Revisions to the Guideline on Air Quality Models (GAQM)

Revisions to the Guideline on Air Quality Models (GAQM)

Guideline on Air Quality Models
What is it? EPA-preferred models Recommended techniques Why have it? Common basis and consistency Where is it? App. W to 40 CFR Part 51 The Guideline on Air Quality Models (Guideline or GAQM) provides the Environmental Protection Agency’s (EPA’s) preferred models and other recommended techniques, as well as guidance for their use in estimating ambient concentrations of air pollutants. The Guideline is used by the EPA; other federal, state, territorial, and local air quality agencies; and industry to prepare and review new and/or modified source permits, State Implementation Plan (SIP) submittals and revisions, conformity, and other air quality assessments required under EPA regulation. The Guideline provides information on EPA-preferred models and other recommended techniques, as well as guidance for their use in predicting ambient concentrations of air pollutants. It should serve as a common measure of acceptable technical analysis when supported by sound scientific judgment. Case-by-case analysis and judgment are frequently required; even so, consistency in the selection and application of models and databases should also be sought. Consistency ensures that air quality control agencies and the general public have a common basis for estimating pollutant concentrations, assessing control strategies, and specifying emission limits. As such, it serves as a means by which national consistency is maintained in air quality analyses for regulatory activities. The Guideline is published as Appendix W to Title 40 Code of Federal Regulations (40 CFR) Part 51. All changes to the Guideline must follow rulemaking requirements since the Guideline is codified in the CFR. To amend this appendix, the EPA will promulgate proposed and final rules in the Federal Register. Appendix W can be found at

Timeline January 17, 2017 May 22, 2017 May 22, 2018
Promulgated revisions to the Guideline May 22, 2017 Effective date May 22, 2018 End of transition period The U.S. EPA promulgated revisions to the Guideline on Air Quality Models (January 17, 2017) with an effective date of May 22, 2017. This final rule starts a one-year transition period that ends on May 22, 2018 for all regulatory applications covered under the Guideline (except for transportation conformity) for integrating revisions to the requirements and recommendations of the Guideline into the regulatory process. During this one-year transition period, protocols for modeling analyses based on the version of the Guideline may be approved at the discretion of the Air Dispersion Modeling Team (ADMT). All applicants are encouraged to consult with the TCEQ as soon as possible to assure acceptance of their modeling protocols and/or modeling demonstration during this period.

Updates: AERMOD Modeling System
New Version AERMOD 16216r Horizontal/capped stack BLP NO2 Tier 2 & 3 AERMET 16216 ADJ_U* Other enhancements/bug fixes As part of the revisions to the Guideline, the EPA has updated the regulatory versions of the AERMOD dispersion model and AERMET meteorological processor to version 16216r and 16216, respectively. Both version 16216r and include bug fixes and updates to the modeling system. The EPA adopted the ADJ_U* option in AERMET as a regulatory option for use in AERMOD for sources using: standard National Weather Service (NWS) airport meteorological data, site-specific meteorological data without turbulence parameters (NonDFAULT if turbulence data is used), or prognostic meteorological inputs derived from prognostic meteorological models. The TCEQ has collected and processed recent ( ) NWS airport meteorological data using the ADJ_U* option in the newest version of AERMET. These AERMOD-ready meteorological files are available on the Agency’s website as of April 2017. The EPA updated the regulatory options in AERMOD to address plume rise for horizontal and capped stacks. These two types of point sources (POINTHOR and POINTCAP) are now regulatory options in AERMOD. The EPA integrated the Buoyant Line and Point Source (BLP) dispersion model into the AERMOD modeling system. The EPA updated the nitrogen dioxide (NO2) Tier 2 and Tier 3 screening techniques coded within AERMOD. The EPA replaced the existing Tier 2 Ambient Ratio Method (ARM) with a revised ARM2 approach (more details in next slide). The EPA incorporated the existing Ozone Limiting Method (OLM) and the updated Plume Volume Molar Ration Method (PVMRM) (proposed as PVMRM2) model options as regulatory options in AERMOD AERMOD – American Meteorological Society/EPA Regulatory Model Landscape image created by Dave from Noun Project

Updates: 3-Tiered Approach for NO2
Tier 1: 100 % conversion (no change) Tier 2: Ambient equilibrium NO:NO2 (ARM to ARM2) Tier 3: NOx:NO2 through ozone titration (PVMRM & OLM) Due to the complexity of NO2 modeling, a multi-tiered approach is used to obtain hourly and annual average estimates of NO2. The tiered approach is not new; although, previously the Guideline only addressed the annual standard, whereas the revised Guideline addresses both standards. Here is an overview of the multi-tiered approach including changes as outlined in the Guideline: Tier 1 – total conversion of nitrous oxide (NO) to NO2 (no change). Tier 2 – assumes ambient equilibrium between NO and NO2. The EPA replaced the existing Tier 2 Ambient Ratio Method (ARM) factors with a revised Ambient Ratio Method 2 (ARM2) approach. Tier 3 – a detailed screening technique, applied on a case-by-case basis, that accounts for the conversion of oxides of nitrogen (NOX) to NO2 through ozone titration. Tier 3 was revised to list the Plume Volume Molar Ratio Method (PVMRM) model option (proposed as PVMRM2) and Ozone Limiting Method (OLM) model option as detailed screening techniques (no longer non-regulatory models). Since these methods are considered screening, their usage shall occur in consultation with the ADMT and EPA Region 6, as applicable.

ARM → ARM2 Based on hourly data of NO2:NOx, Empirically derived equation in AERMOD, min ratio: 0.5 & max ratio: 0.9 Based on hourly data of NO2:NOx ratios Empirically derived equation in AERMOD Min ratio: 0.5 & Max ratio: 0.9 Tier 2 assumes ambient equilibrium between NO and NO2. The EPA replaced the existing Tier 2 ARM factors of 0.75 for annual and 0.8 for one hour with a revised ARM2 approach. ARM2 is based on hourly measurements of the NO2 to NOX ratios and provides more detailed estimates of this ratio based on the total NOX present. ARM2 provides estimates of representative equilibrium ratios of NO2/NOX values based on ambient levels of NO2 and NOX derived from national data from the EPA’s Air Quality System (AQS). The national default for ARM2 includes a minimum ambient NO2/NOX ratio of 0.5 and a maximum ambient ratio of Alternative default minimum NO2/NOX values may be established based on the source’s in-stack emissions ratio, with alternative minimum values reflecting the source’s in-stack NO2/NOX ratios. These should be based on source-specific data, which satisfies all quality assurance procedures that ensure data accuracy for both NO2 and NOX within the typical range of measured values. However, manufacturer test data, state or local agency guidance, peer-reviewed literature, or the EPA’s NO2/NOX ratio database may be used as sources of data.

PVMRM & OLM Regulatory default version of AERMOD, Revised PVMRM, still need to coordinate with us on model inputs Regulatory default version of AERMOD Revised PVMRM ADMT coordination on model inputs Tier 3 is a detailed screening technique, applied on a case-by-case basis, that accounts for ambient ozone and the relative amount of NO and NO2 emitted from a source. Tier 3 was revised to list the PVMRM model option and OLM model option as detailed screening techniques (no longer non-regulatory models). The OLM and the PVMRM model options estimate NOX concentrations and then estimate the conversion of primary NO emissions to NO2, based on the ambient levels of ozone and the plume characteristics. The revised version of the PVMRM (proposed as PVMRM2) model option utilizes relative dispersion coefficients to estimate plume volume during convective conditions and total dispersion coefficients during stable conditions. The revised PVMRM is intended to mitigate potential over-prediction of NO2 conversion in multisource applications. Since these methods are considered screening, their usage shall occur in consultation with the ADMT and EPA Region 6, as applicable.

Revisions: Meteorological Input
AERMINUTE: 1-min ASOS wind data Reduces calm & missing hours Included in TCEQ processing ASOS = automated surface observing station The Guideline recommends the use of the AERMINUTE meteorological data processor for use with for AERMET (the meteorological pre-processor for the AERMOD modeling system).

Processor: 1-min ASOS wind data AERMINUTE: Reduces calm & missing hours Included in TCEQ processing AERMINUTE calculates hourly average wind speed and direction when processing of National Weather Service (NWS) automated surface observing station (ASOS) data for AERMET.

Reduces calm & missing hours AERMINUTE: 1-min ASOS wind data Included in TCEQ processing The AERMINUTE processor was developed to reduce calm and missing hours by taking advantage of the availability of the one-minute ASOS wind data to calculate full hourly average winds to replace standard hourly observations and reduce the number of calm and missing winds in AERMET processing. The AERMINUTE processor, in most cases, should be used to process one-minute ASOS wind data for input into AERMET when processing NWS ASOS sites.

Included in TCEQ processing AERMINUTE: 1-min ASOS wind data Reduces calm & missing hours The TCEQ pre-processed AERMOD-ready meteorological files utilize the AERMINUTE processor to process one-minute ASOS wind data for input into AERMET for NWS ASOS sites.

Preferred screening model
Status of AERSCREEN AERSCREEN Preferred screening model Version 16216 ADJ_U* AERSCREEN is the screening version of AERMOD and EPA’s preferred screening model for most applications in all types of terrain and applications involving building downwash. The EPA incorporated AERSCREEN into the Guideline as the recommended screening model for AERMOD. The regulatory version of AERSCREEN is version Changes since version include bug fixes and the ability to use the ADJ_U*, consistent with AERMET

Single Source Impacts on O3 & Secondary PM2.5
EPA is recommending two-tier approach: Tier 1: relationship precursor emissions - source impact Tier 2: photochemical grid modeling O3 = ozone PM2.5 = particulate matter equal to or less than 2.5 microns in diameter In order to provide the user community flexibility in estimating single-source secondary pollutant impacts, the EPA promulgated a two-tiered demonstration approach. The first tier involves the use of technically credible relationships between precursor emissions and a source’s impact (that may be published in literature; developed from modeling that was previously conducted for an area by a source, a governmental agency, or some other entity that is deemed sufficient; or generated by a reduced form model) in combination with other supportive information and analysis for the purpose of estimating secondary impacts from a particular source. The second tier involves application of more sophisticated case-specific chemical transport models (e.g., photochemical grid models). The appropriate tier for a given application should be selected in consultation with the ADMT and be consistent with EPA guidance.

Tier 1: relationship precursor emissions – source impact Peer –reviewed literature, Previous modeling results (MERPs), Reduced-form model The first tier involves the use of technically credible relationships between precursor emissions and a source’s impact (that may be published in literature; developed from modeling that was previously conducted for an area by a source, a governmental agency, or some other entity that is deemed sufficient; or generated by a reduced form model) in combination with other supportive information and analysis for the purpose of estimating secondary impacts from a particular source (e.g., Modeled Emission Rates for Precursors (MERPs)). Peer-reviewed literature Previous modeling results (MERPs) Reduced-form model

Tier 2: photochemical grid modeling New draft guidance, consult with us early The second tier involves application of more sophisticated case-specific chemical transport models (e.g., photochemical grid models). The appropriate tier for a given application should be selected in consultation with the ADMT and be consistent with EPA guidance. New draft guidance Consult with ADMT early

Modeled Emission Rates for Precursors (MERPs)
Represents a level of emissions of precursors that is not expected to contribute significantly to concentrations of ozone or secondarily-formed PM2.5 A MERP represents a level of emissions of precursors that is not expected to contribute significantly to concentrations of ozone or secondarily-formed PM2.5.

MERP (tpy) = Modeled emission rate from hypothetical source (tpy) Modeled air quality impact from hypothetical source (µg/m3 or ppb) Critical Air Quality Threshold (µg/m3 or ppb) µg/m3 = micrograms per cubic meter ppb = parts per billion ppm = parts per million Based on EPA draft guidance, a MERP can be derived using the modeled precursor emissions from hypothetical sources, their downwind maximum impacts, and a critical air quality threshold using the following equation: MERP (tpy) = Critical Air Quality Threshold (µg/m3 or ppb) * (Modeled emission rate from hypothetical source (tpy) / Modeled air quality impact from hypothetical source (µg/m3 or ppb)) MERPs are expressed as an annual emissions rate in tons per year (tpy) consistent with the modeled emissions rates input to the air quality model to predict a change in pollutant concentrations. The critical air threshold is separately defined and expressed as a concentration for PM2.5 (in µg/m3) or O3 (in ppb or ppm).

Region 8-hr O3 Daily PM2.5 Annual PM2.5 NOx Central US 126 1,693 5,496 Eastern US 170 2,295 10,144 Western US 184 1,075 3,184 SO2 -- 238 839 SO3 628 4,013 SO4 210 2,289 VOC 948 1,159 1,049 NOX = oxides of nitrogen SO2 = sulfur dioxide SO3 = sulfur trioxide SO4 = sulfate VOC = volatile organic compound The appendices to the EPA draft guidance include the results of the modeling of numerous hypothetical sources across the country. The EPA used these results, along with critical air quality thresholds based on EPA draft significant impact level (SIL) guidance, to derive MERPs based on precursor pollutants, region, and secondarily formed pollutant. This table summarizes the lowest MERPs derived (i.e., most conservative).

Using a MERP 1 Precursor Precursor emissions must be < MERP value
An overview for using a MERP value for when one precursor is present versus two precursors. When only one precursor is present, simply confirm the precursor emission rate is below the MERP value.

Using a MERP 2 Precursors
Both precursors’ emissions must be < MERP value Additive impacts analysis PM2.5 must also consider primary impact 2 Precursors When two precursors are present, confirm both precursors’ emission rates are below the MERP values. Next, precursor contributions are considered together in an additive analysis to determine if the source’s air quality impact would exceed the critical air quality threshold. For PM2.5, the primary impacts would also need to be considered. Each of these are covered in the example that follows.

Example Facility with proposed increase of 35 tpy of primary PM2.5
110 tpy of NOx 75 tpy of SO2 Located in the Central U.S. Example: In this scenario, a facility with a proposed increase in emissions of 35 tpy of primary PM2.5, 110 tpy of NOX, and 75 tpy of SO2 is located in the central region of the United States. Note that the primary PM2.5 emissions must be accounted for in assessing PM2.5 along with the secondary impacts of NOX and SO2 precursor emissions as part of the Tier 1 demonstration.

O3 Analysis NOX emissions of 110 tpy are lower than the NOX MERP for 8-hr O3 in the central region of the U.S. Precursor Region 8-hr O3 Daily PM2.5 Annual PM2.5 NOx Central US 126 1,693 5,496 Eastern US 170 2,295 10,144 Western US 184 1,075 3,184 SO2 -- 238 839 SO3 628 4,013 SO4 210 2,289 VOC 948 1,159 1,049 O3 analysis: The NOX emissions of 110 tpy are lower than the (most conservative) NOX MERP for 8-hr O3 in the central and other regions of the U.S. (see Table 1) such that air quality impacts of O3 from this source would be expected to be less than the critical air quality threshold.

PM2.5 Analysis NOX and SO2 emissions are below the lowest daily and annual PM2.5 MERP values of any source modeled in the Central U.S. Precursor Region 8-hr O3 Daily PM2.5 Annual PM2.5 NOx Central US 126 1,693 5,496 Eastern US 170 2,295 10,144 Western US 184 1,075 3,184 SO2 -- 238 839 SO3 628 4,013 SO4 210 2,289 VOC 948 1,159 1,049 PM2.5 analysis: Both the NOX and SO2 emissions are well below the lowest (most conservative) daily and annual PM2.5 MERP values of any source modeled in the Central or any other region in the continental U.S (see Table 1). However, the NOX and SO2 precursor contributions to both daily and annual average PM2.5 are considered together to determine if the source’s air quality impact of PM2.5 would exceed the critical air quality threshold.

PM2.5 Analysis: Additive Secondary Impacts on Daily PM2.5
(110 tpy NOX from source / 1,693 tpy NOX daily PM2.5 MERP) + (75 tpy SO2 from source / 238 tpy SO2 daily PM2.5 MERP) = = .38 * 100 = 38% To determine the additive secondary impacts the proposed emissions increase can be expressed as a percent of the lowest MERP for each precursor and then summed. A value less than 100% indicates that the critical air quality threshold would not be exceeded when considering the combined impacts of these precursors on daily PM2.5.

PM2.5 Analysis: Additive Secondary Impacts on Annual PM2.5
(110 tpy NOX from source / 5,496 tpy NOX annual PM2.5 MERP) + (75 tpy SO2 from source / 839 tpy SO2 annual PM2.5 MERP) = = .11 * 100 = 11% A value less than 100% indicates that the critical air quality threshold would not be exceeded when considering the combined impacts of these precursors on daily PM2.5.

PM2.5 Analysis: Primary + Secondary
Primary 24-hr PM2.5 impact from AERMOD = µg/m3 Primary impact is 35% of the critical air quality threshold: µg/m3 / 1.2 µg/m3 = 35% Primary impact (35%) + secondary impacts (38%) = 73% and <100% In this example, the primary PM2.5 impacts need to be added to the secondary impacts for an appropriate account of total PM2.5 impacts for the comparison to the air quality threshold. The primary PM2.5 impacts should be estimated using AERMOD or an approved alternative model and consistent with EPA guidance for combining primary and secondary impacts of PM2.5 for permit program assessments. The highest impact at any receptor for primary PM2.5 (project modeling) should be divided by the air quality threshold to estimate the percent contribution. The additive secondary impacts plus the percent contribution from the primary impacts should be below 100% suggesting this source’s impact is below the critical air quality threshold. For example, a peak primary 24-hr PM2.5 impact from AERMOD is estimated to be 0.45 µg/m3. Compared with a 1.2 µg/m3 critical air quality threshold means that the primary impact is 35% of the critical air quality threshold. When this primary impact is summed with the secondary impacts of 46%, the total is 81%, which is below 100% suggesting this source impact is below the critical air quality threshold. This same methodology would be applied to the annual analysis of secondary PM2.5.

1 Lowest derived MERPs on a national level 2 Lowest derived MERPs only for Texas sources 3 MERPs derived for a specific Texas source Note that the guidance for MERPs and SILs are draft, and EPA will likely be updating these guidance documents based on comments received. Once new versions are released, the ADMT will update guidance to incorporate the new EPA guidance. With that said, once the MERPs become final, the ADMT agrees that the MERPs can be used as a Tier 1 demonstration tool to evaluate secondarily-formed pollutants using various levels of refinement. The first level of assessment could be based on the lowest derived MERPs on a national level (e.g., using the values provided in the previous example). A second level of assessment could be based on MERPs derived for just the Texas sources. The overall approach would be the same as the first level, just relying on the lowest derived MERPs from the hypothetical sources located in Texas. A third level of assessment could be based on MERPs derived for a specific Texas source. The overall approach would be the same as the first and second level of assessment, just relying on the MERPs derived for a specific Texas source. Regardless of what level of assessment, it should be made clear in the protocol how the chemical and physical environments modeled as part of the existing set of information included in the Tier 1 demonstration tool are relevant to the geographic area of the source and key receptors.

Updates: Cumulative Impact Analysis
Modeling Domain: Geographic area for analysis Includes area extending: distant significant impact 50 kilometers, whichever is less Geographic area for analysis extending: Modeling Domain: distant significant impact Many of the updates to the cumulative impact analysis are based on the EPA clarification memoranda issued since 2010 that were intended to provide the necessary clarification regarding applicability of the GAQM to Prevention of Significant Deterioration (PSD) modeling for new standards. The EPA provided a more definitive definition of the modeling domain. The modeling domain is the geographic area for which the required air quality analyses for the National Ambient Air Quality Standard (NAAQS) and PSD increments are conducted. When conducting a NAAQS or PSD increment assessment, the modeling domain or project’s impact area shall include all locations where the emissions of a pollutant from the new or modifying source(s) may cause a significant ambient impact. This impact area is defined as an area with a radius extending from the new or modifying source to: 1.The most distant location where air quality modeling predicts a significant ambient impact will occur, or 2.The nominal 50 kilometer (km) distance considered applicable for Gaussian dispersion models, whichever is less. or 50km, whichever is less

Emissions Input Data: New/Modified source: allowable emissions Nearby source: actual emissions data Other source: represented by monitoring data New/modified source: allowable emissions Emissions Input Data: Nearby source: actual emissions data As part of a cumulative impact analysis, Table 8-2 of the Guideline allows for the model user to account for actual operations in developing the emissions inputs for dispersion modeling of nearby sources (i.e., relying on actual emissions data), while other sources are best represented by air quality monitoring data. Consultation with the ADMT is recommended on the identification of nearby sources and the establishment of the appropriate emissions inputs for the nearby sources. Other source: represented by monitoring data

Nearby source: in vicinity of source under consideration: explicitly model Other source: all other sources: accounted for by monitoring data The revisions are for situations involving isolated single-sources and multi-source areas with an emphasis on how to determine which nearby sources to explicitly model, based on the concept of significant concentration gradients and the use of monitored background to adequately represent other sources. Nearby sources: These are individual sources in the vicinity of the source under consideration for emission limits that are not adequately represented by ambient monitoring data. Typically, sources that cause a significant concentration gradient in the vicinity of the source under consideration for emission limits are not adequately represented by background ambient monitoring. The ambient contributions from these nearby sources are thereby accounted for by explicitly modeling their emissions. Other sources: The ambient contributions from these sources are typically accounted for through the use of ambient monitoring data or, in some cases, regional-scale photochemical grid modeling results.

Determining appropriate background concentrations Isolated source Multi-source area The EPA made revisions regarding the determination of background concentrations. Many of these revisions are based on the EPA clarification memoranda issued since 2010 that were intended to provide the necessary clarification regarding applicability of the GAQM to PSD modeling for new standards. The revisions are for situations involving isolated single-sources and multi-source areas with an emphasis on how to determine which nearby sources to explicitly model, based on the concept of significant concentration gradients and the use of monitored background to adequately represent other sources.

Isolated: focus on other sources through ambient monitoring data In areas with an isolated source (i.e., for projects with no sources nearby), determining the appropriate background concentration should focus on characterization of contributions from all other sources through adequately representative ambient monitoring data. In most cases, the EPA recommends using data from the monitor closest to and upwind of the project area. If several monitors are available, preference should be given to the monitor with the most similar characteristics as the project area (i.e., impacted by similar or adequately representative sources). Determination of the appropriate background concentrations should be consistent with appropriate EPA and TCEQ modeling guidance and justified in the modeling protocol and Air Quality Analysis (AQA).

Multi-source: determine nearby sources & ambient monitoring data for other sources For multi-source areas (i.e., projects near other sources), determining the appropriate background concentration involves: 1. Identification and characterization of contributions from nearby sources through explicit modeling, and 2. Characterization of contributions from other sources through adequately representative ambient monitoring data.

Thank You! Amanda Jones (512) 239-1229 Amanda.Jones@tceq.texas.gov
Rachel Melton (512) Dianne Anderson Team Leader – Air Dispersion Modeling Team (512)

Revisions to the Guideline on Air Quality Models (GAQM)

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Revisions to the Guideline on Air Quality Models (GAQM)

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