Laboratory experiments studying respiratory virus aerosols rely on the reproducibility of aerosolization and sampling techniques. Conventional techniques could compromise viral structure and antigenicity, particularly for pleomorphic viruses like influenza A (IAV), yet very little information is available on this issue. Here, we evaluate three aerosolization methods: Collison, Blaustein Atomization Modules (BLAM), and jet nebulizers, and three bioaerosol samplers: liquid spot sampler (LSS), wet cyclone, and SKC BioSampler, to determine their efficiency in retaining the structural stability and antigenicity of filamentous IAV virus-like particles (VLPs). VLPs provide a safe and practical alternative for studying highly pathogenic airborne viruses. The BLAM and jet nebulizers maintain 12-21 % of filamentous structures, whereas the Collison nebulizer, which generates higher shear stress, reduces filament recovery to similar to 10 %. The liquid spot sampler (LSS), owing to gentle condensation-based sampling technique, retains approximately 30 % of filamentous VLPs. The SKC BioSampler and wet cyclone sampler cause greater structural disruption due to higher shear stress and impaction forces and retain similar to 10 % and similar to 7 % of filamentous VLPs, respectively. Higher relative humidity (85 %) improves filament retention by similar to 20 % compared to dry conditions (25 % RH). The antigenicity of Neuraminidase (NA), the IAV surface protein responsible for viral release, followed a bimodal distribution, with up to 20 % of small VLPs showing undetectable NA signal post-aerosolization, indicating greater susceptibility to structural degradation. These results point to the necessity of improving upon contemporary aerosolization and sampling strategies to characterize airborne filamentous viruses in controlled laboratory environments more accurately.

A novel electrochemical micro-immunoelectrode (MIE) biosensor utilizing camelid nanobodies has been developed as the sensing element in a point-of-care exhaled breath test for respiratory pathogens, providing results in under 60 seconds from only one deep breath. The MIE comprises a screen-printed electrode specially prepared with a pathogen-specific nanobody, which is covalently bound to the working electrode to concentrate the target pathogen near the surface. Electrical current produced by tyrosine oxidation is measured using square wave voltammetry, and the amount of current produced is proportional to the amount of target bound to the surface. MIE specificity was assessed by comparing peak tyrosine oxidation currents for different concentrations of SARS-CoV-2 and SARS-CoV-1 spike proteins. To determine the biosensor’s limit of detection, a purified inactivated SARS-CoV-2 stock solution was tested in a dilution series at various virus concentrations. To further evaluate system performance, inactivated SARS-CoV-2 virions from three different variants – WA1, Delta, and Omicron (BA.1) – were aerosolized in a controlled laboratory setting in aerosol droplets that mimicked the size distribution of exhaled breath particles. For human studies, the MIE was incorporated into an impaction/condensation surface in a breath collection device. This mechanism accelerates exhaled breath and focuses it onto a chilled surface, where it condenses. This condensate, containing viral particles, is washed onto the biosensor surface for analysis. The biosensor is sensitive to 10-40 SARS-CoV-2 viral particles in a sample, depending on the variant. The breath test device was evaluated for sensitivity, specificity, and the number of breaths required to determine illness. A clinical study at the Washington University in St. Louis School of Medicine tested this device for detecting SARS-CoV-2. Thirty-seven patients with respiratory illnesses provided at total of 42 individual breath samples. The results, verified by RT-qPCR for diagnosis, showed 95.5% sensitivity and 94.4% specificity for SARS-CoV-2 over other respiratory pathogens, such as Influenza A, RSV and rhinovirus. The breath-based MIE diagnostic platform is a rapid, portable, sensitive, and specific way to identify infected patients using breath. While this study focuses on SARS-CoV-2, the MIE is readily adaptable to other respiratory pathogens, and efforts are currently underway to develop a multiplexed MIE that can detect several different pathogens at once for differential diagnosis of respiratory illness.

Airborne transmission via aerosols is a dominant route for the transmission of respiratory pathogens, including avian H5N1 influenza A virus and E. coli bacteria. Rapid and direct detection of respiratory pathogen aerosols has been a long-standing technical challenge. Herein, we develop a novel label-free capacitive biosensor using an interlocked Prussian blue (PB)/graphene oxide (GO) network on a screen-printed carbon electrode (SPCE) for direct detection of avian H5N1 and E. coli. A single-step electro-co-deposition process grows GO branches on the SPCE surface, while the PB nanocrystals simultaneously decorate around the GO branches, resulting in an ultrasensitive capacitive response at nanofarad levels. We tested the biosensor for H5N1 concentrations from 2.0 viral RNA copies/mL to 1.6 × 105 viral RNA copies/mL, with a limit of detection (LoD) of 56 viral RNA copies/mL. We tested it on E. coli for concentrations ranging from 2.0 bacterial cells/mL to 1.8 × 104 bacterial cells/mL, with a LoD of 5 bacterial cells/mL. The detection times for both pathogens were under 5 min. When integrated with a custom-built wet cyclone bioaerosol sampler, our biosensor could detect and quasi-quantitatively estimate H5N1 and E. coli concentrations in air with spatial resolutions of 93 viral RNA copies/m3 and 8 bacterial cells/m3, respectively. The quasi-quantification method, based on dilution and binary detection (positive/negative), achieved an overall accuracy of >90% for pathogen-laden aerosol samples. This biosensor is adaptable for multiplexed detection of other respiratory pathogens, making it a versatile tool for real-time airborne pathogen monitoring and risk assessment.

In this study, we evaluate the relative redox activity of various water-soluble organic aerosol (WSOA) sources in Delhi's winter PM2.5, focusing on their capacity to generate reactive oxygen species (ROS). Using offline-aerosol mass spectrometry (AMS) and positive matrix factorization (PMF), we identified two oxidized factors—more oxidized oxygenated organic aerosol (MO-OOA) and less oxidized oxygenated organic aerosol (LO-OOA)—and three primary factors, namely nitrogen-enriched hydrocarbon-like organic aerosol (NHOA), biomass-burning organic aerosol (BBOA), and solid-fuel combustion organic aerosol (SFC-OA). The ROS-generating capability of PM2.5 was assessed using a real-time oxidative potential (OP) measurement system based on the dithiothreitol (DTT) assay. We employed multivariate linear regression technique (MLR) to explore the association between the DTT activity of water-soluble PM2.5 and these identified factors. We found BBOA, SFCOA, and MO-OOA significantly contributed to volume-normalized OP, with intrinsic water-soluble activities of 39 ± 11, 106 ± 31 and 160 ± 43 pmol/min/μg, respectively. MO-OOA, primarily from non-fossil precursors, serves as a proxy for aged biomass burning, which intensifies during winter and significantly influences the DTT activity. Additionally, OP is significantly influenced by WSOA derived from local incomplete solid fuel combustion sources, including coal and wood burning for household cooking and heating, burning of leaves, biodegradable waste, and garbage along the roadside. Interestingly, water-soluble metals (Mn, Cu, and Fe) showed no discernible contribution to the OP. These findings highlight the need for targeted mitigation strategies addressing local combustion processes and unregulated biomass burning to effectively reduce PM health exposure in Delhi.

This study focuses on filter-based aerosol light absorption measurement biases and their correction algorithms in the coastal urban-industrial area of Houston-Galveston region. Known as a major petrochemical hub in the United States, this area is dominated by industrial flaring emissions. The aerosols in this region are mainly composed of organic carbon and sulfates. We observed that conventional filter-based instruments, despite their cost-effectiveness and simplicity of use, overestimate aerosol light absorption by approximately four times in comparison to reference particle phase instruments, such as photoacoustic spectrometers. To mitigate these unquantifiable measurement biases, we applied and compared different analytical correction algorithms including the widely used Bond-Ogren (2010) and Virkkula (2010), as well as a customized Random Forest Regression (RFR) machine learning algorithm. Our analysis revealed that RFR significantly improved correction efficacy, reducing the wavelength-averaged Root Mean Square Error (RMSE) by approximately 50% compared to traditional analytical methods. We performed SHapley Additive exPlanations (SHAP) analysis to identify the key parameters that influence the accuracy of our RFR correction algorithm. We find that at longer visible wavelengths, dark-brown carbon from flaring emissions in the sampling region exacerbates biases in filter-based measurements. This study underscores the importance of employing advanced correction algorithms for correcting filter-based aerosol light absorption measurements, especially in complex urban settings influenced by industrial emissions.

In this study, we examined the relationship between human activities and emission characteristics of CO2 and particles in three indoor settings: a clean environmental chamber, a café, and a musical club. We determined CO2 and particle emission rates (CER and PER) across various human activities (sitting, reading, and exercise) in both masked (N-95) and unmasked scenarios, as well as scenarios involving normal and clean-room clothing (CRC) in the clean environmental chamber. Masking reduced PER only during the exercise (∼11%), indicating that respiratory sources constitute a tiny fraction of the total particle emissions during passive activities (e.g., sitting and reading). In contrast, wearing CRC reduced the PER by over 55% for all activities, demonstrating that clothing and skin-shedding constitute a dominant fraction of particle emissions from humans. Results from the café and club measurements showed that the CO2 and particle number concentrations were mostly driven by human occupancy. Collectively, the results from the controlled lab and field environments demonstrate that nonrespiratory emissions (e.g., clothing and skin-shedding) from humans outnumber respiratory emissions in an indoor environment, emphasizing the need to better quantify the contribution and evaluate the potential role of nonrespiratory emissions from humans in the airborne transmission of infectious diseases.

Oxidative potential (OP) is increasingly recognized as a more health-relevant metric than particulate matter (PM) mass concentration because of its response to varying chemical compositions. Given the limited research on the OP of complex combustion aerosols, the effects of aging processes on their OP remain underexplored. We used online instruments to track the evolution of OP [via dithiothreitol (DTT) assays] during the aging of wood burning and coal combustion emissions by hydroxyl-radical-driven photooxidation and dark ozonolysis. We observed very substantial increases in the intrinsic OP (OPm DTT) of complex combustion aerosols (e.g., OPm DTT up to 100 pmol min-1 mu g-1 for OH-aged wood burning emissions) within 1 day of equivalent aging. Further analysis in relation to the degree of oxidation revealed a potential for generalizing the OP of carbonaceous aerosols with average carbon oxidation state O S c values ranging from -1.5 to -0.5 by assuming they have a constant OPm DTT value of similar to 10 +/- 6 pmol min-1 mu g-1. Additionally, we uncovered a strong dependency of OPm DTT on both the source/precursor and aging pathway with O S c above similar to-0.5. OH photooxidation was identified as an exceptionally efficient pathway for generating highly oxidized, multifunctionalized, and DTT-active products, particularly from wood burning emissions.

Lateral flow assays (LFAs) are currently the most popular point-of-care diagnostics, rapidly transforming disease diagnosis from expensive doctor checkups and laboratory-based tests to potential on-the-shelf commodities. Yet, their sensitive element, a monoclonal antibody, is expensive to formulate, and their long-term storage depends on refrigeration technology that cannot be met in resource-limited areas. In this work, LCB1 affibodies (antibody mimetic miniproteins) were conjugated to bovine serum albumin (BSA) to afford a high-avidity synthetic capture (LCB1-BSA) capable of detecting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and virus like particles (VLPs). Substituting the monoclonal antibody 2B04 for LCB1-BSA (stable up to 60 °C) significantly improved the thermal stability, shelf life, and affordability of plasmonic-fluor-based LFAs (p-LFAs). Furthermore, this substitution significantly improved the sensitivity of p-LFAs toward the spike protein and VLPs with precise quantitative ability over 2 and 3 orders of magnitude, respectively. LCB1-BSA sensors could detect VLPs at 100-fold lower concentrations, and this improvement, combined with their robust nature, enabled us to develop an aerosol sampling technology to detect aerosolized viral particles. Synthetic captures like LCB1-BSA can increase the ultrasensitivity, availability, sustainability, and long-term accuracy of LFAs while also decreasing their manufacturing costs.

Fine particulate matter (PM2.5) causes millions of premature deaths each year worldwide. Oxidative potential (OP) has been proposed as a better metric for aerosol health effects than PM2.5 mass concentration alone. In this study, we report for the first time online measurements of PM2.5 OP in wintertime Beijing and surroundings based on a dithiothreitol (DTT) assay. These measurements were combined with co-located PM chemical composition measurements to identify the main source categories of aerosol OP. In addition, we highlight the influence of two distinct pollution events on aerosol OP (spring festival celebrations including fireworks and a severe regional dust storm). Source apportionment coupled with multilinear regression revealed that primary PM and oxygenated organic aerosol (OOA) were both important sources of OP, accounting for 41 +/- 12 % and 39 +/- 10 % of the OPvDTT (OP normalized by the sampled air volume), respectively. The small remainder was attributed to fireworks and dust, mainly resulting from the two distinct pollution events. During the 3.5-day spring festival period, OPvDTT spiked to 4.9 nmol min(-1) m(-3) with slightly more contribution from OOA (42 +/- 11 %) and less from primary PM (31 +/- 15 %). During the dust storm, hourly-averaged PM2.5 peaked at a very high value of 548 mu g m(-3) due to the dominant presence of dust-laden particles (88 % of total PM2.5). In contrast, only mildly elevated OPvDTT values (up to 1.5 nmol min(-1) m(-3)) were observed during this dust event. This observation indicates that variations in OPvDTT cannot be fully explained using PM2.5 alone; one must also consider the chemical composition of PM2.5 when studying aerosol health effects. Our study highlights the need for continued pollution control strategies to reduce primary PM emissions, and more in-depth investigations into the source origins of OOA, to minimize the health risks associated with PM exposure in Beijing.

Most fine ambient particulate matter (PM2.5)-based epidemiological models use globalized concentration-response (CR) functions assuming that the toxicity of PM2.5 is solely mass-dependent without considering its chemical composition. Although oxidative potential (OP) has emerged as an alternate metric of PM2.5 toxicity, the association between PM2.5 mass and OP on a large spatial extent has not been investigated. In this study, we evaluate this relationship using 385 PM2.5 samples collected from 14 different sites across 4 different continents and using 5 different OP (and cytotoxicity) endpoints. Our results show that the relationship between PM2.5 mass vs. OP (and cytotoxicity) is largely non-linear due to significant differences in the intrinsic toxicity, resulting from a spatially heterogeneous chemical composition of PM2.5. These results emphasize the need to develop localized CR functions incorporating other measures of PM2.5 properties (e.g., OP) to better predict the PM2.5-attributed health burdens.

Airborne transmission via virus-laden aerosols is a dominant route for the transmission of respiratory diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Direct, non-invasive screening of respiratory virus aerosols in patients has been a long-standing technical challenge. Here, we introduce a point-of-care testing platform that directly detects SARS-CoV-2 aerosols in as little as two exhaled breaths of patients and provides results in under 60 s. It integrates a hand-held breath aerosol collector and a llama-derived, SARS-CoV-2 spike-protein specific nanobody bound to an ultrasensitive micro-immunoelectrode biosensor, which detects the oxidation of tyrosine amino acids present in SARS-CoV-2 viral particles. Laboratory and clinical trial results were within 20% of those obtained using standard testing methods. Importantly, the electrochemical biosensor directly detects the virus itself, as opposed to a surrogate or signature of the virus, and is sensitive to as little as 10 viral particles in a sample. Our platform holds the potential to be adapted for multiplexed detection of different respiratory viruses. It provides a rapid and non-invasive alternative to conventional viral diagnostics.

AbstractReal-time surveillance of airborne SARS-CoV-2 virus is a technological gap that has eluded the scientific community since the beginning of the COVID-19 pandemic. Offline air sampling techniques for SARS-CoV-2 detection suffer from longer turnaround times and require skilled labor. Here, we present a proof-of-concept pathogen Air Quality (pAQ) monitor for real-time (5 min time resolution) direct detection of SARS-CoV-2 aerosols. The system synergistically integrates a high flow (~1000 lpm) wet cyclone air sampler and a nanobody-based ultrasensitive micro-immunoelectrode biosensor. The wet cyclone showed comparable or better virus sampling performance than commercially available samplers. Laboratory experiments demonstrate a device sensitivity of 77–83% and a limit of detection of 7-35 viral RNA copies/m3 of air. Our pAQ monitor is suited for point-of-need surveillance of SARS-CoV-2 variants in indoor environments and can be adapted for multiplexed detection of other respiratory pathogens of interest. Widespread adoption of such technology could assist public health officials with implementing rapid disease control measures.

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winter. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the total AMS SOA (i.e. MO-OOA + LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major SOA component during the daytime, with a 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during the nighttime. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during the daytime and 36.1 % of total SOA mass (11.2 % of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass (11.7 % and 8.5 % of total OA mass), respectively, during the daytime and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass), respectively, during the nighttime. A simple dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the daytime suggests an increased OA toxicity and health-related consequences for the general public.

We investigated the spatiotemporal distribution and sources of cellular oxidative potential (OP) in the Midwest US. Weekly samples were collected from three urban [Chicago (IL), Indianapolis (IN), and St. Louis (MO)], one rural [Bondville (IL], and one roadside site [Champaign (IL)] for a year (May 2018 to May 2019), and analyzed for water-soluble cellular OP using a macrophage reactive oxygen species (ROS) assay. Chemical composition of the samples including several carbonaceous components, inorganic ions, and water-soluble elementals, were also analyzed. The emission sources contributing to water-soluble cellular OP and PM2.5 mass were analyzed using positive matrix factorization. The secondary organic aerosols contributed substantially (≥54%) to PM2.5 cellular OP at urban sites, while the roadside and rural OP were dominated by road dust (54%) and agricultural activities (62%), respectively. However, none of these sources contributed substantially to the PM2.5 mass (≤21%). Other sources contributing significantly to the PM2.5 mass, i.e., secondary sulfate and nitrate, biomass burning and coal combustion (14-26%) contributed minimally to the cellular OP (≤13%). Such divergent profiles of the emission sources contributing to cellular OP vs. PM2.5 mass demonstrate the need of considering more health-relevant metrics such as OP in the design of air pollution control strategies.

We investigated the influence of biomass burning (BURN), Diwali fireworks, and fog events on the ambient fine particulate matter (PM2.5) oxidative potential (OP) during the postmonsoon (PMON) and winter season in Delhi, India. The real-time hourly averaged OP (based on a dithiothreitol assay) and PM2.5 chemical composition were measured intermittently from October 2019 to January 2020. The peak extrinsic OP (OPv: normalized by the volume of air) was observed during the winter fog (WFOG) (5.23 ± 4.6 nmol·min-1·m-3), whereas the intrinsic OP (OPm; normalized by the PM2.5 mass) was the highest during the Diwali firework-influenced period (29.4 ± 18.48 pmol·min-1·μg-1). Source apportionment analysis using positive matrix factorization revealed that traffic + resuspended dust-related emissions (39%) and secondary sulfate + oxidized organic aerosols (38%) were driving the OPv during the PMON period, whereas BURN aerosols dominated (37%) the OPv during the WFOG period. Firework-related emissions became a significant contributor (∼32%) to the OPv during the Diwali period (4 day period from October 26 to 29), and its contribution peaked (72%) on the night of Diwali. Discerning the influence of seasonal and episodic sources on health-relevant properties of PM2.5, such as OP, could help better understand the causal relationships between PM2.5 and health effects in India.

Previous studies have indicated the roles of both organic compounds and metals in driving the cellular generation of reactive oxygen species (ROS); however, their contribution has not been adequately quantified using mechanistic approaches. We developed a novel fractionation scheme for the stepwise removal of various classes of organic compounds and metals using a combination of solid phase extraction columns. We applied this fractionation scheme to 10 PM2.5 samples collected from the midwestern United States. Because both water-soluble organic carbon (WSOC) and Fe have shown good correlations with cellular ROS, we separated them into different fractions and measured their ability to generate ROS in rat alveolar macrophages. Most of the PM2.5 cellular ROS was attributed to the metallic fraction. To further explore the reason for the correlation of WSOC with ROS, we investigated the water solubility of Fe by measuring the total Fe in PM2.5 samples. The water-soluble fraction of Fe was tightly correlated with WSOC (r >= 0.69), indicating WSOC may have an additional role in cellular oxidative potential, probably through complexation of Fe, in enhancing its water solubility and macrophage ROS activity. This work reveals the role of both Fe and organic compounds through different mechanisms in contributing to PM2.5-driven cellular ROS.

We assessed the oxidative potential (OP) of both water-soluble and methanol-soluble fractions of ambient fine particulate matter (PM2.5) in the Midwestern United States. A large set of PM2.5 samples (N=241) was collected from five sites set up in different environments, i.e., urban, rural, and roadside, in Illinois, Indiana, and Missouri during May 2018–May 2019. Five acellular OP endpoints, including the consumption rate of ascorbic acid and glutathione in a surrogate lung fluid (SLF) (OPAA and OPGSH, respectively), dithiothreitol (DTT) depletion rate (OPDTT), and ⚫OH generation rate in SLF and DTT (OPOH−SLF and OPOH−DTT, respectively), were measured for all PM2.5 samples. PM2.5 mass concentrations in the Midwestern US as obtained from these samples were spatially homogeneously distributed, while most OP endpoints showed significant spatiotemporal heterogeneity. Seasonally, higher activities occurred in summer for most OP endpoints for both water- and methanol-soluble extracts. Spatially, the roadside site showed the highest activities for most OP endpoints in the water-soluble extracts, while only occasional peaks were observed at urban sites in the methanol-soluble OP. Most OP endpoints showed similar spatiotemporal trends between mass- and volume-normalized activities across different sites and seasons. Comparisons between two solvents (i.e., water and methanol) showed that methanol-soluble OP generally had higher activity levels than corresponding water-soluble OP. Site-to-site comparisons of OP showed stronger correlations for methanol-soluble OP compared to water-soluble OP, indicating a better extraction of water-insoluble redox-active compounds from various emission sources into methanol. We found a weak correlation and inconsistent slope values between PM2.5 mass and most OP endpoints. Moreover, the poor to moderate intercorrelations among different OP endpoints indicate different mechanisms of OP represented by these endpoints and thus demonstrate the rationale for analyzing multiple acellular endpoints for a better and more comprehensive assessment of OP.

Several automated instruments exist to measure the acellular oxidative potential (OP) of ambient particulate matter (PM). However, cellular OP of the ambient PM is still measured manually, which severely limits the comparison between two types of assays. Cellular assays could provide a more comprehensive assessment of the PM-induced oxidative stress, as they incorporate more biological processes involved in the PM-catalyzed reactive oxygen species (ROS) generation. Considering this need, we developed a semi-automated instrument, the first of its kind, for measuring the cellular OP based on a macrophage ROS assay using rat alveolar macrophages. The instrument named SCOPE – semi-automated instrument for cellular oxidative potential evaluation – uses dichlorofluorescein diacetate (DCFH-DA) as a probe to detect the OP of PM samples extracted in water. SCOPE is capable of analyzing a batch of six samples (including one negative and one positive control) in 5 h and is equipped to operate continuously for 24 h with minimal manual intervention after every batch of analysis, i.e., after every 5 h. SCOPE has a high analytical precision as assessed from both positive controls and ambient PM samples (coefficient of variation (CoV)<17 %). The results obtained from the instrument were in good agreement with manual measurements using tert-butyl hydroperoxide (t-BOOH) as the positive control (slope =0.83 for automated vs. manual, R2=0.99) and ambient samples (slope =0.83, R2=0.71). We further demonstrated the ability of SCOPE to analyze a large number of both ambient and laboratory samples and developed a dataset on the intrinsic cellular OP of several compounds, such as metals, quinones, polycyclic aromatic hydrocarbons (PAHs) and inorganic salts, commonly known to be present in ambient PM. This dataset is potentially useful in future studies to apportion the contribution of key chemical species in the overall cellular OP of ambient PM.