-500 µm, and 500-1000 µm) and were further characterized to study their physicochemical properties and Cr bioaccessibility. The results indicated that the gastrointestinal bioaccessibility estimated by the Solubility Bioaccessibility Research Consortium (SBRC) method was on average 15.9% higher than that by the physiologically based extraction test (PBET) method. The health risk of all samples was within the safe range, and the health risk based on total Cr content may be overestimated by an average of 13.2 times compared to the bioaccessibility-based health risk. The health risk investigated from metal content was mainly contributed by the 50-250 µm fraction, which was 47.5, 50.2, and 43.5% for low-, medium-, and high-level polluted soils, respectively. As for the combined effect, the fractions of 100-250 µm and 500-1000 µm contributed the highest proportion to health risk, which was 57.1, 62.1, and 64.4% for low-level, medium-level, and high-level polluted soils, respectively. These results may further deepen the understanding of health risk assessment and quantify the contribution of the soil particle mass to health risk.

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.

Properties of Particulate Matter in the Air of the Wieliczka Salt Mine and Related Health Benefits for Tourists.

This study aimed to evaluate the mass concentration of size-resolved (PM1, PM2.5, PM4, PM10, PM100) particulate matter (PM) in the Wieliczka Salt Mine located in southern Poland, compare them with the concentrations of the same PM fractions in the atmospheric air, and estimate the dose of dry salt aerosol inhaled by the mine visitors. Measurements were conducted for 2 h a day, simultaneously inside (tourist route, passage to the health resort, health resort) and outside the mine (duty-room), for three days in the summer of 2017 using DustTrak DRX devices (optical method). The highest average PM concentrations were recorded on the tourist route (54-81 µg/m3), while the lowest was in the passage to the health resort (49-62 µg/m3). At the same time, the mean outdoor PM concentrations were 14-20 µg/m3. Fine particles constituting the majority of PM mass (68-80%) in the mine originated from internal sources, while the presence of coarse particles was associated with tourist traffic. High PM deposition factors in the respiratory tract of children and adults estimated for particular mine chambers (0.58-0.70), the predominance of respirable particles in PM mass, and the high content of NaCl in PM composition indicate high health benefits for mine visitors.

What is suitable social distancing for people wearing face masks during the COVID-19 pandemic?

COVID-19 has caused the global pandemic and had a serious impact on people's daily lives. The respiratory droplets produced from coughing and talking of an infected patient were possible transmission routes of coronavirus between people. To avoid the infection, the US Centers for Disease Control and Prevention (CDC) advised to wear face masks while maintaining a social distancing of 2 m. Can the social distancing be reduced if people wear masks? To answer this question, we measured the mass of inhaled droplets by a susceptible manikin wearing a mask with different social distances, which was produced by coughing and talking of an index "patient" (human subject) also wearing a mask. We also used the computational fluid dynamics (CFD) technology with a porous media model and particle dispersion model to simulate the transmission of droplets from the patient to the susceptible person with surgical and N95 masks. We compared the CFD results with the measured velocity in the environmental chamber and found that the social distancing could be reduced to 0.5 m when people wearing face masks. In this case, the mass concentration of inhaled particles was less than two people without wearing masks and with a social distancing of 2 m. Hence, when the social distancing was difficult, wearing masks could protect people. We also found that the leakage between the face mask and the human face played an important role in the exhaled airflow pattern and particle dispersion. The verified numerical model can be used for more scenarios with different indoor environments and HVAC systems. The results of this study would make business profitable with reduced social distancing in transportation, education, and entertainment industries, which was beneficial for the reopening of the economy.

Application of Single-Particle Mass Spectrometer to Obtain Chemical Signatures of Various Combustion Aerosols.

A single-particle mass spectrometer (SPMS) with laser ionization was constructed to determine the chemical composition of single particles in real time. The technique was evaluated using various polystyrene latex particles with different sizes (125 nm, 300 nm, 700 nm, and 1000 nm); NaCl, KCl, MgCO3, CaCO3, and Al2O3 particles with different chemical compositions; an internal mixture of NaCl and KCl; and an internal mixture of NaCl, KCl, and MgCl2 with different mixing states. The results show that the SPMS can be useful for the determination of chemical characteristics and mixing states of single particles in real time. The SPMS was then applied to obtain the chemical signatures of various combustion aerosols (diesel engine exhaust, biomass burning (rice straw), coal burning, and cooking (pork)) based on their single-particle mass spectra. Elemental carbon (EC)-rich and EC-organic carbon (OC) particles were the predominant particle types identified in diesel engine exhaust, while K-rich and EC-OC-K particles were observed among rice straw burning emissions. Only one particle type (ash-rich particles) was detected among coal burning emissions. EC-rich and EC-OC particles were observed among pork burning particles. The single-particle mass spectra of the EC or OC types of particles differed among various combustion sources. The observed chemical signatures could be useful for rapidly identifying sources of atmospheric fine particles. In addition, the detected chemical signatures of the fine particles may be used to estimate their toxicity and to better understand their effects on human health.

Sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms.

Sensor-based material flow characterization (SBMC) promises to improve the performance of future-generation sorting plants by enabling new applications like automatic quality monitoring or process control. Prerequisite for this is the derivation of mass-based material flow characteristics from pixel-based sensor data, which requires known individual particle masses. Since particle masses cannot be measured inline, the prediction of particle masses of lightweight packaging (LWP) waste using machine learning (ML) algorithms is investigated. Five LWP material classes were sampled, preprocessed, and scanned on a custom-made test rig, resulting in a dataset containing 3D laser triangulation (3DLT) images, RGB images, and corresponding masses of n = 3,830 particles. Based on 66 extracted shape measurements, six ML models were trained for particle mass prediction (PMP). Their performance was compared with two state-of-the-art reference models using (i) material-specific mean particle masses and (ii) grammages. Obtained particle masses showed a high variation and significant differences between material classes and particle size classes. After feature selection, both reference models achieving R2-scores of (i) 0.422 ± 0.121 and (ii) 0.533 ± 0.224 were outperformed by all investigated ML models. A random forest regressor with an R2-score of 0.763 ± 0.091 and a normalized mean absolute error of 0.243 ± 0.050 achieved the most accurate PMP. In contrast to studies on primary raw materials, PMP of LWP waste is challenging due to influences of packaging design and post-consumer disposal behavior. ML algorithms are a promising approach for PMP that outperform state-of-the-art methods by 43% higher R2-scores.

Investigation of solvent microparticle formation in spray ionization-quadrupole ion trap-mass spectrometry.

In the present study, a new method has been developed for the real-time analysis of insource created solvent particles based on spray ionization-quadrupole ion trap-mass spectrometry (SI-QIT-MS). This is the first work in the literature reporting the formation of different solvent particles during solvent spray in mass spectrometry. The solvent particles formed from the solvent droplets are detected by a charge detector. Our ion trap system allows the measurement of a wide range particle masses. Various solvents and solvent mixtures such as water, methanol, acetone, toluene, n-butanol, water-methanol, and water-ethanol were sprayed through a cone system, and the mass of the particles was monitored by different trap frequencies and voltages. While polar molecules produce larger and more diverse particles due to their strong intermolecular forces, apolar solvents generally do not produce a significant number of particles. We obtained results using a homemade ion trap mass spectrometer capable of determining the mass of micro-sized solvent and solvent mixture particles weighing up to 1015 (Da). The instrument uses a charge detector connected to the exit of the ion trap. Simultaneous acquisition of particle mass spectra and measurement of the amount of charge in each particle allow mass assignment of each particle. Sprayed solvent particles were examined at various trap frequencies and voltages to find the best instrumental parameters for the highest trapping efficiency. The custom SI-QIT-MS instrument allows the measurement of the mass distribution of charged particles from the solvent spray.

Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure.

An electrothermal piezoresistive cantilever (EPC) sensor is a low-cost MEMS resonance sensor that provides self-actuating and self-sensing capabilities. In the platform, which is of MEMS-cantilever shape, the EPC sensor offers several advantages in terms of physical, chemical, and biological sensing, e.g., high sensitivity, low cost, simple procedure, and quick response. However, a crosstalk effect is generated by the coupling of parasitic elements from the actuation part to the sensing part. This study presents a parasitic feedthrough subtraction (PFS) method to mitigate a crosstalk effect in an electrothermal piezoresistive cantilever (EPC) resonance sensor. The PFS method is employed to identify a resonance phase that is, furthermore, deployed to a phase-locked loop (PLL)-based system to track and lock the resonance frequency of the EPC sensor under cigarette smoke exposure. The performance of the EPC sensor is further evaluated and compared to an AFM-microcantilever sensor and a commercial particle counter (DC1100-PRO). The particle mass-concentration measurement result generated from cigarette-smoke puffs shows a good agreement between these three detectors.

A core-shell box model for simulating viscosity dependent secondary organic aerosol (CSVA) and its application.

Secondary organic aerosol (SOA) plays a key role in air pollution and global climate change. However, the understanding and modelling of SOA properties and evolution are still limited. In this paper, we developed a novel kinetic Core-Shell box model for Viscosity dependent SOA simulation (CSVA), which includes explicit gas-phase reactions (MCM), homogeneous nucleation by H2SO4-NH3-H2O, viscosity dependent mass transfer between gas and particle phases (organic and aqueous phases) and particle-phase reactions. The gas-particle mass transfer is represented by chainlike reactions analogizing to electrical resistance. The CSVA model is verified and applied to chamber experiments of toluene oxidation systems. The monomers and dimers of SOA are determined by coupling the high-resolution Orbitrap mass spectra and MCM mechanism. The majority of dimers are confirmed to be peroxyhemiacetals formed by reactions of hydroperoxides with aldehydes in the particle phase. The results show that CSVA can well capture the following processes: (1) relative humidity (RH) dependent nucleation of the H2SO4-NH3-H2O system, (2) particle size-dependent hygroscopic growth of inorganics (e.g., NaCl and (NH4)2SO4) and organics (levoglucosan and SOA), (3) NOx dependent SOA formation, (4) viscosity-induced evolution of particle size distribution, and (5) effect of RH on SOA formation. In particular, our model reproduces the phenomenon that the evolution of SOA particle size distribution from a one-peak mode into a two-peak mode is due to viscosity.

Inflammatory and coagulatory markers and exposure to different size fractions of particle mass, number and surface area air concentrations in the Swedish hard metal industry, in particular to cobalt.

PURPOSE: To study the relationship between inhalation of airborne particles and cobalt in the Swedish hard metal industry and markers of inflammation and coagulation in blood. METHODS: Personal sampling of inhalable cobalt and dust were performed for subjects in two Swedish hard metal plants. Stationary measurements were used to study concentrations of inhalable, respirable, and total dust and cobalt, PM10 and PM2.5, the particle surface area and the particle number concentrations. The inflammatory markers CC16, TNF, IL-6, IL-8, IL-10, SAA and CRP, and the coagulatory markers FVIII, vWF, fibrinogen, PAI-1 and D-dimer were measured. A complete sampling was performed on the second or third day of a working week following a work-free weekend, and additional sampling was taken on the fourth or fifth day. The mixed model analysis was used, including covariates. RESULTS: The average air concentrations of inhalable dust and cobalt were 0.11 mg/m3 and 0.003 mg/m3, respectively. For some mass-based exposure measures of cobalt and total dust, statistically significant increased levels of FVIII, vWF and CC16 were found. CONCLUSIONS: The observed relationships between particle exposure and coagulatory biomarkers may indicate an increased risk of cardiovascular disease.

[Influence of Burning Fireworks on the Atmosphere During the Spring Festival in Guangzhou in 2020].

Twenty-one air quality monitoring stations including four with single particle aerosol mass spectrometers (SPAMS) were used to observe air quality and aerosol particulates during the 2020 Spring Festival (from January 21 to 28) in Guangzhou. The effect of burning fireworks on the atmosphere of Guangzhou and its eleven administrative regions was examined, and the chemical composition of firework particles was detected and analyzed by single particle aerosol mass spectrometry. The results show that the burning of fireworks had a significant impact on air quality in the discharge area and the prohibited discharge area. The concentrations of PM2.5, PM10, and SO2 sharply increased in Guangzhou on New Year's Eve. Air quality in Zengcheng District, Baiyun District, Huangpu District, and some areas of Tianhe District was also affected by the concentrated burning of fireworks on January 25 between 01:00 and 06:00. A method of fireworks tracing based on SPAMS using Al+ as a tracer was established with a time resolution of 5 min. The main particle types emitted by the burning fireworks were levoglucan, potassium-rich, and mineral. These particles were well mixed with nitrate, but this was not conducive to the formation of ammonium.

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.

How to Get the Best from Low-Cost Particulate Matter Sensors: Guidelines and Practical Recommendations.

Low-cost sensors based on the optical particle counter (OPC) are increasingly being used to collect particulate matter (PM) data at high space and time resolution. In spite of their huge explorative potential, practical guidelines and recommendations for their use are still limited. In this work, we outline a few best practices for the optimal use of PM low-cost sensors based on the results of an intensive field campaign performed in Bologna (44°30' N, 11°21' E; Italy) under different weather conditions. Briefly, the performances of a series of sensors were evaluated against a calibrated mainstream OPC with a heated inlet, using a robust approach based on a suite of statistical indexes capable of evaluating both correlations and biases in respect to the reference sensor. Our results show that the sensor performance is sensibly affected by both time resolution and weather with biases maximized at high time resolution and high relative humidity. Optimization of PM data obtained is therefore achievable by lowering time resolution and applying suitable correction factors for hygroscopic growth based on the inherent particle size distribution.

In-Plane and Out-of-Plane MEMS Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection.

In this study, we investigate the performance of two piezoresistive micro-electro-mechanical system (MEMS)-based silicon cantilever sensors for measuring target analytes (i.e., ultrafine particulate matters). We use two different types of cantilevers with geometric dimensions of 1000 × 170 × 19.5 µm3 and 300 × 100 × 4 µm3, which refer to the 1st and 2nd types of cantilevers, respectively. For the first case, the cantilever is configured to detect the fundamental in-plane bending mode and is actuated using a resistive heater. Similarly, the second type of cantilever sensor is actuated using a meandering resistive heater (bimorph) and is designed for out-of-plane operation. We have successfully employed these two cantilevers to measure and monitor the changes of mass concentration of carbon nanoparticles in air, provided by atomizing suspensions of these nanoparticles into a sealed chamber, ranging from 0 to several tens of µg/m3 and oversize distributions from ~10 nm to ~350 nm. Here, we deploy both types of cantilever sensors and operate them simultaneously with a standard laboratory system (Fast Mobility Particle Sizer, FMPS, TSI 3091) as a reference.