Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. Thes... more Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. These SBTEX are hazardous air pollutants (HAPs) and released from the petrochemical industry, combustion process, transport emission, and solvent usage sources. Although several SBTEX toxic assessment studies have been conducted, they have mainly relied on ambient measurements to estimate exposure and limiting their scope to specific locations and observational periods. To overcome these spatiotemporal limitations, an air quality modeling system over the U.S. Gulf region was created predicting the the spatially and temporally enhanced SBTEX modeling concentrations from May to September 2012. Due to the incompleteness of SBTEX in the official US EPA National Emission Inventory (NEI), Hazardous Air Pollutions Imputation (HAPI) program was used to identify and estimate the missing HAPs emissions. The improved emission data was processed to generate the chemically-speciated hourly gridded emission inputs for the Comprehensive Air Quality Model with Extensions (CAMx) chemical transport model to simulate the SBTEX concentrations over the Gulf modeling region. SBTEX pollutants were modeled using
We applied the USEPA's 2011 National Emission Inventory (NEI) Emissions Modeling Platform (EMP) v... more We applied the USEPA's 2011 National Emission Inventory (NEI) Emissions Modeling Platform (EMP) version 6 (USEPA, 2021b), based on the official 2011 NEI with the SMOKE model system to generate the year 2012 gridded hourly emissions for the CAMx modeling. The following emission source types were processed: 1) area, 2) point, 3) mobile, and 4) biogenic source types. The area emission sources included the following emission sectors: fugitive dust (afdust), commercial marine vessels (cmv), non-point source oil and gas industry (np_oilgas), rail, agriculture (ag), agriculture fire (agfire), non-point (nonpt), and nonroad. The stationary point sources included stationary point electric generating units (ptegu), point source fire or wildfire (ptfire), point source from non-electric generating unit point source (ptnonipm, which include most industrial processes but exclude the "egu" and "oilgas" industry), and point source oil and gas (pt_oilgas) sectors. The details of these emission sectors can be found in the technical supporting
Atmospheric ammonia (NH3) is a critical component of our atmosphere that contributes to air quali... more Atmospheric ammonia (NH3) is a critical component of our atmosphere that contributes to air quality degradation and reactive nitrogen deposition; however, our knowledge of NH3 in urban environments remains limited. Year-long ambient NH3 and related species were measured for concentrations and the nitrogen isotopic compositions (δ 15 N) of NH3 and particulate ammonium (pNH4 + ) to understand the temporal sources and chemistry of NH3 in a northeastern US urban environment. We found that urban NH3 and pNH4 + concentrations were elevated compared to regional rural background monitoring stations, with seasonally significant variations. Local and transported sources of NHx (NH3 + pNH4 + ) were identified using polar bivariate and statistical back trajectory analysis, which suggested the importance of vehicles, volatilization, industry, fuel combustion, and biomass burning emissions. Utilizing a uniquely positive δ 15 N(NH3) emission source signature from vehicles, a Bayesian stable isotope mixing model indicates that vehicles contribute 30.7±11.6% (mean±1σ) to the annual background level of urban NHx, with a strong seasonal pattern with higher relative contribution during winter (45.8±13.0%) compared to summer (20.8±9.7%). The decrease in the relative importance of vehicle emissions during the summer was suggested to be driven by temperature-dependent NH3 emissions from volatilization sources based on wind direction, back trajectory, and NH3 emission inventory analysis. This work highlights that reducing vehicle NH3 emissions should be considered to improve wintertime air quality in this region.
Atmospheric ammonia (NH 3 ) is a critical component of our atmosphere that contributes to air qua... more Atmospheric ammonia (NH 3 ) is a critical component of our atmosphere that contributes to air quality degradation and reactive nitrogen deposition; however, our knowledge of NH 3 in urban environments remains limited. Year-long ambient NH 3 and related species were measured for concentrations and the nitrogen isotopic compositions (δ 15 N) of NH 3 and particulate ammonium (pNH + 4 ) were measured to understand the temporal sources and chemistry of NH 3 in a northeastern US urban environment. We found that urban NH 3 and pNH + 4 concentrations were elevated compared to regional rural background monitoring stations, with seasonally significant variations. Local and transported sources of NH x (NH 3 + pNH + 4 ) were identified using polar bivariate and statistical back trajectory analysis, which suggested the importance of vehicles, volatilization, industry, and stationary fuel combustion emissions. Utilizing a uniquely positive δ 15 N(NH 3 ) emission source signature from vehicles, a Bayesian stable isotope mixing model (SIMMR) indicates that vehicles contribute 46.8±3.5 % (mean ±1σ ) to the annual background level of urban NH x , with a strong seasonal pattern with higher relative contribution during winter (56.4±7.6 %) compared to summer (34.1±5.5 %). The decrease in the relative importance of vehicle emissions during the summer was suggested to be driven by temperature-dependent NH 3 emissions from volatilization sources, seasonal fuel-combustion emissions related to energy generation, and change in seasonal transport patterns based on wind direction, back trajectory, and NH 3 emission inventory analysis. This work highlights that reducing vehicle NH 3 emissions should be considered to improve wintertime air quality in this region.
The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for me... more The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for mesoscale air quality modeling applications. We present ESP v2.0, which expands upon ESP v1.0 by spatially allocating futureyear non-power sector emissions to account for projected population and land use changes. In ESP v2.0, US Census division-level emission growth factors are developed using an energy system model. Regional factors for populationrelated emissions are spatially disaggregated to the county level using population growth and migration projections. The county-level growth factors are then applied to grow a base-year emission inventory to the future. Spatial surrogates are updated to account for future population and land use changes, and these surrogates are used to map projected county-level emissions to a modeling grid for use within an air quality model. We evaluate ESP v2.0 by comparing US 12 km emissions for 2005 with projections for 2050. We also evaluate the individual and combined effects of county-level disaggregation and of updating spatial surrogates. Results suggest that the common practice of modeling future emissions without considering spatial redistribution over-predicts emissions in the urban core and under-predicts emissions in suburban and exurban areas. In addition to improving multidecadal emission projections, a strength of ESP v2.0 is that it can be applied to assess the emissions and air quality implications of alternative energy, population and land use scenarios.
Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. Thes... more Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. These SBTEX are hazardous air pollutants (HAPs) and released from the petrochemical industry, combustion process, transport emission, and solvent usage sources. Although several SBTEX toxic assessment studies have been conducted, they have mainly relied on ambient measurements to estimate exposure and limiting their scope to specific locations and observational periods. To overcome these spatiotemporal limitations, an air quality modeling system over the U.S. Gulf region was created predicting the the spatially and temporally enhanced SBTEX modeling concentrations from May to September 2012. Due to the incompleteness of SBTEX in the official US EPA National Emission Inventory (NEI), Hazardous Air Pollutions Imputation (HAPI) program was used to identify and estimate the missing HAPs emissions. The improved emission data was processed to generate the chemically-speciated hourly gridded emission inputs for the Comprehensive Air Quality Model with Extensions (CAMx) chemical transport model to simulate the SBTEX concentrations over the Gulf modeling region. SBTEX pollutants were modeled using
Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. Thes... more Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. These SBTEX are hazardous air pollutants (HAPs) and released from the petrochemical industry, combustion process, transport emission, and solvent usage sources. Although several SBTEX toxic assessment studies have been conducted, they have mainly relied on ambient measurements to estimate exposure and limiting their scope to specific locations and observational periods. To overcome these spatiotemporal limitations, an air quality modeling system over the U.S. Gulf region was created predicting the the spatially and temporally enhanced SBTEX modeling concentrations from May to September 2012. Due to the incompleteness of SBTEX in the official US EPA National Emission Inventory (NEI), Hazardous Air Pollutions Imputation (HAPI) program was used to identify and estimate the missing HAPs emissions. The improved emission data was processed to generate the chemically-speciated hourly gridded emission inputs for the Comprehensive Air Quality Model with Extensions (CAMx) chemical transport model to simulate the SBTEX concentrations over the Gulf modeling region. SBTEX pollutants were modeled using
SMOKE Digital Object Identifier (DOI): SMOKE-MOVES Integration Tool Development 1. <b>Avera... more SMOKE Digital Object Identifier (DOI): SMOKE-MOVES Integration Tool Development 1. <b>Average Speed Distribution (ASD) Enhancement</b> New feature in <b>Movesmrg</b> for users to optional to apply hour-specific average speed distribution profiles based on 16-speed bins by FIPS, vehicle, road, fuel, process, and day of the week. <strong>USE_AVG_SPD_DIST</strong> [default: N]: Define whether to apply hourly average speed distribution profiles by hour or not. <strong>AVGSPD_SCCXREF</strong>: Average speed distribution SCC mapping input file. <strong>SPDIST</strong>: 16-speed bin average speed distribution profiles sorted by FIPS, SCC, day of the week and hour. The impacts of these ASD and NOx humidity correction applications have been analyzed in the research presentation from the 2019 International Emissions Inventory Conference. 2. <b>NOx Humidity Correction Enhancement</b> New feature in <b>Movesmrg</b&...
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in Engli... more Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre -including this research content -immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Well written and the quality of the paper is excellent. Readability was also great! It covers com... more Well written and the quality of the paper is excellent. Readability was also great! It covers complex bottom-up inventory developments across significantly different types of emissions sources, such as point, transportation, agriculture and so on. Each emission sector has been well described and carefully adapted emissions calculations method from reliable sources. The authors also carefully designed the model for global modeling users on how to adapt their own emissions to use this model. These model developers understand the complexity of emissions inventory development and processing across regions/countries.
Asian journal of atmospheric environment, Mar 1, 2013
Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asi... more Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asian region. Estimation of emissions from fires, however, has been problematic, primarily because of uncertainty in the size and location of sources and in their temporal and spatial variability. Hence, more comprehensive tools to estimate wildfire emissions and that can characterize their temporal and spatial variability are needed. Furthermore, an emission processing system that can generate speciated, gridded, and temporally allocated emissions is needed to support air-quality modeling studies over Asia. For these reasons, a biomass-burning emissions modeling system based on satellite imagery was developed to better account for the spatial and temporal distributions of emissions. The BlueSky Framework, which was developed by the USDA Forest Service and US EPA, was used to develop the Asian biomass-burning emissions modeling system. The sub-models used for this study were the Fuel Characteristic Classification System (FCCS), CONSUME, and the Emissions Production Model (EPM). Our domain covers not only Asia but also Siberia and part of central Asia to assess the large boreal fires in the region. The MODIS fire products and vegetation map were used in this study. Using the developed modeling system, biomass-burning emissions were estimated during April and July 2008, and the results were compared with previous studies. Our results show good to fair agreement with those of GFEDv3 for most regions, ranging from 9.7 % in East Asia to 52% in Siberia. The SMOKE modeling system was combined with this system to generate three-dimensional model-ready emissions employing the fire-plume rise algorithm. This study suggests a practicable and maintainable methodology for supporting Asian air-quality modeling studies and to help understand the impact of air-pollutant emissions on Asian air quality.
The main focus of this study is to develop a dynamic-coupling "inline" air quality modeling syste... more The main focus of this study is to develop a dynamic-coupling "inline" air quality modeling system for the meteorology-induced emissions with simulated meteorological data. To improve the spatiotemporal representations and accuracy of onroad vehicle emissions, which are largely sensitive to local meteorology, 15 we developed the "inline" coupler module called "MetEmis" for Meteorology-Induced Emission sources within the Community Multiscale Air Quality (CMAQ) version 5.3.2 modeling system. It can dynamically estimate meteorology-induced hourly gridded emissions within the CMAQ modeling system using modeled meteorology. The CMAQ air quality modeling system is applied over the continental U.S. for two months (January and July 2019) for two emissions scenarios: a) current "offline" based onroad vehicle emissions, 20 and b) "inline" CMAQ-MetEmis onroad vehicle emissions. Overall, the "MetEmis" coupler allows us to dynamically simulate onroad vehicle emissions from the MOVES onroad emission model for CMAQ with a better spatio-temporal representation compared to the "offline" scenario based on static temporal profiles. With an instance interpolation calculation approach, the new "inline" approach significantly enhances the computational efficiency and accuracy of estimating mobile source emissions, compared to the existing 25 "offline" approach that yields almost identical hourly emission estimation. The domain total of daily VOC emissions from the "inline" scenario shows the largest impacts from the local meteorology, which is approximately 10% lower than the ones from the "offline" scenario. Especially, the major difference of VOC estimates was shown over the California region. These local meteorology impacts on onroad vehicle emissions via CMAQ-MetEmis revealed an improvement in hourly NO2, daily maximum ozone, and daily 30 average PM2.5 patterns with a higher agreement and correlation with daily ground observations.
The Comprehensive Automobile Research System (CARS) is an open-source python-based automobile emi... more The Comprehensive Automobile Research System (CARS) is an open-source python-based automobile emissions inventory model designed to efficiently estimate high quality emissions from motor-vehicle emission sources. It can estimate the criteria air pollutants, greenhouse gases, and air toxics in various temporal resolutions at the national, state, county, and any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road link-level network Geographic Information System (GIS) information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's (EEA) onroad automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from onroad automobile sources. It can optionally utilize road link-specific average speed distribution (ASD) inputs to reflect more realistic vehicle speed variations by road type than a road-specific single averaged speed approach. Also, utilizing high-resolution road GIS data allows the CARS to estimate the road link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a moderate increase of VOC (33%), CO (52%), and fine particulate matter (PM2.5) (15%) emissions while NOx and SOx are reduced by 24% and 17% in the CARS estimates. The main differences are driven by the usage of different vehicle activities and the incorporation of road-specific ASD, which plays a critical role
Climate warming has been implicated as a major driver of recent catastrophic wildfires worldwide ... more Climate warming has been implicated as a major driver of recent catastrophic wildfires worldwide but analyses of regional differences in U.S. wildfires show that socioeconomic factors also have a large role. We previously leveraged statistical projections of annual areas burned (AAB) over the fast-growing Southeastern U.S. that include both climate and socioeconomic changes from 2011 to 2060, and developed wildfire emissions estimates over the region at 12-km x 12-km resolution to enable air quality (AQ) impact assessments for 2010 and selected future years. These estimates employed two AAB datasets, one using statistical downscaling ("statistical d-s"), and another using dynamical downscaling ("dynamical d-s") of climate inputs from the same climate realization. This paper evaluates these wildfire emissions estimates against the U.S. National Emissions Inventory (NEI) as a benchmark in contemporary (2010) simulations with the Community Multiscale Air Quality (CMAQ) model, and against network observations for ozone and particulate matter below 2.5 µm in diameter (PM2.5). We hypothesize that our emissions estimates will yield model results that meet acceptable performance criteria, and are comparable to those using the NEI. The three simulations, which differ only in wildfire emissions, compare closely, with differences in ozone and PM2.5 below 1% and 8% respectively, but have much larger maximum mean fractional biases (MFBs) against observations (25% and 51% respectively). The largest biases for ozone are in the fire-free winter, indicating that modeling uncertainties other than wildfire emissions are mainly responsible. Statistical d-s, with the largest AAB domain-wide, is 7% more positively biased and 4% less negatively biased in PM2.5 on average than the other two cases, while dynamical d-s and the NEI differ only by 2%-3% partly because of their equally large summertime PM2.5 underpredictions. Primary species (elemental carbon, and ammonium from ammonia) have good-to-acceptable results, especially for the downscaling cases, providing confidence in our emissions estimation methodology. Compensating biases in sulfate (positive), and in organic carbon and dust (negative) lead to acceptable PM2.5 performance. As these species are driven by secondary chemistry or non-wildfire sources, their production pathways can be fruitful avenues for CMAQ
Abstract: This report is a guide for airport operators on effective procedures for using air qual... more Abstract: This report is a guide for airport operators on effective procedures for using air quality models in combination with on-site measurement equipment to prepare a comprehensive assessment of air pollutant concentrations in the vicinity of airports. It is ...
Black carbon (BC) is a key component of fine particulate matter that is emitted from aircraft and... more Black carbon (BC) is a key component of fine particulate matter that is emitted from aircraft and other combustion sources at airports. Understanding the contribution of aviation activity to ambient BC is important given the projected growth in aviation transport and decrease ...
Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. Thes... more Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. These SBTEX are hazardous air pollutants (HAPs) and released from the petrochemical industry, combustion process, transport emission, and solvent usage sources. Although several SBTEX toxic assessment studies have been conducted, they have mainly relied on ambient measurements to estimate exposure and limiting their scope to specific locations and observational periods. To overcome these spatiotemporal limitations, an air quality modeling system over the U.S. Gulf region was created predicting the the spatially and temporally enhanced SBTEX modeling concentrations from May to September 2012. Due to the incompleteness of SBTEX in the official US EPA National Emission Inventory (NEI), Hazardous Air Pollutions Imputation (HAPI) program was used to identify and estimate the missing HAPs emissions. The improved emission data was processed to generate the chemically-speciated hourly gridded emission inputs for the Comprehensive Air Quality Model with Extensions (CAMx) chemical transport model to simulate the SBTEX concentrations over the Gulf modeling region. SBTEX pollutants were modeled using
We applied the USEPA's 2011 National Emission Inventory (NEI) Emissions Modeling Platform (EMP) v... more We applied the USEPA's 2011 National Emission Inventory (NEI) Emissions Modeling Platform (EMP) version 6 (USEPA, 2021b), based on the official 2011 NEI with the SMOKE model system to generate the year 2012 gridded hourly emissions for the CAMx modeling. The following emission source types were processed: 1) area, 2) point, 3) mobile, and 4) biogenic source types. The area emission sources included the following emission sectors: fugitive dust (afdust), commercial marine vessels (cmv), non-point source oil and gas industry (np_oilgas), rail, agriculture (ag), agriculture fire (agfire), non-point (nonpt), and nonroad. The stationary point sources included stationary point electric generating units (ptegu), point source fire or wildfire (ptfire), point source from non-electric generating unit point source (ptnonipm, which include most industrial processes but exclude the "egu" and "oilgas" industry), and point source oil and gas (pt_oilgas) sectors. The details of these emission sectors can be found in the technical supporting
Atmospheric ammonia (NH3) is a critical component of our atmosphere that contributes to air quali... more Atmospheric ammonia (NH3) is a critical component of our atmosphere that contributes to air quality degradation and reactive nitrogen deposition; however, our knowledge of NH3 in urban environments remains limited. Year-long ambient NH3 and related species were measured for concentrations and the nitrogen isotopic compositions (δ 15 N) of NH3 and particulate ammonium (pNH4 + ) to understand the temporal sources and chemistry of NH3 in a northeastern US urban environment. We found that urban NH3 and pNH4 + concentrations were elevated compared to regional rural background monitoring stations, with seasonally significant variations. Local and transported sources of NHx (NH3 + pNH4 + ) were identified using polar bivariate and statistical back trajectory analysis, which suggested the importance of vehicles, volatilization, industry, fuel combustion, and biomass burning emissions. Utilizing a uniquely positive δ 15 N(NH3) emission source signature from vehicles, a Bayesian stable isotope mixing model indicates that vehicles contribute 30.7±11.6% (mean±1σ) to the annual background level of urban NHx, with a strong seasonal pattern with higher relative contribution during winter (45.8±13.0%) compared to summer (20.8±9.7%). The decrease in the relative importance of vehicle emissions during the summer was suggested to be driven by temperature-dependent NH3 emissions from volatilization sources based on wind direction, back trajectory, and NH3 emission inventory analysis. This work highlights that reducing vehicle NH3 emissions should be considered to improve wintertime air quality in this region.
Atmospheric ammonia (NH 3 ) is a critical component of our atmosphere that contributes to air qua... more Atmospheric ammonia (NH 3 ) is a critical component of our atmosphere that contributes to air quality degradation and reactive nitrogen deposition; however, our knowledge of NH 3 in urban environments remains limited. Year-long ambient NH 3 and related species were measured for concentrations and the nitrogen isotopic compositions (δ 15 N) of NH 3 and particulate ammonium (pNH + 4 ) were measured to understand the temporal sources and chemistry of NH 3 in a northeastern US urban environment. We found that urban NH 3 and pNH + 4 concentrations were elevated compared to regional rural background monitoring stations, with seasonally significant variations. Local and transported sources of NH x (NH 3 + pNH + 4 ) were identified using polar bivariate and statistical back trajectory analysis, which suggested the importance of vehicles, volatilization, industry, and stationary fuel combustion emissions. Utilizing a uniquely positive δ 15 N(NH 3 ) emission source signature from vehicles, a Bayesian stable isotope mixing model (SIMMR) indicates that vehicles contribute 46.8±3.5 % (mean ±1σ ) to the annual background level of urban NH x , with a strong seasonal pattern with higher relative contribution during winter (56.4±7.6 %) compared to summer (34.1±5.5 %). The decrease in the relative importance of vehicle emissions during the summer was suggested to be driven by temperature-dependent NH 3 emissions from volatilization sources, seasonal fuel-combustion emissions related to energy generation, and change in seasonal transport patterns based on wind direction, back trajectory, and NH 3 emission inventory analysis. This work highlights that reducing vehicle NH 3 emissions should be considered to improve wintertime air quality in this region.
The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for me... more The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for mesoscale air quality modeling applications. We present ESP v2.0, which expands upon ESP v1.0 by spatially allocating futureyear non-power sector emissions to account for projected population and land use changes. In ESP v2.0, US Census division-level emission growth factors are developed using an energy system model. Regional factors for populationrelated emissions are spatially disaggregated to the county level using population growth and migration projections. The county-level growth factors are then applied to grow a base-year emission inventory to the future. Spatial surrogates are updated to account for future population and land use changes, and these surrogates are used to map projected county-level emissions to a modeling grid for use within an air quality model. We evaluate ESP v2.0 by comparing US 12 km emissions for 2005 with projections for 2050. We also evaluate the individual and combined effects of county-level disaggregation and of updating spatial surrogates. Results suggest that the common practice of modeling future emissions without considering spatial redistribution over-predicts emissions in the urban core and under-predicts emissions in suburban and exurban areas. In addition to improving multidecadal emission projections, a strength of ESP v2.0 is that it can be applied to assess the emissions and air quality implications of alternative energy, population and land use scenarios.
Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. Thes... more Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. These SBTEX are hazardous air pollutants (HAPs) and released from the petrochemical industry, combustion process, transport emission, and solvent usage sources. Although several SBTEX toxic assessment studies have been conducted, they have mainly relied on ambient measurements to estimate exposure and limiting their scope to specific locations and observational periods. To overcome these spatiotemporal limitations, an air quality modeling system over the U.S. Gulf region was created predicting the the spatially and temporally enhanced SBTEX modeling concentrations from May to September 2012. Due to the incompleteness of SBTEX in the official US EPA National Emission Inventory (NEI), Hazardous Air Pollutions Imputation (HAPI) program was used to identify and estimate the missing HAPs emissions. The improved emission data was processed to generate the chemically-speciated hourly gridded emission inputs for the Comprehensive Air Quality Model with Extensions (CAMx) chemical transport model to simulate the SBTEX concentrations over the Gulf modeling region. SBTEX pollutants were modeled using
Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. Thes... more Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX) are established neurotoxicants. These SBTEX are hazardous air pollutants (HAPs) and released from the petrochemical industry, combustion process, transport emission, and solvent usage sources. Although several SBTEX toxic assessment studies have been conducted, they have mainly relied on ambient measurements to estimate exposure and limiting their scope to specific locations and observational periods. To overcome these spatiotemporal limitations, an air quality modeling system over the U.S. Gulf region was created predicting the the spatially and temporally enhanced SBTEX modeling concentrations from May to September 2012. Due to the incompleteness of SBTEX in the official US EPA National Emission Inventory (NEI), Hazardous Air Pollutions Imputation (HAPI) program was used to identify and estimate the missing HAPs emissions. The improved emission data was processed to generate the chemically-speciated hourly gridded emission inputs for the Comprehensive Air Quality Model with Extensions (CAMx) chemical transport model to simulate the SBTEX concentrations over the Gulf modeling region. SBTEX pollutants were modeled using
SMOKE Digital Object Identifier (DOI): SMOKE-MOVES Integration Tool Development 1. <b>Avera... more SMOKE Digital Object Identifier (DOI): SMOKE-MOVES Integration Tool Development 1. <b>Average Speed Distribution (ASD) Enhancement</b> New feature in <b>Movesmrg</b> for users to optional to apply hour-specific average speed distribution profiles based on 16-speed bins by FIPS, vehicle, road, fuel, process, and day of the week. <strong>USE_AVG_SPD_DIST</strong> [default: N]: Define whether to apply hourly average speed distribution profiles by hour or not. <strong>AVGSPD_SCCXREF</strong>: Average speed distribution SCC mapping input file. <strong>SPDIST</strong>: 16-speed bin average speed distribution profiles sorted by FIPS, SCC, day of the week and hour. The impacts of these ASD and NOx humidity correction applications have been analyzed in the research presentation from the 2019 International Emissions Inventory Conference. 2. <b>NOx Humidity Correction Enhancement</b> New feature in <b>Movesmrg</b&...
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in Engli... more Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre -including this research content -immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Well written and the quality of the paper is excellent. Readability was also great! It covers com... more Well written and the quality of the paper is excellent. Readability was also great! It covers complex bottom-up inventory developments across significantly different types of emissions sources, such as point, transportation, agriculture and so on. Each emission sector has been well described and carefully adapted emissions calculations method from reliable sources. The authors also carefully designed the model for global modeling users on how to adapt their own emissions to use this model. These model developers understand the complexity of emissions inventory development and processing across regions/countries.
Asian journal of atmospheric environment, Mar 1, 2013
Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asi... more Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asian region. Estimation of emissions from fires, however, has been problematic, primarily because of uncertainty in the size and location of sources and in their temporal and spatial variability. Hence, more comprehensive tools to estimate wildfire emissions and that can characterize their temporal and spatial variability are needed. Furthermore, an emission processing system that can generate speciated, gridded, and temporally allocated emissions is needed to support air-quality modeling studies over Asia. For these reasons, a biomass-burning emissions modeling system based on satellite imagery was developed to better account for the spatial and temporal distributions of emissions. The BlueSky Framework, which was developed by the USDA Forest Service and US EPA, was used to develop the Asian biomass-burning emissions modeling system. The sub-models used for this study were the Fuel Characteristic Classification System (FCCS), CONSUME, and the Emissions Production Model (EPM). Our domain covers not only Asia but also Siberia and part of central Asia to assess the large boreal fires in the region. The MODIS fire products and vegetation map were used in this study. Using the developed modeling system, biomass-burning emissions were estimated during April and July 2008, and the results were compared with previous studies. Our results show good to fair agreement with those of GFEDv3 for most regions, ranging from 9.7 % in East Asia to 52% in Siberia. The SMOKE modeling system was combined with this system to generate three-dimensional model-ready emissions employing the fire-plume rise algorithm. This study suggests a practicable and maintainable methodology for supporting Asian air-quality modeling studies and to help understand the impact of air-pollutant emissions on Asian air quality.
The main focus of this study is to develop a dynamic-coupling "inline" air quality modeling syste... more The main focus of this study is to develop a dynamic-coupling "inline" air quality modeling system for the meteorology-induced emissions with simulated meteorological data. To improve the spatiotemporal representations and accuracy of onroad vehicle emissions, which are largely sensitive to local meteorology, 15 we developed the "inline" coupler module called "MetEmis" for Meteorology-Induced Emission sources within the Community Multiscale Air Quality (CMAQ) version 5.3.2 modeling system. It can dynamically estimate meteorology-induced hourly gridded emissions within the CMAQ modeling system using modeled meteorology. The CMAQ air quality modeling system is applied over the continental U.S. for two months (January and July 2019) for two emissions scenarios: a) current "offline" based onroad vehicle emissions, 20 and b) "inline" CMAQ-MetEmis onroad vehicle emissions. Overall, the "MetEmis" coupler allows us to dynamically simulate onroad vehicle emissions from the MOVES onroad emission model for CMAQ with a better spatio-temporal representation compared to the "offline" scenario based on static temporal profiles. With an instance interpolation calculation approach, the new "inline" approach significantly enhances the computational efficiency and accuracy of estimating mobile source emissions, compared to the existing 25 "offline" approach that yields almost identical hourly emission estimation. The domain total of daily VOC emissions from the "inline" scenario shows the largest impacts from the local meteorology, which is approximately 10% lower than the ones from the "offline" scenario. Especially, the major difference of VOC estimates was shown over the California region. These local meteorology impacts on onroad vehicle emissions via CMAQ-MetEmis revealed an improvement in hourly NO2, daily maximum ozone, and daily 30 average PM2.5 patterns with a higher agreement and correlation with daily ground observations.
The Comprehensive Automobile Research System (CARS) is an open-source python-based automobile emi... more The Comprehensive Automobile Research System (CARS) is an open-source python-based automobile emissions inventory model designed to efficiently estimate high quality emissions from motor-vehicle emission sources. It can estimate the criteria air pollutants, greenhouse gases, and air toxics in various temporal resolutions at the national, state, county, and any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road link-level network Geographic Information System (GIS) information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's (EEA) onroad automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from onroad automobile sources. It can optionally utilize road link-specific average speed distribution (ASD) inputs to reflect more realistic vehicle speed variations by road type than a road-specific single averaged speed approach. Also, utilizing high-resolution road GIS data allows the CARS to estimate the road link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a moderate increase of VOC (33%), CO (52%), and fine particulate matter (PM2.5) (15%) emissions while NOx and SOx are reduced by 24% and 17% in the CARS estimates. The main differences are driven by the usage of different vehicle activities and the incorporation of road-specific ASD, which plays a critical role
Climate warming has been implicated as a major driver of recent catastrophic wildfires worldwide ... more Climate warming has been implicated as a major driver of recent catastrophic wildfires worldwide but analyses of regional differences in U.S. wildfires show that socioeconomic factors also have a large role. We previously leveraged statistical projections of annual areas burned (AAB) over the fast-growing Southeastern U.S. that include both climate and socioeconomic changes from 2011 to 2060, and developed wildfire emissions estimates over the region at 12-km x 12-km resolution to enable air quality (AQ) impact assessments for 2010 and selected future years. These estimates employed two AAB datasets, one using statistical downscaling ("statistical d-s"), and another using dynamical downscaling ("dynamical d-s") of climate inputs from the same climate realization. This paper evaluates these wildfire emissions estimates against the U.S. National Emissions Inventory (NEI) as a benchmark in contemporary (2010) simulations with the Community Multiscale Air Quality (CMAQ) model, and against network observations for ozone and particulate matter below 2.5 µm in diameter (PM2.5). We hypothesize that our emissions estimates will yield model results that meet acceptable performance criteria, and are comparable to those using the NEI. The three simulations, which differ only in wildfire emissions, compare closely, with differences in ozone and PM2.5 below 1% and 8% respectively, but have much larger maximum mean fractional biases (MFBs) against observations (25% and 51% respectively). The largest biases for ozone are in the fire-free winter, indicating that modeling uncertainties other than wildfire emissions are mainly responsible. Statistical d-s, with the largest AAB domain-wide, is 7% more positively biased and 4% less negatively biased in PM2.5 on average than the other two cases, while dynamical d-s and the NEI differ only by 2%-3% partly because of their equally large summertime PM2.5 underpredictions. Primary species (elemental carbon, and ammonium from ammonia) have good-to-acceptable results, especially for the downscaling cases, providing confidence in our emissions estimation methodology. Compensating biases in sulfate (positive), and in organic carbon and dust (negative) lead to acceptable PM2.5 performance. As these species are driven by secondary chemistry or non-wildfire sources, their production pathways can be fruitful avenues for CMAQ
Abstract: This report is a guide for airport operators on effective procedures for using air qual... more Abstract: This report is a guide for airport operators on effective procedures for using air quality models in combination with on-site measurement equipment to prepare a comprehensive assessment of air pollutant concentrations in the vicinity of airports. It is ...
Black carbon (BC) is a key component of fine particulate matter that is emitted from aircraft and... more Black carbon (BC) is a key component of fine particulate matter that is emitted from aircraft and other combustion sources at airports. Understanding the contribution of aviation activity to ambient BC is important given the projected growth in aviation transport and decrease ...
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Papers by Bok Baek