1 health impacts of air quality on the bishop paiute reservation focus on particulate matter toni...
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1
HEALTH IMPACTS OF AIR QUALITY
ON THE BISHOP PAIUTE RESERVATION
FOCUS ON PARTICULATE MATTER
TONI RICHARDS, Ph.D., AIR QUALITY SPECIALISTENVIRONMENTAL MANAGEMENT OFFICEBISHOP PAIUTE TRIBEtoni.richards@bishoppaiute.orgwww.bishoptribeemo.com
THE BISHOP PAIUTE RESERVATION IS 60 MILES FROM THE LARGEST SOURCE OF PM-10
IN THE NATION
THE OWENS DRY LAKE!
dustdust
3
WHERE IS THE BISHOP PAIUTE TRIBE?
On the California-Nevada BorderIn the “deepest valley” at 4,000ft between the Sierra Nevada and White Mountains200 miles South of Reno270 miles East of Las Vegas300 miles North of Los Angeles
Bishop Paiute Tribe
Los Angeles 300 miles
Las Vegas 270 miles
Reno 200 miles
Dry Lake
4
Separated high PM-10 days:dry lake probably had an impact on Bishop air quality
did not have an impact
Half the time, high PM-10 concentrations in Bishop were associated with dry lake activity
Conclusion: the dry lake has a significant Conclusion: the dry lake has a significant impact on Bishop Reservation air qualityimpact on Bishop Reservation air quality
EARLIER STUDY:Days with the highest PM-10
concentration on the Bishop Paiute Reservation 2003-2006
5
PM-10: DustOwens dry lake
Other barren lands
Dirt roads and parking lots
PM-2.5: SmokeWood smoke for home heating
Wildfires
Controlled burns
BISHOP’S PARTICULATE MATTER SOURCES
6
HISTORICAL PARTICULATE CONCENTRATIONS
2009 593 Apr 7
2008 1,064 Feb 13
2007 698 Sept 27
2006 370 Nov 11
2005 550 Jun 25
2004 1,220 Oct 19
PM-10 HOURLY MAX
2009 74 Jan 1
2008 94 Dec 94
2007 156 Jul 18
2006 96 Jan 28
2005 96 Dec 20
2004 109 Nov 24
PM-2.5 HOURLY MAX*
* Excludes July 4
AQI: PM-10 above 154 is unhealthyAQI: PM-10 above 154 is unhealthy AQI: PM-2.5 above 65.4 is unhealthyAQI: PM-2.5 above 65.4 is unhealthy
7
HEALTH STUDY: BACKGROUND
Few or no studies of the impacts of the dry lake
No studies of the impact of particulate matter on Reservation populations
WHY?
Can’t use standard methods (mortality / hospitalizations) on sparse rural populations
8
HEALTH STUDY: OUR APPROACH
Short term impacts: 3-5 daysDaily clinic visits as a measure of health
All visitsUnder age 5 / age 65 and overRespiratory / circulatory
Link visits to daily PM-10 and PM-2.5 concentrations
Hourly maximum in a 24-hour period24-hour average
Data: October 2006 to September 2007
9
HEALTH STUDY: METHODS
Descriptive statisticsExplore data structureVerify data quality
Time series correlationsVerify relationships among health variables and PM
All visitsUnder age 5 / age 65 and overRespiratory / circulatory
ModelingStandard time series methodsPoisson regression
10
DESCRIPTIVE STATISTICS: VISITSNumber of Visits Per Day
3 3 2
39
11
0
10
20
30
40
50
60
70
80
90
Total Under age 5 Over age 65 Respiratory Circulatory
Patient Type
Nu
mb
er o
f vi
sits
Max Num VisMin Num VisAvg Daily Visits
11
DESCRIPTIVE STATISTICS: PM-10Particulate Matter Less than 10 Microns
0
100
200
300
400
500
600
700
800
Oct-06
Nov-06
Dec-06
Jan-07
Feb-07
Mar-07
Apr-07
May-07
Jun-07
Jul-07
Aug-07
Sep-07
Month
PM
in m
icro
gra
ms
per
cu
bic
met
er
Average 24-Hr MCMax 24-Hr MCMax Hourly
12
DESCRIPTIVE STATISTICS: PM-2.5
Particulate Matter Less than 2.5 Microns
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
Oct-06
Nov-0
6
Dec-0
6
Jan-
07
Feb-0
7
Mar
-07
Apr-0
7
May
-07
Jun-
07
Jul-0
7
Aug-0
7
Sep-0
7
Month
PM
in m
icro
gra
ms
per
cu
bic
met
er
Average 24-Hr MCMax 24-Hr MCMax Hourly
13
DESCRIPTIVE STATISTICS: autocorrelations for visits
Autocorrelation Functions for Visits
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
Lag 1 Lag 2 Lag 3 Lag 4
Lag
Co
rrel
atio
n Total VisitsUnder Age 5Age 65 and overRespiratoryCirculatory
14
DESCRIPTIVE STATISTICS:cross correlations
Cross Correlation of Visits and Average PM-10
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Co
rre
lati
on
Total Visits
Visits under age 5
Visits 65 and over
Respiratory visits
Circulatory visits
Cross Correlations between Visits and Maximum PM-10
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Co
rre
lati
on
Total Visits
Visits under age 5
Visits 65 and over
Respiratory visits
Circulatory visits
Cross Correlation of Visits and Average PM-2.5
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Co
rre
lati
on
Total Visits
Under 5
65 and over
Respiratory
Circulatory
Cross Correlation of Visits and Maximum PM-2.5
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Co
rre
lati
on
total x PM2.5max
under5 x PM2.5max
over65 x PM2.5max
resp x PM2.5max
circ x PM2.5max
PM
-10
PM
-2.5
24-hour average Daily hourly maximum
15
PRELIMINARY MODELING:time series methods
Data Structure: 2nd order autoregressive process with indicator for days following a weekend or holiday
Visitst = α + β weekend/holiday + µt
Where µt = ρ1 µt-1 + ρ2 µt-2 + εt
Distributed Lag ModelVisitst = α + β PMt + β1 PMt-1 + β2 PMt-2 + β3 PMt-3
+ β4 PMt-4 + β5 weekend/holiday + µt
Where µt = ρ1 µt-1 + ρ2 µt-2 + εt
And t indexes days
16
TIME SERIES RESULTS:response of visits to PMResponse of Visits to Average PM-10
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Co
eff
icie
nt
Total Visits
Under age 5
65 and over
Respiratory
Circulatory
Response of Visits to Maximum PM-10
-0.015
-0.01
-0.005
0
0.005
0.01
0.015
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Co
eff
icie
nt Total Visits
Under age 5
65 and over
Respiratory
Circulatory
Response of Visits to Average PM-2.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Co
eff
icie
nt Total Visits
Under age 5
65 and over
Respiratory
Circulatory
Response of Visits to Maximum PM-2.5
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Co
eff
icie
nt Total Visits
Under age 5
65 and over
Respiratory
Circulatory
PM
-10
PM
-2.5
24-hour average Daily hourly maximum
17
FINAL MODELING:Poisson Regression
The number of visits follows a Poisson distribution with clustering within weeks
Visitswt = exposuret (exp (β PMt + β1 PMt-1
+ β2 PMt-2 + β3 PMt-3 + β4 PMt-4 + µ wt
where corr (µwt , µvs) = ρ if v=w = 0 otherwise
t indexes days and w indexes weeks
and exposuret = 1 (unknown) by assumption
The coefficients expβ compare the ratio of visits on days where PM increased by 1 microgram to those where it did not. Values >1 indicate a positive effect.
18
POISSON MODEL RESULTS:response of visits to PM
Incidence Rate Ratios of Visits to Average PM-10
0.98
0.99
1.00
1.01
1.02
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Inci
den
ce r
ate
rati
o total visits
under age 5
65 and over
respiratory
circulatory
Incidence Rate Ratios of Visits to Maximum PM-2.5
0.985
0.990
0.995
1.000
1.005
1.010
1.015
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Inci
den
ce r
ate
rati
o total visits
under 5
65 and over
respiratory
circulatory
Incidence Rate Ratio of Visits to Average PM-2.5
0.97
0.98
0.99
1.00
1.01
1.02
1.03
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Inci
den
ce r
ate
rati
o total visits
under age 5
65 and over
respiratory
circulatory
Incidence Rate Ratio of Visits to Maximum PM-10
0.997
0.998
0.999
1.000
1.001
1.002
1.003
lag 0 lag 1 lag 2 lag 3 lag 4
Lag in days
Inci
den
ce R
ate
Rat
io total visits
under age 5
65 and over
respiratory
circulatory
PM
-10
PM
-2.5
24-hour average Daily hourly maximum
19
Pilot study are clinic visits a measure of health that responds to air quality?Two approaches: time series and Poisson modelsResults broadly consistent across modelsSome evidence of impact of PM-10 for Some evidence of impact of PM-10 for circulatory visitscirculatory visitsModest evidence of impact of PM-2.5 for Modest evidence of impact of PM-2.5 for pediatric visitspediatric visits
LESSONS LEARNED
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