ACS ES&T Air最新文献

In this study, we assessed the performance of PurpleAir PM_2.5 sensors and developed calibration models in Dhaka, Bangladesh─one of the global hotspots most severely affected by extreme air pollution. We collocated an array of PurpleAir (PA-II-SD) sensors alongside a beta attenuation monitor (BAM: MetOne BAM-1020) across the dry and wet seasons. Specifically, we collocated 10 sensors during the wet season and 20 sensors during the dry season, collecting one month of colocation data per season, covering a wide range of pollution levels and meteorological conditions. Quality-assured hourly concentrations from different PurpleAir units have shown good consistency, with pairwise R² values generally exceeding 0.95. We developed empirical correction models by testing 29 multiple linear regression (MLR) forms and Random Forest models. Results showed that for hourly average PM_2.5 concentrations measured by PurpleAir, a simple linear correction model achieved an accuracy (nRMSE) within 17–18% of hourly BAM measurements. More complex MLR models incorporating several meteorological variables and interaction terms improved accuracy (nRMSE) slightly, to ∼15%. Random Forest models slightly outperformed all MLR models, at 12–14% (nRMSE) accuracy relative to BAM. Our findings highlight that existing correction models─particularly those developed for U.S. cities and used in the PurpleAir map─are inadequate for Bangladeshi conditions. Uncorrected PurpleAir cf_atm PM_2.5 data yielded accuracy within 25% of BAM measurements. Further research is needed to assess sensor performance in rural and suburban environments and to evaluate long-term performance under diverse climatological and source conditions in Bangladesh and South Asia.

This study presents the first multiyear investigation of per- and polyfluoroalkyl substances (PFAS) in the atmosphere over the Great Lakes. The levels of ∑PFAS exhibit a broad range from 2.9 to 280 pg/m³. Higher concentrations of PFAS at the urban-influenced site compared to the rural site signified spatial variability between the two sampling locations. Despite the absence of a consistent seasonal pattern, concentrations of perfluorooctanoic acid (PFOA) and neutral PFAS (nPFAS) tend to be higher during the summer months. This pattern appears to be influenced by the seasonal variations of air back trajectories and potential influences from south of the sampling site. The study elucidates potential shifts in atmospheric PFAS dynamics during the COVID-19 pandemic. Applying a modified model with a probabilistic approach, we investigated the transport and fate of PFOA and perfluorooctanesulfonic acid (PFOS) within Lake Ontario. The estimated loadings of PFOA and PFOS from air-to-water were 9.5 ± 3.6 and 17 ± 6.6 kg/y, respectively, close to the values from wet deposition (10 ± 3.9 and 31 ± 12 kg/y, respectively) but much lower than those from inflow and all other sources. The primary output pathways for PFOA and PFOS were from water outflow.

PFAS in the Great Lakes’ atmosphere reveal various spatial and seasonal variability impacted by potential sources and COVID-19, with fate modeling underscoring unaccounted for PFOA inputs to Lake Ontario.

Free amino acids (FAAs) are crucial components of organic nitrogen (ON) in aerosols. We investigated the characteristics of FAAs in PM_2.5 in a cruise campaign from the South China Sea (SCS) to the Eastern Indian Ocean (EIO). The contribution of marine and continental sources to FAAs was assessed in both the continent-surrounded sea area and the open-sea area. Higher total FAA concentrations were observed in the continental-influenced samples than in the oceanic-influenced samples. Among the FAAs, the abundance of Glycine (Gly) was significantly enhanced (71.5%) due to the transport of continental air masses. Moreover, the d/l-aspartate acid ratio exhibited an elevation under the impact of biomass burning. Under marine influence, the abundance of l-tryptophan (l-Trp) increased by 94.8% in the EIO. We propose that Gly and l-Trp can serve as the indicators for continental and marine sources, respectively, and we applied the l-Trp/Gly ratio to quantify their relative contributions to FAAs in marine aerosols. The results reveal the impact of continental transport and marine emissions on atmospheric ON. This study deepens the understanding of features of the nitrogen cycle and its implications for biogeochemical cycling over the ocean.

Particulate 2-methyltetrols (2-MT), 2-methylglyceric acid (2-MG), and C₅-alkene triols serve as critical tracers for identifying isoprene-derived secondary organic aerosols (SOAs). Field observations have identified significant concentrations of C₅-alkene triols, yet no air quality models currently account for the formation pathways of these compounds. Simultaneously, the 2-MT concentrations are usually overestimated by 4–5 times compared to field observations due to the lack of the C₅-alkene triols formation pathway. In this study, we expanded the isoprene-SOA scheme and implemented it into the Community Multiscale Air Quality (CMAQ) model with the latest Community Regional Atmospheric Chemistry Multiphase Mechanism version 2 (CRACMM2) mechanism. The missing formation of C₅-alkene triols via the low NO_x isoprene epoxydiols (IEPOX) pathway, the gas-particle partitioning of isoprene-derived SOA tracers, and the high NO_x SOA formation pathway are comprehensively considered in this expanded isoprene-SOA scheme. Results indicate that the C₅-alkene triols contribute to 68% of all isoprene-derived SOA tracers, with approximately 50% in the gas phase and the remainder in the particle phase. The 2-MT concentrations are better represented in the new scheme compared to the default CRACMM2 based on the field observations. The overall SOA is improved by 0–4 μg/m³ in China with the expanded scheme. The isoprene-derived SOA is highly influenced by anthropogenic emissions, especially SO_x (SO₂ + SO₄^2–), and the reduction in SO_x reduces both isoprene aerosol tracers and organosulfates significantly, while a similar reduction in NO_x leads to small increases in these species for both particle and gas phases.

Measurements of ambient ethane concentrations at a regional air quality monitor in the Eagle Ford oil and gas production region are compared to concentrations predicted using site-level hydrocarbon emission inventories coupled with a Gaussian puff dispersion model (CALPUFF). To account for more than half of mean concentrations due to routine emissions, sites at distances 20–50 km from the receptor site were included in the simulations. Nearly all of the highest observed concentrations were observed at night. For each night in the simulation, the location and magnitude of the maximum predicted concentration and maximum observed concentration were compared, and approximately two-thirds of the highest observed nighttime maximum concentrations were accounted for by routine emissions. In contrast, approximately a third of the highest daytime maxima could be accounted for by routine emissions. Most of the large observed maxima that are attributable to routine emissions are predicted to be caused by sources that were within 10 km of the receptor site, but sources up to 20 or more kilometers from the receptor also contributed to the predicted concentrations. A case study is provided demonstrating the potential of coupling site-level inventories of routine emissions with dispersion modeling for attributing sources of elevated hydrocarbon concentrations.

The largest hydrocarbon enhancements observed at regional monitors are due to both routine emissions and emission events from nearby and distant sources.

The reaction mechanisms and rate constants for the aqueous phase reactions of atmospherically relevant carbonyl organonitrates (ONs) and organosulfates (OSs) were determined via bulk kinetics experiments using nuclear magnetic resonance (NMR) spectroscopy. The OSs and the nonalpha carbonyl substituted ONs were found to exclusively undergo a hydrolysis mechanism which is like that previously found for monofunctional and alpha hydroxy substituted ONs and OSs. However, the nonalpha carbonyl-substituted ONs were also found to undergo hydrolysis via sulfate catalysis (in addition to the previously found Brønsted acid catalyzed pathway), which has not been previously reported for any ONs. The alpha carbonyl-substituted ONs were found to react predominantly according to the E_CO2 mechanism (via both Brønsted acid and sulfate catalyzed pathways) which has also not been previously reported for any atmospherically relevant ONs. Because the E_CO2 mechanism leads to alpha-substituted dicarbonyl compounds, this process could be a precursor to accretion reactions which are needed to explain low volatility ambient secondary organic aerosol (SOA). The kinetics data indicate that the Brønsted acid catalyzed pathways could be relevant for SOA with pH < 1, while the sulfate catalyzed pathways are predicted to be relevant for SOA well below efflorescence point for (NH₄)₂SO₄.

This study estimates ground-level fine particulate matter (PM_2.5) concentrations using geostationary satellites-derived Aerosol Optical Depth (AOD) and radiance measurements and meteorological parameters from the High-Resolution Rapid Refresh (HRRR) model, with AirNow PM_2.5 measurements over the contiguous United States (CONUS). A Deep Neural Network (DNN) was adopted and compared with other machine learning (ML) models (i.e., Random Forest and Light Gradient-Boosting Machine) to estimate surface PM_2.5 concentrations. The DNN model (without the tropospheric emissions: monitoring of pollution (TEMPO); 1 year) estimated PM_2.5 with an interquartile range (IQR) of 4.32 μg/m³, and outperformed ML models, with up to 44.68% better index of agreement (IOA) and 45.28% smaller relative root-mean-square error (rRMSE), particularly in high PM_2.5 cases. The hourly estimated PM_2.5 closely matched the observed PM_2.5 in both temporal trend and spatial distribution across the eastern CONUS. ML modeling was further enhanced to include TEMPO Level 1b (L1b) data. The DNN model with TEMPO improved performance, with an 8% higher R² and a 25% lower rRMSE than the DNN model without TEMPO. The more significant improvement was seen during high smoke events using the TEMPO data. For the first time, we demonstrate the use of TEMPO L1b spectrally resolved radiances data to capture high PM_2.5 concentrations during the wildfire events, enhancing our understanding of PM_2.5 dynamics. This study provides a framework to integrate data from multiple geostationary satellites with HRRR model outputs to estimate surface air quality at high temporal resolution.

This study provides enhanced PM_2.5 monitoring estimated through deep neural networks, particularly during wildfire events, and supporting public health responses.

Inhaling smoke PM_2.5 can lead to acute health effects like asthma and lung irritation, making it essential to estimate smoke exposure on short time scales. Epidemiological studies that assess these effects need daily, fire-specific ground-level PM_2.5 data, and missing emission information can lead to underestimates. This paper presents a method to estimate daily fire-specific PM_2.5 smoke concentrations in the western United States from 2007 to 2019. Our model uses fire characteristics (e.g., fuel type, fire size, and distance) and updated fire emission inputs in an atmospheric dispersion model to simulate where smoke travels and at what concentration. We then apply a Bayesian time-series model to ground-based EPA monitors to isolate the smoke-specific portion of total PM_2.5, accounting for meteorology and season. This approach allows us to assess spatial variation in smoke exposure and investigate the role of fire attributes. For example, Lindon, UT experienced 398 fires with modest average concentrations (∼2 μg m³), while Carson City, NV saw fewer fires (177) but more intense exposures (∼6 μg m³, max 159 μg m³). These contrasts highlight the value of linking fire characteristics to daily exposure in health studies and underscore the need to consider transported smoke in fire management strategies.

Submicron particles (PM₁) play a crucial role in air quality and human health. This study investigates the influence of long-range transport (LRT) on urban aerosol levels in Leipzig, Germany, using high-resolution aerosol mass spectrometry measurements at two sites: an urban traffic site (Eisenbahnstrasse, Eiba) and a rural background site (Melpitz), located ∼50 km apart. The sites were analyzed during winter 2017 under two dominant wind regimes: East and West. These sites were directly linked to each other, which was supported by cross-correlation analysis, with a typical time lag of −2 h in East and +4 h in West. Eastern winds brought higher concentrations (Melpitz: 35.50 μg m^–3, Eiba: 37.47 μg m^–3), while Western winds led to cleaner conditions. After being corrected for time lag, the Urban Increment (UI) was estimated, showing that during Eastern wind, only ∼9% of PM₁ mass measured at Eiba was attributed to urban sources, highlighting the dominant contribution of regionally transported aerosol. Furthermore, source apportionment of organic aerosol (OA) identified five major factors─three primary OA and two oxygenated OA─at both sites. The findings underscore the significant role of regional pollution in shaping urban air quality and the need for cross-border emission reduction strategies.

The findings of this study highlight the issue of long-range transport (LRT) aerosols from Eastern Europe and their impact on urban air quality in Eastern Germany.

Carbonyls and small organic acids are ubiquitous in indoor and outdoor atmospheres; carbonyls also have significant health implications. In this study, we validate a rapid, simple, and cost-effective method for carbonyl and small organic acids quantification by ultraviolet–visible (UV–vis) spectroscopy detection after derivatizing the C═O moiety with 2,4-dinitrophenylhydrazine (DNPH). The spectroscopic method is benchmarked against accurate mass speciation by using high-performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS). Complex natural mixtures relevant to indoor and outdoor air quality were examined: electronic (e-) cigarette aerosols from nicotine and cannabinoid sources, woodsmoke aerosols, and secondary organic aerosols from limonene ozonolysis. The spectroscopy method measured the UV–vis absorption of quinoidal ions, formed from DNPH hydrazones in alkaline solution, at 530 nm (A₅₃₀). A significant correlation was established between the two methods across a range of aerosol mass and chemical compositions tested, resulting in a recommended calibration factor (CF) of ∼5.16–5.34 × 10^–5 (M cm), where C_carbonyls (M) = A₅₃₀ × CF (M cm) × path length^–1 (cm^–1). The calibration factors, determined via two approaches─(1) by measuring the effective molar absorptivity of quinoidal ions produced from eight carbonyl-DNPH and acid-DNPH calibration standards, and (2) empirically from the correlation data without assumptions, were in good agreement with one another. The simple UV–vis spectroscopic method was a robust total carbonyl quantification method for multiple aerosol systems of environmental interest, which has utility in functional group apportionment, teaching laboratories, student projects, and preliminary screenings of carbonyl-related toxicity prior to more-detailed analyses.