El Paso AirNow vs TEMPO

Timeseries comparison of NO2 from AirNow and TEMPO in El Paso, TX during unvalidated release 2023-12-17 to 2023-12-30.

https://ofmpub.epa.gov/rsig/rsigserver?SERVICE=wcs&VERSION=1.0.0&REQUEST=GetCoverage&FORMAT=ascii&TIME=2023-12-23T00:00:00Z/2023-12-26T23:59:59Z&BBOX=-106.7,31.39,-105.95,32.0&COVERAGE=airnow.no2&COMPRESS=1
Calling RSIG elpaso/airnow.no2_2023-12-23T000000Z_2023-12-26T235959Z.csv.gz
Retrieving ..
https://ofmpub.epa.gov/rsig/rsigserver?SERVICE=wcs&VERSION=1.0.0&REQUEST=GetCoverage&FORMAT=ascii&TIME=2023-12-23T00:00:00Z/2023-12-26T23:59:59Z&BBOX=-106.7,31.39,-105.95,32.0&COVERAGE=tempo.l2.no2.vertical_column_troposphere&COMPRESS=1&MAXIMUM_CLOUD_FRACTION=1.0&MINIMUM_QUALITY=normal&KEY=none&CORNERS=1
Calling RSIG elpaso/tempo.l2.no2.vertical_column_troposphere_2023-12-23T000000Z_2023-12-26T235959Z.csv.gz
Retrieving ...

import matplotlib.pyplot as plt
import pyrsig
import pandas as pd
import os

# Create an RSIG api isntance
# Define a Time and Space Scope during unvalidated release around El Paso TX
rsigapi = pyrsig.RsigApi(
    bdate='2023-12-23T00', edate='2023-12-26T23:59:59',
    bbox=(-106.70, 31.39, -105.95, 32.00), workdir='elpaso'
)

# For the unvalidated data release, you do not need a key. To expand,
# outside the release, use a key.
# tkey = open(os.path.expandusrer('~/.tempokey'), 'r').read().strip()
tkey = 'none'
rsigapi.tempo_kw['api_key'] = tkey

# Get AirNow NO2 with dates parsed and units removed from column names
andf = rsigapi.to_dataframe(
    'airnow.no2', parse_dates=True, unit_keys=False, verbose=9
)

# Get TEMPO NO2
tempodf = rsigapi.to_dataframe(
    'tempo.l2.no2.vertical_column_troposphere',
    unit_keys=False, parse_dates=True, verbose=9
)


# Create spatial medians for TEMPO and AirNow
tempods = tempodf.groupby(pd.Grouper(key='time', freq='1h')).median(numeric_only=True)[
    'no2_vertical_column_troposphere'
]
ands = andf.groupby(['time']).median(numeric_only=True)['no2']

# Subset AirNow to overpass times
oands = ands.loc[ands.index.isin(tempods.dropna().index.floor('1h'))]  # just overpass t
# Create axes with shared x
fig, ax = plt.subplots(figsize=(12, 4),
                       gridspec_kw=dict(bottom=0.25, left=0.05, right=0.95))
ax.tick_params(axis='x', labelrotation=90)
tax = ax.twinx()

# Add AirNow with markers at overpasses
ax.plot(ands.index.values, ands.values, color='k')
ax.scatter(oands.index.values, oands.values, marker='o', color='b')

# Add TEMPO NO2
tax.plot(tempods.index.values, tempods.values, marker='s', color='r')

# Configure axes
ax.set(ylabel='AirNow NO2 ppb', ylim=(0, 10))
tax.set(ylim=(0.3e15, 4e15), ylabel='TropOMI NO2 molecules/cm$^2$')

plt.show()
# Or save out figure
fig.savefig('el_paso.png')