ACS ES&T Air最新文献

This study introduced a high-resolution mobile measurement method and a new Gaussian vertical dispersion parametrization for plume dispersion in urban surface, with an application to CH₄ emission measurements of compressed natural gas (CNG) stations in urban Beijing, China. Tracer release experiments were conducted at two urban sites, and the results showed that the vertical plume dispersion coefficient in urban surface has an exponential relationship with plume travel distance, despite the surface topology. The results were fitted to a new vertical dispersion parametrization: ln(D_z) = −2.42 × ln(x) + 6.28. Using the new vertical dispersion parametrization along with lateral dispersion widths mapped through mobile measurements, the CH₄ emission rates of CNG fueling stations were determined, with an average of 2.5 (95% CI [0.21,30]) kg h^–1 for normal operating conditions, and 0.56 (95% CI [0.07,4.4]) kg h^–1 for stations under nonoperating conditions. A CH₄ emission factor (EF) of CNG fueling stations in Beijing was calculated based on the measured CH₄ emission rate and the annual CNG consumption in transportation, with an average of 0.010 (95% CI [0.001,0.087]) kg/kg. This indicates that the CH₄ emission of CNG fueling stations is nonnegligible compared with the emission of natural gas vehicles and should be considered in an inventory study.

Farms are a suspected source of dissemination of antibiotic resistance genes (ARGs) to the atmosphere, but their contribution remains poorly quantified. This study investigated the concentrations, emission rates, and particle size distributions of ARGs in air around a dairy farm and swine farm, as well as in farm wastewater and soil as potential sources, during a yearlong sampling campaign. Analysis targeted genes corresponding to a cross-section of antibiotic classes used in human and veterinary medicine, along with 16S rRNA and intI1 as indicators of total bacterial load and anthropogenic sources of ARGs, respectively. Two approaches were demonstrated for estimating emissions to account for the physical configurations of the farms. A custom sampler that collected size-resolved aerosol particles at a flow rate of 2.25 L/min only when the wind originated from the direction of interest was used to collect aerosol particles near potential sources. At the dairy and swine farms, bla_CTX-M1 concentrations varied significantly by sampling location, averaging 10² gene copies per cubic meter (gc m^–3) across seasons and peaking at 10⁴ gc m^–3 during the summer sampling period. At the swine farm, maximum concentrations reached 10⁵ gc m^–3 for intI1, ermF, and qnrA near the buildings’ exhaust fans. Emission rates reached ∼ 10⁵ gc s^–1 for some ARGs, including bla_CTX-M1, and 10⁶ gc s^–1 for intI1. ARGs were predominantly associated with coarse particles (>5 μm) near emission sources and were also present in fine (<1 μm) and accumulation (1–5 μm) mode particles near the source and at downwind locations, indicating potential for inhalation exposure and long-range transport.

An observational study reveals insights into sources, emissions, and transport of antibiotic resistance genes in the atmosphere around swine and dairy farms.

The widespread use of face masks on a global scale calls for a comprehensive evaluation of the associated chemical exposure. However, researchers mainly focus on the chemicals directly emitted from masks, with no consideration given to their possible transformation chemistry and the associated potential health impacts during the use of masks. Here, via mask wearing experiments (by volunteers), ambient air-mask interaction experiments, and ozone-mask reaction experiments, we find that the chemical exposure will be substantially changed during mask wearing due to gas-mask multiphase reactions with ambient ozone. In particular, this ozone oxidation chemistry leads to the formation of gaseous carbonyl compounds and a surface-bound organophosphate ester tris(2,4-di-tert-butylphenyl) phosphate (TDtBPP, an emerging contaminant), with concentrations up to five times higher than those present in unused masks. While exposure assessments indicate that the health risks posed by gaseous carbonyl compounds and surface-bound organophosphate esters are likely to be minor, the present work emphasizes the importance of considering the dynamic evolution of mask-related chemicals when evaluating the overall chemical exposure related to face mask usage.

New particle formation (NPF) is a major source of atmospheric aerosol particles, significantly influencing particle number concentrations in urban environments. High condensation and coagulation sinks at highly trafficked roadside sites should suppress NPF due to the low survival probability of clusters and new particles, however, observations show that roadside NPF is frequent and intense. Here, we investigate NPF at an urban background and roadside site in Central Europe using simultaneous measurements of sulfuric acid, amines, highly oxygenated organic molecules (HOMs), and particle number size distributions. We demonstrate that sulfuric acid and amines, particularly traffic-derived C₂-amines, are the primary participants in particle formation. C₂-amine concentrations at the roadside are enhanced by over a factor of 4 relative to the background, overcoming the effect of enhanced coagulation and condensation sinks. Using machine learning we identify a further but uncertain enhancing role of HOMs. These findings reveal the critical role of traffic emissions in urban NPF.

Traffic is a source of amines which enhance the formation rates of new particles.

Far ultraviolet-C (UVC) light, especially germicidal UV light at 222 nm (GUV₂₂₂), has received considerable attention for its potential to deactivate airborne pathogens indoors and prevent the spread of infectious disease. However, GUV₂₂₂ also generates ozone (O₃), posing human health risks and initiating additional photochemistry that may degrade indoor air quality. Air cleaners present an opportunity to counteract the drawbacks of GUV₂₂₂ by removing harmful byproducts; however, to our knowledge, this has never been demonstrated. Here, we conduct laboratory experiments in a 7.5 m³ Teflon chamber using two commercially available air cleaners─a manganese-oxide-catalyst “ozone cleaner” and an activated-carbon-HEPA “volatile organic compound (VOC) + particulate matter (PM) cleaner”─each simultaneously with a GUV₂₂₂ lamp. We show that both cleaner types remove a wide range of key pollutants, including O₃, NO₂, formaldehyde, VOCs, and particles. Application of chamber results to a photochemical model simulating chemistry in a 150 m³ room suggests that a single cleaner can achieve modest reductions in O₃ levels and substantial reductions in secondary pollutant levels within typical indoor environments. These results indicate that indoor air pollutants from GUV₂₂₂ can be mitigated through the use of air cleaning technology, thereby improving indoor air quality while maximizing the potential benefit of germicidal UV for human health.

Human exposure to radon and radon progenies can lead to the development of respiratory diseases, including lung cancer. Accurate predictive models are needed to better understand the effects of inhaled radionuclides on human health. This study aimed at developing a theoretical/analytical approach to simultaneously predict time-dependent changes in the distributions of radon and radon progenies in the air, in aerosols, and on surfaces. Various indoor, aerosol, and radiological processes were combined to develop the approach. The developed approach conserves the total number of nuclides, but it neither requires iteration nor assumes a steady state or radioactive equilibrium. We verified the theoretical/analytical approach by comparing its accuracy to the analytical and steady-state solutions of kinetic equations for the behavior of radon and radon progenies, as well as the numerical analysis of the equations and measurements for the indoor concentrations of the radionuclides. The results of this study can provide useful insights that can enhance our understanding of the behavior of radon and radon progenies in indoor environments with limited ventilation (e.g., underground facilities) and facilitate radiation-induced health risk assessment in areas where precautions are warranted because of the potential for long-term exposure to radon.

Radon and radon progenies are potential lung carcinogens. This study presents simple approaches to better understand the behavior of radon and radon progenies in indoor environments.

Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models are the only models able to capture this diversity in particle physiochemical properties, and these models are computationally expensive. As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC. GLAD implements a graph-network-based simulator (GNS), a machine learning framework that has been used to emulate particle-based fluid dynamics simulations. In GLAD, each particle is represented as a node in a graph, and the evolution of the particle population over time is simulated through learned message passing. We demonstrate our GNS approach on a simple aerosol system that includes the condensation of sulfuric acid onto particles composed of sulfate, black carbon, organic carbon, and water. A graph with particles as nodes is constructed, and a graph neural network (GNN) is then trained by using the model output from PartMC-MOSAIC. The trained GNN can then be used for simulating and predicting aerosol dynamics over time. Results demonstrate the framework’s ability to accurately learn chemical dynamics and generalize across different scenarios, achieving efficient training and prediction times. We evaluate the performance across three scenarios, highlighting the framework’s robustness and adaptability in modeling aerosol microphysics and chemistry.

Globally distributed measurements of the diurnal variation of fine particulate matter (PM_2.5) reveal a remarkable overall consistency with similar bimodal patterns and some regional variation, neither of which is well understood. We interpret these observations using the GEOS-Chem global model of atmospheric composition in its high-performance configuration (GCHP) at fine resolution of C180 (∼50 km). The base simulation overestimates the PM_2.5 accumulation overnight, leading to excessive diurnal amplitude and earlier PM_2.5 morning peaks than observations. These biases are reduced by applying sector- and species-wise diurnal scaling factors on anthropogenic emissions, by resolving the aerosol subgrid vertical gradient within the surface model layer, by applying revised wet deposition, and by revising the mixing coefficient in the boundary layer. Budget analyses indicate that the morning peak of PM_2.5 is likely driven by changes in the aerosol subgrid vertical gradient with fumigation after sunrise, that the concentration decrease until late afternoon is driven by boundary layer mixing and thermodynamic partitioning of a semivolatile aerosol to the gas phase, that the concentration increase during evening is driven by enhanced secondary chemical production and persistent primary anthropogenic emissions, and that the consistently high concentration overnight is driven by the balance between emissions, chemical production, and boundary layer mixing and deposition.

Continuous monitoring systems (CMS) that utilize fixed-point sensors provide high temporal resolution point-in-space measurements of ambient methane concentration. This study introduces a modular framework for optimizing CMS configurations, encompassing sensor density (number of sensors) and near-optimal placement. By introducing a metric called ‘blind time’, this study attempts to capture periods where the network fails to make detections that could satisfy the regulatory requirement of quantifying emissions every 12 h. This framework is then applied to 124 operational oil and gas production facilities with a wide variety of site characteristics and meteorological conditions. This study determines a representative blind time for near-optimum CMS configurations for operational facilities and then investigates the impact of different sensor network densities on the performance of the CMS. The results demonstrate that 3-sensor networks, when placed in near-optimum arrangements, can achieve blind time of less than 10% and a mean time to detection of approximately 82 min.

Cannabis electronic cigarette (CEC) products, used to “vape” or aerosolize cannabinoids and their mixtures, have proliferated in recent years and are emerging sources of indoor air pollution. This work characterizes the chemical composition and emissions of inhalable aerosol from CEC vaping of the popular synthetic cannabinoids Δ8- and Δ10-tetrahydrocannabinol (Δ8-THC and Δ10-THC, respectively). Commercial Δ8-THC and Δ10-THC distillates were found to have variable purity, with the Δ10-THC distillate comprising roughly one-third of Δ8-THC. The addition of a commercial terpene oil mixture (rich in d-limonene, β-caryophyllene, β-myrcene, and others) at 0%, 7.5%, and 15% by mass to the Δ8-THC and Δ10-THC distillates significantly increases emissions of carbonyls up to 9-fold, but not those of cannabinoid oxidation products. On average, 6 ± 1 mg of aerosol was produced per puff, which did not significantly vary with added terpenes. Molecular analysis confirmed that a majority of the carbonyl products originate from the chemical oxidation of terpenes during vaping. The most abundantly observed carbonyls were acetone, acetaldehyde, propionaldehyde, and terpene-derived carbonyls. We concluded that high terpene content in CEC products gives rise to more carbonyl emissions in the aerosol due to terpene oxidation, which has adverse implications for inhalation toxicology in an indoor environment.