University of North Carolina Institute for the Environment Regional Modeling Emissions Inventories to Support Regional Haze Planning Zac Adelman University of North Carolina Institute for the Environment Tom Moore Western States Air Resources Council and Western Regional Air Partnership Emissions means multiple inputs on a regional basis, not individual sources

Emissions Inventories: What and Why? What are Emissions Inventories? Current comprehensive listing by sources of air pollutant emissions in a geographical area during a specific time period The basis for numerous efforts including trends analysis, regional, and local scale air quality modeling/planning, regulatory impact assessments, and human exposure modeling Why do we have Emissions Inventories? Section 110(a)(2)(F) of CAA: Requiring SIPs to provide for the reporting of criteria air pollutants Section 169(a): Authority for emission inventories to be required in SIPs developed to protect visibility in Class I federal areas Section 172(c)(3): Discretionary authority to require other emissions data Many inventories are flat datasets – all emissions at the same time, space, and chemical profile resolution

Emissions Inventories: What and Why? Bottom-up inventories are based on a basic parameterization of emissions fluxes: E = A x EF E = Emissions (e.g. tons/year) A = Activity (e.g. # emitting process/year) EF = Emissions Factor ( e.g. tons/emitting process) EF relates the quantity of a pollutant released into the atmosphere with the activity that releases it Equation becomes more complex when A and EF vary under different conditions (space, time, technology differences, control programs…) Both A and EF can be parameterized as “process-based” models

Emissions Modeling Emissions Models are software that estimate emissions processes (“process-based”) MOVES (onroad mobile) MEGAN and BEIS (biogenics) Emissions Processors are software that convert inventories to the terms/formats required by air quality models SMOKE Assign emissions inventory data to grid cells for photochemical grid modeling, e.g., 4km, 12km, and 36km Apply chemical profiles, e.g., total VOC mass to individual species Estimate hourly emissions rates from annual or seasonal inventories

How is Emissions Inventory Processing done? Emissions Modeling How is Emissions Inventory Processing done? Emissions inventories are generally annual, county totals for criteria pollutants Regional air quality models and dispersion models used for attainment and maintenance demonstrations have prescriptive formats for input emissions (daily or hourly, uniform spatial grids, detailed chemistry) SMOKE “parallel” sparse matrix emissions processing paradigm

Emissions Inventories and SIP Modeling MJO/S/L/T EPA Inventory Planning Develop Inventory NEI Forecast Emissions EPA National Emissions Inventory (NEI) SIP Planning Inventory (e.g., NAA progress) SIP Modeling Inventory (e.g., attainment demo) Emissions Modeling NO Base Air Quality Future Air Quality Projection/Future-year Inventory Estimate of future emissions with growth and controls For RH, must consider all reasonable controls NAAQS Met? Attainment Test Photochemical grid modeling is typically beyond the scope of most air agencies, this isn’t dispersion modeling; widely used by national EPA, MJOs, some states, a few locals, no tribes Tools are open source and anyone can do it, anyone can hire contractors to do it, but there aren’t many applications for this type of work YES Modeling is done by EPA, FLMs, MJOs, S/L/Ts Configuration of modeling platforms are specified in advance and vary depending on the modeling group SIP

Types of Emissions Data Inventories Natural source data Ancillary Data (spatial/chemical/temporal/codes) Source types Stationary point and area (a.k.a., non-point) sources Mobile on-road/off-network sources Off-road mobile sources Biogenic sources Event sources (i.e. wildfires or emergency releases) Meteorology and Landuse Impact Emissions Examples: Temperatures impact energy use, wind and moisture impact windblown dust and fires Landuse/Landcover/change determines location of sources

How Complete are Emissions Inventories? 2014 NEI v1 - contributions of S/L/T agency data to total emissions for each major data category. https://www.epa.gov/sites/production/files/2016-12/documents/nei2014v1_tsd.pdf States have different mixes of anthropogenic and natural sources States have different regulatory programs for anthro sources affecting the emission rates and tracking of inventory data Non-point anthro and/or natural source data reporting by state varies quite a bit, and “gap-filling” by EPA may or may not be representative for air quality planning For the 2014 NEI v1, Figures 2-1 and 2.2 on the following slides show the proportion of Criteria Air Pollutant emissions from various data sources in the NEI for point and nonpoint sources, and mobile sources, respectively. For regional haze planning, selected emissions categories can also be extracted from the NEI dataset Then changes can be made to the data and/or “base year represented” Likely necessary for regionally representative data as well as for robust projections

How Complete are Emissions Inventories? 2014 NEIv1 proportion of CAP emissions from various data sources for point and nonpoint sources. For the nonpoint data in the Figure 2.1 (left 7 bars), most of the emissions come from EPA sources of data, with S/L/T agency data the majority for VOC and SO2. The large “EPA Other” bar for PM10 is predominantly dust sources from unpaved roads (11.4 million tons), agricultural dust from crop cultivation (5.8 million tons), and construction dust (1.4 million tons). For point data in the figure (right 7 bars), most of the emissions come from S/L/T agency data, with EPA data making up a large proportion only for the PM2.5 with the EPA PM Augmentation dataset (a component of the “EPA HAP & PM Aug” in the figure, see Section 2.2.3).

How Complete are Emissions Inventories? 2014 NEI v1 - proportion of CAP emissions from various data sources for mobile sources. data sources for the emissions from nonroad and onroad data categories are shown in Figure 2-2. California, which uses its own onroad and nonroad mobile models, was the only state that provided emissions rather than inputs for EPA models (this is in accordance with the AERR). All other states were required to provide inputs to the EPA models; therefore, the S/L/T bars in this figure represent only California. All other data were generated by the EPA MOVES model and are comprised primarily of data from the EPA. Onroad and nonroad mobile data categories use the MOVES emissions model, and the EPA primarily collected model inputs from S/L agencies for these categories and ran the models using these inputs to generate the emissions.

Sources of Emissions Data NEI 2014 v1 Triennial schedule, 2-3 year lag; national scale, directed by EPA Usually at least 2 versions Each version requires QA for planning purposes and then emissions / air quality modeling The Intermountain West Data Warehouse (IWDW) has been fully supported by multiple federal and state agencies, with assistance from WESTAR and WRAP, since its creation through the 3SAQS Pilot Study. The IWDW provides air quality data, emissions modeling data, photochemical grid modeling products, and analysis tools to support various air quality applications Datasets can be customized for regional haze analysis and planning studies at various scales, including projection of future year emissions, along with complete documentation.

Customizable Source of Emissions Data

Projection Year data for Planning

How to Assess & Evaluate Emissions Data Regional emissions data are impossible to measure Exceptions are stack tests: Continuous Emissions Monitors (CEMs) Evaluation of inventories are done by proxy Comparison of past or similar inventories Analysis by experts Data sharing and graphical summaries Air quality model evaluation

Inventories for Regional Haze Planning Key pollutants for regional haze Primary & secondary PM precursors: PM, NH3, SO2, and NOx CO and VOCs less important Air toxics not important at all Primary fine PM (PM2.5) is speciated to SO4, NO3, NH4, EC, OC, soil, other Source-based profiles from EPA SPECIATE database S/L/Ts can provide profiles for inclusion in SPECIATE Assignment of profiles to sources based on expert judgement, primarily by EPA

Strengths/Weaknesses of Emissions Modeling Emissions modeling has evolved towards process-based models and disaggregating the inventories to more detailed sectors Can lose connection to native EIs provided by different air quality mgmt. agencies, which are based on regulations and permits Allows for more control over how sources are simulated, evaluated, substituted, and projected The trade off for more detail is more complexity in the modeling process Areas needing improvement Comparable EI inputs from outside the U.S. Mobile sources Nonpoint stationary Natural / Quasi-natural: fire and dust emissions of all types, ammonia, biogenics Chemical speciation Consistent/systematic QA/QC of emissions and modeling