Interagency Monitoring Of Protected Visual Environments Research Articles

Long-term Measurements of Carbonaceous Aerosol at Cape Hedo, Okinawa, Japan: Effects of Changes in Emissions in East Asia

ABSTRACT We investigated the changes in the carbonaceous-aerosol sources and their effects on the long-term elemental carbon (EC) and organic carbon (OC) concentration trends at the Cape Hedo Atmosphere and Aerosol Monitoring Station (CHAAMS) in Okinawa, Japan, during the period 2004–2013. We obtained the EC and OC concentrations by conducting semi-real-time measurements using a carbon monitor, and performing an offline thermal/optical filter analysis according to the Interagency Monitoring of Protected Visual Environments (IMPROVE) protocol. The annual average concentration of the EC remained constant between 2004 and 2013, but that of the OC decreased at a rate of 0.11 µg C m–3 y–1 (α > 0.05). The secondary OC (SOC)/OC ratio showed an increasing trend from 2004 till 2011, which may have been caused by a reduction in primary emissions of OC and compositional changes in the organic compounds originating in China, from which air pollutants were frequently transported during spring and winter. Although the EC concentration did not change appreciably in either season, the OC concentration decreased at rates of 0.10 µg C m–3 y–1 and 0.11 µg C m–3 y–1 during spring and winter, respectively. We estimated the contributions from the various sources of carbonaceous aerosol, viz., biomass burning, fossil-fuel combustion, and air-pollutant transport from China, based on the OC/EC ratio, which decreased from 5.7 to 2.4 in terms of the annual average. The growing share from fossil-fuel combustion is responsible for the decline in biofuel-burning OC emissions transported from China to CHAAMS.

Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM<sub>2.5</sub> forecasts across China

Abstract. The authors developed a three-dimensional variational (3-DVAR) aerosol extinction coefficient (AEC) and aerosol mass concentration (AMC) data assimilation (DA) system for aerosol variables in the Weather Research and Forecasting–Chemistry (WRF–Chem) model with the WRF–Chem using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) scheme. They establish an AEC observation operator and its corresponding adjoint based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) equation and investigate the use of lidar AEC and surface AMC DA to forecast mass concentration (MC) profiles of PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) across China. Two sets of data were assimilated: AEC profiles captured by five conventional Mie scattering lidars (positioned in Beijing, Shijiazhuang, Taiyuan, Xuzhou, and Wuhu) and PM2.5 and PM10 MC data obtained from over 1500 ground environmental monitoring stations across China. Three DA experiments (i.e., a PM2.5 (PM10) DA experiment, a lidar AEC DA experiment, and a simultaneous PM2.5 (PM10) and lidar AEC DA experiment) with a 12 h assimilation period and a 24 h forecast period were conducted. The PM2.5 (PM10) DA reduced the root mean square error (RMSE) of the surface PM2.5 MC in the initial field of the model by 38.6 µg m−3 (64.8 %). When lidar AEC data were assimilated, this reduction was 10.5 µg m−3 (17.6 %), and a 38.4 µg m−3 (64.4 %) reduction occurred when the two data sets were assimilated simultaneously, although only five lidars were available within the simulation region (approximately 2.33 million km2 in size). The RMSEs of the forecasted surface PM2.5 MC 24 h after the DA period in the three DA experiments were reduced by 6.1 µg m−3 (11.8 %), 1.5 µg m−3 (2.9 %), and 6.5 µg m−3 (12.6 %), respectively, indicating that the assimilation and hence the optimization of the initial field have a positive effect on the PM2.5 MC forecast performance over a period of 24 h after the DA period.

Spatial and temporal variability of brown carbon in United States: implications for direct radiative effects.

A newly developed dataset from the Interagency Monitoring of PROtected Visual Environments (IMPROVE) observation network, combined with a 3-D chemical transport model, is used to evaluate the spatial and temporal variability of brown carbon (BrC) in the United States. The model with BrC emitted from biomass burning and biofuel emissions agrees with the seasonal and spatial variability of BrC planetary boundary layer (PBL) absorption aerosol optical depth (AAOD) observations within a factor of 2. The model without whitening, the tendency for absorption to decrease with aerosol aging, overestimates the observed BrC PBL AAOD, and does not reflect the measured BrC PBL AAOD spatial variability. The model shows higher absorption direct radiative effects (DRE) from BrC at northern high latitudes than at mid-latitudes in spring and summer, due to boreal fire emissions, long whitening lifetimes and high surface albedos. These findings highlight the need to study BrC over the Arctic region.

Dust Impacts of Rapid Agricultural Expansion on the Great Plains

AbstractClimate change and land use are altering the landscape of the U.S. Great Plains, producing increases in windblown dust. These increases are investigated by combining coarse mode aerosol observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor in addition to the Aerosol Robotic Network (AERONET) and Interagency Monitoring of Protected Visual Environments (IMPROVE) aerosol monitoring networks. Increasing trends of up to 5%/year in MODIS aerosol optical depth for dust observations are observed throughout the Great Plains (2000–2018). Cropland coverage has increased 5–10% over the majority of the Great Plains (2008–2018), and positive monthly trends in IMPROVE (1988–2018) and AERONET (1995–2018) coarse mode 90th percentile observations coincide with planting and harvesting seasons of predominant crops. Presently, results suggest increased dust due to agricultural expansion is negatively influencing human health and visibility in the Great Plains. Furthermore, results foreshadow a future where desertification becomes an increasing risk in the Great Plains.

Comparison of recent speciated PM2.5 data from collocated CSN and IMPROVE measurements

As long-term speciated PM2.5 monitoring programs, the Interagency Monitoring of Protected Visual Environments (IMPROVE) and Chemical Speciation Network (CSN) were designed with different objectives but apply similar analytical methods to 24hr filter samples and report many of the same species. The two networks have different operating structures, sampling practices, analytical methods, analytical facilities, and data handling and validation practices, which require attention when data from the two networks are combined in an analysis. Data from collocated CSN and IMPROVE sites from January 1, 2016 through September 30, 2018 are presented to document the comparability between the networks. While species measured well above the method detection limit (MDL) generally agree well during this period, there is evidence of some inter-network bias for fine-soil-related elements at specific locations, as well as subtle biases for some well-measured species. Many species – particularly for CSN – are measured at or near the MDL and have poor inter- and intra-network collocated agreement; caution should be used when advancing findings on such measurements. However, comparison of reconstructed mass shows good inter-network agreement suggesting that the networks are effective at quantifying predominant mass species.

Observation of Aerosol Optical Properties and New Particle Formation in the Yangtze River Delta

In a recent field campaign focused on air quality study, aerosol optical properties, particle number concentration, and PM2.5 components were monitored in Changzhou, Jiangsu Province, from May 27 to June 27, 2019. An array of instruments were deployed that included scanning mobility particle size spectrometer (SMPS), aethalometer (AE33), cavity attenuation phase shift single albedo monitor (CAPS-ALB), monitor for aerosols and gases in ambient air (MARGA) and RT-4 organic carbon/elemental carbon (OC/EC) carbon analyzer to study the ① changes in chemical composition and optical parameters of the new particles generated during the campaign period. ② comparison of the aerosol extinction coefficient recorded by these instruments and measured value in the reconstruction of IMPROVE (interagency monitoring of protected visual environment) and the calculated coefficient using MIE theory model were carried out. During the entire campaign, two new particle generation events were observed and also found that the particle size continued to increase from 4 nm to 64 nm. It was monitored that in the initial stage of new particle generation, sulfate contributed greatly. The measured average aerosol extinction coefficient during the period of particle generation, using these instruments was 95.40 Mm-1, while the average aerosol extinction reconstruction using the IMPROVE model was observed to be 140.20 Mm-1. The theoretical calculations based on Mie theory model yielded an average extinction coefficient of 93.54 Mm-1. It was found that the average aerosol extinction in Changzhou is lower than the average value of the urban aerosol extinction coefficient, which is measured to be 300 Mm-1 in China, during this period. The deployment of multiple instruments in a single campaign is more desirable because the combination of all observations helped in better characterization of the physicochemical properties of ambient aerosols from various aspects, including particle size spectrum and chemical composition.

Characterization and source apportionment of aerosol light scattering in a typical polluted city in the Yangtze River Delta, China

Abstract. Through online observation and offline chemistry analysis of samples at suburban, urban and industrial sites (NJU, PAES and NUIST, respectively) in Nanjing, a typical polluted city in the Yangtze River Delta, we optimized the aerosol light scattering estimation method, identified its influencing factors and quantified the contributions of emission sources to aerosol scattering. The daily average concentration of PM2.5 during the sampling period (November 2015–March 2017) was 163.1±13.6 µg m−3 for the heavily polluted period, 3.8 and 1.6 times those for the clean (47.9±15.8 µg m−3) and lightly polluted (102.1±16.4 µg m−3) periods, respectively. The largest increase in PM concentration and its major chemical components was found at the size range of 0.56–1.0 µm for the heavily polluted period, and the contributions of nitrate and sulfate were the greatest in the 0.56–1.0 µm fraction (19.4 %–39.7 % and 18.1 %–34.7 %, respectively) for all the three periods. The results indicated that the large growth of nitrate and sulfate was one of the major reasons for the polluted periods. Based on measurements at the three sites, the US Interagency Monitoring of Protected Visual Environments (IMPROVE) algorithm was optimized to evaluate aerosol scattering in eastern China. The light absorption capacity of organic carbon (OC) was estimated to account for over half of the methanol-soluble organic carbon (MSOC) at NJU and PAES, whereas the fraction was lower at NUIST. Based on the Mie theory, we found that the high relative humidity (RH) could largely enhance the light scattering effect of accumulation particles, but it had few effects on the mixing state of particles. The scattering coefficients of particles within the 0.56–1.0 µm range contributed the most to the total scattering (28 %–69 %). The mass scattering efficiency (MSE) of sulfate and nitrate increased with the elevated pollution level, whereas a low MSE of organic matter (OM) was found for the heavily polluted period, probably because a proportion of OM had only a light absorption property. A coupled model of positive matrix factorization (PMF) and the Mie theory was developed and applied for the source apportionment of aerosol light scattering. Coal burning, industry and vehicles were identified as the major sources of the reduced visibility in Nanjing, with an estimated collective contribution at 64 %–70 %. The comparison between the clean and polluted period suggested that the increased primary particle emissions from vehicles and industry were the major causes of the visibility degradation in urban and industrial regions, respectively. In addition, secondary aerosols were a great contributor to the reduced visibility.

Thirty years of the Clean Air Act Amendments: Impacts on haze in remote regions of the United States (1990–2018)

The Clean Air Act Amendments of 1990 were aimed at reducing major environmental threats in the United States, such as acid rain, urban air pollution, toxic air emissions, and regional haze. To this end, there have been major reductions in anthropogenic gaseous emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx). SO2 and NOx are also major contributors to particulate haze, which affects visibility in both urban and rural environments. The Interagency Monitoring of Protected Visual Environments (IMPROVE) network has tracked trends in haze in remote regions of the United States since the late 1980s by reconstructing total light extinction (bext) from speciated particulate concentrations. Regional trends in bext were examined using aggregated data from individual sites. Regional, annual mean short-term trends analyses (2002–2018) indicated strong reductions in bext associated with reduced SO2 and NOx emissions, especially in the eastern United States (−4.3% yr−1, p < 0.001) where haze historically was dominated by sulfate particles. Less improvement occurred in the Intermountain West/Southwest (−0.9% yr−1, p = 0.03) and trends were negative but insignificant along the West Coast (−1.5% yr−1, p = 0.19). On average, across the continental remote United States, bext has decreased at a rate of −2.8% yr−1 (p < 0.001) from 2002 through 2018 and -1.8% yr−1 (p < 0.001) from 1992 through 2018. The composition of haze has shifted away from being sulfate-dominated to having higher contributions from carbonaceous and crustal aerosols. This shift points to the success of combined regulatory activities aimed at reducing anthropogenic emissions over the last three decades. As emissions from regulated sources of SO2 and NOx continue to decline, the contributions to haze from unregulated sources, both anthropogenic and natural, such as oil and gas extraction, international sources, biomass burning, and dust, have increased in importance. Reducing haze from these sources would require additional mitigation strategies and resource management plans.

Mulitphase Atmospheric Chemistry in Liquid Water: Impacts and Controllability of Organic Aerosol.

Liquid water is a dominant and critical tropospheric constituent. Over polluted land masses low level cumulus clouds interact with boundary layer aerosol. The planetary boundary layer (PBL) is the lowest atmospheric layer and is directly influenced by Earth's surface. Water-aerosol interactions are critical to processes that govern the fate and transport of trace species in the Earth system and their impacts on air quality, radiative forcing, and regional hydrological cycling. In the PBL, air parcels rise adiabatically from the surface, and anthropogenically influenced hygroscopic aerosols take up water and serve as cloud condensation nuclei (CCN) to form clouds. Water-soluble gases partition to liquid water in wet aerosols and cloud droplets and undergo aqueous-phase photochemistry. Most cloud droplets evaporate, and low volatility material formed during aqueous phase chemistry remains in the condensed phase and adds to aerosol mass. The resulting cloud-processed aerosol has different physicochemical properties compared to the original CCN. Organic species that undergo multiphase chemistry in atmospheric liquid water transform gases to highly concentrated, nonideal ionic aqueous solutions and form secondary organic aerosol (SOA). In recent years, SOA formation modulated by atmospheric waters has received considerable interest.Key uncertainties are related to the chemical nature of hygroscopic aerosols that become CCN and their interaction with organic species. Gas-to-droplet or gas-to-aqueous aerosol partitioning of organic compounds is affected by the intrinsic chemical properties of the organic species in addition to the pre-existing condensed phase. Environmentally relevant conditions for atmospheric aerosol are nonideal. Salt identity and concentration, in addition to aerosol phase state, can dramatically affect organic gas miscibility for many compounds, in particular when ionic strength and salt molality are outside the bounds of limiting laws. For example, Henry's law and Debye-Hückel theory are valid only for dilute aqueous systems uncharacteristic of real atmospheric conditions. Chemical theory is incomplete, and at ambient conditions, this chemistry plays a determining role in total aerosol mass and particle size, controlling factors for air quality and climate-relevant aerosol properties.Accurate predictive skill to understand the impacts of societal choices and policies on air quality and climate requires that models contain correct chemical mechanisms and appropriate feedbacks. Globally, SOA is a dominant contributor to the atmospheric organic aerosol burden, and most mass can be traced back to precursor gas-phase volatile organic compounds (VOCs) emitted from the biosphere. However, organic aerosol concentrations in the Amazon Rainforest, the largest emitter of biogenic VOCs, are generally lower than in U.S. national parks. The Interagency Monitoring of Protected Visual Environments (IMPROVE) air quality network, with sites located predominantly in national parks, provides the longest continuous record of organic aerosol measurements in the U.S. Analysis of IMPROVE data provides a useful chemical climatology of changing air resources in response to environmental rules and shifting economic trends. IMPROVE data provides an excellent test bed for case studies to assess model skill to accurately predict changes in organic aerosol concentrations in the context of a changing climate.

Changing Nature of Organic Carbon over the United States.

Total organic carbon (TOC) mass concentrations are decreasing across the contiguous United States (CONUS). We investigate decadal trends in organic carbon (OC) thermal fractions [OC1 (volatilizes at 140 °C), OC2 (280 °C), OC3 (480 °C), OC4 (580 °C)] and pyrolyzed carbon (PC), reported at 121 locations in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network from 2005 to 2015 for 23 regions across the CONUS. Reductions in PC and OC2 drive decreases in TOC (TOC = OC1 + OC2 + OC3 + OC4 + PC) mass concentrations. OC2 decreases by 40% from 2005 to 2015, and PC decreases by 34%. The largest absolute mass decreases occur in the eastern United States, and relative changes normalized to local concentrations are more uniform across the CONUS. OC is converted to organic mass (OM) using region- and season-specific OM:OC ratios. Simulations with GEOS-Chem reproduce OM trends and suggest that decreases across the CONUS are due to aerosol liquid water (ALW) chemistry. Individual model species, notably aerosol derived from isoprene oxidation products and formed in ALW, correlate significantly (p < 0.05) with OM2, even in arid regions. These findings contribute to literature that suggests air quality rules aimed at SO2 and NOx emissions induce the cobenefit of reducing organic particle mass through ALW chemistry, and these benefits extend beyond the eastern United States.

WRF-Chem Simulation of Winter Visibility in Jiangsu, China, and the Application of a Neural Network Algorithm

In this paper, the winter visibility in Jiangsu Province is simulated by WRF-Chem (Weather Research and Forecasting (WRF) model coupled with Chemistry) with high spatiotemporal resolutions. Simulation results show that WRF-Chem has good capability to simulate the visibility and related local meteorological elements and air pollutants in Jiangsu in the winters of 2013–2017. For visibility inversion, this study adopts the neural network algorithm. Meteorological elements, including wind speed, humidity and temperature, are introduced to improve the performance of WRF-Chem relative to the visibility inversion scheme, which is based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) extinction coefficient algorithm. The neural network offers a noticeable improvement relative to the inversion scheme of the IMPROVE visibility extinction coefficient, substantially improving the underestimation of winter visibility in Jiangsu Province. For instance, the correlation coefficient increased from 0.17 to 0.42, and root mean square error decreased from 2.62 to 1.76. The visibility inversion results under different humidity and wind speed levels show that the underestimation of the visibility using the IMPROVE scheme is especially remarkable. However, the underestimation issue is essentially solved using the neural network algorithm. This study serves as a basis for further predicting winter haze events in Jiangsu Province using WRF-Chem and deep-learning methods.

Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

Abstract. Multiple observation data sets – Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, the Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign – are used to evaluate the HMS–BlueSky–SMOKE (Sparse Matrix Operator Kernel Emission)–CMAQ (Community Multi-scale Air Quality Model) fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information was valuable for deriving both fire emissions and forecast evaluation. This study also identified that the operational NAQFC did not include fire contributions through lateral boundary conditions, resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to retrieve fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.

Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations

Abstract. The Fourier transform infrared (FTIR) spectra of fine particulate matter (PM2.5) contain many important absorption bands relevant for characterizing organic matter (OM) and obtaining organic matter to organic carbon (OM∕OC) ratios. However, extracting this information quantitatively – accounting for overlapping absorption bands and relating absorption to molar abundance – and furthermore relating abundances of functional groups to that of carbon atoms poses several challenges. In this work, we define a set of parameters that model these relationships and apply a probabilistic framework to identify values consistent with collocated field measurements of thermal–optical reflectance organic carbon (TOR OC). Parameter values are characterized for various sample types identified by cluster analysis of sample FTIR spectra, which are available for 17 sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network (7 sites in 2011 and 10 additional sites in 2013). The cluster analysis appears to separate samples according to predominant influence by dust, residential wood burning, wildfire, urban sources, and biogenic aerosols. Functional groups calibrations of aliphatic CH, alcohol COH, carboxylic acid COOH, carboxylate COO, and amine NH2 combined together reproduce TOR OC concentrations with reasonable agreement (r=0.96 for 2474 samples) and provide OM∕OC values generally consistent with our current best estimate of ambient OC. The mean OM∕OC ratios corresponding to sample types determined from cluster analysis range between 1.4 and 2.0, though ratios for individual samples exhibit a larger range. Trends in OM∕OC for sites aggregated by region or year are compared with another regression approach for estimating OM∕OC ratios from a mass closure equation of the major chemical species contributing to PM fine mass. Differences in OM∕OC estimates are observed according to estimation method and are explained through the sample types determined from spectral profiles of the PM.

Total sulfur analysis of fine particulate mass on nylon filters by ICP-OES.

Sulfur (S) and sulfate (SO4 2- ) in fine particulate matter (PM2.5 ) are monitored by the Interagency Monitoring of Protected Visual Environments (IMPROVE) network at remote and rural sites across the United States. Within the IMPROVE network, S is determined from X-ray fluorescence (XRF) spectroscopy from a Teflon filter, and SO4 2- is determined via ion chromatography (IC) from a nylon filter. Differences in S and SO4 2- estimates may indicate the presence of organosulfur (OS) species or biases between sampling and analytical methods. To reduce potential biases, an inductively coupled plasma-optical emission spectroscopy (ICP-OES) method was developed to allow for analysis of SO4 2- and S from a single filter extract. Sulfur (ICP-OES) and SO4 2- (IC) estimates from 2016 IMPROVE filters correlated strongly, suggesting that, on average, ICP-OES accurately estimated S. However, observed differences between slopes suggested the presence of water-soluble OS species, especially during summer. Organosulfur species are important indicators of secondary organic aerosols formed through reactions of biogenic and anthropogenic pollutants and can be quantified through laboratory techniques such as reverse-phase liquid chromatography (RPLC) or hydrophilic liquid interaction chromatography (HILIC) coupled to electrospray ionization-high-resolution tandem mass spectrometry (RPLC/ESI-HR-MS/MS and HILIC/ESI-HR-MS/MS, respectively), and field techniques using Aerodyne aerosol mass spectrometry (AMS). However, these methods are costly and introduce relatively large uncertainties when scaled for large networks such as IMPROVE. The method described in this report provides an inexpensive complement to XRF, which measures total S (insoluble and water-soluble S) to estimate water-soluble S and OS concentrations in PM.

Implications of Organic Mass to Carbon Ratios Increasing Over Time in the Rural United States

AbstractThe thermal evolution procedure used by most monitoring programs in the United States to determine carbonaceous aerosol concentrations is referred to as the thermal‐optical reflectance method, where an aerosol sample that has been collected on a quartz filter is heated and evolved carbon is characterized as either organic (OC) or light absorbing carbon (LAC). Evolved carbon assigned to OC is multiplied by a factor, Roc, to achieve an estimate of organic mass. Over the last 10–15 years, Roc, estimated through multiple linear regression analysis of data collected in the Interagency Monitoring of Protected Visual Environments (IMPROVE) program, has increased at about a rate of about 0.02 per year, reaching values above 2.0 in many regions of the United States. Analysis of evolved carbon concentration temporal trends suggests that thermal‐optical reflectance analysis, on the average, inaccurately bifurcates particulate carbon into the OC and LAC fractions with some LAC being inadvertently and wrongly assigned to the OC fraction. It is shown that misapportioned LAC assigned to OC is decreasing faster than true OC, resulting in a compensating increase in the Roc assigned to reported OC over time. A first‐order model is proposed to correct for the misapportioned carbon.

How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America

Abstract. Fires and the aerosols that they emit impact air quality, health, and climate, but the abundance and properties of carbonaceous aerosol (both black carbon and organic carbon) from biomass burning (BB) remain uncertain and poorly constrained. We aim to explore the uncertainties associated with fire emissions and their air quality and radiative impacts from underlying dry matter consumed and emissions factors. To investigate this, we compare model simulations from a global chemical transport model, GEOS-Chem, driven by a variety of fire emission inventories with surface and airborne observations of black carbon (BC) and organic aerosol (OA) concentrations and satellite-derived aerosol optical depth (AOD). We focus on two fire-detection-based and/or burned-area-based (FD-BA) inventories using burned area and active fire counts, respectively, i.e., the Global Fire Emissions Database version 4 (GFED4s) with small fires and the Fire INventory from NCAR version 1.5 (FINN1.5), and two fire radiative power (FRP)-based approaches, i.e., the Quick Fire Emission Dataset version 2.4 (QFED2.4) and the Global Fire Assimilation System version 1.2 (GFAS1.2). We show that, across the inventories, emissions of BB aerosol (BBA) differ by a factor of 4 to 7 over North America and that dry matter differences, not emissions factors, drive this spread. We find that simulations driven by QFED2.4 generally overestimate BC and, to a lesser extent, OA concentrations observations from two fire-influenced aircraft campaigns in North America (ARCTAS and DC3) and from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, while simulations driven by FINN1.5 substantially underestimate concentrations. The GFED4s and GFAS1.2-driven simulations provide the best agreement with OA and BC mass concentrations at the surface (IMPROVE), BC observed aloft (DC3 and ARCTAS), and AOD observed by MODIS over North America. We also show that a sensitivity simulation including an enhanced source of secondary organic aerosol (SOA) from fires, based on the NOAA Fire Lab 2016 experiments, produces substantial additional OA; however, the spread in the primary emissions estimates implies that this magnitude of SOA can be neither confirmed nor ruled out when comparing the simulations against the observations explored here. Given the substantial uncertainty in fire emissions, as represented by these four emission inventories, we find a sizeable range in 2012 annual BBA PM2.5 population-weighted exposure over Canada and the contiguous US (0.5 to 1.6 µg m−3). We also show that the range in the estimated global direct radiative effect of carbonaceous aerosol from fires (−0.11 to −0.048 W m−2) is large and comparable to the direct radiative forcing of OA (−0.09 W m−2) estimated in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). Our analysis suggests that fire emissions uncertainty challenges our ability to accurately characterize the impact of smoke on air quality and climate.

Incorporation of Remote PM2.5 Concentrations into the Downscaler Model for Spatially Fused Air Quality Surfaces.

The United States Environmental Protection Agency (EPA) has implemented a Bayesian spatial data fusion model called the Downscaler (DS) model to generate daily air quality surfaces for PM2.5 across the contiguous U.S. Previous implementations of DS relied on monitoring data from EPA’s Air Quality System (AQS) network, which is largely concentrated in urban areas. In this work, we introduce to the DS modeling framework an additional PM2.5 input dataset from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network located mainly in remote sites. In the western U.S. where IMPROVE sites are relatively dense (compared to the eastern U.S.), the inclusion of IMPROVE PM2.5 data to the DS model runs reduces predicted annual averages and 98th percentile concentrations by as much as 1.0 and 4 μg m−3, respectively. Some urban areas in the western U.S., such as Denver, Colorado, had moderate increases in the predicted annual average concentrations, which led to a sharpening of the gradient between urban and remote areas. Comparison of observed and DS-predicted concentrations for the grid cells containing IMPROVE and AQS sites revealed consistent improvement at the IMPROVE sites but some degradation at the AQS sites. Cross-validation results of common site-days withheld in both simulations show a slight reduction in the mean bias but a slight increase in the mean square error when the IMPROVE data is included. These results indicate that the output of the DS model (and presumably other Bayesian data fusion models) is sensitive to the addition of geographically distinct input data, and that the application of such models should consider the prediction domain (national or urban focused) when deciding to include new input data.

A Decadal Climatology of Chemical, Physical, and Optical Properties of Ambient Smoke in the Western and Southeastern United States

AbstractBiomass burning is a major source of summertime fine particulate matter in the United States, degrading air quality and affecting human health and climate. Fuels, burn conditions, and fire types vary across the United States, which may impact smoke composition and optical properties. We use the Hazard Mapping System to track smoke plumes for 10 years from 2008 to 2017, focusing on three U.S. regions: Southeast, Pacific West, and Southwest. Combining this geospatial data set with AErosol RObotic NETwork (AERONET) columnar data, and Interagency Monitoring of Protected Visual Environments (IMPROVE) network in situ observations, we define “smoke‐influenced days” and derive the properties of smoke aerosol for April through September, encompassing the main fire seasons across the United States. We find in the IMPROVE surface measurement that the elemental carbon fraction of smoke in the Southeast is statistically different from and lower than that in the west, consistent with more‐smoldering prescribed fires in the southeast. We find in the AERONET columnar observations that the mean single scattering albedo of smoke (at 675 nm) is lower in the two western regions (~0.94) compared to the southeast (~0.96), consistent with the differences in composition and fire types, which implies that the compositional differences at the surface may be representative of the column. Higher relative humidity in the southeast and less dust associated with smoke also appear to contribute to the higher smoke single scattering albedo relative to the west. Our results show that smoke properties vary regionally, with implications for models, data assimilation, and remote‐sensing.

Optical Properties of Aerosols and Chemical Composition Apportionment under Different Pollution Levels in Wuhan during January 2018

To clarify the aerosol optical properties under different pollution levels and their impacting factors, hourly organic carbon (OC), elemental carbon (EC), and water-soluble ion (WSI) concentrations in PM2.5 were observed by using monitoring for aerosols and gases (MARGA) and a semicontinuous OC/EC analyzer (Model RT-4) in Wuhan from 9 to 26 January 2018. The aerosol extinction coefficient (bext) was reconstructed using the original Interagency Monitoring of Protected Visual Environment (IMPROVE) formula with a modification to include sea salt aerosols. A good correlation was obtained between the reconstructed bext and measured bext converted from visibility. bext presented a unimodal distribution on polluted days (PM2.5 mass concentrations &gt; 75 μg⋅m−3), peaking at 19:00. bext on clean days (PM2.5 mass concentrations &lt; 75 μg⋅m−3) did not change much during the day, while on polluted days, it increased rapidly starting at 12:00 due to the decrease of wind speed and increase of relative humidity (RH). PM2.5 mass concentrations, the aerosol scattering coefficient (bscat), and the aerosol extinction coefficient increased with pollution levels. The value of bext was 854.72 Mm−1 on bad days, which was 4.86, 3.1, 2.29, and 1.28 times of that obtained on excellent, good, acceptable, and poor days, respectively. When RH &lt; 95%, bext exhibited an increasing trend with RH under all pollution levels, and the higher the pollution level, the bigger the growth rate was. However, when RH &gt; 95%, bext on acceptable, poor and bad days decreased, while bext on excellent and good days still increased. The overall bext in Wuhan in January was mainly contributed by NH4NO3 (25.2%) and organic matter (20.1%). The contributions of NH4NO3 and (NH4)2SO4 to bext increased significantly with pollution levels. On bad days, NH4NO3 and (NH4)2SO4 contributed the most to bext, accounting for 38.2% and 27.0%, respectively.

Characterization of aerosol chemical composition and the reconstruction of light extinction coefficients during winter in Wuhan, China.

To evaluate light extinction contributions of aerosol chemical constituents and their impacts on atmospheric visibility, the PM2.5 and its chemical components, light scattering (bsp) and absorption (bap) were continuously measured in Wuhan from January to February 2018. The average of PM2.5 concentration, bsp and bap were 96.5 ± 13.7 μg m-3, 564 ± 124 Mm-1 and 44 ± 8 Mm-1 during polluted days, respectively, which was about 2.0, 2.1 and 1.6 times higher than those of clean days, respectively. Compared with the clean days, the increase of the mass concentrations of SNA (SO42-, NO3-, NH4+) during polluted days was higher than those of organic (OC) and elemental (EC) carbon, indicated the increase of SNA was the main cause of air pollution. The PM2.5 concentration threshold was 66 μg m-3, corresponding to the visibility lower than 10 km. The revised Interagency Monitoring of Protected Visual Environments (IMPROVE) algorithm was used to reconstruct the light extinction coefficient (bext) in Wuhan. The sum of light extinction coefficients of (NH4)2SO4, NH4NO3 and organic matter (OM) accounted for 70.5% and 83.9% of bext during clean and polluted days, respectively. The backward trajectory and potential source contribution function (PSCF) analysis revealed that regional transport accounted for 55.6% of the total airflow, which originated from south, northwest and west of Wuhan. The increases of (NH4)2SO4 and NH4NO3 concentrations, emitted from local vehicle exhaust and coal combustion, and their hygroscopic growth in ambient were the major causes of pollution in Wuhan.