Characterization of PM2.5 Episodes in the Wintertime San

Characterization of PM2.5 Episodes in the Wintertime San Joaquin Valley using Surface and Aircraft Observations

Chris Cappa UC Davis

Dept. of Civil & Environmental Engineering

California Air Resources Board 27 November 2018

UCDAVIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

1

UCDAVIS

CALIFORNIA AIR RESOURCES BOARD

Acknowledgements Qi Zhang Prof. Qi Zhang (co-PI)

Dr. Gouri Prabhakar Dr. Dominique (Nicky) Young Dr. Caroline Parworth, Dr. Xiaolu Zhang, Dr. Hwajin Kim, Dr. Sonya Collier Gouri Prabhakar Caroline Parworth Hwajin Kim Dr. Sally Pusede (Univ. Virginia) The entire DISCOVER-AQ Team (NASA)

This work supported by ARB

Nicky Young Xiaolu Zhang Sonya Collier Project #14-307

Disclaimer: The statements and conclusions in this report are those of the contractor and not necessarily those of the California Air Resources Board. The mention of commercial products, their source, or their use in connection with material reported herein is not to be construed as actual or implied endorsement of such products. 2

Publications Resulting from this Work

*Prabhakar, G., Parworth, C., Zhang, X., Kim, H., Young, D. E., Beyersdorf, A. J., Ziemba, L. D., Nowak, J. B., Bertram, T. H., Faloona, I. C., Zhang, Q., and Cappa, C. D.: Observational assessment of the role of nocturnal residual-layer chemistry in determining daytime surface particulate nitrate concentrations, Atmos. Chem. Phys., 17, 14,747-714,770, 10.5194/acp-17-14747-2017, 2017.

Pusede, S. E., Duffey, K. C., Shusterman, A. A., Saleh, A., Laughner, J. L., Wooldridge, P. J., Zhang, Q., Parworth, C. L., Kim, H., Capps, S. L., Valin, L. C., Cappa, C. D., Fried, A., Walega, J., Nowak, J. B., Weinheimer, A. J., Hoff, R. M., Berkoff, T. A., Beyersdorf, A. J., Olson, J., Crawford, J. H., and Cohen, R. C.: On the effectiveness of nitrogen oxide reductions as a control over ammonium nitrate aerosol, Atmos. Chem. Phys., 16, 2575-2596, 10.5194/acp-16-2575-2016, 2016.

*Young, D. E., Kim, H., Parworth, C., Zhou, S., Zhang, X., Cappa, C. D., Seco, R., Kim, S., and Zhang, Q.: Influences of emission sources and meteorology on aerosol chemistry in a polluted urban environment: results from DISCOVER-AQ California, Atmos. Chem. Phys., 16, 5427-5451, 10.5194/acp-16-5427-2016, 2016.

UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

3

80

-('( 60 E C) :::L --LO

c--J

~ 40 a_

20

-3 Ave= 14.2 µgm

-3 Ave= 19.5 µgm -3

Ave= 11 .7 µgm -3

Ave= 14.5 µgm -3 Ave= 12.7 µg m

-3 Ave= 15.8 µgm

-3 Ave* = 15.9 µgm

0 - ........................................................ ....-........................................................ ......-+.........-................... --............................................... ........, ................... ........, ......... --.........., ................... ........, ................... - ........................................................ ....-....................................................... .....-

1/1/2012 1/1/2013 1/1/2014 1/1/2015 1/1/2016 Date

1/1/20 17 1/1/201 8 1/1/201 9


Wintertime PM2.5 in the San Joaquin Valley

Source: ARB AQMIS, Site: Fresno-Garland 4

Wintertime PM2.5 in the San Joaquin Valley

Fresno 2012 - 2018

Image: NASA SeaWiFS Dec. 21, 1999

Source: ARB AQMIS, Site: Fresno-Garland

...-... <?

E 30 C) .....

...I.. ..__.,

~ N ~ a.. 20

1 2 3 4 5 6 7 Month of Year

8 9 10 11 12

UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC 5

Project Objectives Use aircraft and ground measurements from the NASA DISCOVER-AQ to:

• Elucidate the sources that contribute most to PM2.5 episodes

• Improve understanding of the atmospheric processes that lead to build-up & dissipation of pollution episodes

• Update the conceptual model for wintertime PM2.5 formation in the SJV

Project Tasks Task 1: Analysis of spatial and temporal distributions of PM2.5

Task 2: Numerical modeling of PM2.5 during DISCOVER-AQ in support of conceptual model development

Task 3: Updating of conceptual model of PM2.5 formation in the SJV UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

6

42

DISCOVER-AQ • NASA-led measurement campaign during Jan/Feb 2013 • Aircraft + ground measurements

• Fresno “supersite” • Vertical profiles over 6 SJV cities 3 times per day

40

38 a,

] ii ...J

36

34

-124 -122 -120 long11:u<1e

-118 -116 -120.5 -120.0 -119.5 -119.0 Longitude

4000

8000 ,, ~ co ~

6000 t 0

8' 4000 II'

!

' !!.

- 2000 3 1

0 0

-1 18.5 -118.0 \ITAL

7

Ground (Fresno) Aircraft

P3B B200

In Situ • PM composition & concentration • PM concentration & composition • HR-AMS = NO3-, SO4

-, NH4+, Organics • Scattering • SP2 = Black carbon fast NO3• hygroscopicity

-

• Size Distributions • size distribution • Gas-phase + Met • Water soluble ions (slow NO3 , SO4 , NH4

+) • NO2, O3, NO, SO2 • Gas-phase + Met • PTRMS + Canister samples • NO2, O3, NO, SO2• Solar Radiation, wind • NH3, HNO3, CH4speed/direction, RH, T • RH, T • NH3 Remote Sensing

• PM backscatter profiles (HSRL)

}


- -

8

Ground Aircraft Day-specific vertical profiles over Continuous time-series of pollutant multiple locations in morning, concentrations at the surface midday and afternoon

I I I I I I

25 - -

---(") I

E 20 - -~ ---Q) ..... co 15 I.... - -..... z Q) ~ ..... co ::J 10 (.)

t:: co a..

5

0

- ~ ~DI ~ -

' ~ ,

- ' 1111

ii~~ - ~

j I I I I I I I

1/16/2013 1/21/2013 1/26/2013 1/31/2013 2/5/2013 2/10/2013

Date and Ti me

-.. E ~ -Q) "'C ::J

:'!:: ..... <(

1. 0 ---------..-------------------,

- morning ( ~9:30 am) - middday ( ~12:00 pm) - afternoon (~2:00 pm)

0.8

0.6

0.4

0.2

0.0 ---------------0 5 10 15 20 25 30

Estimated Nitrate (µg m-3)


9

Part I: Composition and variability of surface particulate matter


10

Two major pollution events during DISCOVER-AQ

Fresno 120

100

- 80 C") I

E 0) :::l ---,--, 60 ..---

~ a.. ..........

40

20

1/1 6/2013 1/21/2013 1/26/2013 1/31/2013

Date and Time (local)

2/5/2013 2/1 0/2013


11

Valley-wide Impact of Events

37.5 60

50 -0 ~ 37.0 N

(J1

()

40 0 ::, (")

36.5 ro ::, ,-+, -,

30 ll) ,-+,

6" 36.0 ::,

..--.. 1= (0

20 -3 "" 35.5

........

10

35.0

-121.0 -120.0 -119.0 -118.0

100........,,..........---,----r---r---r--.--.--.----.--.-r--,r"T-r--r-r-r7 80 60 40 20

Hanford ,. Episode 1 ., ,. Episode 2., ... ,-.... -• •

100-------- •••• ·---•---80 Tranqui lity •

60 e\ 40 .... .. ·

1 ~~ ~=-2.4 ... _C.. e lft:~ 80 Corcoran 60 = .. · _ ...... ·• •

,_ 40 e 7 ~ '

E 20 •-• __ _ 0) 1009Z----c---_ 2: 80 § 60 ;; ~ 40

+-'

C 20 Q)

Huron

g 100 1--------0 80 u

Lr) 60 N ~ 40 a.. 20

100 80 60 40 20

100

Porterville

Bakersfield

80 • Fresno i

~~ .. _...._. ...... ~ 20 • ••••

0 L...JL......J---1----1.....J...-L.-L.....L.-..&...-..L......:~L......1'---I. _________ _

1/11 /2013 1/21/2013 1/31/2013 2/10/2013

Sampling Date >NMENTAL .AQRC

Source: ARB AQMIS 12

37° N (a) Fresno

\ 36° N

Bakersfield 35 ° N .___ _ ____.__ __ .....___ _ ____.______.

120° w 119° w

10 12 14 16 18 N03(g+p) (µ g m- 3)

37° N (b)

\ 36° N

35° N .___ _ ____._ __ ___.___ __ ..________. 120° w 119° w

5 10 15 NOX (ppb)

37° N (c)

36° N

35° N .___ _ _____.__ __ __.___ _ ____.____, 120° w 11 9° w

20 40 60 80 100 NH 3 (ppb)


Spatial Distribution of Pollutants in the SJV • Enhancement of total nitrate and NOx over cities, but wide spatial extent • Highest ammonia concentrations outside cities, but elevated throughout SJV

[Pusede et al., ACP, 2016] 13

Particle Composition During DISCOVER-AQ

Period 1 Period 2

56% 54%

Nitrate Chloride Sulfate Black carbon Ammonium Organic Aerosol

[Young et al., ACP, 2016]

(h) (i) 2% -C"')

9% I

E 40 O> 1% :i 5% -C: 0 30 :.::; ro I,....

.6,,,,1

C: Q)

20 0 C: 0 0 (/) (/) 10 ctJ E 0)

Avg. PM 1 = 44 µg m > 0 <(

U) -C"') I

E 40 O> :i -C: 0 30 ~ ro I,....

.6,,,,1

C: Q)

20 0 C 0 0 (/) (/) 10 ro E

-3 C) > 0 <(

(k) 3%

10 %

Avg. PM1 = 36 µg m -3


14

Distinctive diel variability

• Substantial difference between secondary and primary species

• Peak in BC, organics and chloride at night

• Peak in nitrate, sulfate and ammonium during day


(a) 3.0

2.0

- 1.0 ('I)

E C) 0.0

::l.. (b}40

.._..

C: 0 30 ·-+-' cu L.. 20 +-' C: Q) 10 0 C

0 0 0

(c} (/) (/)

m 15

~ 10

5

__ o 0

BC

Org.

(d}

(e}

(f}

1.5

1.0

0.5

0.0

6

4

2 ,. -

..................... ...,iP

. , .., ........... .. .. .... ,. . ,. ...

0 ~'l"""'O""'--"l"""T""'__,..T""""'P"""'P""T..,._~_....,

0.8

0.6

0.4

0.2

0 .0+-,-m--r-r"T'""T"""r--r-T""'T"T""'r"'T,"'T""T""T"'T"T--r-T"l

4 8 12 16 20 24 0 4 8 12 16 20 24 Hour of day (PST) Hour of day (PST)


15

(g) 0 10 20 30 4

6

1008 ' .. .. .. 2

\

4 ;

6 ,

10008

(h) 0 10 20 4 ' 6 ' • 8 i,

100 J, I \

2 l ' ! \

i ' ... .. 4 .. .. 6

, 8 ,

1000

- so 2-+ (i) 0 1 2 3 - Org. - N03 - ■■ NH4 Org44 4

-- 16 <? (")

2.0 I

1.2 5 E E 20 14 O> O> 1.0 ::i. ::i.. 1.5 12 4 --co 0.8 15 10 ~ ::,. a a

8 3 0 0 0.6 1.0 .-.- 10 O> O> 6 2 0 0 0.4 0) 0 2 4 6 8 -0 0.5 4

-0 5 -- 1 e, 2> 0.2 2 0

0 c::: -0 0.0 0 0 0 0 --0

4 4 5 6 7 8 9 1000 •

' .. o va (nm)

... '

10008

0 2 4 6 8 10 12 14 16 18 20 22 24

Hour of day (PST) NTAL .

Species-specific size distributions • Organic and chloride size

distributions differ from major inorganic species

• Greater diel variability in organic size distribution

Organics

Nitrate

Sulfate

Ammonium

16 [Young et al., ACP, 2016] Di

amet

er (n

m)

Diam

eter

(nm

) Di

amet

er (n

m)

Diam

eter

(nm

)

Organic Aerosol “Factors”: Positive Matrix Factorization • Hydrocarbon-like OA (HOA) • Oxygenated OA (OOA) • Cooking-related OA (COA) • “semi-volatile” (SV)

• “low-volatility” (LV)• Biomass-burning OA (BBOA) • Two distinct types

PMF

Prim

ary

(s)

------· 20°/o

13%~~---....... 18%

COA

16% UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC


Secondary

17

Distinctive diel variability • All primary OA factors peak at night

with strong diel variability • HOA exhibits rush hour bump • Secondary OA relatively constant,

with daytime peak

Conc

entr

atio

n (µ

g m

-3)

0 2 4 6 8 0 12 4 6 18 20 22 24

Hour of day PST

8

0 ....,:...,;..;c.=z=.:z:.=.m.z.:c.::a:.w~ltl-t,l,,ll.w,11:!i.=t--_....;...,;....;.....=-t

2

I

BBOAQ i I I I I

I I

Hour of day (PST UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

Conc

entr

atio

n (µ

g m

-3)

[Young et al., ACP, 2016] 18

,__5 ,-.. (") C")

I I

E .5 E4 C) C)

::::i.. ...__... ...__...

~3 "O

0 T"""

C) g>2 0 -"'O

"'O O .5 ..._,

~ 0 0 l: "'O 0 "'O 0.0

50 ..._,

D va (nm)

Cl. 20

0 0

Cl.

8c8

...--.. "'C co

2 3 I

v,)

0 ...__...


OA Factor Size Distributions • HOA and BBOA exist in smaller particles compared to OOA and COA • OOA size distribution very similar to secondary inorganic size distribution • COA has unique size distribution

HOA COA BBOA OOA


C: 0

~ ... -C

~ C 0 0 1/) V'J ro ::

w 2.5 Q) ·0 2.0 ~ 1.5 w 1.0 ... :;: 0.5 BC a. 0.0-+-r-..........................

.... .9 0 ffl -<( 0

0 8 16 24

4 HOA 3

2

1 Q-+-o-..........................

0 8 16 24

~ ifl 1.2 .c -- E o.8 a. irl a. V 1/) a. .S 0 4 ro (/) .

C)

ro . !.2 0)

0 ffl o... rn O"O Q)

Q) ~

co 0.0 .+9-.......... ......,..,........,.... 0 8 16 24

1.0 0.8 0.6 0.4 0.2 0.0-+i-~""'"-.....

100 80

l 60 :c 40 a:: 20

0 8 16 24

0 -+-,-,..,........,... ............ 0 8 16 24

- Weekday average -- Weekend average

30

20

10 O Org.

0 8 16 24

8 COA 6

4

2 Q-+-,-,..,.........,......,.. ....

0 8 16 24

00 V 80 60 40 20 NOx 0 +.-, ........ ......,.......,. ....

0 8 16 24

1.5

1.0

0.5

0.0-+ri~..,..,.......,. .... 0 8 16 24

6 0 ,~

~12 \ ~ J -e 8 \

,w - ' ' 8- 4 ~ 0 ............ ., ........................ ~

0 8 16 24

12

8

4

0 -+-.--............................

4 3 2 1

0 8 16 24

0-+-.--............................

40 30 20 10

0 8 16 24

Ox 0 +,-........ ......,.......,. .....

0 8 16 24

~:::V\ / 0.2- \J 0.0 lsoorene

0 8 16 24

1.2 ~

0.8

0.4

0.0 ...................................

12

8

4

1.0

0 8 16 24

8 16 24

0.8 ~ 0.6 , . 0.4

8 16 24

Acetonitrile

8 16 24

8 16 24 Hour or Day (PST)

4 3 2 1 NH.i +

0 -+-.--.......................... 0 8 16 24

4

3 ._~

2 "'V' I ~ SV-OOA

80 ~60 140

20

0 8 16 24

O+t-........ ......,._ .....

25 20 15 10 5

0 8 16 24

0 Methanol

~ 0 ,_, 8 300 = 0 200 ~ '5 100 -0 C:

0 8 16 24

~ 0 ................................... 0 8 16 24

0.6

0.4

0.2

0.0 .................................

5 4 3 2

0 8 16 24

1 LV-OOA 0 ..................................

30

{ 20 ~10

0 8 16 24

NOi O+.-........ ,.............,..,...

4 3 2 1

0 8 16 24

0 Acetaldehyde

'7 Ill 2.0

.S 1.5

] 1.0

liro.5 "8 ~ 0.0

0 8 16 24

0

""'I ,., JI \

JI ,-.,. \ ' ,.

8 16 24

CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

Weekday vs. Weekend • Some species or

factors exhibit strong differences in shape of diel variation

• BC, HOA, CO, NOx, SO2, benzene, toluene, isoprene

• Some exhibit negligible differences

• Nitrate, sulfate, SV-OOA, BBOA2, COA, Ox, acetonitrile


Relationship between particle composition and concentration

• As the PM concentration increases…

• Fraction of primary species increases

• SV-OOA fraction of OOA increases

• Nitrate fraction of secondary species increases

2- + • N03 • S04 NH4 • Cl • BC

• HOA • COA • BBOA 1 • BBOA2 Total OA

SV-OOA • LV-OOA

1.0

0.8 C: 0 .....

0.6 (.)

cu I....

'+-C/)

0.4 C/)

cu ~

0.2

0.0 T"""

f'-

15.4

0 CJ) 00 f'- c..o T"""

N N L{) 00 T""" C'0 C'0 ~ L{) f'- 0

T"""

24.8 22.5 24.6 9.3 2.5 0.9 Total PM1 mass bin mid-point (µg m-3)

Fraction of total data points per bin (%)



Summary: Part I • Wintertime pollution events are spatially wide-spread in the SJV

• Particle composition dominated by organic aerosol and ammonium nitrate

• OA is contributed from primary emissions and secondary formation

• Primary OA contributed by biomass combustion, vehicles, cooking

• Primary and secondary species exhibit distinct diel behavior allows for separation and some identification of sources

• Vehicle-associated species tend to show greater weekend-weekday differences in diel behavior

• Organic aerosol contribution generally larger when total PM1 is larger


22

Part II: Processes Driving Variability in Ammonium Nitrate


23

Regulatory (and other) models continue to have difficulty accurately simulating the mass concentrations of wintertime particulate nitrate in the SJV

Green = Observations

Blue & Red = Simulations

Time of Day (local) Month of the Year

Blue & Green = Observations

Red = Simulations

Report to California Air Resources Board (2017) [Walker et al., ACP, 2012]

-C 0 ·-~

'ff '

'ffO

t= 6 '<l) 0

g V

0. ~-----..,,.........,,............----,,---.--.,.........,,.............---,---.--~r---r----r----.---.-T"'"""""'T----.----.-"""T"'""""'"'>

110 1 0

Q) +-' cu s.,_

+-'

z

15

10

5

2

Nitrate - Fresno, CA

4 6 8 10 12


24

The story of a diurnal profile in particulate nitrate

20

15

10

5

o-L-~----,---r---;-----0 10 20 UCDAYIS


Particulate Nitrate

Conc

entr

atio

n (µ

g m

-3 )

Hour of Day [Prabhakar et al., ACP, 2017] 25

Particulate Nitrate Diurnal Variability

During DISCOVER-AQ

Nitrate

Hour of Day (local)

• Sharp ↑ in secondary PM at the surface in early morning

• Nearly a doubling of particulate nitrate in early morning

• Peak surface concentration around 10 am

• Late morning decrease that continues with small afternoon bump

• Relatively constant concentration overnight

-M I

E 20 0) ::J.. ...._.....

C: 15 0 ...., co "-....,

10 C: a.> (.} C: 0 5 (.)

CJ) CJ) co 0 :E

0 5 10 15 20


26

Processes Impacting Surface Concentrations of AN

• Chemical production

• Mixing and entrainment

• Advection

• Dry deposition

Model these processes using observational constraints to determine their absolute and relative importance, supporting conceptual model refinement

[Prabhakar et al., ACP, 2017] UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

27

Particulate Nitrate Formation

Daytime

O3 + hv O(1D) + O2 H2O + O(1D) 2 OH HONO + hv OH + NO CH2O + hv OH + CH OH + NO2 HNO3

NH3

AN

Nighttime

O3 + NO2 NO3 + O2 NO3 + NO2 ↔ N2O5 NO3 + VOC products N2O5 + particles(aq) 2 HNO3

HNO3 + ClNO2

NH3

AN II / --I I \

UCDAYIS ~~R~O~N:;;M~E~NITT.:AAIL

CIVIL AND ERNli~ & AQRC ENGINEE

28

O3 + NO2 NO3 + O2 NO3 + NO2 ↔ N2O5 NO3 + VOC products N2O5 + particles(aq) 2 HNO3

HNO3 + ClNO2

O3 + hv O(1D) + O2 H2O + O(1D) 2 OH HONO + hv OH + NO CH2O + hv OH + CH OH + NO2 HNO3

Nighttime Daytime

AN NH3

AN NH3

Particulate Nitrate Formation

• RATE of nighttime conversion depends on: • particle surface area, and • reactive uptake coefficient (depends on composition, RH)

• EQUILIBRIUM state depends on total (gas + particulate) ammonia:nitrate ratio

' I UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

29

Atmospheric Boundary Layer

Entrainment of FT air

Well-mixed atmospheric

boundary layer

Free Troposphere

Mixed Boundary Layer

Free Troposphere

Residual Layer

Weak entrainment of FT air

(Nocturnal) Residual Layer w/ limited mixing

Shallow surface layer ----------~-, -----------1 t:N\:IINt:t:KIN\:I & A'-{Kl.

30

Processes Impacting Surface Concentrations

f'ree ,·roposphere

• s.unrse sunset

surlac m:onitor


Daytime mixed boundary layer

Nocturnal residual layer

Nocturnal boundary layer

Modified from Pusede et al. (2016) 31

f'ree ,·roposphere

• s.unrse sunset

surlac m:onitor


Processes Impacting Surface Concentrations Nighttime Production Aloft

N2O5 chemistry in the nocturnal residual layer leads to production of HNO3 and particulate nitrate


f'ree ,·roposphere

• s.unrse sunset

surlac m:onitor


Processes Impacting Surface Concentrations Nighttime Production at the Surface

Shallow layer, and high NO emissions titrate O3 and limit N2O5 chemistry (and HNO3 production) at the surface


f'ree ,·roposphere

• s.unrse sunset

surlac m:onitor


Processes Impacting Surface Concentrations Mixing & entrainment at daybreak

Starting at sunrise, entrainment of air from the residual layer brings air from above to the surface


f'ree ,·roposphere

• s.unrse sunset

surlac m:onitor


Processes Impacting Surface Concentrations Daytime Production

Daytime photochemical production of HNO3 from NO2 + OH throughout the mixed boundary layer


Entrainment of “clean” air in afternoon from FT ↓ surface concentrations

Daytime “loss” via entrainment


Modified from Pusede et al. (2016)

f'ree ,·roposphere

• s.unrse sunset

surlac m:onitor


36

Deposition of HNO3 to the surface ↓ ambient concentrations

Daytime loss via dry deposition


Modified from Pusede et al. (2016)

f'ree ,·roposphere

I l l l

• s.unrse sunset

surlac m:onitor


37

f'ree ,·roposphere

• s.unrse sunset

-

surlac m:onitor


Processes Impacting Surface Concentrations Establishing Nighttime Conditions

Late afternoon air becomes residual layer, and sets nighttime reaction conditions


f'ree ,·roposphere

• s.unrse sunset

-

surlac m:onitor


Processes Impacting Surface Concentrations Establishing Nighttime Conditions

Late afternoon air becomes residual layer, and sets nighttime reaction conditions

Dry deposition of HNO3


We have characterized the contribution of each process

20

15

10

5

0 --1------r------r-----;-----.l ---

0 5 201 __ _ AVIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

Particulate Nitrate

Conc

entr

atio

n (µ

g m

-3 )

Hour of Day [Prabhakar et al., ACP, 2017] 40

Chemical Production Constrain nighttime and daytime production as a function of altitude based on observed:

• concentrations of precursor gases, sunlight, RH profiles • particle surface area, particle composition

NO2 (ppb)

O3 (ppb)

NO (ppb)

Surface Area (µm2 cm-3)

Sunlight

Particle Composition

[Based on Prabhakar et al., ACP, 2017]

30

20

10

150

100

50

0 5 10 15 20

1600

1200

800

400

0

40

30

20

10

0 -

0 5 10 15 20

1.0

0.8

0.6

0.4

0.2

0.0 -+---"----.-------------..... 0 5 10 15 20


41

Mixing and Entrainment Determine day-specific evolution of entrainment rate and mixed layer height based on:

• Observed time-dependent vertical profiles in T, RH, CO, PM, CH4, NO2, AN • Observed time-dependent surface sensible heat flux • Using the Chemistry Land-surface Atmosphere Soil Slab (CLASS) model

Plateau (12 pm)

Rapid rise (10-11 am)

Slow rise (8 am)

400 1.0

- morning ( ~9:30 am) • Observed] 20 - middday ( ~1 2:00 pm)

- Fit - afternoon (~2:00 pm) 0.8 ..-

E 300 .._ 150 -.c.

C) C: ...-... - E 0.6 CD o · ~ I ::::, ---I,,.. 200 10~ a,

CD i:::::, >- Q. :::J ct! :t:

0 ..... 0.4 ....J -, <l::

"'C CD

5 _x 100 ~

0.2

-----

0 0.0 0 5 10 15 20 0 5 10 15 20 25 30

Hour of Day -3 Estimated Nitrate (µg m ) L

Cl"\:Jll"CCl'Ul"\:J CX 1-\'-(n\..

[Based on Prabhakar et al., ACP, 2017] 42

Nighttime Horizontal Advection Constrain using wind profiler measurements

• Notable vertical variability in mean wind speed and directionality

• Surface winds much slower than winds aloft

• Greater directional variability near surface

[Prabhakar et al., ACP, 2017]

1 . 0 ,-----.-------,...--------.------.

0.9

0.8

0.7

0) 0.6 ctl

~ 0.5 -Q)

-g 0.4 -:.;::; <(

0.3

0.2

0.1

(a)

o.o------- --------------0.0 0.5 1.0 1.5

Horizontal wind speed (m s-1)

(b3) 285 m 0

180

(b1) 10m o

180

(b4) 450 m o

180

(b2) 150 m o

180

UCDAYIS

Wind speed (m s·1) ■ 0-0.5 ■ 0.5-1 ■ 1-1.5

1.5-2 ■ 2-2.5 ■ 2.5-3

3-3.5 • 3.5-4 ■ 4-4.5 • 4.5-5


43

Nighttime Horizontal Advection

• Strong relationship between mean altitude-specific nighttime winds and [AN] the following morning


-C"') I

E

25

20

~ 15 C) > (ti ,........, a: -

' C"') 10 0 z

5

\ \

\ \

\ \

\ \

\

\\~

\\ . ' ' ' \

il.o ___ o.L.5 ___ ..J1 .-=--0------=-1~.5~-----:2;--;.o

Mean Wind Speed (mis)

0.6

0.5

)> ;::::;:

0.4 ~ C C. CD

0.3 ~ 3 )>

0.2 G) r ---

0.1

0.0


44

Dry Deposition Dry deposition rate depends on:

• Fraction of nitrate in gas-phase (fNO3,g) • Surface deposition velocity • Higher fNO3,g when T is high and RH is low • Wind-speed dependent • Dependence on ammonia availability • Height of surface boundary layer • Faster loss when fNO3,g is large • Varies with time-of-day


1.0 50 10 ... T = 290 K, RH = 40 % (typically observed) - ■ · T = 290 K, RH= 70 %

0.8 • T = 280 K, RH = 40 % • 40 - • NH, (g+p) I §

I z C') I

0 0.6 / 30 ; z / + - ~ 0

/ -C • 'C 0 / 20 (Q - . (.) ,,,

3 ct! ,,, w ... LL ..,. -..,.

10 0.2 Fresno

-- 8 (") I

E 0) 6 :::::s .._

,.........., -a. 4 -......, Gas-fraction of nitrate

(") - 1% - 3% 0 5% 10% z 2 - 15% - 20% .__,

- 25% - 35% •· .............. - 50% - 75%

0.0 0 0 0 1 2 3 4 s UCDAYIS

NHx(g+pl: N03. (g+p) (molar) CIVIL AND ENVIRONMENTAL Time (hours) ENGINEERING & AQRC

45

--- [N 0 3- + H N03] (b) Alli data

D H NO3 (day) D HNI0 3 (night)

• N 0 3- (day) D I N 0 3- (night)

1 J)1

ro ....... 0 .._ ~

0 C: 0 ~ (.)

02 m i,.;,

LL 0.0

11/20/13 11.22113 1/24/ 3 1/26/13 1/28/13 Date

(c) Day

91%

1/30/13 2/1/13 2/3/13 215/13,

I z SJ


Dry Deposition Constrain based on:

• Observations of daytime and nighttime fNO3,g (Parworth et al., JGR, 2017) • Observed surface wind speed • Observed time-varying T and RH

[Parworth et al., JGR, 2017] 46

Add these processes together to understand the overall behavior

Hour of Day

and develop a refined conceptual model of AN formation and loss

Particulate Nitrate

Conc

entr

atio

n (µ

g m

-3 )


20

15

10

5

0 --1------r------r-----;-----.l ---

0 5 201 __ _ AVIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

47

Nighttime:• Limited chemical production of AN at surface due to titration of O3

• Strong chemical production of AN above surface • determined by [precursors] near sunset

• Altitude-dependent advection ↓ AN and precursor gases, reshaping vertical profile

• Limited loss via dry deposition at surface due to low T and high RH Sunrise/Morning:

• Entrainment of air in residual layer to surface • Rapid change in surface concentration results from shallowness of nocturnal

boundary layer and shape of vertical profile Afternoon:

• Substantial photochemical production of HNO3 even in winter • Entrainment of cleaner FT air offsets some production

Sunset: • Short period when dry deposition is enhanced due to shallow mixed layer

but still sufficiently high T and RH • Conversion of daytime mixed layer into residual layer


48

Chemical Production: Both nighttime formation in residual layer (above surface) and daytime formation (at surface) are important Mixing: Sunrise engenders entrainment of residual layer to the surface Entrainment: Entrainment of FT air exports surface pollution

Advection: Reshapes vertical profile overnight

Dry Deposition: Sunset transition region is key period of influence: Limited daytime influence due to higher mixed layer small nighttime influence, due to low T/high RH

30 400 s: 1.0 (a) -+- Observed

>< (b) (1)

25 --- Model Q. C 0 0.8 - D ML height 300 .....

C".) ll> I :J

E 20 '< .0 (1) .i::

0) "'"' ..... 0.6 C Initial surface Photochemistry ::i -=r- 0 __. 15 200 (1) (.) Nocturnal - co a. co 0.4 chemistry - -=r- C C".) 10 ,-+ 0 + 0 -100 3 ..... carryover z (.) co 0.2 5 ll> '--LL

co 0 0 - 0.0 __.

00:00 06:00 12:00 18:00 24:00 00:00 06:00 12:00 18:00 24:00

Time of day (h) Time of day (h)


[Prabhakar et al., ACP, 2017] 49

25 (a)

.--.. 20 er)

I

E C) 15 --::::i.. ._...

---Cl. -(Y) 10

0 z

5

0 -----------------------------00:00 05:00 10:00 15:00 20:00

Hour of day (local)

30 (b) •

25

20

• • 15

10

5

o~----------------------' 1/15/2013 1/17/2013 1/19/2013 1/21 /2013

Time and date (local)


Pollution Event Build Up

Day-over-day build up depends on ∆[NO3-] between sunrise and

boundary layer decoupling (~3-4 pm)

∆[NO3-] = 1.3 µg/day

[Prabhakar et al., ACP, 2017] 50

Summary: Part II Key Processes in Conceptual Model • Altitude-specific nocturnal chemical production and transport (advection) • Daytime chemical production • Mixing and entrainment of residual layer to surface • Entrainment of clean FT air and export of surface pollution • Time-dependent dry deposition of HNO3 and repartitioning of AN

Overall • Substantial source/process specific information embedded in composition-

specific diurnal profiles • Specifics of pollution build-up will vary by location, depending on particular

contributions of processes


51

Extra Slides

UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC 52

Nitrate formation in the Nocturnal Residual Layer

• Net production at low altitudes (< 150 m)

• Net loss at higher altitudes (150-350 m)

• Combined influence of production and transport

• Potential result of differential advection in residual layer

* Afternoon profile scaled to surface [NO3-] at 3 pm

-E ~ ---(]) ,::::, ::J

:!:: +-'

<{

1.0 - ----r---r------r---..-------

- morn ing - afternoon

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2 < < 0.0 ----------------'-------J 0 0 o 5 1 o 15 20 25 30 · 0....._ _ ___ ---'-1 ---..L.2---....L3---...J4

Estimated Nitrate (µg m-3)


53


O3 + NO2 NO3 + O2 NO3 + NO2 ↔ N2O5 N2O5 + particles(aq) 2 HNO3

HNO3 + ClNO2

-Conversion of NO2 to pNO3 depends on:

• Initial [O3] and [NO2] • Particle composition & RH (uptake coefficient) • Particle concentration (surface area)


54

55


• Predict production of ~15-30 µg m-3 of nitrate overnight Surface Area (µm2 cm-3)

• Reasonably consistent with observed ↑ at low altitudes

Initial model NO2 (ppb)

conditions from Likely observations at O3 (ppb) range 3 pm

NO (ppb) O

vern

ight

Nitr

ate

Prod

uctio

n (µ

g m

-3 )

1600

1200

800

400

0

40

30

20

10

0

30

20

10

0

150

100

50

0 0 5 10 15 20

35

- y =3x 10 -2

30 - y =3x 10 -3

- y =2x 10 -3

-3 25 - y = 1 x 10

- y = 0.5 X 10

20

15

10

5

0 0 2

-3

4 6 8 Reaction Time (hrs)

10 12


Daytime nitrate formation Daytime HNO3 production rate peaks around noon (not 10 am) • Daytime production

Hour of Day

[NO

2 ] (p

pb)

[OH]

(nor

mal

ized)

• Accumulates over the day (no peak) 1.0

0.8

0.6

0.4

0.2

0.0---'-----~ 1--~-~ r-=a----,r--_.._

40

30

20

10

0 -+------r--..---""T"'6------r----

0 5 10 15 20

10-------..--------r------,r-----,

C)

::i. ..._ ,.......

C")

0 4 z I ........

2

0 ____ ......__IIIE...._L.....-__ ___.__ __ ____,ji..,.._ __

0 5 10 15 Hour of Day

20


56

Historical Observations Inform Trends in AN

As NO2 has decreased, so has ammonium nitrate

Open = weekend Filled = weekday

[Pusede et al., ACP, 2016]

Fresno 12

(a)

10 Last decade

,,..__ 8 \ 7

E Last decade 0)

6 \ :j__ ---I (") 0 Present z 4

\ 2

., Present

0 0 5 10 15

N0,2 (ppb)

,,..__

7 E 0) :j__ ---I (") 0 z

20

12 (b)

10

8

Bakersfield

Last decade ~. Last decade

~ 6

4

2

◊ Present • •

\a,◊

a~-______._--~--~-~ 0 20


57

Bakersfield - 36 ~

a) '>-ro ,:,

('t') 30

I E 0) 24

..__ C

18 0 t,

Present :, ,:,

12 0 L

a. ('t')

◊ Next decade ·O 6 z

V I

00 z 5 10 15 20

N02 (ppb)

1- 36 (b) >-ro

""O 30 ('I')

I E rn 24 -C

18 0

u :J

""O 12 0 L

a. ('I')

0 6 z

'-.:t I

00 z

Bakersfield

I \

Next decade

5 10 N02 (ppb)

15 20

JCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

Observed and Expected Changes in AN Production

Nighttime production is more sensitive than daytime to changes in NOx at current concentrations

Nighttime Daytime

[Pusede et al., ACP, 2016] 58

Dry deposition of HNO3 gas can impact particulate nitrate The gas-particle partitioning of HNO3 depends on:

Particles deposit HNO3 gas slowly deposits fast • NH3 availability

• T and RH 1.0

~ ~ 0.8 <==> ,-...

~ 0) '-"

<')

0 0.6 z ~

0

I C 0 0.4 ..., 0 ro I...

LL

0.2

0.0

50 -+- T = 290 K, RH = 40% (Typically observed) -■ - T = 290 K, RH = 70% • .. • T = 280 K, RH = 40% • 40 - ■ NHX (g+p) I

• I z

• I / X

• 30 -/ (0

+ • "O / -.....,

/ ,-...

1= • 20 (0 ' ,, .. .. • 3 ' ,;

' • l, .. ,,,. ....., ' ..

.. .,,,,.. .. _,,,,,,. ... .. Fresno 10 ·•. .. ,,, II._,- •-------------► -. :a. .. --... . . ...

1

0

10 NHx (g+p): N03. (g+p) (molar) - - ---- ---


[Prabhakar et al., 2017] 59

Dry deposition of HNO3 gas can impact particulate nitrate • Daytime impact limited by large BLH

• Only 10-15% of nitrate is in • Critical period when boundary layer collapses the gas-phase (excess NH3)

• Gas-phase fraction ↓ at night, due to lower T/higher RH

[Parworth et al., JGR, 2017]

300 -E ._, ..... ~

----- [N 0 3- + H N 0 3] .!2> Q)

HNO3 (day) HN 0 3 (night) =: 200 □ D

NO3- (night) Q) • NO3- (day) □ >-m

{d), I I ight ....J

(c) Day ""C Q)

-~ ~

91% 100 9%

0

14

12

10 -u 0 ._, Q) 8 I...

::J ..... m I...

~6 E Q)

I-4

2

0

0 5 10 Hour of Day

• -

15

80

70 ::0 CD D) -<" CD 60 I C:

3 a. ;::;:

50 -< -~ 0 ._,

40


60

Dry deposition is important right after the boundary layer collapses

Without dry deposition With dry deposition + evaporation

-C') I

E C)

2, ,......., C'0 0 z ........

30------------------------

25

20

15

10

5

Daytime • Nighttime

- Observed

o-----~---~----~---~--__:.

0 5 10 15 20 Hour of Day

-C') I

E C)

2, ,......., C'0 0 z ........

30------------------------

25

20

15

10

5


- Observed

o-----~---~----~---~----0 5 10 15 20

Hour of Day UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

Key period

61

Daytime entrainment is important once boundary layer stops growing • Estimate afternoon entrainment velocity from

afternoon decline in non-volatile sulfate aerosol • Compares well with in situ estimate of Trousdell et

al. for wintertime SJV (ACP, 2016)

Low [X] Slope = 8.8%/hr ve = 1.2 cm/s

High [X]

Sulfate

Time of Day

Entrainment of FT air

800 >1 pm

700

600

2.0 -Free Troposphere M I 500 E - c, C) E 2: 1.5 --(1)

C: -c 400 0 :::,

:,.::::; :t::: - m <( '-- 1.0 300 C: Q) t) C: 0 200 t)

CJ) 0.5 CJ)

m 100 ~

surface mixed layer 0.0 0 0 5 10 15 2o ucDAYIS


62

Daytime entrainment is important once boundary layer stops growing

Without daytime entrainment With daytime entrainment

-C') I

E C)

2, ,......., C'0 0 z ........

30------------------------

25

20

15

10

5


- Observed

o-----~---~----~---~--__:.

0 5 10 15 20 Hour of Day

-C') I

E C)

2, ,......., C'0 0 z ........

30------------------------

25

20

15

10

5


- Observed

o-----~---~----~---~----0 5 10 15 20

Hour of Day UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

Active time

63

Most of the PM is organic aerosol or ammonium nitrate

2010 2013 2016

Organic Compounds Data from Zhang (UCD) Sulfate and Russell (UCSD) groups Nitrate Chloride AN

Ammonium Black Carbon } UCDAYIS CIVIL AND ENVIRONMENTAL ENGINEERING & AQRC

64

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