Observational Constraints on the Aerosol Optical Depth-Surface PM2.5 Relationship during Alaskan Wildfire Seasons

Wildfire is one of the main sources of PM2.5 (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 PM2.5 during wildfire season in Alaska. Here we aim at proposing a quick and direct method to estimate surface PM2.5 over Alaska, especially in places exposed to strong wildfire events with limited measurements. We compare the AOD-surface PM2.5 conversion factor (η = PM2.5/AOD; AOD, aerosol optical depth) from the chemical transport model GEOS-Chem (ηGC) and from observations (ηobs). We show that ηGC is biased high compared to ηobs 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 ηGC can be to some extent improved by increasing the injection height of wildfire emissions. We constructed a piecewise function for ηobs across different AOD ranges based on VIIRS-SNPP AOD and PurpleAir surface PM2.5 measurements over Alaska in the 2019 summer and then applied it on VIIRS AOD to derive daily surface PM2.5 over continental Alaska in the 2021 and 2022 summers. The derived satellite PM2.5 shows a good agreement with corrected PurpleAir PM2.5 in Alaska during the 2021 and 2022 summers, suggesting that aerosol vertical distribution likely represents the largest uncertainty in converting AOD to surface PM2.5 concentrations. This piecewise function, η'obs, shows the capability of providing an observation-based, quick and direct estimation of daily surface PM2.5 over the whole of Alaska during wildfires, without running a 3-D model in real time.

Trends and drivers of aerosol vertical distribution over China from 2013 to 2020: Insights from integrated observations and modeling

Understanding aerosol vertical distribution is of great importance to climate change and atmospheric chemistry, but there is a dearth of systematical analysis for aerosol vertical distribution amid rapid emission decline after 2013 in China. Here, the GEOS-Chem model and multiple-sourced observations were applied to quantify the changes of aerosol vertical distributions in response to clean air actions. In 2013-2020, the MODIS aerosol optical depth (AOD) presented extensive decreasing trends by -7.9 %/yr to -4.2 %/yr in summer and -6.1 %/yr to -5.8 %/yr in winter in polluted regions. Vertically, the aerosol extinction coefficient (AEC) from CALIPSO decreased by -8.0 %/yr to -5.5 %/yr below ~1 km, but the trends weakened significantly with increasing altitude. Compared with available measurements, the model can reasonably reproduce 2013-2020 trends and seasonality in AOD and vertical AEC. Model simulations confirm that emission reduction was the dominant driver of the 2013-2020 decline in AOD, while the effect of meteorology varied seasonally, with contributions ranging from -2 % to 13 % in summer and -67 % to -2 % in winter. Vertical distributions of emission-driven AEC trends strongly depended on emission reductions, local planetary boundary layer height, and relative humidity. For aerosol components, sulfate accounted for ~50 % of the AOD decline during summer, followed by ammonium and organic aerosol, while in winter the contribution of organic aerosol doubled (24 %-35 %), and nitrate exhibited a weak increasing trend. Chemical production and meteorological conditions (e.g., relative humidity) primarily drove the nitrate contribution, but emission reduction and hygroscopicity were decisive for other components. This work provides an integrated observational and modeling effort to better understand rapid changes in aerosol vertical distribution over China.

Global Health and Climate Effects of Organic Aerosols from Different Sources

The impact of aerosols on human health and climate is well-recognized, yet many studies have only focused on total PM2.5 or changes from anthropogenic activities. This study quantifies the health and climate effects of organic aerosols (OA) from anthropogenic, biomass burning, and biogenic sources. Using two atmospheric chemistry models, CAM-chem and GEOS-Chem, our findings reveal that anthropogenic primary OA (POA) has the highest efficiency for health effects but the lowest for direct radiative effects due to spatial and temporal variations associated with population and surface albedo. The treatment of POA as nonvolatile or semivolatile also influences these efficiencies through different chemical processes. Biogenic OA shows moderate efficiency for health effects and the highest for direct radiative effects but has the lowest efficiency for indirect effects due to the reduced high cloud, caused by stabilized temperature profiles from aerosol-radiation interactions in biogenic OA-rich regions. Biomass burning OA is important for cloud radiative effect changes in remote atmospheres due to its ability to be transported further than other OAs. This study highlights the importance of not only OA characteristics such as toxicity and refractive index but also atmospheric processes such as transport and chemistry in determining health and climate impact efficiencies.

Influence of Air Mass Source Regions on Signatures of Surface-Active Organic Molecules in Size Resolved Atmospheric Aerosol Particles

The physical and chemical properties of atmospheric aerosol particles depend on their sources and lifetime in the atmosphere. In coastal regions, sources may include influences from marine, continental, anthropogenic, and natural emissions. In this study, particles in ten diameter-size ranges were collected, and particle number size distributions were measured, at Skidaway Island, GA in May and June 2018. Based on air mass back trajectories and concentrations of major ions in the particles, the air mass source regions were identified as Marine Influenced, Mixed, and Continental Influenced. Organic molecules were extracted from the particles using solid-phase extraction and characterized using tensiometry and high-resolution mass spectrometry. The presence of surfactants was confirmed in the extracts through the observation of significant surface tension depressions. The organic formulas contained high hydrogen-to-carbon (H/C) and low oxygen-to-carbon (O/C) ratios, similar to surfactants and lipid-like molecules. In the Marine Influenced particles, the fraction of formulas identified as surfactant-like was negatively correlated with minimum surface tensions; as the surfactant fraction increased, the surface tension decreased. Analyses of fatty acid compounds demonstrated that organic compounds extracted from the Marine Influenced particles had the highest carbon numbers (18), compared to those of the Mixed (15) and Continental Influenced (9) particles. This suggests that the fatty acids in the Continental Influenced particles may have been more aged in the atmosphere and undergone fragmentation. This is one of the first studies to measure the chemical and physical properties of surfactants in size-resolved particles from different air mass source regions.

Effects of Meteorology Changes on Inter-Annual Variations of Aerosol Optical Depth and Surface PM2.5 in China—Implications for PM2.5 Remote Sensing

PM2.5 retrieval from satellite-observed aerosol optical depth (AOD) is still challenging due to the strong impact of meteorology. We investigate influences of meteorology changes on the inter-annual variations of AOD and surface PM2.5 in China between 2006 and 2017 using a nested 3D chemical transport model, GEOS-Chem, by fixing emissions at the 2006 level. We then identify major meteorological elements controlling the inter-annual variations of AOD and surface PM2.5 using multiple linear regression. We find larger influences of meteorology changes on trends of AOD than that of surface PM2.5. On the seasonal scale, meteorology changes are beneficial to AOD and surface PM2.5 reduction in spring (1–50%) but show an adverse effect on aerosol reduction in summer. In addition, major meteorological elements influencing variations of AOD and PM2.5 are similar between spring and fall. In winter, meteorology changes are favorable to AOD reduction (−0.007 yr−1, −1.2% yr−1; p < 0.05) but enhanced surface PM2.5 between 2006 and 2017. The difference in winter is mainly attributed to the stable boundary layer that isolates surface PM2.5 from aloft. The significant decrease in AOD over the years is related to the increase in meridional wind speed at 850 hPa in NCP (p < 0.05). The increase of surface PM2.5 in NCP in winter is possibly related to the increased temperature inversion and more stable stratification in the boundary layer. This suggests that previous estimates of wintertime surface PM2.5 using satellite measurements of AOD corrected by meteorological elements should be used with caution. Our findings provide potential meteorological elements that might improve the retrieval of surface PM2.5 from satellite-observed AOD on the seasonal scale.

Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species

Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, and PM2.5 constituents: SO42-, NO3-, NH4+, EC and OC) between 2005 and 2014 for the continental United States with little spatial or temporal bias. R2 ranged from 0.45 to 0.96, depending upon pollutant. Additional analysis found that temporal R2 ranged from 0.84 to 0.99 and spatial R2 values ranged from 0.76 to 0.96 across species. Four-fold cross-validation was performed to assess the model's predictive power, and ranged from 0.40 for PM10 to 0.94 for SO4 with other pollutants falling within this range. Largest improvements were found for PM10 which had substantial bias in the CMAQ fields that varied east-to-west; smallest improvements were for SO4 which was already well simulated. The random forest model results to correct the simulation biases, while largely consistent year-to-year, did show slight variation due in part to changes in the distribution of monitors and changes in CMAQ simulation inputs.

PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μg/m3, and a root mean square error (RMSE) of 5.8 μg/m3. This study also found that the model performance as well as the site measured PM2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R2) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R2 scores correlate positively with the mean measured PM2.5 concentration at monitoring sites. Mean measured PM2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM2.5 concentrations. In addition, the reconstructed PM2.5 surfaces serve as an important data source for pollution event tracking and PM2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM2.5 estimates were made for 10 km by 10 km and the PM2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM2.5 concentrations.

Biomass Burning Over the United States East Coast and Western North Atlantic Ocean: Implications for Clouds and Air Quality

Biomass burning (BB) aerosol events were characterized over the U.S. East Coast and Bermuda over the western North Atlantic Ocean (WNAO) between 2005 and 2018 using a combination of ground-based observations, satellite data, and model outputs. Days with BB influence in an atmospheric column (BB days) were identified using criteria biased toward larger fire events based on anomalously high AERONET aerosol optical depth (AOD) and MERRA-2 black carbon (BC) column density. BB days are present year-round with more in June-August (JJA) over the northern part of the East Coast, in contrast to more frequent events in March-May (MAM) over the southeast U.S. and Bermuda. BB source regions in MAM are southern Mexico and by the Yucatan, Central America, and the southeast U.S. JJA source regions are western parts of North America. Less than half of the BB days coincide with anomalously high PM2.5 levels in the surface layer, according to data from 14 IMPROVE sites over the East Coast. Profiles of aerosol extinction suggest that BB particles can be found in the boundary layer and into the upper troposphere with the potential to interact with clouds. Higher cloud drop number concentration and lower drop effective radius are observed during BB days. In addition, lower liquid water path is found during these days, especially when BB particles are present in the boundary layer. While patterns are suggestive of cloud-BB aerosol interactions over the East Coast and the WNAO, additional studies are needed for confirmation.

Predicting Spatial Variations in Multiple Measures of Oxidative Burden for Outdoor Fine Particulate Air Pollution across Canada

Fine particulate air pollution (PM_2.5) is a leading contributor to the overall global burden of disease. Traditionally, outdoor PM_2.5 has been characterized using mass concentrations which treat all particles as equally harmful. Oxidative potential (OP) (per μg) and oxidative burden (OB) (per m³) are complementary metrics that estimate the ability of PM_2.5 to cause oxidative stress, which is an important mechanism in air pollution health effects. Here, we provide the first national estimates of spatial variations in multiple measures (glutathione, ascorbate, and dithiothreitol depletion) of annual median outdoor PM_2.5 OB across Canada. To do this, we combined a large database of ground-level OB measurements collected monthly prospectively across Canada for 2 years (2016-2018) with PM_2.5 components estimated using a chemical transport model (GEOS-Chem) and satellite aerosol observations. Our predicted ground-level OB values of all three methods were consistent with ground-level observations (cross-validation R² = 0.63-0.74). We found that forested regions and urban areas had the highest OB, predicted primarily by black carbon and organic carbon from wildfires and transportation sources. Importantly, the dominant components associated with OB were different than those contributing to PM_2.5 mass concentrations (secondary inorganic aerosol); thus, OB metrics may better indicate harmful components and sources on health than the bulk PM_2.5 mass, reinforcing that OB estimates can complement the existing PM_2.5 data in future national-level epidemiological studies.

Comparison of Aerosol Optical Depth from MODIS Product Collection 6.1 and AERONET in the Western United States

Recent observations reveal that dust storms are increasing in the western USA, posing imminent risks to public health, safety, and the economy. Much of the observational evidence has been obtained from ground-based platforms and the visual interpretation of satellite imagery from limited regions. Comprehensive satellite-based observations of long-term aerosol records are still lacking. In an effort to develop such a satellite aerosol dataset, we compared and evaluated the Aerosol Optical Depth (AOD) from Deep Blue (DB) and Dark Target (DT) product collection 6.1 with the Aerosol Robotic Network (AERONET) program in the western USA. We examined the seasonal and monthly average number of Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua DB AOD retrievals per 0.1∘×0.1∘ from January 2003 to December 2017 across the region’s different topographic, climatic, and land cover conditions. The number of retrievals in the southwest United States was on average greater than 37 days per 90 days for all seasons except summer. Springtime saw the highest number of AOD retrievals across the southwest, consistent with the peak season for synoptic-scale dust events. The majority of Arizona, New Mexico, and western Texas showed the lowest number of retrievals during the monsoon season. The majority of collocating domains of AOD from the Aqua sensor showed a better correlation with AERONET AOD than AOD from Terra, and the correlation coefficients exhibited large regional variability across the study area. The correlation coefficient between the couplings Aqua DB AOD-AERONET AOD and Terra DB AOD-AERONET AOD ranges from 0.1 to 0.94 and 0.001 to 0.94, respectively. In the majority of the sites that exhibited less than a 0.6 correlation coefficient and few matched data points at the nearest single pixel, the correlations gradually improved when the spatial domain increased to a 50 km × 50 km box averaging domain. In general, the majority of the stations revealed significant correlation between MODIS and AERONET AOD at all spatiotemporal aggregating domains, although MODIS generally overestimated AOD compared to AERONET. However, the correlation coefficient in the southwest United States was the lowest and in stations from a higher latitude was the highest. The difference in the brightness of the land surface and the latitudinal differences in the aerosol inputs from the forest fires and solar zenith angles are some of the factors that manifested the latitudinal correlation differences.

Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem

The burning of fossil fuels - especially coal, petrol, and diesel - is a major source of airborne fine particulate matter (PM_2.5), and a key contributor to the global burden of mortality and disease. Previous risk assessments have examined the health response to total PM_2.5, not just PM_2.5 from fossil fuel combustion, and have used a concentration-response function with limited support from the literature and data at both high and low concentrations. This assessment examines mortality associated with PM_2.5 from only fossil fuel combustion, making use of a recent meta-analysis of newer studies with a wider range of exposure. We also estimated mortality due to lower respiratory infections (LRI) among children under the age of five in the Americas and Europe, regions for which we have reliable data on the relative risk of this health outcome from PM_2.5 exposure. We used the chemical transport model GEOS-Chem to estimate global exposure levels to fossil-fuel related PM_2.5 in 2012. Relative risks of mortality were modeled using functions that link long-term exposure to PM_2.5 and mortality, incorporating nonlinearity in the concentration response. We estimate a global total of 10.2 (95% CI: -47.1 to 17.0) million premature deaths annually attributable to the fossil-fuel component of PM_2.5. The greatest mortality impact is estimated over regions with substantial fossil fuel related PM_2.5, notably China (3.9 million), India (2.5 million) and parts of eastern US, Europe and Southeast Asia. The estimate for China predates substantial decline in fossil fuel emissions and decreases to 2.4 million premature deaths due to 43.7% reduction in fossil fuel PM_2.5 from 2012 to 2018 bringing the global total to 8.7 (95% CI: -1.8 to 14.0) million premature deaths. We also estimated excess annual deaths due to LRI in children (0-4 years old) of 876 in North America, 747 in South America, and 605 in Europe. This study demonstrates that the fossil fuel component of PM_2.5 contributes a large mortality burden. The steeper concentration-response function slope at lower concentrations leads to larger estimates than previously found in Europe and North America, and the slower drop-off in slope at higher concentrations results in larger estimates in Asia. Fossil fuel combustion can be more readily controlled than other sources and precursors of PM_2.5 such as dust or wildfire smoke, so this is a clear message to policymakers and stakeholders to further incentivize a shift to clean sources of energy.

Weather Effects of Aerosols in the Global Forecast Model

The weather effects of aerosol types were investigated using well-posed aerosol climatology through the aerosol sensitivity test of thermodynamic and hydrometeor fields, and the weather forecast performances in July of 2017. The largest aerosol direct radiative forcing (ADRF) in July was due to dust aerosols at the surface and atmosphere, and sulfate at the top of the atmosphere (TOA), respectively. The ADRF of total aerosols had unilateral tendencies in thermodynamic and hydrometeor fields. The contribution of individual aerosols was linearly additive to those of total aerosols in the heat fluxes, heating rates, humidity, and convective precipitation. However, no such linearity existed in temperature, geopotential height, cloud liquid or ice contents, and large-scale precipitation. Dust was the most influential forcing agent in July among five aerosol types due to the largest light-absorption capacity. Such unilateral tendencies of total aerosols and a part of the linearity of individual aerosols were exerted on the weather systems. The verification of medium-range forecasts showed that aerosols alleviated the overestimation of surface shortwave (SW) downward fluxes, the negative biases of temperature and geopotential heights at TOA and surface, and the underestimation in light and moderate precipitation. In contrast, they enhanced warm biases at the mid-atmosphere and underestimation in heavy precipitations, particularly negative biases in the intertropical convergence zone (ITCZ). Weather forecast scores including current aerosol information were improved in geopotential height (GPH) of the northern hemisphere (NH); however, they got worse in the temperature and the upper atmosphere GPH of the southern hemisphere (SH), which was mostly due to black carbon (BC) aerosols in the tropical regions. The missing mechanisms such as aerosol–cloud interactions, better aerosol spectral optical properties including mixing states and aging, and the near-real-time (NRT) based aerosol loading data are worthwhile to be tried in the near future for fixing the intrinsic underestimation of precipitation in ITCZ and surface radiative fluxes in the desert and biomass burning area.

Satellite Observations of PM2.5 Changes and Driving Factors Based Forecasting Over China 2000–2025

In China, atmospheric fine particulate matter (PM2.5) pollution is a challenging environmental problem. Systematic PM2.5 measurements have started only in 2013, resulting in a lack of historical data which is a key obstacle for the analysis of long-term PM2.5 trends and forecasting the evolution over this hot region. Satellite data can provide a new approach to derive historical PM2.5 information provided that the column-integrated aerosol properties can adequately be converted to PM2.5. In this study, a recently developed formulation for the calculation of surface PM2.5 concentrations using satellite data is introduced and applied to reconstruct a PM2.5 time series over China from 2000 to 2015. The formulated model is also used to explore the PM2.5 driving factors related to anthropogenic or meteorological parameters in this historical period. The results show that the annually averaged PM2.5 over China’s polluted regions increased rapidly between 2004 and 2007 (with an average rate of 3.07 μg m−3 yr−1) to reach values of up to 61.1 μg m−3 in 2007, and decreased from 2011 to 2015 with an average rate of −2.61 μg m−3 yr−1, to reach a value of 46.9 μg m−3 in 2015. The analysis shows that the increase in PM2.5 before 2008 was mainly associated with increasing anthropogenic factors, further augmented by the effect of meteorological influences. However, the decrease in PM2.5 after 2011 is mainly attributed to the effect of pollution control measures on anthropogenic factors, whereas the effects of meteorological factors have continued to increase since 2000. The results also suggest that further reduction in anthropogenic emissions is needed to accelerate the decrease in PM2.5 concentrations to reach the target of 35 μg m−3 over major polluted areas in China before 2025.

Wildfire and prescribed burning impacts on air quality in the United States

Air quality impacts from wildfires have been dramatic in recent years, with millions of people exposed to elevated and sometimes hazardous fine particulate matter (PM _2.5 ) concentrations for extended periods. Fires emit particulate matter (PM) and gaseous compounds that can negatively impact human health and reduce visibility. While the overall trend in U.S. air quality has been improving for decades, largely due to implementation of the Clean Air Act, seasonal wildfires threaten to undo this in some regions of the United States. Our understanding of the health effects of smoke is growing with regard to respiratory and cardiovascular consequences and mortality. The costs of these health outcomes can exceed the billions already spent on wildfire suppression. In this critical review, we examine each of the processes that influence wildland fires and the effects of fires, including the natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry, and human health impacts. We highlight key data gaps and examine the complexity and scope and scale of fire occurrence, estimated emissions, and resulting effects on regional air quality across the United States. The goal is to clarify which areas are well understood and which need more study. We conclude with a set of recommendations for future research.

IMPLICATIONS

In the recent decade the area of wildfires in the United States has increased dramatically and the resulting smoke has exposed millions of people to unhealthy air quality. In this critical review we examine the key factors and impacts from fires including natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry and human health.

Simulation of airborne trace metals in fine particulate matter over North America

Trace metal distributions are of relevance to understand sources of fine particulate matter (PM2.5), PM2.5-related health effects, and atmospheric chemistry. However, knowledge of trace metal distributions is lacking due to limited ground-based measurements and model simulations. This study develops a simulation of 12 trace metal concentrations (Si, Ca, Al, Fe, Ti, Mn, K, Mg, As, Cd, Ni and Pb) over continental North America for 2013 using the GEOS-Chem chemical transport model. Evaluation of modeled trace metal concentrations with observations indicates a spatial consistency within a factor of 2, an improvement over previous studies that were within a factor of 3-6. The spatial distribution of trace metal concentrations reflects their primary emission sources. Crustal element (Si, Ca, Al, Fe, Ti, Mn, K) concentrations are enhanced over the central US from anthropogenic fugitive dust and over the southwestern U.S. due to natural mineral dust. Heavy metal (As, Cd, Ni and Pb) concentrations are high over the eastern U.S. from industry. K is abundance in the southeast from biomass burning and high concentrations of Mg is observed along the coast from sea spray. The spatial pattern of PM2.5 mass is most strongly correlated with Pb, Ni, As and K due to their signature emission sources. Challenges remain in accurately simulating observed trace metal concentrations. Halving anthropogenic fugitive dust emissions in the 2011 National Air Toxic Assessment (NATA) inventory and doubling natural dust emissions in the default GEOS-Chem simulation was necessary to reduce biases in crustal element concentrations. A fivefold increase of anthropogenic emissions of As and Pb was necessary in the NATA inventory to reduce the national-scale bias versus observations by more than 80 %, potentially reflecting missing sources.

Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5 μm (PM_2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10 km vs. 3 km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM_2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM_2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM_2.5 data can improve the spatial coverage, especially in areas where PM_2.5 ground monitors are lacking, and studying the connections between PM_2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.

Estimated Long-Term (1981-2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing, and Ground-Based Measurements

Accurate data concerning historical fine particulate matter (PM_2.5) concentrations are needed to assess long-term changes in exposure and associated health risks. We estimated historical PM_2.5 concentrations over North America from 1981 to 2016 for the first time by combining chemical transport modeling, satellite remote sensing, and ground-based measurements. We constrained and evaluated our estimates with direct ground-based PM_2.5 measurements when available and otherwise with historical estimates of PM_2.5 from PM₁₀ measurements or total suspended particle (TSP) measurements. The estimated PM_2.5 concentrations were generally consistent with direct ground-based PM_2.5 measurements over their duration from 1988 onward ( R² = 0.6 to 0.85) and to a lesser extent with PM_2.5 inferred from PM₁₀ measurements from 1985 to 1998 ( R² = 0.5 to 0.6). The collocated comparison of the trends of population-weighted annual average PM_2.5 from our estimates and ground-based measurements was highly consistent (RMSD = 0.66 μg m^-3). The population-weighted annual average PM_2.5 over North America decreased from 22 ± 6.4 μg m^-3 in 1981, to 12 ± 3.2 μg m^-3 in 1998, and to 7.9 ± 2.1 μg m^-3 in 2016, with an overall trend of -0.33 μg m^-3 yr^-1 (95% CI: -0.35, -0.31).

Constraining chemical transport PM2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R 2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3 -, 0.78 and 0.23 for SO4 2-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4 2- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

Aerosol Optical Depth Over India

Tropospheric aerosol optical depth (AOD) over India was simulated by Goddard Earth Observing System (GEOS)-Chem, a global 3-D chemical-transport model, using SMOG (Speciated Multi-pOllutant Generator from Indian Institute of Technology Bombay) and GEOS-Chem (GC) (current inventories used in the GEOS-Chem model) inventories for 2012. The simulated AODs were ~80% (SMOG) and 60% (GC) of those measured by the satellites (Moderate Resolution Imaging Spectroradiometer and Multi-angle Imaging SpectroRadiometer). There is no strong seasonal variation in AOD over India. The peak AOD values are observed/simulated during summer. The simulated AOD using SMOG inventory has particulate black and organic carbon AOD higher by a factor ~5 and 3, respectively, compared to GC inventory. The model underpredicted coarse-mode AOD but agreed for fine-mode AOD with Aerosol Robotic Network data. It captured dust only over Western India, which is a desert, and not elsewhere, probably due to inaccurate dust transport and/or noninclusion of other dust sources. The calculated AOD, after dust correction, showed the general features in its observed spatial variation. Highest AOD values were observed over the Indo-Gangetic Plain followed by Central and Southern India with lowest values in Northern India. Transport of aerosols from Indo-Gangetic Plain and Central India into Eastern India, where emissions are low, is significant. The major contributors to total AOD over India are inorganic aerosol (41-64%), organic carbon (14-26%), and dust (7-32%). AOD over most regions of India is a factor of 5 or higher than over the United States.

Aqueous-phase mechanism for secondary organic aerosol formation from isoprene: application to the Southeast United States and co-benefit of SO2 emission controls

Isoprene emitted by vegetation is an important precursor of secondary organic aerosol (SOA), but the mechanism and yields are uncertain. Aerosol is prevailingly aqueous under the humid conditions typical of isoprene-emitting regions. Here we develop an aqueous-phase mechanism for isoprene SOA formation coupled to a detailed gas-phase isoprene oxidation scheme. The mechanism is based on aerosol reactive uptake coefficients (γ) for water-soluble isoprene oxidation products, including sensitivity to aerosol acidity and nucleophile concentrations. We apply this mechanism to simulation of aircraft (SEAC⁴RS) and ground-based (SOAS) observations over the Southeast US in summer 2013 using the GEOS-Chem chemical transport model. Emissions of nitrogen oxides (NO_x ≡ NO + NO₂) over the Southeast US are such that the peroxy radicals produced from isoprene oxidation (ISOPO₂) react significantly with both NO (high-NO_x pathway) and HO₂ (low-NO_x pathway), leading to different suites of isoprene SOA precursors. We find a mean SOA mass yield of 3.3 % from isoprene oxidation, consistent with the observed relationship of total fine organic aerosol (OA) and formaldehyde (a product of isoprene oxidation). Isoprene SOA production is mainly contributed by two immediate gas-phase precursors, isoprene epoxydiols (IEPOX, 58% of isoprene SOA) from the low-NO_x pathway and glyoxal (28%) from both low- and high-NO_x pathways. This speciation is consistent with observations of IEPOX SOA from SOAS and SEAC⁴RS. Observations show a strong relationship between IEPOX SOA and sulfate aerosol that we explain as due to the effect of sulfate on aerosol acidity and volume. Isoprene SOA concentrations increase as NO_x emissions decrease (favoring the low-NO_x pathway for isoprene oxidation), but decrease more strongly as SO₂ emissions decrease (due to the effect of sulfate on aerosol acidity and volume). The US EPA projects 2013-2025 decreases in anthropogenic emissions of 34% for NO_x (leading to 7% increase in isoprene SOA) and 48% for SO₂ (35% decrease in isoprene SOA). Reducing SO₂ emissions decreases sulfate and isoprene SOA by a similar magnitude, representing a factor of 2 co-benefit for PM_2.5 from SO₂ emission controls.

Aqueous-phase mechanism for secondary organic aerosol formation from isoprene: application to the Southeast United States and co-benefit of SO2 emission controls

Isoprene emitted by vegetation is an important precursor of secondary organic aerosol (SOA), but the mechanism and yields are uncertain. Aerosol is prevailingly aqueous under the humid conditions typical of isoprene-emitting regions. Here we develop an aqueous-phase mechanism for isoprene SOA formation coupled to a detailed gas-phase isoprene oxidation scheme. The mechanism is based on aerosol reactive uptake coefficients (γ) for water-soluble isoprene oxidation products, including sensitivity to aerosol acidity and nucleophile concentrations. We apply this mechanism to simulation of aircraft (SEAC⁴RS) and ground-based (SOAS) observations over the Southeast US in summer 2013 using the GEOS-Chem chemical transport model. Emissions of nitrogen oxides (NO_x ≡ NO + NO₂) over the Southeast US are such that the peroxy radicals produced from isoprene oxidation (ISOPO₂) react significantly with both NO (high-NO_x pathway) and HO₂ (low-NO_x pathway), leading to different suites of isoprene SOA precursors. We find a mean SOA mass yield of 3.3 % from isoprene oxidation, consistent with the observed relationship of total fine organic aerosol (OA) and formaldehyde (a product of isoprene oxidation). Isoprene SOA production is mainly contributed by two immediate gas-phase precursors, isoprene epoxydiols (IEPOX, 58% of isoprene SOA) from the low-NO_x pathway and glyoxal (28%) from both low- and high-NO_x pathways. This speciation is consistent with observations of IEPOX SOA from SOAS and SEAC⁴RS. Observations show a strong relationship between IEPOX SOA and sulfate aerosol that we explain as due to the effect of sulfate on aerosol acidity and volume. Isoprene SOA concentrations increase as NO_x emissions decrease (favoring the low-NO_x pathway for isoprene oxidation), but decrease more strongly as SO₂ emissions decrease (due to the effect of sulfate on aerosol acidity and volume). The US EPA projects 2013-2025 decreases in anthropogenic emissions of 34% for NO_x (leading to 7% increase in isoprene SOA) and 48% for SO₂ (35% decrease in isoprene SOA). Reducing SO₂ emissions decreases sulfate and isoprene SOA by a similar magnitude, representing a factor of 2 co-benefit for PM_2.5 from SO₂ emission controls.

High-resolution satellite-based analysis of ground-level PM2.5 for the city of Montreal

Satellite remote sensing offers the opportunity to determine the spatial distribution of aerosol properties and could fill the gap of ground-level observations. Various algorithms have recently been developed in order to retrieve the aerosol optical depth (AOD) at continental scales. However, they are, to some extent, subject to coarse spatial resolutions which are not appropriate for intraurban scales as usually needed in health studies. This paper presents an improved AOD retrieval algorithm for satellite instrument MODIS at 1-km resolution for intraurban scales. The MODIS-retrieved AODs are used to derive the ground-level PM2.5 concentrations using the aerosol vertical profiles and local scale factors obtained from the GEOS-Chem model simulation. The developed method has been applied to retrieve the AODs and to evaluate the ground-level PM2.5 over the city of Montreal, Canada for 2009 on daily, monthly and annual scales. The daily and monthly results are compared with the monitoring values with correlations R(2) ranging from 0.86 to 0.93. Especially, the annual mean PM2.5 concentrations are in good agreement with the measurement values at all monitoring stations (r=0.96, slope=1.0132 ± 0.0025, intercept=0.5739 ± 0.0013). This illustrates that the developed AOD retrieval algorithm can be used to retrieve AODs at a higher spatial resolution than previous studies to further derive the regional full coverage PM2.5 results at finer spatial and temporal scales. The study results are useful in health risk assessment across this region.

Tropospheric aerosols: size-differentiated chemistry and large-scale spatial distributions

Worldwide interest in atmospheric aerosols has emerged since the late 20th century as a part of concerns for air pollution and radiative forcing of the earth's climate. The use of aircraft and balloons for sampling and the use of remote sensing have dramatically expanded knowledge about tropospheric aerosols. Our survey gives an overview of contemporary tropospheric aerosol chemistry based mainly on in situ measurements. It focuses on fine particles less than 1-2.5 microm in diameter. The physical properties of particles by region and altitude are exemplified by particle size distributions, total number and volume concentration, and optical parameters such as extinction coefficient and aerosol optical depth. Particle chemical characterization is size dependent, differentiated by ubiquitous sulfate, and carbon, partially from anthropogenic activity. Large-scale particle distributions extend to intra- and intercontinental proportions involving plumes from population centers to natural disturbances such as dust storms and vegetation fires. In the marine environment, sea salt adds an important component to aerosols. Generally, aerosol components, most of whose sources are at the earth's surface, tend to dilute and decrease in concentration with height, but often show different (layered) profiles depending on meteorological conditions. Key microscopic processes include new particle formation aloft and cloud interactions, both cloud initiation and cloud evaporation. Measurement campaigns aloft are short term, giving snapshots of inherently transient phenomena in the troposphere. Nevertheless, these data, combined with long-term data at the surface and optical depth and transmission observations, yield a unique picture of global tropospheric particle chemistry.

Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America

We improve the accuracy of daily ground-level fine particulate matter concentrations (PM(2.5)) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM(2.5). This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM(2.5), and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m(3) + 67%) to ± (1 μg/m(3) + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM(2.5) estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m(3) + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM(2.5) as compared to day-specific relationships.

Impact of aging mechanism on model simulated carbonaceous aerosols

Carbonaceous aerosols including organic carbon and black carbon have significant implications for both climate and air quality. In the current global climate or chemical transport models, a fixed hydrophobic-to-hydrophilic conversion lifetime for carbonaceous aerosol (τ) is generally assumed, which is usually around one day. We have implemented a new detailed aging scheme for carbonaceous aerosols in a chemical transport model (GEOS-Chem) to account for both the chemical oxidation and the physical condensation-coagulation effects, where τ is affected by local atmospheric environment including atmospheric concentrations of water vapor, ozone, hydroxyl radical and sulfuric acid. The updated τ exhibits large spatial and temporal variations with the global average (up to 11 km altitude) calculated to be 2.6 days. The chemical aging effects are found to be strongest over the tropical regions driven by the low ozone concentrations and high humidity there. The τ resulted from chemical aging generally decreases with altitude due to increases in ozone concentration and decreases in humidity. The condensation-coagulation effects are found to be most important for the high-latitude areas, in particular the polar regions, where the τ values are calculated to be up to 15 days. When both the chemical aging and condensation-coagulation effects are considered, the total atmospheric burdens and global average lifetimes of BC, black carbon, (OC, organic carbon) are calculated to increase by 9% (3%) compared to the control simulation, with considerable enhancements of BC and OC concentrations in the Southern Hemisphere. Model evaluations against data from multiple datasets show that the updated aging scheme improves model simulations of carbonaceous aerosols for some regions, especially for the remote areas in the Northern Hemisphere. The improvement helps explain the persistent low model bias for carbonaceous aerosols in the Northern Hemisphere reported in literature. Further model sensitivity simulations focusing on the continental outflow of carbonaceous aerosols demonstrate that previous studies using the old aging scheme could have significantly underestimated the intercontinental transport of carbonaceous aerosols.

Recent changes in particulate air pollution over China observed from space and the ground: effectiveness of emission control

The Chinese government has moved aggressively since 2005 to reduce emissions of a number of pollutants including primary particulate matter (PM) and sulfur dioxide (SO(2)), efforts inadvertently aided since late 2008 by economic recession. Satellite observations of aerosol optical depth (AOD) and column nitrogen dioxide (NO(2)) provide independent indicators of emission trends, clearly reflecting the sharp onset of the recession in the fall of 2008 and rebound of the economy in the latter half of 2009. Comparison of AOD with ground-based observations of PM over a longer period indicate that emission-control policies have not been successful in reducing concentrations of aerosol pollutants at smaller size range over industrialized regions of China. The lack of success is attributed to the increasing importance of anthropogenic secondary aerosols formed from precursor species including nitrogen oxides (NO(x)), non-methane volatile organic compounds (NMVOC), and ammonia (NH(3)).