Session Presentations

Nicholas Heath, Matthew Alvarado, Amy McVey, Elizabeth Bettencourt, Chase Calkins, Karen Cady-Pereira, Jeana Mascio, Mark Shephard, Michael Sitwell, Jonathan Pleim, Limei Ran, Hansen Cao, Daven Henze

Ammonia (NH3) negatively affects air quality and human health, primarily through its role as a PM2.5 precursor. Therefore, accurate emissions estimates of NH3 are critical to air quality managers and forecasters. However, current emission inventories have significant uncertainties that inhibit reliable estimates of NH3. Observations from the satellite-based Cross-track Infrared Spectrometer (CrIS) provide an opportunity to improve NH3 emissions estimates using inversion-based modeling techniques. In the current study, CrIS NH3 observations are used in a finite-difference mass-balance inversion to constrain NH3 emissions in the Community Multiscale Air Quality (CMAQ) model. A unique aspect of our inversion is that it is applied using an NH3 bidirectional flux scheme. CMAQ is run over the continental United States using 12 km grid spacing for June 2015. A baseline simulation is made with unperturbed NH3 emissions. Then, a second simulation is performed with NH3 emissions perturbed by 20%. The resulting surface NH3 concentrations are compared to CrIS observations to derive a monthly-mean scaling factor for the a priori NH3 emissions. Lastly, an optimized simulation performed with the updated emissions improves agreement with CrIS observations. Encouragingly, the inversion also increases the correlation with independent NH3 observations from the Ammonia Monitoring Network (AMoN), increasing it from 0.66 to 0.7, while also reducing the overall error from 0.70 ug m-3 to 0.58 ug m-3. Similarly, we found that CMAQ simulations performed 36 km grid spacing spanning all of North America are also improved through this satellite-based inversion technique. Overall, this project illustrates the usefulness of applying satellite observations in a top-down inversion using NH3 bidirectional flux for improving NH3 emissions. The resulting emissions data will be made publicly available to air quality managers and other stakeholders for use in their air quality modeling applications.

Abi Lawal, Armistead Russell (Ph.D.), Jennifer Kaiser (Ph.D.)

Airports are high emitters of harmful pollutants species, leading to elevated concentrations and health implications as they tend to be located near city centers. Thus, better characterization of airport emissions is essential to understanding its effects on air quality. In this study, impacts of emissions from the Atlanta Hartsfield-Jackson airport (ATL) on ozone (O3), ultrafine particulates (UFPs) and particulate matter (PM2.5), are evaluated using the Community Multiscale Air Quality (CMAQ) model and high resolution satellite observations from TROPOMI. Two airport inventories are compared, an inventory using the default emissions where Landing and Take Off (LTO) processes are allocated at the same altitude and geolocation of the airport (default), and a modified (3D) inventory that has LTO and cruise emissions which are vertically and horizontally distributed, accounting for aircraft climb and descend rates. The 3D showed improved Normalized Mean Bias [NMB -46%/-43%] and Root Mean Square Error [RMSE 1.21/1.12 (1015 molecules/cm2)] between CMAQ and TROPOMI NO2 vertical column densities (VCDs), and improved agreement in ATL associated grids [RMSE 1.76/1.33 (1015 molecules/cm2)]. Close agreement of TROPOMI observations to modeled concentrations from measured sources (i.e. two power plants) was also observed. Our analysis indicated that ATL emissions yielded maximum increases in UFP particle number and PM2.5 mass by 6.5x108 #/m3 (~38%), and 0.7ug/m3 (~8%) respectively. Modelled Daily Maximum 8hr O3 with the modified inventory predicted an increase between 6ppb and 3ppb over the default inventory. Projected increases in future aviation emissions led to ozone increases up to 3 ppb around the airport as well. The results show 1) spatial allocation of airport emissions have notable effects on air quality and will be of further importance as airports become a larger part of the total urban emissions, 2) the applicability of high resolution satellite retrieval to better understand emissions from facilities such as airports.

Jesse Bash, Ben Murphy, Darrell Sonntag, Claudia Toro, Guy Burke, Karl Seltzer, Heather Simon, Kristen Foley, Alison Eyth, Chris Allen, Janice Godfrey

In urban areas, mobile sources are important sources of ammonia (NH3) emissions that contribute to secondary formation of particulate matter. Recent studies have suggested that mobile ammonia emissions are underestimated in national and state emission inventories. In this study, we compared roadside, and tunnel ammonia measurements from light-duty gasoline and heavy-duty diesel vehicles to ammonia emission rates from EPA’s latest version of the MOtor Vehicle Emission Simulator (MOVES3). The roadside and tunnel comparisons suggest that the ammonia emission rates for onroad vehicles are underestimated in MOVES3. From the evaluated roadside and tunnel data, we developed revised MOVES ammonia emission rates by vehicle class, model year, vehicle age, and operating mode for light-duty gasoline and heavy-duty vehicles for use in this study.

We explored the effect of the revised onroad ammonia emission rates on US air quality using Community Multiscale Air Quality (CMAQ) simulations for the calendar year 2017 that were performed as part of the EPA’s Air QUAlity TimE Series Project (EQUATES) to develop modeled meteorology, emissions, air quality and pollutant deposition for 2002 through 2017. Modeled datasets covering the Conterminous US (CONUS) were estimated using 12 km horizontal grid spacing, MOVES3 onroad emissions, Weather Research and Forecasting Model (WRFv4.1.1) meteorology, and CMAQv5.3.2 air quality modeling. A sensitivity simulation was performed with ammonia emissions from onroad non-diesel (primarily gasoline) vehicles scaled by a factor of 2.08 and onroad diesel vehicles scaled by a factor of 1.54; the factors were based on national-level MOVES output for calendar year 2017 using MOVES3 and the revised emission rates. All other model inputs and parameters were held constant. We explored the impact of these changes on PM2.5 concentrations. These updates increased three-month average PM2.5 by up to 0.5 ug/m3 in winter with regional impacts spread across the Eastern US and in some Western urban areas. Three-month average PM2.5 concentrations increased by up to 0.2 ug/m3 in summer with changes focused in major urban areas.

Hossein Khajehpour, Farzaneh Taksibi, Mohammad Sadegh Hassanvand

The global trend of urbanization and limited environmental capacities have caused air pollution in Megacities, as one of the most prominent environmental concerns. In recent years, PM2.5 has been the major air pollutant in the Megacity of Tehran and the leading environmental risk factor causing health effects. Identification of the sources of this pollution is necessary for an effective environmental management program. Therefore, numerous studies have focused on source apportioning of PM2.5 in Tehran through emission inventory, source apportionment, and sensitivity analysis of the pollutants to the emission sources. A comparison of the methods and the derived results on relative shares of mobile and stationary sources in Tehran is studied in this short review. Our review shows that as the emission inventory approach is highly dependent on the geographic and sectoral coverage of the inventory, the results of this group of studies are not consistent. However, most of the source apportionment studies, although from different sampling locations and analytical methods, show similar shares of mobile and stationary sources in Tehran (in the range of 40% to 50%). This range is consistent with a published sensitivity analysis results which show that 47% of the PM2.5 concentrations in Tehran has resulted from mobile sources. The paper is concluded with a discussion on the methodological limitations of the methods, and finally, some suggestions for further developments of the study are proposed.

Jiaoyan (Joey) Huang¹, Jeff Houk¹, Shih-Ying Chang¹, Ray Minjares^2
1Sonoma Technology, Petaluma, CA, USA

²International Council on Clean Transportation, San Francisco, CA, USA

Fifteen states and the District of Colombia (D.C.) have signed a memorandum of understanding (MOU) that calls for 100% of new medium-duty (MD) and heavy-duty (HD) truck and bus sales to be zero emission vehicles by 2050, with an interim target of 30% by 2030. The California Air Resources Board (CARB) has adopted the Advanced Clean Trucks (ACT) and California HD Vehicle Phase II regulations and has proposed the low-NOx Omnibus regulation to accelerate MD and HD electric vehicle penetration and reduce on-road mobile source emissions in California. To understand the benefits of adopting these regulations and the resulting zero emission truck targets in other states, we designed a systematic method to estimate NOx, PM2.5, and CO2 equivalent (CO2e) benefits from these regulations. We developed five scenarios that used county-level on-road data for the year 2017 from the U.S. Environmental Protection Agency (EPA)’s MOVES3 model and EPA’s Air QUAlity TimE Series Project (EQUATES), as well as data provided by state/local agencies, to model on-road emissions in 13 states (CO, CT, MA, MD, ME, NC, NJ, NY, OR, PA, RI, VT, WA) and D.C. for 2017, 2020, 2025, 2030, 2035, 2040, 2045, and 2050.

The first scenario that was developed was a business-as-usual (BAU) scenario. We then post-processed total on-road NOx, PM2.5, and CO2e emissions for each state and D.C. based on four additional scenarios: • Scenario 2: Dual Harmonization (ACT Rule, Low-NOx Omnibus Rule with urban buses) from model year (MY) 2025 • Scenario 3: Only ACT Rule from MY2025 • Scenario 4: Only Low-NOx Omnibus Rule with urban buses from MY2025 • Scenario 5: Only California Phase II GHG Standard from MY2025 (benefits of Phase 2 trailer rule only) Emissions rates from the U.S. Department of Energy’s Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies Model (GREET) model were used to calculate the impact on upstream emissions from electric utilities and petroleum fuel production.

Compared to the BAU scenario, the typical NOx emission reduction (well-to-pump and vehicle emissions) is largest (25-30%) in scenario 2, and PM2.5 and CO2 emission reductions (well-to-pump and vehicle emissions) are 10-15% in scenarios 2 and 3. Some states had higher or lower reductions due to differences in fleet makeup and electrical grid emission rates. Detailed NOx, PM2.5, and CO2 emission reductions estimated for each state will be discussed, and the modeled future-year population of electric trucks will be presented. This information can help policymakers and scientists understand the benefits of zero-emission and low-NOx regulations.

Elena C. McDonald-Buller, Gary McGaughey, John Grant, Tejas Shah, Yosuke Kimura, and Greg Yarwood

Energy reform initiated under the Pacto por México in 2013 catalyzed transformational changes in Mexico’s energy sector. Among the most significant outcomes was the allowance for private and foreign investment and participation for oil and gas exploration and extraction, reducing the influence of the state-owned monopoly of Petróleos Mexicanos (Pemex). Between 2015 and 2018, Mexico awarded more than 100 contracts for exploration and extraction of its onshore conventional, shallow water, and deepwater hydrocarbon resources. This study developed a bottom-up emissions inventory for criteria pollutants and precursors from upstream and midstream sector sources including onshore well sites, off-shore platforms, flaring, natural gas processing plants, and natural gas compressor stations for the 2016 base year and examined the status and spatial implications of blocks awarded to date through the bid rounds. The Comprehensive Air Quality Model with extensions (CAMx) was used with the base year inventory to examine contributions to ozone and fine particulate matter concentrations in Mexican and U.S. border states associated with onshore and offshore oil and gas producing regions in Mexico.

Ben Murphy¹, Karl Seltzer², Havala Pye¹, Emma D’Ambro¹, Colette Heald³, and Jesse Kroll³

¹Office of Research and Development, Research Triangle Park, NC USA

²Office of Air and Radiation, Research Triangle Park, NC USA

³Massachusettes Institute of Technology, Cambridge, MA USA

Organic emissions lead to ozone and particulate matter air pollution, which are known threats to both human and environmental health at global, regional, and local scales. Historically, the EPA has inventoried organic gas- and particle-phase emissions separately as VOC and PM-OC, respectively. However, the development of novel instrumentation and modeling capabilities has led to new terms and frameworks that often blur the line between gas- and particle-phase organics, which in turn have caused communication challenges between the regulatory and research-oriented air quality modeling communities. This is best highlighted by the inconsistent definition of “semivolatile.” The emissions testing community definition is based on species that actively partition in concentrated stack, plume or tailpipe conditions (~50-10,000 μg m^-3), while the atmospheric science community definition is based on ambient conditions (0.1-10 μg m^-3). Overall, two core challenges for air quality modeling that result from these communication challenges are the inclusion of semivolatile primary organic aerosol and the emission of intermediate volatility organic compounds, which are known to efficiently produce additional aerosol after atmospheric oxidation. Given the influence and impact of regulatory and research data products, it is vitally important to resolve inconsistencies and decrease the barrier of incorporating new science to inform policy modeling, uncertainty analysis, and source attribution.

We propose unifying VOC, semivolatile and organic PM emissions modeling tools under the reactive organic carbon (ROC) framework to consider the enormous breadth of sources, properties, and impacts relevant for these critical classes of air pollutants. This presentation will demonstrate the limitations in conventional methods for interpreting VOC and PM emissions, explain how these limitation are resolved with the ROC framework, share insights about potential emissions testing gaps that emerge using the ROC framework as a guide, and identify the specific modeling, data-reporting, and testing definition updates that should be made to fully realize the benefits of this comprehensive approach.

Karl Seltzer, Ben Murphy, Elyse Pennington, Chris Allen, Kevin Talgo, Havala Pye

Volatile chemical products (VCPs) are a broad assortment of residential, commercial, institutional, and industrial sources of reactive organic carbon (ROC) emissions. Collectively, these sources currently contribute ~3.1 Tg of ROC emissions in the United States, with a substantial fraction of the sector (>20% by mass) serving as secondary organic aerosol (SOA) precursors. Here, we incorporate a new nationwide VCP inventory into the Community Multiscale Air Quality (CMAQ) model, with VCP-specific updates to better model air quality impacts. These CMAQ updates include representation of secondary air pollutant formation pathways for alkane-like intermediate volatility organic compounds (IVOCs), oxygenated IVOCs, and siloxanes. VCPs most enhance anthropogenic SOA formation in densely populated areas, with modeled, population-weighted annual average SOA increasing by 15 – 30% in Southern California and New York City due to VCP emissions (contribution of 0.2 - 0.5 µg m-3). Annually, VCP emissions enhance total population-weighted PM2.5 by up to 10% and generally ~5% in California, ~3% in New York, New Jersey, and Connecticut, and 1 – 2% in most other states. While the ozone enhancements from VCP emissions are more modest on average, their influence can cause a several ppb increase on select days in major cities and can account for up to 8.9% of annual-average maximum daily 8-hour ozone. Emissions from printing inks, cleaning products, paints and coatings contribute ~75% to the modeled VCP-derived SOA, and emissions from cleaning products, paints, coatings, and personal care products contribute ~81% to the modeled VCP-derived ozone. Overall, improved VCP emissions enhance modeled formation of multiple criteria pollutants throughout the United States, with the largest impacts in urban cores.

Jeff Willison, Jonathan E. Pleim, David Wong, Francis S. Binkowski, and Christian Hogrefe

Biogenic emissions of volatile organic compounds (BVOC) and nitric oxide can significantly contribute to surface concentrations of ozone and particulate matter. The current release of the Community Multiscale Air Quality (CMAQ) model offers the Biogenic Emission Inventory System (BEIS) as the only option for simulating inline biogenic emissions. This presentation will summarize the integration and performance of a second option, MEGAN v3.1, that will be included in CMAQ v6.0. We will present results from WRF-CMAQ simulations over the contiguous United States and from MPAS-CMAQ for a global domain. Comparisons with BEIS show that BVOC and nitric oxide production from MEGAN are strongly dependent on the configuration and runtime options. Enabling drought stress reduces BVOC production and PM2.5 concentration relative to BEIS, while improving the simulated organic carbon. Consistent with previous work, the Berkeley-Dalhousie Soil Nitric Oxide (NO) Parameterization simulates more NO and subsequently more ozone than the Yienger and Levy options that are available in both BEIS and MEGAN. The results from these sensitivity experiments will help CMAQ users improve the biogenic emissions configuration for their applications.

Junhua Zhang, Verica Savic-Jovcic, Alexandru Lupu, and Michael D. Moran

Vegetation is an important source of VOC (volatile organic compound) emissions, accounting for about 80-90% of global total VOC emissions. Accurate estimation of biogenic VOC emissions is critical for air quality modeling, particularly for summertime when O3 production is high due to favorable meteorological conditions. The Canadian air quality model, GEM-MACH (Global Environmental Multiscale—Modelling Air-quality and Chemistry), employs the Biogenic Emission Inventory System (BEIS) for estimating biogenic emissions (https://www.epa.gov/air-emissions-modeling/biogenic-emission-inventory-system-beis). BEIS3.09 with updated VOC emissions factors for needleleaf trees is currently being used in GEM-MACH and VOC emissions estimated from BEIS3.61 are being tested. However, a new BEIS version, v3.70, developed within the Community Multiscale Air Quality (CMAQ) model has recently been released. Compared to BEIS3.61, emissions factors in BEIS3.70 have changed significantly for many types of vegetation. Leaf Area Index (LAI) value for crops have also been updated. In this presentation, changes to biogenic VOC emissions due to changes to emissions factors in various versions of BEIS will be investigated for the GEM-MACH regional 10-km North American grid. The impacts of these changes on GEM-MACH predictions will be discussed too.

George Pouliot, Art Diem, Ying Hsu, Frank Divita, Venkatesh Rao, Casey Bray, Karl Seltzer, Havala Pye, Ben Murphy,Marc Menetrez

SPECIATE is a database of elements, compounds, particulate matter (PM) and other emissions from stationary and mobile sources. The SPECIATE project seeks to compile data from peer-reviewed literature or other sources on the identity of chemical species and composition of air pollutant emissions from all sources. Speciation profiles in the database contribute to emissions inventories and air quality modeling analyses that assess the impact of air emission sources on human health and the environment. For example, SPECIATE is used to support development of the National Emissions Inventory and associated modeling platforms, atmospheric modeling of regional haze, and prediction of human and ecological exposure. The SPECIATE database is a valuable tool used by air quality modelers, source-receptor modelers, emission inventory developers, and others who need a speciated breakdown of emissions. This presentation will summarize the newest updates to be included in SPECIATE 5.2 release including a major update to the species properties table.

Israel Pestana Soares, Daniella Correa Gonçalves de Rezende and Karinnie Nascimento de Almeida*

One of the main challenges of the industry is to control fugitive emissions. Those can be defined as emissions that are not released by stacks, ducts or vents. Generation of fugitive emissions is intrinsic to the processes of steel plants and can be understood as a sequence of events, starting with feedstock preparation, transport and final product until its storage. Sinter plants and coal yard, for example, are areas that requires attention. There are a few alternatives that can be used to mitigate dust emissions in different areas of a steel plant. One of the most recent technology that is calling attention are the fog canyons. Those can spread a fine mist of water on the source of interest, increasing the density of the particles and consequently its sedimentation on the ground. This mechanism is called wet deposition and it acts similarly to rain, whereby particulate matter mix with water and wash out through atmosphere, minimizing pollutant dispersion. The main objectives of this study were determinate control systems (fog canyons) efficiency and criteria of its position in order to optimize the particulate matter control in critical areas of a steel plant. The OTM 32 (US EPA) - Exposure Profiling Method – is widely applied and is one of the best methods to monitor fugitive emissions. This method was developed to measure open source emissions of a pollutant, with exposure defined as the time-integrated mass flux of a pollutant at a sampling point. The mass flux is the product of pollutant concentration and wind speed, which gives the pollutant mass per unit of cross-section of the plume per unit of time. The total emissions from the source during the sampling period is found by spatial integration of the exposure over the cross-section of the plume. The efficiency of the control system performed relative to parameters such as wind speed, fog canyons high, angle and its position regarding the monitoring source of interest.

Israel Pestana Soares, Daniella Correa Gonçalves de Rezende and Karinnie Nascimento de Almeida

Generation of fugitive emissions in a steel plant can be understood as a sequence of events, starting with feedstock preparation, transport and final product until its storage. One of the steelmaking processes that requires attention is the liquid pig iron production, which involves both its production as well as the converting process, when necessary. The converting of pig iron by torpedo cars only happens when the quality of the material are not within the established standards. When this occurs, they are directed to specific yards that are properly prepared to receive it. The fall of the pig iron causes great movement of liquid metal, promoting the detachment of particles and therefore the emission of particulate matter. In this study were monitored different fractions of particulate matter: Total Suspended Particles (TSP), Coarse Particles (PM10) e Fine Particles (PM2,5) on the pig iron dumping process in a Steel Plant in Belo Horizonte, Minas Gerais, to determinate the efficiency of the system control using liquid CO2 during the pig iron dumping. The OTM 32 - exposure profiling method (US EPA, 2013) is one of the best methods to monitor fugitive emissions. This method was developed to measure open source emissions of a pollutant, with exposure defined as the time-integrated mass flux of a pollutant at a sampling point. In order to evaluate the efficiency of the control system used on this study it were realized comparative tests on the pig iron converting Yard with and without liquid CO2 injection. The results of the tests without the application of liquid CO2 shown up that the high temperatures reached by pig iron elevates the vertical gradient plume dispersion, provoking a quick rise of the plume and less horizontal reach. As expected, it was noticed the increase of particulate matter concentration during the pig iron converting without the CO2 injection. Considering the overall average of particulate matter emission rate it was observed a percentage drop of 68% for TSP, 71,5% for PM10 and 66,5% for PM2,5.