Study of PM10 Annual Arithmetic Mean in USA

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Study of PM10 Annual Arithmetic Mean in USA

• Particulate matter is the term for solid or
  liquid particles found in the air
• The smaller particles penetrate deep in the
  respiratory systems causing adverse health effect
• PM10 Particulate matter in the air with
  aerodynamic size less than or equal to 10
  micrometers
• PM2.5 diameter lt 2.5 microns

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Standards for PM10
Set of limits established to protect human health

• National Standards
• 24-hour Average 150 mg/m3
• Annual Arithmetic Mean 50 mg/m3
• California Standard
• Annual Geometric Mean 35 mg/m3
• Egypt Standard
• 24-hour Average 70 mg/m3

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Location of the 1168 monitoring stations in the
US
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The annual arithmetic average of PM10
Z(p) Z(s,t)
where s spatial coordinate, t time, T1
year, C(s,u) instantaneous PM10 concentration
at s and time u
Obtaining Z from monitoring station s, year t
At a monitoring station s , throughout a year t,
tT we have nobvs, number of PM10
observations, Ci, i1,, nobvs C0.95, 95
quantile of the Ci observation values Cave
, average of the Ci
observation values Cave is a measurement of Z
at the space/time point (s,t) nobvs and
(C0.95-Cave) characterize the uncertainty of Cave

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The dataset of annual PM10 data
1168 Monitoring Stations with (nobvs , C0.95,
Cave) from 1984 to 2000
Frequency distribution of the number of
observations, nobvs
nobvs
Disparity of nobvs from data point to data
point nobvs varies from 1 to over 300
there are 46 data points with nobvs1
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Obtaining the soft data
For a data point p(s,t), we know nobvs, C0.95
and Cave Under ergotic assumption that
, the
soft PDF for Z at p (s,t) is given by
fS(Z)1 /sn t( (Z- Cave)/sn ) where sn s/
s (C0.95-Cave) / 1.65 t(.)
student-t PDF of degree nobvs-1 This soft PDF is
wider (has more uncertainty) for small nobvs and
large (C0.95-Cave)
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Soft data for monitoring station 1
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Soft data for monitoring station 829
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Soft data in California in 1997
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Movie of soft data for California,1987-1997
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BME space/time mapping
Random Field representation Y(s,t)m(s,t)
X(s,t) Modeling of the spatial and seasonal
trend m(s,t) ms(s) mt(t) Covariance
modeling of the Space/time variability
cx(s,t s,t)E (X(s,t)- mx(s,t)) (X(s,t)-
mx(s,t))
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Movie of the Y space/time mean trend
m(s,t) ms (s) mt (t)
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Covariance the model selected
cx(r,t) c1 exp(-3r/ar1-3t/at1) c2
exp(-3r/ar2-3t/at2) First component represents
weather related fluctuations (448 Km / 1 years)
c1 0.0141 (log mg/m3)2 , ar1448 Km ,
at1 1 years Second component represents large
scale fluctuations (16.8 Km / 45 years) c2
0.0798 (log mg/m3)2 , ar216.8 Km , at2 45
years We hypothesize that the first component
(448 Km /1 years) is related to the physical
environment (weather) the second component (16.8
Km / 45 years) is linked to human activity
? Lasting effect of human activity
(urbanism, pollution) on air quality
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Covariance experimental data and model
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Space/time composite view of covariance cX(r,t)
Time lag t (years)
Spatial lag r (Km)
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BME estimation of PM10 annual arithmetic
average
Using BMElib (the numerical implementation of
BME) we estimate Z across space and time
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BME estimation at monitoring station 1
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BME estimation at monitoring station 829
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Spatiotemporal map of the BME median estimate
Annual PM10 arithmetic average (mg/m3)
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Spatiotemporal map of mapping estimation error
Length of the 68 confidence interval (mg/m3)
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Spatiotemporal map of normalized estimation error
Ratio of posterior error variance by prior
variance
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Spatiotemporal map of non-attainment areas
Areas not-attaining the 35 mg/m3 limit with a
confidence of at least 50
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Spatiotemporal map of the 80 quantile
PM10 80 quantile (mg/m3) such that Prob
Annual PM10 arithmetic average lt PM10 80
quantile0.8
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Spatiotemporal map of non-attainment areas
Areas not-attaining the 35 mg/m3 limit with a
confidence of at least 80
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Spatiotemporal map of non-attainment areas
Areas not-attaining the 35 mg/m3 limit with a
confidence of at least 99
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Conclusions of the PM10 study in the US

• Soft probabilistic data are useful to represent
  the information available about the annual
  arithmetic mean of PM10 in the US
• A composite space/time analysis provides a
  realistic view of the distribution of the PM10
  arithmetic mean across space and time
• The BME posterior pdf allows to efficiently
  delineate non-attainment area at any confidence
  level required
• BMElib provides an efficient library for
  Computational Geostatistics that is particularly
  useful for space/time analysis and for dealing
  with hard and soft data