ACS ES&T Air最新文献

The understanding of the mechanisms of synergistic bioaerosol disinfection using 222 nm far-UVC and negative air ions is limited. In this study, we employed a fabricated aerosol chamber to investigate the synergistic disinfection effects of these two technologies and their impact on oxidative stress responses in airborne Staphylococcus epidermidis, Salmonella enterica, and Escherichia coli. We measured the log reduction in airborne survival of bacteria, lipid peroxidation, and the activities of antioxidant enzymes and total antioxidants. The results show that bacterial inactivation by the simultaneous operation of both technologies was significantly greater than the sum of the inactivation values by individual treatments, demonstrating a synergistic disinfection effect. The Pearson correlation coefficient indicates a strong correlation between airborne bacterial survival and lipid peroxidation (r ≥ 0.83). Furthermore, 222 nm far-UVC was found to be able to inactivate the antioxidant defense in airborne bacteria. Our results together suggest that the simultaneous operation of 222 nm far-UVC and negative air ions induces oxidative stress while deactivating antioxidant defense, contributing to the observed synergistic disinfection effect. Findings from the current study contribute to the mechanistic understanding of the synergistic effect by 222 nm far-UVC and negative air ions in bioaerosol disinfection, offering a new research opportunity for system design in high-efficiency air disinfection.

Air quality management benefits from an in-depth understanding of the emissions associated with, and composition of, local PM_2.5 concentrations. Here, we investigate the changing role of biomass burning emissions to North American PM_2.5 exposure by combining multiple satellite-, ground-, and simulation-based data sets biweekly at a 0.01° × 0.01° resolution from 2000 to 2022. We also developed a Buffered Leave Cluster Out (BLeCO) method to address autocorrelation and computational cost in cross-validation. Biomass burning emissions contribute an increasingly large fraction to PM_2.5 exposure in the United States and Canada, with national annual population-weighted mean contributions increasing from 0.4 μg/m³ (3-5%) in 2000-2004 to 0.8-0.9 μg/m³ (9-14%) by 2019-2022, led by western North American 2019-2022 annual contributions of 1.4-1.9 μg/m³ (15-27%) and maximum seasonal contributions of 3.3-5.5 μg/m³ (29-49%). Other components such as nonbiomass burning Organic Matter (OM) and nitrate can be regionally as (or more) important, albeit with distinct seasonal variability. The contribution of total OM to PM_2.5 exposure in the United States in 2016-2022 is 42.2%, comparable to all other anthropogenically sourced components combined. Comparison of BLeCO and random 10-fold cross-validation suggests that random 10-fold cross-validation may significantly underrepresent true uncertainty for total PM_2.5 concentrations due to the clustered nature of PM_2.5 ground-based monitoring.

Air quality management benefits from an in-depth understanding of the emissions associated with, and composition of, local PM_2.5 concentrations. Here, we investigate the changing role of biomass burning emissions to North American PM_2.5 exposure by combining multiple satellite-, ground-, and simulation-based data sets biweekly at a 0.01° × 0.01° resolution from 2000 to 2022. We also developed a Buffered Leave Cluster Out (BLeCO) method to address autocorrelation and computational cost in cross-validation. Biomass burning emissions contribute an increasingly large fraction to PM_2.5 exposure in the United States and Canada, with national annual population-weighted mean contributions increasing from 0.4 μg/m³ (3–5%) in 2000–2004 to 0.8–0.9 μg/m³ (9–14%) by 2019–2022, led by western North American 2019–2022 annual contributions of 1.4–1.9 μg/m³ (15–27%) and maximum seasonal contributions of 3.3–5.5 μg/m³ (29–49%). Other components such as nonbiomass burning Organic Matter (OM) and nitrate can be regionally as (or more) important, albeit with distinct seasonal variability. The contribution of total OM to PM_2.5 exposure in the United States in 2016–2022 is 42.2%, comparable to all other anthropogenically sourced components combined. Comparison of BLeCO and random 10-fold cross-validation suggests that random 10-fold cross-validation may significantly underrepresent true uncertainty for total PM_2.5 concentrations due to the clustered nature of PM_2.5 ground-based monitoring.

Microplastics (MP) are ubiquitous in the environment; their atmospheric relevance is being increasingly recognized. Because of their atmospheric concentrations, there is the question of whether MP can act as ice nucleating particles in the atmosphere. This study investigates the immersion freezing activity of lab-prepared MP of four different compositions─low density polyethylene (LDPE), polypropylene (PP), poly(vinyl chloride) (PVC), and poly(ethylene terephthalate) (PET)─using droplet freezing assays. The MP are also exposed to ultraviolet light, ozone, sulfuric acid, and ammonium sulfate to mimic environmental aging of the plastics to elucidate the role that these processes play in the ice nucleating activity of MP. Results show that all studied MP act as immersion nuclei, and aging processes can modify this ice nucleating activity, leading, primarily, to decreases in ice nucleating activity for LDPE, PP, and PET. The ice nucleating activity of PVC generally increased following aging, which we attribute to a cleaning of chemical defects present on the surface of the stock material. Chemical changes were monitored with infrared spectroscopy (ATR-FTIR), and the growth of a peak at 1650–1800 cm^–1 was associated with a decrease in ice nucleating activity while loss of an existing peak in that region was associated with an increase in ice nucleating activity. The studied MP have ice nucleating activities sufficient to be a non-negligible source of ice nucleating particles in the atmosphere if present in sufficiently high concentrations.

Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to nonexistent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors, which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was colocated at the Physics Department, University of Ghana, with a reference grade instrument to evaluate its performance for estimating PM_2.5 pollution accurately at fine scales and the value of these data in identification of local sources and their behavior over time. For this study, 15 weeks of data at hourly resolution with approximately 2500 data pairs were generated and analyzed (June 1, 2023, to September 15, 2023). For this time period a coefficient of determination (r²) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μg m^–3 between the pre local calibration micro air sensor (i.e., out of the box) and the reference-grade instrument. Following currently accepted best practice methods (see, e.g., PAS4023) a domain specific (i.e., local) calibration factor was generated using a multilinear regression model, and when this factor is applied to the micro air sensor data, a reduction, i.e. improvement, in MAE to 1.43 μg m^–3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM_2.5/PM₁₀ ratio scatter and count plots, to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of the box, domain calibrated, and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyperlocalized sources associated with urban areas, such as those found in major African cities.

Research on micro air sensor data for emission source identification is patchy. This study reports PM_2.5 emission sources in an urban setting using relative and calibrated micro air sensor data compared to reference grade data with implications for low-cost air pollution management and control.

Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to nonexistent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors, which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was colocated at the Physics Department, University of Ghana, with a reference grade instrument to evaluate its performance for estimating PM_2.5 pollution accurately at fine scales and the value of these data in identification of local sources and their behavior over time. For this study, 15 weeks of data at hourly resolution with approximately 2500 data pairs were generated and analyzed (June 1, 2023, to September 15, 2023). For this time period a coefficient of determination (r ²) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μg m^-3 between the pre local calibration micro air sensor (i.e., out of the box) and the reference-grade instrument. Following currently accepted best practice methods (see, e.g., PAS4023) a domain specific (i.e., local) calibration factor was generated using a multilinear regression model, and when this factor is applied to the micro air sensor data, a reduction, i.e. improvement, in MAE to 1.43 μg m^-3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM_2.5/PM₁₀ ratio scatter and count plots, to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of the box, domain calibrated, and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyperlocalized sources associated with urban areas, such as those found in major African cities.

The consumption of natural gas (NG) in China has quadrupled over the past decade. However, there is an absence of measurement-based assessment of methane emissions from NG consumption, including those from the urban distribution network. Here, we conducted a mobile survey with concurrent measurements of ambient methane concentrations, carbon dioxide concentrations, and ¹³CH₄ isotopic signatures in the center and suburban areas of Hangzhou, a megacity in East China, from November 2021 to June 2022. We detected 176 leak indications in the 1408 km road covered with 32% attributed to NG leaks based on the source isotopic signature and the methane-to-carbon dioxide ratio derived from the Keeling plot and measured concentrations. We quantified the flux using Weller’s empirical equation, which yielded an emission factor of 115 L·d^–1·km^–1 in Hangzhou. The value ranges from 16 to 314 L·d^–1·km^–1 by accounting for various uncertainty sources. This emission factor falls on the lower end compared to previous studies conducted in North American and European cities. Our findings confirm the effectiveness of mobile surveys in detecting methane emission sources in urban China.

This study investigates air quality in a Toronto community located between an airport and an expressway. A community science approach was adopted for data collection and interpretation, and a partnership was formed between a local neighborhood association, university researchers, the municipal government, and the local airport authority. Community scientists placed low-cost sensors on outdoor balconies and inside homes for 28 weeks between 2020 and 2022, measuring particle number (PN) concentrations of particulate matter (PM) with diameters between 0.5 and 2.5 μm. Indoors, the PN concentrations increased during cooking and other activities. During periods with minimal indoor activities, indoor levels closely followed the outdoor signal. Median indoor/outdoor (IO) ratios varied between 0.4 and 0.87 across sampling months. Median outdoor PN concentrations varied from 1 to 4 #/cm³ and were influenced by local and regional sources. Outdoor PN concentrations were significantly correlated to PM_2.5 and nitrogen dioxide at a downtown reference station; the latter suggests that traffic emissions from the nearby expressway contribute to PN concentrations in the neighborhood. An analysis of outdoor ultrafine particle (UFP) data collected at a single location suggests that the airport is a source of UFP in the neighborhood. Community engagement was enabled through involvement in study design, execution, and knowledge mobilization.

This study investigated the size distributions, oxidative potential (OP_v), and acute ecotoxicity index (TI) of atmospheric organic aerosols during haze and nonhaze periods in a coastal city in China. Results indicated higher OP_v and TI levels during haze periods, with trimodal variations: the highest OP_v peak in the 7.2–10 μm and the highest TI peak in the 0–0.49 μm. For the first time, multilayer perception analysis was applied to predict both OP_v and ecotoxicity, offering enhanced accuracy by capturing synergistic and antagonistic interactions among various chemical components. Lung deposition doses of size-resolved PM inducing OP_v and TI within the human respiratory system were estimated. Findings revealed that the largest particles (7.2–10 μm) predominantly affected the head airways, whereas particles in the 1.5–3.0 μm significantly impacted the pulmonary region. This behavior is attributed to quinones and high-molecular-weight polycyclic aromatic hydrocarbons (HMW-PAHs), which have higher deposition efficiency in the head airways and elevated concentrations in the pulmonary region, respectively. To mitigate health risks associated with these toxicants, efforts should target their size-dependent properties and lung deposition efficiency, considering various health end points. This study underscores the need for size-specific mitigation strategies to effectively address the differential impacts of PM on respiratory health.