ACS ES&T Air最新文献

The U.S. EPA’s Community Multiscale Air Quality (CMAQ)-adjoint model is used to map monetized health benefits (defined here as benefits of reduced mortality from chronic PM_2.5 exposure) in the form of benefits per ton (of emissions reduced) for the U.S. and Canada for NOx, SO₂, ammonia, and primary PM_2.5 emissions. The adjoint model provides benefits per ton (BPTs) that are location-specific and applicable to various sectors. BPTs show significant variability across locations, such that only 20% of primary PM_2.5 emissions in each country makes up more than half of its burden. The greatest benefits in terms of BPTs are for primary PM_2.5 reductions, followed by ammonia. Seasonal differences in benefits vary by pollutant: while PM_2.5 benefits remain high across seasons, BPTs for reducing ammonia are much higher in the winter due to the increased ammonium nitrate formation efficiency. Based on our location-specific BPTs, we estimate a total of 91,000 U.S. premature mortalities attributable to natural and anthropogenic emissions.

Due to the spatiotemporal variabilities in benefit per ton of emission reductions, reducing 20% of the primary emissions would result in over half the societal health benefits in both the U.S. and Canada.

The inhalation of fine particulate matter (PM_2.5) is a major contributor to adverse health effects from air pollution worldwide. An important toxicity pathway is thought to follow oxidative stress from the formation of exogenous reactive oxygen species (ROS) in the body, a proxy of which is oxidative potential (OP). As redox-active transition metals and organic species are important drivers of OP in urban environments, we investigate how seasonal changes in emission sources, aerosol chemical composition, acidity, and metal dissolution influence OP dynamics. Using a kinetic model of the lung redox chemistry, we predicted ROS (O₂ ^•-, H₂O₂, ^•OH) formation with input parameters comprising the ambient concentrations of PM_2.5, water-soluble Fe and Cu, secondary organic matter, nitrogen dioxide, and ozone across two years and two urban sites in Canada. Particulate species were the largest contributors to ROS production. Soluble Fe and Cu had their highest and lowest values in summer and winter, and changes in Fe solubility were closely linked to seasonal variations in chemical aging, the acidity of aerosol, and organic ligand levels. The results indicate three conditions that influence OP across various seasons: (a) low aerosol pH and high organic ligand levels leading to the highest OP in summer, (b) opposite trends leading to the lowest OP in winter, and (c) intermediate conditions corresponding to moderate OP in spring and fall. This study highlights how atmospheric chemical aging modifies the oxidative burden of urban air pollutants, resulting in a seasonal cycle with a potential effect on population health.

The inhalation of fine particulate matter (PM_2.5) is a major contributor to adverse health effects from air pollution worldwide. An important toxicity pathway is thought to follow oxidative stress from the formation of exogenous reactive oxygen species (ROS) in the body, a proxy of which is oxidative potential (OP). As redox-active transition metals and organic species are important drivers of OP in urban environments, we investigate how seasonal changes in emission sources, aerosol chemical composition, acidity, and metal dissolution influence OP dynamics. Using a kinetic model of the lung redox chemistry, we predicted ROS (O₂^•–, H₂O₂, ^•OH) formation with input parameters comprising the ambient concentrations of PM_2.5, water-soluble Fe and Cu, secondary organic matter, nitrogen dioxide, and ozone across two years and two urban sites in Canada. Particulate species were the largest contributors to ROS production. Soluble Fe and Cu had their highest and lowest values in summer and winter, and changes in Fe solubility were closely linked to seasonal variations in chemical aging, the acidity of aerosol, and organic ligand levels. The results indicate three conditions that influence OP across various seasons: (a) low aerosol pH and high organic ligand levels leading to the highest OP in summer, (b) opposite trends leading to the lowest OP in winter, and (c) intermediate conditions corresponding to moderate OP in spring and fall. This study highlights how atmospheric chemical aging modifies the oxidative burden of urban air pollutants, resulting in a seasonal cycle with a potential effect on population health.

Using field measurements and model simulations, this work investigates if seasonal changes in emission sources, aerosol acidity and composition, and metal dissolution influence the oxidative potential of urban air.

Nitrous acid (HONO) is a key precursor of the hydroxyl radical (•OH), playing an important role in atmospheric oxidation capacity. However, unknown sources of HONO (P_unknown) are frequently reported and the potential sources are controversial. Here, we explored P_unknown during COVID-19 in different seasons and epidemic control phases in Shanghai by eXtreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP) for the first time. They demonstrated that the decrease of anthropogenic activity would inhibit secondary formation of HONO, as epidemic control policies turned strict. The explainable machine learning revealed that nitrogen dioxide (NO₂) had significant impacts on the P_unknown during spring 2020 (P1), where P_unknown could be fully explained by including light-induced heterogeneous conversion of NO₂ on ground, building, and aerosol surfaces. With the untightening of epidemic control in spring 2021 (P3), the HONO budget came to balance after further addition of the photolysis of particulate nitrate (NO₃^–) and soil HONO emission. As for P2 (summer), P_unknown decreased by 54% with all new sources added. These results provide new insights into HONO chemistry in response to reduced anthropogenic emissions, improving the predictions of atmospheric oxidation capacity.

We conducted thermal desorption measurements and machine learning analysis to investigate the volatility and precursors of ambient oxygenated organic aerosols (OOA), with a focus on the link between them, in a variety of urban and marine locations. We found that the OOA species measured by an iodide-based Chemical Ionization Mass Spectrometer equipped with a Filter Inlet for Gases and AEROsol (FIGAERO-CIMS) accounted for 16 ± 13% of OA in those urban and marine locations and represented mostly the secondary and moderate-volatility portion of ambient OA. On average, 25.1% in species number and 26.8% in mass of the OOA species detected by the FIGAERO-CIMS in a winter campaign at an urban site in Wuhan, a megacity in central China, might be attributed to thermal decomposition fragments. Our results show that the volatility and precursor of ambient OOA differed systematically according to location, season, and pollution level. The OOA in the ocean atmosphere was characterized by high fractions of extremely low volatility organic compounds (ELVOC) and aliphatic species, while the inland urban OOA was characterized by aromatic species and fell primarily into the low volatility organic compounds (LVOCs) and semivolatile organic compounds (SVOCs) range. The volatilities of OOA in the inland urban atmosphere in summer, pollution days, and daytime were lower than those in winter, clean days, and nighttime. When the PM episode developed from clean to particle growth and then to pollution period, the OOA species shifted from Low-Mw OOA species to Median-Mw OOA species and then to highly nonvolatile species. The study of ambient OOA volatility in this work also provides important data for future closure studies of SOA formation and its precursors.

Adjoint modeling, using U.S. EPA's Community Multiscale Air Quality (CMAQ), has been performed to provide location-specific monetized health benefits from the controls of primary PM_2.5 and PM_2.5 precursors (NO _x , SO₂, and NH₃) across North America. Source-to-health benefit relationships are quantified using a benefit-per-ton (BPT) metric, accounting for the impacts on premature mortality due to long-term exposure to fine particulate matter. In the base analysis, the approach used a 12 km resolution, four 2-week episodes chosen to capture annual responses, emissions for 2016, and the Global Exposure Mortality Model (GEMM) to link exposures to premature mortality. Here, we investigate the impacts those choices have on results using a range of sensitivity analyses. The choice of four representative episodes led to relatively little bias and error. Finer model resolution, investigated by comparing 36, 12, 4, and 1 km simulations over two urban areas, tended to increase BPT estimates, though the impact was inconsistent between different regions. While BPTs and burden estimates were consistent across resolutions over New York City, they sharply increased for Los Angeles, particularly for NOx and ammonia, leading to 90% increase in burden estimates at 1 km resolution. We find that, for primary PM_2.5 emissions, better resolved population distribution is the main contributing factor to higher BPTs, but for secondary precursor emissions (ammonia and NOx), higher model resolution that avoids dilution in coarser grids is more important. Changing emissions from 2016 to 2001 and 2028 resulted in fairly consistent primary PM_2.5 BPTs but impacted the BPTs for NOx and ammonia more significantly due to changes in SO₂ emissions. We found that BPTs tend to stabilize, as emission changes in 2028 lead to a lower deviation from 2016 BPTs compared to changes from the 2001 episode. The role of the epidemiological model also led to relatively modest uncertainties, 15-30% depending on the species, even when different shapes of concentration-response functions were employed. We found the impact of the choice of CRF to be larger or comparable in size to the reported epidemiological model uncertainties for log-linear CRFs. The adjoining approach proved robust to modeling choices in providing BPT estimates that are highly granular across locations and emitted species.

Adjoint modeling, using U.S. EPA’s Community Multiscale Air Quality (CMAQ), has been performed to provide location-specific monetized health benefits from the controls of primary PM_2.5 and PM_2.5 precursors (NO_x, SO₂, and NH₃) across North America. Source-to-health benefit relationships are quantified using a benefit-per-ton (BPT) metric, accounting for the impacts on premature mortality due to long-term exposure to fine particulate matter. In the base analysis, the approach used a 12 km resolution, four 2-week episodes chosen to capture annual responses, emissions for 2016, and the Global Exposure Mortality Model (GEMM) to link exposures to premature mortality. Here, we investigate the impacts those choices have on results using a range of sensitivity analyses. The choice of four representative episodes led to relatively little bias and error. Finer model resolution, investigated by comparing 36, 12, 4, and 1 km simulations over two urban areas, tended to increase BPT estimates, though the impact was inconsistent between different regions. While BPTs and burden estimates were consistent across resolutions over New York City, they sharply increased for Los Angeles, particularly for NOx and ammonia, leading to 90% increase in burden estimates at 1 km resolution. We find that, for primary PM_2.5 emissions, better resolved population distribution is the main contributing factor to higher BPTs, but for secondary precursor emissions (ammonia and NOx), higher model resolution that avoids dilution in coarser grids is more important. Changing emissions from 2016 to 2001 and 2028 resulted in fairly consistent primary PM_2.5 BPTs but impacted the BPTs for NOx and ammonia more significantly due to changes in SO₂ emissions. We found that BPTs tend to stabilize, as emission changes in 2028 lead to a lower deviation from 2016 BPTs compared to changes from the 2001 episode. The role of the epidemiological model also led to relatively modest uncertainties, 15–30% depending on the species, even when different shapes of concentration–response functions were employed. We found the impact of the choice of CRF to be larger or comparable in size to the reported epidemiological model uncertainties for log–linear CRFs. The adjoining approach proved robust to modeling choices in providing BPT estimates that are highly granular across locations and emitted species.

While modeling study design and assumptions give rise to uncertainties to varying degrees, location-specific benefits-per-ton (BPTs) from full-complexity model simulations remain robust to these inevitable uncertainties.

Wildfire is one of the main sources of PM_2.5 (particulate matter with aerodynamic diameter < 2.5 μm) in the Alaskan summer. The complexity in wildfire smokes, as well as limited coverage of ground measurements, poses a big challenge to estimate surface PM_2.5 during wildfire season in Alaska. Here we aim at proposing a quick and direct method to estimate surface PM_2.5 over Alaska, especially in places exposed to strong wildfire events with limited measurements. We compare the AOD-surface PM_2.5 conversion factor (η = PM_2.5/AOD; AOD, aerosol optical depth) from the chemical transport model GEOS-Chem (η_GC) and from observations (η_obs). We show that η_GC is biased high compared to η_obs under smoky conditions, largely because GEOS-Chem assigns the majority of AOD (67%) within the planetary boundary layer (PBL) when AOD > 1, inconsistent with satellite retrievals from CALIOP. The overestimation in η_GC can be to some extent improved by increasing the injection height of wildfire emissions. We constructed a piecewise function for η_obs across different AOD ranges based on VIIRS-SNPP AOD and PurpleAir surface PM_2.5 measurements over Alaska in the 2019 summer and then applied it on VIIRS AOD to derive daily surface PM_2.5 over continental Alaska in the 2021 and 2022 summers. The derived satellite PM_2.5 shows a good agreement with corrected PurpleAir PM_2.5 in Alaska during the 2021 and 2022 summers, suggesting that aerosol vertical distribution likely represents the largest uncertainty in converting AOD to surface PM_2.5 concentrations. This piecewise function, η'_obs, shows the capability of providing an observation-based, quick and direct estimation of daily surface PM_2.5 over the whole of Alaska during wildfires, without running a 3-D model in real time.

Wildfire is one of the main sources of PM_2.5 (particulate matter with aerodynamic diameter < 2.5 μm) in the Alaskan summer. The complexity in wildfire smokes, as well as limited coverage of ground measurements, poses a big challenge to estimate surface PM_2.5 during wildfire season in Alaska. Here we aim at proposing a quick and direct method to estimate surface PM_2.5 over Alaska, especially in places exposed to strong wildfire events with limited measurements. We compare the AOD–surface PM_2.5 conversion factor (η = PM_2.5/AOD; AOD, aerosol optical depth) from the chemical transport model GEOS-Chem (η_GC) and from observations (η_obs). We show that η_GC is biased high compared to η_obs under smoky conditions, largely because GEOS-Chem assigns the majority of AOD (67%) within the planetary boundary layer (PBL) when AOD > 1, inconsistent with satellite retrievals from CALIOP. The overestimation in η_GC can be to some extent improved by increasing the injection height of wildfire emissions. We constructed a piecewise function for η_obs across different AOD ranges based on VIIRS-SNPP AOD and PurpleAir surface PM_2.5 measurements over Alaska in the 2019 summer and then applied it on VIIRS AOD to derive daily surface PM_2.5 over continental Alaska in the 2021 and 2022 summers. The derived satellite PM_2.5 shows a good agreement with corrected PurpleAir PM_2.5 in Alaska during the 2021 and 2022 summers, suggesting that aerosol vertical distribution likely represents the largest uncertainty in converting AOD to surface PM_2.5 concentrations. This piecewise function, η′_obs, shows the capability of providing an observation-based, quick and direct estimation of daily surface PM_2.5 over the whole of Alaska during wildfires, without running a 3-D model in real time.

We investigated the light-absorption properties of brown carbon (BrC) as part of the Georgia Wildland-Fire Simulation Experiment. We constructed fuel beds representative of three ecoregions in the Southeastern U.S. and varied the fuel-bed moisture content to simulate either prescribed fires or drought-induced wildfires. Based on decreasing fire radiative energy normalized by fuel-bed mass loading (FRE_norm), the combustion conditions were grouped into wildfire (Wild), prescribed fire (Rx), and wildfire involving duff ignition (WildDuff). The emitted BrC ranged from weakly absorbing (WildDuff) to moderately absorbing (Rx and Wild) with the imaginary part of the refractive index (k) values that were well-correlated with FRE_norm. We apportioned the BrC into water-soluble (WSBrC) and water-insoluble (WIBrC). Approximately half of the WSBrC molecules detected using electrospray-ionization mass spectrometry were potential chromophores. Nevertheless, k of WSBrC was an order of magnitude smaller than k of WIBrC. Furthermore, k of WIBrC was well-correlated with FRE_norm while k of WSBrC was not, suggesting different formation pathways between WIBrC and WSBrC. Overall, the results signify the importance of combustion conditions in determining BrC light-absorption properties and indicate that variables in wildland fires, such as moisture content and fuel-bed composition, impact BrC light-absorption properties to the extent that they influence combustion conditions.