voc e missions and trends in los angeles basin n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
VOC e missions and trends in Los Angeles basin PowerPoint Presentation
Download Presentation
VOC e missions and trends in Los Angeles basin

Loading in 2 Seconds...

play fullscreen
1 / 17

VOC e missions and trends in Los Angeles basin - PowerPoint PPT Presentation


  • 85 Views
  • Uploaded on

VOC e missions and trends in Los Angeles basin. Agnès Borbon 1,2,3 , Jessica B. Gilman 2,3 , William C. Kuster 2 , Stuart McKeen 2,3 , John S. Holloway 2,3 , Carsten Warneke 2,3 , Joost de Gouw 2,3 1. LISA, CNRS, University of Paris Est-Créteil and Paris-Diderot, Créteil, France

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'VOC e missions and trends in Los Angeles basin' - lyle


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
voc e missions and trends in los angeles basin

VOC emissions and trends in Los Angeles basin

Agnès Borbon1,2,3, Jessica B. Gilman2,3, William C. Kuster2, Stuart McKeen2,3, John S. Holloway2,3, Carsten Warneke2,3,

Joost de Gouw2,3

1. LISA, CNRS, Universityof Paris Est-Créteil and Paris-Diderot, Créteil, France

2. Chemical Sciences Division, ESRL, NOAA, Boulder, Colorado, USA.

3. CIRES, University of Colorado, Boulder, Colorado, USA.

CalNex workshop – May 16th 2011 – Sacramento, CA - USA

slide2

Outline

  • Motivation and data
  • Determination of urban emission ratios
  • Week-end vs week-day
  • Preliminary comparison with inventories
  • Summary
motivation and data
Motivation and data

How can we improve the emissions inventory for greenhouse gases, ozone and aerosol precursors (extracted from CalNex White Paper - Jan. 2008)

A first step : evaluation of the emission inventory

Target species : VOC

emission

inventory

observations

[VOC]/[tracer]

QVOC/Qtracer

Detailed VOC composition

- CalNex 2010 at Pasadena

Hourly emissions of major regulated and non regulated pollutants :

- NEI (national) – 2005/dec.2008

- CARB (regional) - 2008

pasadena groundsite

Night

1-2 m.sec-1

Mid-day

LA plume transport

Pasadena groundsite

GC-MS instrument (Gilman, 2010)

C2-C11 NMHC, C6-C9 aromatics, OVOC, DMS

30-min time resolution

0.5 m.sec-1

observations diurnal profiles
Observations : diurnal profiles

primary

secondary

signature of primary emissions, mixing and chemistry

Ox

CO

need to define a set of ‘no-chemistry’ conditions to determine urban ER

acetaldehyde

acetylene

benzaldehyde

1,2,4-trimethylbenzene

enhancement ratios voc co or c2h2
Enhancement ratios VOC/CO (or C2H2)

benzene

m+p-xylenes

acetaldehyde

Photochemical

depletion

+

Photochemical

production

Emission

+

Photochemical age

(Calvert, 1976) :

methods to estimate urban er
Methods to estimate urban ER

Linear regression fit method (LRF)

from nightime data (22:00 – 06:00) when t < 1 h

Photochemical age method (OH)

(de Gouw, 2005 ; Warneke, 2007)

benzene

benzene

[VOC]/[C2H2]

m+p-xylenes

m+p-xylenes

see also de Gouw’s poster

on Tuesday

[VOC]/[C2H2]

photochemical age (h)

acetylene ppb

linear regression fit method lrf
Linear regression fit method (LRF)

R2 > 85%

Alkanes >C4

Aromatics

Alkenes

Aldehydes

acetaldehyde

ethylene

60% < R2 < 80%

C2-C4 alkanes

Ketones

Alcohols

n-butane

methy ethyl ketone

 dominant anthropogenic origin at night

estimation of urban er from lrf
Estimation of urban ER from LRF

ER of 42 VOC

ppt/ppb CO

ppt/ppb CO

performance
Performance
  • for primary anthropogenic VOCs

ER (C2H2) comparison

Relative difference vs rate coefficient

1:1

+20%

linear regression fit

(ER photochem – ER LRF / ER LRF ) (%)

- 20%

photochemical age fit

kOH (cm3.molecules-1.sec-1)

short-lived

long-lived

  • relative difference  ± 20%
  • very good agreement
trends la vs boston nyc 2004
Trends : LA vs Boston/NYC-2004

VOC/C2H2

ethanol

slope : 0.94 ± 0.06

R2 = 0.91

very good agreement except for ethanol (biofuel additive to gasoline)

LA - 2010

Boston/NYC - 2004

week end vs week days
Week-end vs week-days

diesel-fueled vehicle exhaust related

undecane

acetaldehyde

abundances constant

no statistical differences between slopes (t-test ;  = 5%)

  •  observed VOC and CO emissions are driven by gasoline-fueled LD vehicles
  • no weekend-weekday effect from the inventory either
evaluation of emission inventories
Evaluation of emission inventories
  • preliminary
  • 2 grids : great LA and Pasadena area
evaluation of emission inventory nei
Evaluation of emission inventory : NEI

ER (VOC/CO)

Consistency for aromatics

 vehicle exhaust origin

Large discrepancies for other VOC

tracer : acetylene

alkenes < propane < oxygenates

 area source contribution for alkanes and oxygenates

NEI - 2005

CalNex - 2010

carb 2008 vs nei 2005
CARB-2008 vs NEI-2005

Hourly emissions of CO

3 non-lumped species :

ethylene, xylenes, methanol

inventory

Hourly trafic counts

CalNex - 2010

PeMS network

summary
Summary
  • Determination of urban emission ratios :
  • ER derived for 42 VOCs at ± 20%  efficiency of both independent approaches.
  • ER consistent with previous datasets.
  • Weekend vs weekday :
  • No weekend effect at the groundsite  gasoline vehicle exhaust emissions dominate VOC composition (at least observed ones).
  • Preliminary evaluation of emission inventory :
  • Large discrepancies with NEI (overestimation of CO emissions). Consistent previous studies.
  • Emission ratios with CARB-2008 seem to agree better