From Filamentary Networks to Dense Cores in Molecular Clouds ...
Dense networks and geostationary and air quality …...Dense networks and geostationary satellites:...
Transcript of Dense networks and geostationary and air quality …...Dense networks and geostationary satellites:...
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Dense networks and geostationary satellites: A vision for the future of NOx and air quality observations
Ronald C. Cohen UC Berkeley
$ BAAQMD, NSF, NASA, UC Berkeley, HEI,
Koret Foundation
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A different talk RONO2 chemistry (Day 2/3; night 1/3) governs the lifetime of NOx and HNO3 and is a major source of aerosol.
e.g. Romer et al. ACP, 2016, Perring et al. Chem. Rev. 2013, Rollins et al. Science, 2012
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-2- (Turner et al., 2016)
CO2 Emission Inventory
Current models of emissions have few parts that respond to day-to-day variations in human behavior or weather.
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Tropospheric NOx Column
Satellite remote sensing is changing how we think about emissions and air quality
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OMI Berkeley High-resolution Retrieval (BEHR) 0 1 2 3 4 5 6 7 8 9 10x1015
NO2 (molecules cm–2)
April-September 2005
Nitrogen oxides (NOx) are concentrated over cities
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OMI Berkeley High-resolution Retrieval (BEHR) 0 1 2 3 4 5 6 7 8 9 10x1015
NO2 (molecules cm–2)
April-September 2015
Large decreases over the last decade in U.S. result in smaller spatial extent of urban plumes
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On a neighborhood scale inexpensive sensing might change how we think about emissions and air quality
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BEACO2N: 2.5m – 130m AGL
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Alphasense B4 Electrochemical O3, CO, NO & NO2 Sensors
($216 ea.)
Vaisala GMP343
NDIR CO2 Sensor
($2,800)
Shinyei PPD42NS nephelometric
particulate matter sensor ($16)
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BEACO2N
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Quality vs. Quantity
Network of roughly BEACO2N’s size (25) & precision (1ppm)
Network of three much more precise instruments (0.1ppm)
36% accuracy
Turner et al. ACP 2016
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BEACO2N CO2 2013
Sites: Laurel Korematsu HeadRoyce Burckhalter Kaiser ODowd ElCerrito Prescott CollegePrep StLiz NOakland
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Question
What can we do to understand the processes affecting air quality at the neighborhood scale? Viewed (mostly) through a lens of NOx
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Surface Ozone
d[NOx]/dt ~ Emissions – Chemistry NOx ≡ NO + NO2 τ ~ 100 s
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Mixing
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Global pollution monitoring constellation
TEMPO (hourly)
Sentinel-4 (hourly) GEMS
(hourly)
High space and time resolution measurements of NO2, H2CO and O3 will soon (3-4 years?) be routinely available
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GOME-2
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OMI
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TEMPO – Actual is twice resolution shown
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Riyadh
L Valin et al., GRL 2013
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Surface Ozone NOx regulates its own removal rate through
its effect on OH
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Rural — Suburban — Urban
OH (or PO3) vs. NO2(x)
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L Valin et al., GRL 2013
Emissions don’t depend on winds; the burden and lifetime does
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Daytime
slow fast slow
RONO2
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<xy> ≠ <x><y> x=NO2; y=OH
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BErkeley
Atmospheric
CO2
Observation
Network 2km
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BEACO2N— pointwise surface network at ~2km resolution
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from Choi et al. 2014
observations modeled fit 1σ variation range
Particulate Matter (co-emitted with CO2, NOx, CO, …)
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2km
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Vehicle # per hour
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Vehicle # per hour
WEEKDAY
WEEKEND
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-17-
near highway
Analyze every plume
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(NOx/CO2<2)
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2km
Port of Oakland Affected by
shipping? Or just traffic?
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Port Aug/Sept Diurnal Cycle
weekdays (M-F) weekends (Sat/Sun)
3 PM 8 PM 10 AM 5 AM
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Port Diurnal Cycle by Ship Movement
ships stationary ships in motion
3 PM 8 PM 10 AM 5 AM
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Comparing observations 1 node at a time to a model of emissions and
transport with 1 km spatial resolution and sub-hourly time resolution
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BEACO2N observations (September averages)
WRF forecasts (given 1km bottom-up emissions inventory)
well predicted under predicted over predicted
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Inverse model using all BEACO2N nodes as a single instrument
Alex Turner
10 km
10 km
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BEACO2N: A high spatial resolution observing system for GHGs (CO2) and air quality (CO, O3, NO, NO2, particles)
CO2 A.A. Shusterman, V. Teige, A.J. Turner, C. Newman, J. Kim, and R.C. Cohen: The BErkeley Atmospheric CO2 Observation Network: initial evaluation, Atmos. Chem. Phys., doi:10.5194/acp-2016-530, 2016. A.J. Turner, A.A. Shusterman, B.C. McDonald, V. Teige, R.A. Harley and R.C. Cohen, Network design for quantifying urban CO2 emissions: Assessing tradeoffs between precision and network density Atmos. Chem. Phys. Disc., 2016. AQ gases J. Kim, and above team, Network of AQ sensors, in prep
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Conclusions High space and time resolution observations from in situ and space based platforms will offer a new window into mechanisms affecting emissions and chemistry in cities. Challenges will be: 1) learning to Interpret dense networks as more than
the sum of individual instruments. 2) Learning to think about daily variability in ways th
at teach us about processes.
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Thank you!
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