atmospheric aerosols: health, environmental and policy of particulates in the us-mexico border...
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Atmospheric Aerosols: Health, Environmental and Policy of Particulates in the US-Mexico Border Region
July 14, 2005
2003 Field Measurement Campaign
Mexico City Metropolitan Area
Mario Molina
University of California, San Diego
Mario Molina Center, Mexico City
Summary of the First Phase of the Mexico City Air Quality Program
Chapter 1. Air Quality Impacts: A Global and Local Concerns
Chapter 2. Cleaning the Air: A Comparative Overview
Chapter 3. Forces Driving Pollutant Emissions in the MCMA
Chapter 4. Health Benefits of Air Pollution Control
Chapter 5. Air Pollution Science in the MCMA: Understanding Source-Receptor Relationships Through Emissions Inventories, Measurements and Modeling
Chapter 6. The MCMA Transportation System: Mobility and Air Pollution
Chapter 7. Key Findings and Recommendations(Kluwer Academic Publishers, 2002)
Visibility in the
Mexico City Metropolitan Area
Estimated Health Benefits of a 10% Reduction of Pollution Levels in the MCMA
PM10 Background Rate (case-persons-yr)
Risk Coefficient(% per 10µg/m3)
Risk Reduction (cases/yr)
Cohort Mortality
10/1000 3 2000
Time Series Mortality
5/1000 1 1000
Chronic Bronchitis
14/1000 10 10 000
OzoneBackground Rate (case-persons-yr)
Risk Coefficient (% per 10µg/m3)
Risk Reduction (cases/yr)
Time Series Mortality
5/1000 0.5 300
Minor Restricted Activity Days
8000/1000 1.0 2,000,000
Chapter 4. Health Benefits of Air Pollution Control: John Evans, Jonathan Levy, James Hammitt, Carlos Santos Burgoa, and Margarita Castillejos (2002).
Air pollution harms children's lungs for life
Children exposed to higher levels of particulate matter and other air pollutants had significantly lower lung function
Other transport
10%Private
cars12%
Buses15%
HD-diesel Vehicles
32%
Vehicles< 3 ton 8%
Other5%
Soil erosion
6%
Industrialcombustion
3%
Electricitygeneration
3%
Manufacturingindustry 6%
Other transport
7%
Other7%
Metals industry
9%
Buses9%
Private cars9%
Soil erosion
17%
HD- diesel vehicles20%
Chemical industry
4%
Vehicles< 3 ton 5%
Manufacturingindustry 13%
PM2.5 PM10
Percentage of emissions from the MCMA in 2000 by source category
Summary of MCMA-2003 Field Measurement Campaign
• Exploratory mission (February 2002)
• Intensive 5-week field measurement (Spring 2003)
• Special Session on “Megacity Impacts on Air Quality” at the Fall 2004 AGU Meeting, San Francisco, CA
• Special Issue of the MCMA 2003 Campaign in ACP (Atmospheric Chemistry and Physics)
• NARSTO sanctioned field campaign – data will be posted on NARSTO website
• Photochemical/Transport Modeling in progress (CIT, MM5, CAMx, etc.)
• Sponsors: CAM, NSF, MIT/AGS, PEMEX, DOE, others
ChaseDetailed mobile source emissions characterizationPlume tracer flux measurements
Mobile Sampling/MappingMotor vehicle pollution emission ratiosLarge source plume identificationAmbient background pollution distributions
Stationary SamplingHigh time resolution point samplingQuality Assurance for conventional air monitoring sites
Mobile Laboratory Modes of OperationFebruary 2002 & April 2003
Chalco
Teotihuacan
Tula
Ajusco
CENICA
Cuautitlan
Aerosol Mass Spectrometer (AMS) at CENICA
100% transmission (60-600 nm), aerodynamic sizing, linear mass signal.• Jayne et al., Aerosol Science and Technology 33:1-2(49-70), 2000.• Jimenez et al., J. Geophys. Res.- Atmospheres, 108(D7), 8425, doi:10.1029/ 2001JD001213, 2003.
Aerosol measurements (April 15-17, 2003)35
30
25
20
15
10
5
0
PM
1.0
Ma
ss C
on
cen
tra
tion
(g
m-3
)
12:00 AM4/15/2003
12:00 PM 12:00 AM4/16/2003
12:00 PM 12:00 AM4/17/2003
12:00 PM
Nitrate Sulphate Water Ammonium Organics PAH Chloride
PM2.5 Concentration
Gas or Particle Signal
Signal
Emission Ratio = Signal / CO2
“In-plume” Sampling indicated by above-ambient CO2 levels
800
700
600
500
400
30017:54
7/10/0117:55 17:56 17:57 17:58
Time
CO2 (ppm)
CO2
Ambient background level
Emission perturbed level
Vehicle Chase Experiments
Kolb et al., A31D-02 / Zavala et al., A31D-08 / Knighton et al., A14A-03
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.00
50
100
150
200
250
SCuO
C
X-r
ay
inte
nsi
ty
keV
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.00
50
100
150
200
CuO
C
X-r
ay
inte
nsi
ty
keV
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.00
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
Si
Cu
O
CX-r
ay
inte
nsi
ty (
a.u
)
keV
Heterogeneity in a single soot particle
S inclusion
Only Carbon
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.00
50
100
150
200
250
KSCu
O
C
X-r
ay
inte
nsi
ty
keV
S, K inclusions
Si inclusion
(Source: MIT/PNNL)
2 µm 500 nm
0.0 0.5 1.0 1.5 2.0 2.5 3.00
5
10
15
20
25
30
35
40
45
50
55
60
65
70
SO
C
X-r
ay in
tens
ity (
a.u)
keV
0.0 0.5 1.0 1.5 2.0 2.5 3.00
5
10
15
20
25
30
35
40
SO
C
X-r
ay in
tens
ity (
a.u)
keV
Fresh soot in city traffic
Processed soot at CENICA
Processing of SootFrom “Chase” Studies In Ambient Air
PIXESpectra
AromaticVOCs
Glyoxal
SOA Precursors SOA
East South South-West
MCMA 2003: Glyoxal and SOA precursors
DOAS-1L= 860mH= 16m
• Benzene, Toluene, Styrene• m-xylene, p-xylene, ethylbenzene• Benzaldehyde, Phenol, pCresol• Naphtalene • HCHO, Glyoxal (DOAS-2)
CENICA
First time DOAS detection of Glyoxal in the atmosphere
Conclusions: PM Measurements
• Rich PM dataset during MCMA-2003
• 58% organics, 26% Inorg., 14% BC– Org: 2/3 OOA, 1/3 POA– Little soil / metals– Intense condensation SIA and SOA– More SOA than in chambers
• “Natural” Holy Week experiment
• PAH measurements