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Record W6905006345 · doi:10.15139/s3/tcr6bb

2017 v1 NEI Emissions Modeling Platform (Premerged CMAQ-ready Emissions)

2020· dataset· en· W6905006345 on OpenAlexaboutno aff

Bibliographic record

VenueUNC Dataverse · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCMAQGreenhouse gasEmission inventoryPollutantAir pollutionSmokeMethane emissions

Abstract

fetched live from OpenAlex

Introduction The emissions modeling platform uses SMOKE v4.7 to apportion the emissions inventories into the grid cells used by CMAQ and temporalizes the emissions into hourly values. In addition, the pollutants in the inventories (e.g., NOx, PM and VOC) are split into the chemical species needed by CMAQ. For the purposes of preparing the CMAQ- ready emissions, the NEI emissions inventories by data category are split into emissions modeling platform “sectors”; and emissions from sources other than the NEI are added, such as the Canadian, Mexican, and offshore inventories. Emissions within the emissions modeling platform are separated into sectors for groups of related emissions source categories that are run through all of the SMOKE programs, except the final merge, independently from emissions categories in the other sectors. (See pre-merged sector files below.) The final merge program called Mrggrid combines low-level sector-specific gridded, speciated and temporalized emissions to create the final CMAQ-ready emissions inputs. For biogenic emissions, the CMAQ model allows for biogenic emissions to be included in the CMAQ-ready emissions inputs, or for biogenic emissions to be computed within CMAQ itself (the “inline” option). (See merged 2D files below) This study uses the inline biogenic emissions option. Table 3-1 from the following report https://www.epa.gov/sites/production/files/2020-11/documents/2017_emissionschapter.pdf presents the sectors in the emissions modeling platform used to develop the year 2017 emissions for this project. The emissions inventories created for input to SMOKE, are based on the April, 2020 version of the 2017 NEI. The NEI includes five main data categories: a) nonpoint (formerly called “stationary area”) sources; b) point sources; c) nonroad mobile sources; d) onroad mobile sources; and e) fires. For CAPs, the NEI data are largely compiled from data submitted by state, local and tribal (S/L/T) agencies. HAP emissions data are often augmented by EPA when they are not voluntarily submitted to the NEI by S/L/T agencies. The NEI was compiled using the Emissions Inventory System (EIS). EIS includes hundreds of automated QA checks to improve data quality, and it also supports release point (stack) coordinates separately from facility coordinates. EPA collaboration with S/L/T agencies helped prevent duplication between point and nonpoint source categories such as industrial boilers. The 2017 NEI Technical Support Document describes in detail the development of the 2017 emission inventories and is available at https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-technical-support-document-tsd (EPA, 2020a). Point source data from the 2017 NEI, including data submitted to EIS by S/L/T agencies, were used for this study. EPA used the SMARTFIRE2 system and the BlueSky emissions modeling framework to develop year 2017 fire emissions. SMARTFIRE2 categorizes all fires as either prescribed burning or wildfire categories, and the Bluesky framework includes emission factor estimates for both types of fires. Onroad and nonroad mobile source emissions for year 2017 were developed by running MOVES2014b (https://www.epa.gov/moves). Canadian emissions interpolated to the year 2017 from 2015 and 2023 were used, and Mexican emissions were for the year 2016. The 2017 Emission Modeling Platform is primarily based on the 2017gb emissions case prepared by EPA, but there are differences between this package and the EPA emissions case: - This package includes a newer airports inventory from 2017 NEI than was used in the original 2017gb emissions case. This newer version, which was not available until after the 2017gb emissions case was completed in May 2020, corrects an overestimation of emissions from airports. - This package includes average speed distributions (SPDIST) from 2017. The original 2017gb emissions case used average speed distributions from 2016. This is an input to SMOKE-MOVES and affects the "RPD" category of onroad emissions. Data Summary: U.S. EPA National Emissions Inventory 2017 Modeling Platform SMOKE Output Data Note:The datasets are on a Google Drive. The metadata associated with this DOI contain the link to the Google Drive folder and instructions for downloading the data. These packages contain the CMAQ-ready, premerged and merged, gridded, speciated hourly emissions files for 2017 including 10 days of spinup days. File Location and Download Instructions: Link to SMOKE Output FilesDownload instructions File Sizes and Name of merged files File Size, File Name 176G, merged2D_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar Contents of Merged 2D Tar file (375 days = 365 days + 10 days spin-up) emis_mole_all_2017{month}{day}_12US1_nobeis_norwc_2017gb_17j.ncf.gz File Sizes and Name of pre-merged sector files File Size, File Name 15 G, ptagfire_ptfire_othna_ptfire_pt_oilgas_ptegu_2017_[casename].tar 50G, rwc_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 43G, ptnonipm_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 22G, othpt_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 65G, beis_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 2.5G, othptdust_adj_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 14G, othar_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 6.9G, othafdust_adj_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 55G, onroad_mex_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 7.4G, onroad_can_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 92G, onroad_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 7.8G, np_oilgas_rail_airports_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 27G, nonpt_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 36G, ag_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar 22G, afdust_adj_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar Contents of beis_2017_12US1_cmaq_cb6ae7_2017gb_17j.tar ./beis/emis_mole_beis_2016{month}{day}_12US1_cmaq_cb6ae7_2017gb_17j.ncf.gz Note: the other sector tar files have a similar structure File Format:The case name for these SMOKE output files is "12US1_cmaq_cb6ae7_2017gb_17j", which serves as the basis of EPA's 2017gb_17j platform for air quality modeling. CMAQ model-ready emissions generated using these packages with SMOKE v4.5 should be identical to those used in EPA's 2017 platform. Documentation for the platform, along with the inputs to SMOKE including inventories, ancillary files, and run scripts are available from EPA's Air Emissions Modeling website. EPA NEI 2017 Modeling Platform Website Supporting documentation is available

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.368
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0040.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0200.388

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.088
GPT teacher head0.315
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2020
Admission routes1
Has abstractyes

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