Dense networks and geostationary satellites: A vision for the future of NO x and air quality observations

Transcription

1 Dense networks and geostationary satellites: A vision for the future of NO x and air quality observations Ronald C. Cohen UC Berkeley $ BAAQMD, NSF, NASA, UC Berkeley, HEI, Koret Foundation

2 A different talk RONO 2 chemistry (Day 2/3; night 1/3) governs the lifetime of NO x and HNO 3 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

3 Current models of emissions have few parts that respond to day-to-day variations in human behavior or weather. CO 2 Emission Inventory -2- (Turner et al., 2016)

4 Satellite remote sensing is changing how we think about emissions and air quality Tropospheric NO x Column

5 Nitrogen oxides (NO x ) are concentrated over cities April-September x10 15 NO 2 (molecules cm 2 ) OMI Berkeley High-resolution Retrieval (BEHR)

6 Large decreases over the last decade in U.S. result in smaller spatial extent of urban plumes April-September x10 15 NO 2 (molecules cm 2 ) OMI Berkeley High-resolution Retrieval (BEHR)

7 On a neighborhood scale inexpensive sensing might change how we think about emissions and air quality

8 BEACO 2 N: 2.5m 130m AGL

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10 Alphasense B4 Electrochemical O 3, CO, NO & NO 2 Sensors ($216 ea.) Vaisala GMP343 NDIR CO 2 Sensor ($2,800) Shinyei PPD42NS nephelometric particulate matter sensor ($16)

11 BEACO 2 N

12 Quality vs. Quantity 36% accuracy Network of roughly BEACO 2 N s size (25) & precision (1ppm) Network of three much more precise instruments (0.1ppm) Turner et al. ACP 2016

13 BEACO 2 N CO Sites: Laurel Korematsu HeadRoyce Burckhalter Kaiser ODowd ElCerrito Prescott CollegePrep StLiz NOakland

14 Question What can we do to understand the processes affecting air quality at the neighborhood scale? Viewed (mostly) through a lens of NO x

15 d[no x ]/dt Surface ~ Ozone Emissions Chemistry NO x NO + NO 2 τ ~ 100 s

16 Mixing

17 High space and time resolution measurements of NO 2, H 2 CO and O 3 will soon (3-4 years?) be routinely available TEMPO (hourly) Sentinel-4 (hourly) Global pollution monitoring constellation GEMS (hourly)

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19 GOME-2

20 OMI

21 TEMPO Actual is twice resolution shown

22 Riyadh L Valin et al., GRL 2013

23 Surface Ozone NO x regulates its own removal rate through its effect on OH

24 OH (or PO 3 ) vs. NO 2(x) Rural Suburban Urban

25 Emissions don t depend on winds; the burden and lifetime does L Valin et al., GRL 2013

26 Daytime RONO 2 slow fast slow

27 <xy> <x><y> x=no 2 ; y=oh

28 BErkeley Atmospheric CO 2 Observation 2km Network

29 BEACO 2 N pointwise surface network at ~2km resolution

30 Particulate Matter (co-emitted with CO 2, NO x, CO, ) observations modeled fit 1σ variation range from Choi et al. 2014

31 2km

32 Vehicle # per hour

33 WEEKDAY WEEKEND Vehicle # per hour

34 Analyze every plume near highway -17-

35 (NOx/CO2<2)

36 Port of Oakland Affected by shipping? Or just traffic? 2km

37 Port Aug/Sept Diurnal Cycle weekdays (M-F) weekends (Sat/Sun) 5 AM 10 AM 3 PM 8 PM

38 Port Diurnal Cycle by Ship Movement ships stationary ships in motion 5 AM 10 AM 3 PM 8 PM

39 Comparing observations 1 node at a time to a model of emissions and transport with 1 km spatial resolution and sub-hourly time resolution

40 well predicted under predicted over predicted BEACO 2 N observations (September averages) WRF forecasts (given 1km bottom-up emissions inventory)

41 Inverse model using all BEACO 2 N nodes as a single instrument 10 km 10 km Alex Turner

42 BEACO 2 N: A high spatial resolution observing system for GHGs (CO 2 ) and air quality (CO, O 3, NO, NO 2, particles) CO 2 A.A. Shusterman, V. Teige, A.J. Turner, C. Newman, J. Kim, and R.C. Cohen: The BErkeley Atmospheric CO 2 Observation Network: initial evaluation, Atmos. Chem. Phys., doi: /acp , A.J. Turner, A.A. Shusterman, B.C. McDonald, V. Teige, R.A. Harley and R.C. Cohen, Network design for quantifying urban CO 2 emissions: Assessing tradeoffs between precision and network density Atmos. Chem. Phys. Disc., AQ gases J. Kim, and above team, Network of AQ sensors, in prep

43 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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45 Thank you!

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