ACS ES&T Air最新文献

As the largest Hg reservoir in terrestrial ecosystems, forests provide valuable insights into atmospheric Hg levels through observations of mercury deposition. In this study, we conducted a one-year observation of Hg deposition, by both throughfall and litterfall, in a forest located in Chongqing, an industrial hub in southwestern China, in 2021. We compared the results with observations made at the same sampling site in 2010–2011. The results showed that there were significant decreases in both Hg concentrations (from 115 μg·kg^–1 to 80.2 μg·kg^–1 for litterfall and from 68.4 ng·L^–1 to 4.01 ± 2.09 ng·L^–1 for throughfall) and Hg deposition fluxes (from 89.8 μg·m^–2·yr^–1 to 19.9 μg·m^–2·yr^–1 for total deposition, decreasing by 77.8%) in the forest from 2010 to 2021. The decrease in deposition might result from the synergistic effect of reduction in both anthropogenic (inventory-listed or nonquantitative) and re-emission sources. Over the decade since the initiation of the Minamata Convention on Mercury, atmospheric Hg levels have decreased significantly, mainly due to the co-benefits of conventional air pollutant control, indicating the effectiveness of China’s Hg pollution control policies.

Wildland fire (i.e., prescribed fire and wildfire) smoke exposure is an emerging public health threat, in part due to climate change. Previous research has demonstrated disparities in ambient fine particulate matter (PM_2.5) exposure, with Black people, among others, exposed to higher concentrations; yet, it remains unclear how wildland fire smoke may contribute to additional disproportionate exposure. Here, we investigate the additional PM_2.5 burden contributed by wildland fire smoke in the contiguous United States by race and ethnicity, urbanicity, median household income, and language spoken at home, using modeled total, non-fire, and fire PM_2.5 concentrations from 2007 to 2018. Wildland fires contributed 7% to 14% of total population weighted PM_2.5 concentrations annually, while non-fire PM_2.5 concentrations declined by 24% over the study period. Wildland fires contributed to greater PM_2.5 exposure for Black and American Indian or Alaska Native people and those who live in nonurban areas. Disproportionate mean non-fire PM_2.5 concentrations for Black people (9.1 μg/m³, compared to 8.7 μg/m³ overall) were estimated to be further exacerbated by additional disproportionate concentrations from fires (1.0 μg/m³, compared to 0.9 μg/m³ overall). These results can inform equitable strategies by public health agencies and air quality managers to reduce smoke exposure in the United States.

This study investigates the contribution of wildland fire smoke to total fine particulate matter exposure for different population groups to inform equitable exposure reduction strategies in the United States.

This study examines greenhouse gas (GHG) and criteria air pollutant (CAP) emissions trade-offs for renewable diesel across 12 scenarios, involving different biochemical conversion designs, biorefinery scales, and feedstocks. A conventional design uses lignin for on-site heat and power, which exports excess power to the grid. An alternative design exports lignin pellets, offsetting other pellet production methods but requiring grid electricity to meet biorefinery power demands. Net emissions were quantified in Iowa and Georgia, selected considering feedstock availability, coproduct displacement, and regional power grids, assuming grid-exported power avoids coal or low-carbon electricity. Results for the conventional design remained consistent across the electricity displacement scenarios. When comparing lignin utilization strategies, pelletizing lignin reduces sulfur dioxide, carbon monoxide, nitrogen oxides, and volatile organic compounds (net emissions −0.66 mg MJ^–1, 25 mg MJ^–1, 25 mg MJ^–1, 7.8 mg MJ^–1, respectively). However, lignin pelletization increases net particulate matter (fine and coarse) and ammonia (net emissions of 4.7 mg MJ^–1, 13 mg MJ^–1, and 0.26 mg MJ^–1, respectively), alongside indirect GHG emissions due to grid electricity dependence. Additionally, processing 2000 tonnes corn stover daily minimizes emissions for both designs. Only lignin pelletization with renewable electricity and additional particulate matter and ammonia controls reduces all CAP and GHG emissions simultaneously.

Optimizing biorefinery design for scale, feedstock, and lignin management can achieve simultaneous reductions in greenhouse gas and air pollutant emissions from renewable diesel production.

Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, aqueous chemistry, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of 4 in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). In addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol number size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model.

Nitrogen dioxide (NO₂) pollution is associated with adverse health effects, but its spatial variability between ground monitors is poorly characterized. NO₂ column observations from the Tropospheric Monitoring Instrument (TROPOMI) have unprecedented spatial resolution and high accuracy over the globe. Land-use regression (LUR) models predict surface-level NO₂ with relevance for epidemiological and environmental justice studies. We use TROPOMI NO₂ columns in a land use regression (LUR) model to improve surface NO₂ concentration estimates over the United States. The TROPOMI LUR predictions have improved correlation with ground monitors (Adj. R ² = 0.72) and bias (Mean Bias, MB = 14.2%) compared with an existing LUR using less granular NO₂ data from a legacy satellite instrument (Adj. R ² = 0.54 and MB = 49%; for North America). Removing TROPOMI NO₂ from the LUR decreased R ² by 29.1%, 8.1 times the impact of removing road system information. These findings reveal that novel Earth observing satellites can enhance surface NO₂ surveillance by capturing pollution variation between monitors without relying heavily on other data sources.

Nitrogen dioxide (NO₂) pollution is associated with adverse health effects, but its spatial variability between ground monitors is poorly characterized. NO₂ column observations from the Tropospheric Monitoring Instrument (TROPOMI) have unprecedented spatial resolution and high accuracy over the globe. Land-use regression (LUR) models predict surface-level NO₂ with relevance for epidemiological and environmental justice studies. We use TROPOMI NO₂ columns in a land use regression (LUR) model to improve surface NO₂ concentration estimates over the United States. The TROPOMI LUR predictions have improved correlation with ground monitors (Adj. R² = 0.72) and bias (Mean Bias, MB = 14.2%) compared with an existing LUR using less granular NO₂ data from a legacy satellite instrument (Adj. R² = 0.54 and MB = 49%; for North America). Removing TROPOMI NO₂ from the LUR decreased R² by 29.1%, 8.1 times the impact of removing road system information. These findings reveal that novel Earth observing satellites can enhance surface NO₂ surveillance by capturing pollution variation between monitors without relying heavily on other data sources.

Improved satellite air pollution observations from TROPOMI further support that surface NO₂ can be estimated without heavy reliance on other data.

The advent of large-scale mobile monitoring using fast-response instruments has enabled hyperlocal mapping (≤100 m) of traffic-related air pollution (TRAP), with important implications for air quality management. However, most related studies have been confined within small areas due to the high cost and labor intensity. This study pioneers a cost-effective TRAP mapping method by incorporating large-scale mobile monitoring and land-use machine learning (LUML). Here, over 4.6 million 1 Hz high-frequency measurements (∼1300 h) were collected on a part of major roadways in the Chinese megacity of Shenzhen. Unmeasured locations were estimated by LUML models to reduce measurement costs and labor intensity. Various ML algorithms and varying spatial aggregation segment lengths were incorporated to optimize the model performance. Hyperlocal maps of NO, NO₂, and PM_2.5 were predicted across the entire road network covering over 1700 km². Based on our results, LU-RF (random forest) for mapping NO and NO₂ and LU-GBM (Gradient Boosting Machine) for mapping PM_2.5, demonstrated superior performance. Deep learning models, in contrast, did not yield comparable results. Finer partitioning of road segments (≤100 m) improved NO prediction performance, but worsened that for NO₂ and PM_2.5. By deployment of optimal ML algorithms and segment lengths, the TRAP mapping accuracy increased by 20–80% compared to conventional land-use regression models. This study provides a promising and cost-effective approach to hyperlocal air pollution mapping and management in cities worldwide.

Chemical transport models are used for federal compliance demonstrations when areas are out of attainment, but there is no guidance for choosing a chemical mechanism. With the 2024 change of the annual PM_2.5 standard and the prevalence of multiday wintertime inversion episodes in the western U.S., understanding the wintertime performance of chemical transport models is important. This study explores the impact of chemical mechanism choice on the Community Multiscale Air Quality (CMAQ) model performance for PM_2.5 and implications for attainment demonstration in inversion-prone areas in the western United States. Total and speciated PM_2.5 observations were used to evaluate wintertime CMAQ simulations using four chemical mechanisms. The study evaluated intermechanism differences in total and secondary PM_2.5 and the impact of meteorology at sites with observed multiday temperature inversions. Model performance for total PM_2.5 was similar across chemical mechanisms, but intermechanism differences for total and secondary PM_2.5 were exacerbated during inversion periods, suggesting that modeled chemistry contributes to the model bias. Results suggest that nitrate, ammonium, and organic carbon are secondary species for which model results do not agree or perform to standard evaluation metrics in scientific literature. These findings show a need for mechanistic investigations of the causes of these differences.

Chemical transport models are used for federal compliance demonstrations when areas are out of attainment, but there is no guidance for choosing a chemical mechanism. With the 2024 change of the annual PM_2.5 standard and the prevalence of multiday wintertime inversion episodes in the western U.S., understanding the wintertime performance of chemical transport models is important. This study explores the impact of chemical mechanism choice on the Community Multiscale Air Quality (CMAQ) model performance for PM_2.5 and implications for attainment demonstration in inversion-prone areas in the western United States. Total and speciated PM_2.5 observations were used to evaluate wintertime CMAQ simulations using four chemical mechanisms. The study evaluated intermechanism differences in total and secondary PM_2.5 and the impact of meteorology at sites with observed multiday temperature inversions. Model performance for total PM_2.5 was similar across chemical mechanisms, but intermechanism differences for total and secondary PM_2.5 were exacerbated during inversion periods, suggesting that modeled chemistry contributes to the model bias. Results suggest that nitrate, ammonium, and organic carbon are secondary species for which model results do not agree or perform to standard evaluation metrics in scientific literature. These findings show a need for mechanistic investigations of the causes of these differences.

This study compared chemical mechanisms in the CMAQ model. Modeled secondary aerosol significantly diverged during inversions. This is concerning, as CMAQ is used for demonstrations of emission controls and the U.S. EPA does not issue guidance on chemical mechanism choice.

With the increase in acres burned from wildfire over the last few decades, wildfire smoke is an increasing global public health threat. To date, wildfire smoke research, risk communication, and public health action has focused on short-term (or daily) smoke exposures. However, the patterns of wildfire smoke exposure are transitioning to include longer duration and repeated exposures occurring within and across years. Epidemiologic and experimental studies represent important lines of evidence that have informed risk communication and public health actions for short-term smoke exposures; however, they have yet to provide the science needed to refine public health approaches to include other dynamic exposure durations such as repeated, episodic, or cumulative. This commentary provides an overview of methodological approaches used and recent findings from epidemiologic and experimental studies that examined longer duration, repeated smoke exposures. Based on the current science, we recommend that future epidemiologic and experimental studies of wildfire smoke examine multiple exposure metrics to capture the duration, frequency, and intensity of exposures. Such studies would improve the science produced to best support the needs of the public as we strive to further protect public health in a world projected to have more smoke.

Assessing the health implications of longer duration wildland fire smoke exposures requires that epidemiologic and experimental studies embark on developing and testing new approaches to account for these dynamic exposures.