Aerosol and Air Quality Research最新文献

Polycyclic Aromatic Hydrocarbons (PAHs), a subset of air toxics, pose severe health risks even at low concentrations due to their carcinogenic and reproductive effects. Despite their significance, spatial distribution studies of PAHs remain limited, largely due to the absence of real-time, chemically specific methods. This study applies high-resolution aerosol mass spectrometry (AMS) for quantification and spatial analysis of particle-phase PAHs. Laboratory experiments were conducted with four PAH standards (anthracene, pyrene, 9-methyl anthracene, and acenaphthylene) to determine their mass spectra and ionization efficiency. AMS mass spectra of the PAH standards were highly correlated with NIST reference spectra (R² > 0.95), though the aerosol mass spectra exhibited greater fragmentation than the reference spectra. Subsequently, field sampling was carried out in the city of Pittsburgh. A mobile laboratory was driven over defined routes in areas of varying source activities (e.g., the downtown core, residential areas, and industrial areas). These measurements revealed significant spatial variability. The median neighborhood-scale total particle-phase PAH concentrations ranged from 70 ng/m³ to 150 ng/m³ indicating a 2.1-fold difference, with the downtown neighborhood exhibiting the highest intra-neighborhood variability (Q3/Q1 = 3.4). Compared to Black Carbon (BC), particle-phase PAHs showed greater spatial variability. Most of the detected particle-phase PAHs had low background concentrations and the time series of concentration was punctuated by high concentration spikes. These spikes occurred most frequently in high-traffic zones such as Downtown and on major highways, suggesting contributions from vehicle traffic. Additional spikes in industrial areas suggest contributions from industrial sources. Ratio-ratio plots (e.g., PAH/BC) and comparison of particle-phase PAH concentrations with the concentrations of hydrocarbon-like organic aerosol (HOA), an indicator of vehicle emissions, further indicate the importance of vehicle emissions on ambient particle-phase PAH concentrations. However, vehicle emissions cannot fully explain particle-phase PAH concentrations in the mobile sampling data. Other sources including metallurgical coke emissions and asphalt paving emissions may also be important.

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Supplementary information: The online version contains supplementary material available at 10.1007/s44408-026-00109-2.

Particulate matter (PM) poses a significant public health concern, particularly in industrialized communities. While modern sophisticated air monitors like those deployed by the United States Environmental Protection Agency (US EPA) provide accurate 24-h PM2.5 (PM ≤ 2.5 µm) data, limited spatial and temporal coverage may create monitoring gaps. Low-cost monitors, such as a PurpleAir monitor (PA-II), offer an accessible alternative; however, their accuracy can be influenced by local environmental conditions and PM characteristics. This study evaluated the site-specific performance of PA-II in an industrial region within northern Lake County (N = 6) and a suburban region of Tippecanoe County (N = 4), both in Indiana, United States. As a reference measurement, a deployable particulate sampler (DPS) was co-located with each PA-II. For microscopic analysis, PM was collected on a glass substrate. PA-II data were assessed in raw form and following calibration using two methods, with performance assessed by root mean square error (RMSE) and the coefficient of determination (R²). Microscopic analysis revealed the presence of dark, irregular particles near industrial sites in northern Lake County, which may affect sensor readings by altering light-scattering efficiency. Raw PA-II data showed weak absolute agreement with DPS measurements in northern Lake County (RMSE: 2.04 µg/m³, R²: 0.25), while the US-wide calibration exhibited the best performance (RMSE: 1.40 µg/m³; R²: 0.67). In contrast, raw data showed the best performance in Tippecanoe County (RMSE: 0.62 µg/m³; R²: 0.94). These findings highlight the relevance of site-specific calibration approaches to enhance the accuracy of low-cost PM2.5 monitors, especially in environmentally impacted communities.

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Supplementary information: The online version contains supplementary material available at 10.1007/s44408-026-00112-7.

Low-cost particle monitors have been widely evaluated in laboratory and ambient monitoring settings, but we have less knowledge about their performance for long-term indoor and outdoor monitoring in residential settings. We seek to provide practical guidance for using a type of low-cost particle monitors that have become widespread for indoor and outdoor monitoring in community field studies, PurpleAir PA-II monitors. We base our insights on experiences in a community-led residential field study in and around homes of predominantly agricultural workers in California's San Joaquin Valley. Our guidance spans three categories: (1) providing tools for handling and merging disparate data structures resulting from Wi-Fi-transmitted data and data collected on onboard microSD cards, (2) assessing performance metrics of PA-II monitors from laboratory co-location and field measurements, and (3) assessing data collection success rates of Wi-Fi data transmission and microSD card data acquisition from our study locations. The post-processing methods we demonstrate can successfully align data from both Wi-Fi transmission and microSD cards. Laboratory co-location measurements demonstrated that > 90% of the tested monitors performed well relative to each other (high precision), with only a few problematic monitors that warranted further investigation or exclusion from use. The application of co-location factors generated using the mean of all co-located monitors as a reference did not significantly affect distributions of field-measured indoor or outdoor PM_2.5 concentrations. Relying solely on Wi-Fi data transmission in our study would have resulted in large data loss (i.e., < 50% success rate); using microSD card storage with PA-II-SD monitors increased the data collection success rate to over 80% in these settings. This work contributes to the growing body of knowledge on low-cost particle sensor performance and usability.

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Supplementary information: The online version contains supplementary material available at 10.1007/s44408-025-00048-4.

The viability of viruses collected on membrane filters during air sampling is compromised by desiccation. This leads to an underestimation of the concentration of viable viruses in the air. We investigated whether adding a hygroscopic glycerol coating to membrane filters could enhance the recovery of viable bacteriophage MS2 collected through air sampling. Mixed cellulose ester (MCE) filters were coated with glycerol and compared with as-packaged filters (APF) as a control to determine the impact of glycerol on MS2 viability. First, MS2 viability upon direct deposition of MS2 suspension on APF vs. glycerol-coated filters (GCF) was assessed at benchtop. On average 121% more viable MS2 were recovered from GCF than APF (p = 0.031). MS2 was then aerosolized and collected onto APF, GCF, phosphate-buffered saline (PBS)-wetted filters (PWF), and gelatin filters (GF). All filter types collected statistically similar (p > 0.05) amounts of aerosolized virus at 50% RH according to RT-qPCR analysis. Viable virus recovery from GCF was significantly lower than from APF (p = 0.011) at 50% RH but significantly higher (p = 0.0004) at 80% RH. APF and GCF preconditioned for 30 minutes at 80% RH were also assessed for MS2 aerosol sampling. Results showed higher viable virus recovery from GCF than either APF (p = 0.021) or GF (p = 0.006). High RH in aerosols facilitated viability conservation of MS2 on GCF, leading to improved recovery of viable virus from membrane filters.

Air pollution is the leading environmental cause of death globally, and most mortality occurs in resource-limited settings such as sub-Saharan Africa. The African continent experiences some of the worst ambient air pollution in the world, yet there are relatively little African data characterizing ambient pollutant levels and source admixtures. In Uganda, ambient PM_2.5 levels exceed international health standards. However, most studies focus only on urban environments and do not characterize pollutant sources. We measured daily ambient PM_2.5 concentrations and sources in Mbarara, Uganda from May 2018 through February 2019 using Harvard impactors fitted with size-selective inlets. We compared our estimates to publicly available levels in Kampala, and to World Health Organization (WHO) air quality guidelines. We characterized the leading PM_2.5 sources in Mbarara using x-ray fluorescence and positive matrix factorization. Daily PM_2.5 concentrations were 26.7 μg m^-3 and 59.4 μg m^-3 in Mbarara and Kampala, respectively (p<0.001). PM_2.5 concentrations exceeded WHO guidelines on 58% of days in Mbarara and 99% of days in Kampala. In Mbarara, PM_2.5 was higher in the dry as compared to the rainy season (30.8 vs 21.3, p<0.001), while seasonal variation was not observed in Kampala. PM_2.5 concentrations did not vary on weekdays versus weekends in either city. In Mbarara, the six main ambient PM_2.5 sources identified included (in order of abundance): traffic-related, biomass and secondary aerosols, industry and metallurgy, heavy oil and fuel combustion, fine soil, and salt aerosol. Our findings confirm that air quality in southwestern Uganda is unsafe and that mitigation efforts are urgently needed. Ongoing work focused on improving air quality in the region may have the greatest impact if focused on traffic and biomass-related sources.

Portable air cleaners (PACs) equipped with HEPA filters are gaining attention as cost-effective means of decreasing indoor particulate matter (PM) air pollutants and airborne viruses. However, the performance of PACs in naturalistic settings and spaces beyond the room containing the PAC is not well characterized. We conducted a single-blinded randomized cross-over interventional study between November 2020 and May 2021 in the homes of adults who tested positive for COVID-19. The intervention was air filtration with PAC operated with the HEPA filter set installed ("filter" condition) versus removed ("sham" condition, i.e., control). Sampling was performed in 29 homes for two consecutive 24-hour periods in the primary room (containing the PAC) and a secondary room. PAC effectiveness, calculated as reductions in overall mean PM_2.5 and PM₁₀ concentrations during the filter condition, were for the primary rooms 78.8% and 63.9% (n = 23), respectively, and for the secondary rooms 57.9% and 60.4% (n = 22), respectively. When a central air handler (CAH) was reported to be in use, filter-associated reductions of PM were statistically significant during the day (06:00-22:00) and night (22:01-05:59) in the primary rooms but only during the day in the secondary rooms. Our study adds to the literature evaluating the real-world effects of PACs on a secondary room and considering the impact of central air systems on PAC performance.

Many studies use machine learning to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. This systematic review will focus on reviewing studies that have utilized interpretable machine learning models to enhance interpretability while maintaining high accuracy for air pollution prediction. The search terms "air pollution," "machine learning," and "interpretability" were used to identify relevant studies published between 2011 and 2023 from PubMed, Scopus, Web of Science, Science Direct, and JuSER. The included studies were assessed for quality based on an ecological checklist for maximizing reproducibility of ecological niche models. Among the 5,396 identified studies, 480 focused on air pollution prediction, with 56 providing model interpretations. Among the studies, 20 methods were identified: 8 model-agnostic methods, 4 model-specific methods, and 8 hybrid models. Shapley additive explanations was the most commonly used method (46.4%), followed by partial dependence plots (17.4%), both of which are model-agnostic methods. These methods identify important atmospheric features, enhancing researchers' understanding and making machine learning outcomes more accessible to non-experts. This can enhance prediction and prevention of adverse weather events and air pollution, benefiting public health.

Future estimates of atmospheric pollutant concentrations serve as critical information for policy makers to formulate current policy indicators to achieve future targets. Tropospheric burden of O₃ is modulated not only by anthropogenic and natural precursor emissions, but also by the downward transport of O₃ associated with stratosphere to troposphere exchange (STE). Hence changes in the estimates of STE and its contributions are key to understand the nature and intensity of future ground level O₃ concentrations. The difference in simulated O₃ mixing ratios with and without the O₃-Potential Vorticity (PV) parameterization scheme is used to represent the model estimated influence of STE on tropospheric O₃ distributions. Though STE contributions remain constant in Northern hemisphere as a whole, regional differences exist with Europe (EUR) registering increased STE contribution in both spring and winter while Eastern China (ECH) reporting increased contribution in spring in 2050 (RCP8.5) as compared to 2015. Importance of climate change can be deduced from the fact that ECH and EUR recorded increased STE contribution to O₃ in RCP8.5 compared to RCP4.5. Comparison of STE and non-STE meteorological process contributions to O₃ due to climate change revealed that contributions of non-STE processes were highest in summer while STE contributions were highest in winter. EUR reported highest STE contribution while ECH reported highest non-STE contribution. None of the 3 regions show consistent low STE contribution due to future climate change (< 50%) in all seasons indicating the significance of STE to ground level O₃.

Electronic cigarettes (ECIGs) generate high concentrations of particulate matter (PM), impacting the air quality inhaled by humans through secondhand exposure. ECIG liquids are available commercially and some users create their own "do-it-yourself" liquids, and these liquids often vary in the amounts of their chemical ingredients, including propylene glycol (PG) and vegetable glycerin (VG). Previous studies have quantified PM concentrations in ECIG aerosol generated from liquids containing different PG/VG ratios. However, the effects of these ratios on aerosol instrument filter correction factors needed to measure PM concentrations accurately have not been assessed. Thus, ECIG aerosol filter correction factors for multiple aerosol instruments (SMPS + APS, MiniWRAS, pDR, and SidePak) were determined for five different PG/VG ratios 1) 0PG/100VG, 2) 15PG/85VG, 3) 50PG/50VG, 4) 72PG/28VG, and 5) 90PG/10VG and two different PM sizes, PM₁ (1 μm and smaller) and PM_2.5 (2.5 μm and smaller). ECIG aerosols were generated inside a controlled exposure chamber using a diaphragm pump and a refillable ECIG device for all the ratios. In addition, the aerosol size distribution and mass median diameter were measured for all five ECIG ratios. PM_2.5 correction factors (5-7.6) for ratios 1, 2, 3, and 4 were similar for the SMPS + APS combined data, and ratios 1, 2, 3 were similar for the MiniWRAS (~2), pDR (~0.5), and SidePak (~0.24). These data suggest different correction factors may need to be developed for aerosol generated from ECIGs with high PG content. The higher correction factor values for the 90PG/10VG ratio may have resulted from greater PG volatility relative to VG and sensor losses. The correction factors (ratios 1-4) for PM_2.5 were SMPS + APS data (4.96-7.62), MiniWRAS (2.02-3.64), pDR (0.50-1.07), and SidePak (0.22-0.40). These data can help improve ECIG aerosol measurement accuracy for different ECIG mixture ratios.