Description of Dataset and 1.33km and 4km Base CAMx modeling results

Description of dataset 

Eagle Ford Shale CH4 and CO2 emissions (10.5281/zenodo.10278516)

1. Filename: Eagle Ford Shale CH4 and CO2 emissions 11Sep2023.xslx
2. Description: CH4 and CO2 emissions at well-level, gathering-level and plant-level
3. Processing step: both CH4 and CO2 emissions are processed into a gridded format using SMOKE, with no temporal variations.

CAMx gridded outputs

1. Domain definition (GRIDDESC)

'LAM_40N97W'

 2       33.000        45.000       -97.000       -97.000        40.000

'txs_4km'

'LAM_40N97W'  -324000.000 -1584000.000  4000.000  4000.000 189 234   1

'txs_133km'

'LAM_40N97W'  -324000.000 -1476000.000  1333.333  1333.333 378 459   1

1. CAMx configurations:
2. Version 7.10
3. Simulation time period: 2019 April 1st – October 31st (with a 15-day spin-up)
4. Chemistry turned off
5. Zero initial and boundary concentration of CH4/CO2
6. Output:

Gridded surface concentration and column concentration are provided in the following folder structure: 

• domain/scenario/surface for surface concentration
• domain/scenario/column for column concentration.

Two domains are available: txs_4km and txs_133km. There are ten sets of scenarios for each domain:

• base: routine CO2/CH4 emissions (10.5281/zenodo.10278516)
• Event Scenarios 1.33km: 10.5281/zenodo.10261056; 4km: 10.5281/zenodo.10255627
• low_100kg/1000kg: 100/1000 kg/hr emission event added with low routine emissions
• med_100kg/1000kg: 100/1000 kg/hr emission event added with medium routine emissions
• high_100kg/1000kg: 100/1000 kg/hr emission event added with high routine emissions
• nearby_low/med/high: 100/1000kg/hr emission events added at nearby locations

For example, files under 'txs_4km/base/surface' represent hourly gridded surface CH4/CO2 concentration (ppmv) with a spatial resolution of 4km x 4km. Similarly, files under 'txs_13km/nearby_high/column' represent hourly gridded CH4 column concentration (mol/m2) with a spatial resolution of 4km x 4km under scenario 'nearby_high'.