ACS ES&T Air最新文献

Gas-phase organic acids are ubiquitous in the atmosphere with mixing ratios of several species, such as formic acid and acetic acid, often as high as several parts per billion by volume (ppbv). Organic acids are produced via photochemical reactions and are also directly emitted from various sources, including combustion, microbial activity, vegetation, soils, and ruminants. We present measurements of gas-phase formic, acetic, propionic, pyruvic, and pentanoic acids from a site near Boise, Idaho, in August 2019 made by iodide-adduct chemical ionization mass spectrometry (CIMS). The site is adjacent to a major interstate highway and beyond the urban/suburban core is surrounded by national forests to the north and northeast and by farmland to the west and south. Maximum mixing ratios of formic, acetic, propionic, and pentanoic acid were typically near 10, 3, 0.4, and 0.2 ppbv, respectively. Observed daytime concentrations of these acids were mostly consistent with other studies, but concentrations were persistently the highest at night between 20:00 to 8:00 (local standard time). Such elevated nighttime concentrations are unlike most other reported organic acid measurements. Although there were times when organic acid concentrations were enhanced by mobile source emissions, the organic acid concentrations appear to be mainly controlled by noncombustion surface primary emissions. Source apportionment by positive matrix factorization (PMF) supports the importance of significant noncombustion, nonphotochemical emissions. Two agricultural surface sources were identified and estimated to contribute to greater than half of total observed concentrations of formic and acetic acid. In contrast to the other measured organic acids, but in agreement with all other reported measurements in the literature, pyruvic acid concentrations peaked during the daytime and were largely controlled by photochemistry.

In this study, measurements of several carboxylic acids near Boise, Idaho, are presented. Notably, the concentrations of carboxylic acids were elevated at nighttime, indicating a significant nearby surface source, likely from agriculture.

Sulfur-containing volatile organic compounds emitted during wildfire events, such as dimethyl sulfide, are known to form secondary aerosols containing inorganic sulfate (SO₄^2–) and surfactant-like organic compounds; however, little is known about the fate of sulfur in other emitted reduced organosulfur species. This study aimed to determine the sulfurous product distribution resulting from the nighttime oxidation of thiophene as a model system. Ion chromatography (IC) and aerosol mass spectrometry (a mini aerosol mass spectrometer, mAMS) were used to constrain the proportions of sulfurous compounds produced under wildfire-relevant conditions ([NO₂]/[O₃] = 0.1). With constraints from IC, results indicated that the sulfurous particle mass consisted of 30.3 ± 6.6% SO₄^2–, while mAMS fractionation attributed 24.5 ± 1.6% of total sulfate signal to SO₄^2–, 15.4 ± 1.9% to organosulfates, and 60.1 ± 0.9% to sulfonates. Empirical formulas of organosulfur products were identified as C1–C8 organosulfates and sulfonates using complementary mass spectrometry techniques. This study highlights the nighttime oxidation of thiophene and its derivatives as a source of SO₄^2– and particulate organosulfur compounds, which have important implications for the atmospheric sulfur budget and aerosol/droplet physical and chemical properties.

Little is known about the atmospheric oxidation of volatile organosulfur compounds found in biomass burning emissions. This study investigates the fate of volatile organosulfur compounds and the distribution of sulfurous products in secondary aerosols, with implications for aerosol physical and chemical properties.

Wildfires are increasing in intensity and more often threatening the wildland urban interface (WUI) where buildings and homes coexist with the natural environment. WUI emissions have not been as well-studied as emissions from vegetation. Thus, there is a need to quantify the emissions of building materials used in home construction under flaming and smoldering conditions to study their impacts to human health, visibility, air quality, and climate. Here, in a controlled laboratory setting, we quantify emissions of aerosols and trace gases including formaldehyde, particulate matter, and black carbon. We focus on the combustion of traditional single-source wood-based construction fuels. Our results indicate that, similar to natural fuels, the aerosol optical properties were more related to combustion conditions than the fuel type. Overall, we observed significant variability in the gas and particle emissions. Consistent trends include high formaldehyde (HCHO) and carbon monoxide (CO) emissions for smoldering conditions and higher carbon dioxide (CO₂), nitrogen oxides (NO_x), and black carbon for flaming conditions. These observations highlight the need to better characterize emissions for materials in the built environment to assess large-scale climate and human health impacts of fires at the WUI.

Prescribed fire is applied across the United States as a fuel treatment to manage the impact of wildfires and restore ecosystems. While the recent application of prescribed fire has largely been confined to the southeastern US, the increase in catastrophic wildfires has accelerated the growth of prescribed fire more broadly. To effectively achieve wildfire risk reduction benefits, which includes reducing the amount of smoke emitted, the area treated by prescribed fire must come into contact with a subsequent wildfire. In this study, we applied timely and consistent geospatially resolved data sets of prescribed fires and wildfires to estimate the rate at which an area treated by prescribed fire encounters a subsequent wildfire. We summarize these encounter rates across time intervals, prescribed fire treatment area, and number of previous prescribed fires and by region. On all U.S. Forest Service lands across the Conterminous US (CONUS) 6.2% of prescribed fire treated area from 2003–2022 encountered a subsequent wildfire in 2004–2023. Encounter rates were highest in western US forests, which tend to be more impacted by wildfire than the eastern US, and lower in the eastern US. Encounter rates increased with treatment area in the southeastern US but were relatively flat in the northwest. For the CONUS, encounter rates increased with longer time intervals, associated with diminished potential for reducing wildfire severity, between prescribed fire and the subsequent wildfire area burned. Our results provide timely information on prescribed fire and wildfire interactions that can be leveraged to optimize analyses of the trade-offs between prescribed fire and wildfire.

To date few studies show the rate at which a wildfire encounters a previous prescribed fire. Here we show that a small fraction of prescribed fires spatially intersect with a subsequent wildfire.

Civil aviation gas turbine engines emit ultrafine particles (UFPs, diameter < 100 nm). UFPs degrade air quality because they efficiently transport their chemical content, including engine oil, into the body. Yet, little is known about how and when oil containing UFPs are formed. Results here describe the thrust and flight phase-dependent oil emission and reveal the particle size-dependent transfer of vaporized oil to UFPs with molecular level resolution. All six engines studied emitted oil containing UFPs. Lower volatility oil molecules are enriched on particles <30 nm. Further, the particulate oil mass size distribution aligns with the emitted surface area distribution, suggesting oil vapor condensation onto primary particles and the potential for oil nucleation. However, the oil gas-to-particle transfer in hot exhaust is likely incomplete at least 50 m downwind thus limiting current emission studies. The measured engine oil consumption provides an upper-limit oil emission index at idle of 240 mg oil per kg fuel. The emission index at cruise is 110 mg kg^–1, which is a factor of 10 greater than black carbon. For any flight >2 h, 95% of oil emission is expected to occur at cruise altitude, highlighting the unknown effects of oil emission in the upper atmosphere.

This work provides details on when and how oil containing ultrafine particles are formed from real-world aircraft emissions. The results reveal important implications for oil particle growth and injection in the upper atmosphere.

India’s public health is seriously endangered by air pollution and linked to ∼0.8–0.98 million and 0.17 million premature deaths annually from chronic exposure to particulate matter (PM_2.5) and ozone, respectively. Air quality forecasting tools are needed to assess the spatiotemporal variability of air pollutants and evaluate the impact of potential mitigation measures. Gridded concentrations of PM_2.5 and ozone are typically derived from first principle models called Chemical Transport Models (CTMs). Widespread application of CTMs, particularly for regulatory purposes, requires an understanding of model performance in comparison with observation data. A landscape approach that systematically reviews CTM model predictions and proposes benchmarks for assessing model performance can offer a way forward to build confidence in CTM-backed forecasting tools. Following similar efforts in the United States and China, we systematically shortlisted and examined model performance outcomes for 46 CTM studies reporting PM_2.5 and ozone predictions in India between 2008 and 2023. The reported statistical metrics for each pollutant (normalized mean bias (NMB), normalized mean error (NME), coefficient of correlation (r), and index of agreement (IOA) were rank ordered to identify two types of benchmarks: “goals” (highest achievable model accuracy based on top one-third performing studies) and “criteria” (typical model accuracy based on the top two-third performing studies). The identified goal performance for PM_2.5 was 17% (NMB), 34% (NME), and 0.67 (r), and ozone was 14% (NMB), 43% (NME), and 0.89 (r). These benchmarks are less restrictive than those reported for the United States and China. This highlights the need for the CTM community to coalesce around a common set of practices in evaluating CTMs. Additionally, complementary efforts in developing representative emissions inventories and including air pollution data from the expanding observational networks are required to update such benchmarks periodically. This study seeks to advance the capabilities of CTMs as a cornerstone of air quality forecasting tools and augment air quality management frameworks in India.

Computational models of atmospheric composition are not always physically consistent. For example, not all models respect fundamental conservation laws such as conservation of atoms in an interconnected chemical system. In well performing models, these unphysical deviations are often ignored because they are frequently minor, and thus only need a small nudge to perfectly conserve mass. Here we introduce a method that anchors a prediction from any numerical model to physically consistent hard constraints, nudging concentrations to the nearest solution that respects the conservation laws. This closed-form model-agnostic correction uses a single matrix operation to minimally perturb the predicted concentrations to ensure that atoms are conserved to machine precision. To demonstrate this approach, we train a gradient boosting decision tree ensemble to emulate a small reference model of ozone photochemistry and test the effect of the correction on accurate but nonconservative predictions. The nudging approach minimally perturbs the already well-predicted results for most species, but decreases the accuracy of important oxidants, including radicals. We develop a weighted extension of this nudging approach that considers the uncertainty and magnitude of each species in the correction. This species-level weighting approach is essential to accurately predict important low concentration species such as radicals. We find that applying the species-weighted correction slightly improves overall accuracy by nudging unphysical predictions to a more likely mass-conserving solution.

Computational models of atmospheric composition are not always physically consistent. For example, not all models respect fundamental conservation laws such as conservation of atoms in an interconnected chemical system. In well performing models, these unphysical deviations are often ignored because they are frequently minor, and thus only need a small nudge to perfectly conserve mass. Here we introduce a method that anchors a prediction from any numerical model to physically consistent hard constraints, nudging concentrations to the nearest solution that respects the conservation laws. This closed-form model-agnostic correction uses a single matrix operation to minimally perturb the predicted concentrations to ensure that atoms are conserved to machine precision. To demonstrate this approach, we train a gradient boosting decision tree ensemble to emulate a small reference model of ozone photochemistry and test the effect of the correction on accurate but nonconservative predictions. The nudging approach minimally perturbs the already well-predicted results for most species, but decreases the accuracy of important oxidants, including radicals. We develop a weighted extension of this nudging approach that considers the uncertainty and magnitude of each species in the correction. This species-level weighting approach is essential to accurately predict important low concentration species such as radicals. We find that applying the species-weighted correction slightly improves overall accuracy by nudging unphysical predictions to a more likely mass-conserving solution.

Computational models of atmospheric composition do not always make scientifically trustworthy predictions. This corrective approach minimally adjusts the predicted concentrations of chemical species to guarantee conservation of mass.

Airports are a large and growing source of NO_x. Tracking airport-related emissions is especially difficult, as a portion of emissions are elevated above the surface. While satellite-based NO₂ observations show hot-spots near airports, near-source retrievals often have large biases related to uncertainties in the NO₂ vertical distribution and resultant air mass factors (AMF). Here we use observations from UV–vis spectrometers (Pandora 1S, SciGlob) deployed near the Atlanta Hartsfield-Jackson International airport from April 2020–May 2021 to assess the impact of aviation on NO₂ vertical profiles. We show the first near-airport sky-scanning Pandora observations, which are used to distinguish the airport plume from the urban background. We find that increasing aviation leads to higher NO₂ over the airport, and the enhancement is distributed across the mixed layer near-equally. We compare observed profiles with those modeled by the Goddard Earth Observing System composition forecast (GEOS-CF) system. We find that modeled profiles attribute a larger portion of the column closer to the surface and underestimate the NO₂ mixing height. Observed profiles typically exhibited greater NO₂ concentrations up to 2.5 km above ground level. Air mass factors (AMF) calculated using observations (AMF_Fused) are similar over Hartsfield-Jackson to those calculated using GEOS-CF (AMF_GEOS-CF). The unexpected similarity in alternative AMFs is attributed to the altitude-dependent sensitivity of AMF_Fused to changes in NO₂ concentration. Using either AMF_Fused or AMF_GEOS-CF to evaluate TROPOMI NO₂ against independent direct-sun observations produces consistent normalized mean differences of −22% and −29%, respectively. Overall, these results demonstrate the benefits of a combined ground and satellite-based approach for probing a complex distribution of NO_x emissions in an urban area.

Observational constraints on airport-related emissions are limited. We use 3D observations from the Pandora instrument to map the near-airport NO₂ enhancement and discuss implications of discrepancies in measured and modeled NO₂ vertical profiles for interpretation of satellite-based observations.

Airports are a large and growing source of NO _x . Tracking airport-related emissions is especially difficult, as a portion of emissions are elevated above the surface. While satellite-based NO₂ observations show hot-spots near airports, near-source retrievals often have large biases related to uncertainties in the NO₂ vertical distribution and resultant air mass factors (AMF). Here we use observations from UV-vis spectrometers (Pandora 1S, SciGlob) deployed near the Atlanta Hartsfield-Jackson International airport from April 2020-May 2021 to assess the impact of aviation on NO₂ vertical profiles. We show the first near-airport sky-scanning Pandora observations, which are used to distinguish the airport plume from the urban background. We find that increasing aviation leads to higher NO₂ over the airport, and the enhancement is distributed across the mixed layer near-equally. We compare observed profiles with those modeled by the Goddard Earth Observing System composition forecast (GEOS-CF) system. We find that modeled profiles attribute a larger portion of the column closer to the surface and underestimate the NO₂ mixing height. Observed profiles typically exhibited greater NO₂ concentrations up to 2.5 km above ground level. Air mass factors (AMF) calculated using observations (AMF_Fused) are similar over Hartsfield-Jackson to those calculated using GEOS-CF (AMF_GEOS-CF). The unexpected similarity in alternative AMFs is attributed to the altitude-dependent sensitivity of AMF_Fused to changes in NO₂ concentration. Using either AMF_Fused or AMF_GEOS-CF to evaluate TROPOMI NO₂ against independent direct-sun observations produces consistent normalized mean differences of -22% and -29%, respectively. Overall, these results demonstrate the benefits of a combined ground and satellite-based approach for probing a complex distribution of NO _x emissions in an urban area.