using auxiliary data for air pollutant spatial interpolation
DESCRIPTION
Using Auxiliary Data for Air Pollutant Spatial Interpolation. Liyun Xie May 6, 2004. Background and Rationale. Air pollutants (fine particles, heavy metal, ozone, etc.) are harmful to human health Low resolution of monitoring data - PowerPoint PPT PresentationTRANSCRIPT
Using Using AuxiliaryAuxiliary Data Data for Air for Air Pollutant Spatial InterpolationPollutant Spatial Interpolation
Liyun XieLiyun Xie
May 6, 2004May 6, 2004
Background and RationaleBackground and Rationale
Air pollutants (fine particles, heavy metal, Air pollutants (fine particles, heavy metal, ozone, etc.) are harmful to human healthozone, etc.) are harmful to human health
Low resolution of monitoring dataLow resolution of monitoring data Possible relationship between air pollutant Possible relationship between air pollutant
data and other datadata and other data– PM2.5 mass concentration and visibilityPM2.5 mass concentration and visibility– Lead concentration and emissionLead concentration and emission
Using spatial data analysis tool to Using spatial data analysis tool to interpolate/ extrapolate the air pollutant mapinterpolate/ extrapolate the air pollutant map
DataData
Fine particlesFine particles– Daily mean from June 24, 2003 to Jun 28, 2003 Daily mean from June 24, 2003 to Jun 28, 2003 – Eastern states in USEastern states in US– PM2.5 Concentration (µg/mPM2.5 Concentration (µg/m33): EPA AIR database): EPA AIR database– Visibility (light extinction coeff, MmVisibility (light extinction coeff, Mm-1-1): National Weather ): National Weather
Service Service LeadLead
– Annual mean in 2000 Annual mean in 2000 – 4 states: MO, IL, ID, OH4 states: MO, IL, ID, OH– Concentration (µg/mConcentration (µg/m33): EPA TRI database): EPA TRI database– Emission (lbs/y): EPA AIR database Emission (lbs/y): EPA AIR database
Methods and ToolsMethods and Tools
Interpolation methodsInterpolation methods– IDWIDW– KirgingKirging– CoKirgingCoKirging
ToolsTools– Correlation: Excel 2003Correlation: Excel 2003– Variogram: VarioWin 2.21 Variogram: VarioWin 2.21
Developed by Yvan Pannatier, 1996 Developed by Yvan Pannatier, 1996
– Interpolation: ESRI ArcGis 8.3Interpolation: ESRI ArcGis 8.3
Data FlowData Flow
Download Pollutant Data Download Auxiliary Data
Decide model and parameters
Analyze spatial distribution Analyze correlation
IDW Interpolation Kirging Interpolation CoKirging Interpolation
Compare methods
Results - LeadResults - Lead
TRI and AIR within 5km
00.5
11.5
22.5
33.5
44.5
5
0 50000 100000 150000 200000 250000 300000
TRI (lbs/y)
AIR
(ug
/m3)
080000
160000240000
320000400000
480000560000
640000720000
00.070.140.210.280.350.420.490.560.63
0.7
|h|
(|h|)
436
410
362
634
776228
224
Correlation and VariogramCorrelation and Variogram
Results – PM 2.5Results – PM 2.5
Monitoring LocationMonitoring Location
Results – PM 2.5 (Cont.)Results – PM 2.5 (Cont.)
CorrelationCorrelationPM2.5 vs Visibility (June 25, 2003)
0
100
200
300
400
500
600
0 10 20 30 40 50 60 70 80
PM2.5 (µg/m3)
Vis
ibil
ity
(M
m-1
)
PM2.5 vs Visibility (June 24, 2003)
0
100
200
300
400
0 10 20 30 40 50 60
PM2.5(µg/m3)
Vis
ibil
ity
(M
m-1
)
PM2.5 vs Visibility (June 25, 2003)
0
100
200
300
400
500
0 10 20 30 40 50 60
PM2.5 (µg/m3)
Vis
ibil
ity
(M
m-1
)
PM2.5 vs Visibility (June 27, 2003)
0
100
200
300
400
500
600
700
800
900
0 10 20 30 40 50 60 70 80 90 100
PM2.5 (µg/m3)
Vis
ibil
ity
(M
m-1)
PM2.5 vs Visibility (June 28, 2003)
0
100
200
300
400
0 10 20 30 40
PM2.5 (µg/m3)
Vis
ibil
ity
(M
m-1
)
Results – PM 2.5 (Cont.)Results – PM 2.5 (Cont.) Variogram (6/26/03)Variogram (6/26/03)
– PM 2.5PM 2.5
– VisibilityVisibility
Results – PM 2.5 (Cont.)Results – PM 2.5 (Cont.)
Interpolation Methods ComparisonInterpolation Methods Comparison– Surface estimation mapsSurface estimation maps
Results – PM 2.5 (Cont.)Results – PM 2.5 (Cont.)
Interpolation Methods ComparisonInterpolation Methods Comparison– Estimation difference mapsEstimation difference maps
Results – PM 2.5 (Cont.)Results – PM 2.5 (Cont.)
Interpolation Methods Interpolation Methods ComparisonComparison– RMSERMSE
Date 24-Jun 25-Jun 26-Jun 27-Jun 28-JunIDW 6.725 5.518 5.741 6.571 4.189
Kirging 6.894 5.187 5.251 6.463 4.617CoKirging 6.895 5.351 5.349 7.322 4.604
Results – PM 2.5 (Cont.)Results – PM 2.5 (Cont.)
Temporal trendsTemporal trends
SummarySummary
LeadLead– Not suitable for spatial interpolationNot suitable for spatial interpolation
PM 2.5PM 2.5– Kirging and CoKirging are better than IDWKirging and CoKirging are better than IDW– Comparing to Kirging, CoKiring doesn’t improve Comparing to Kirging, CoKiring doesn’t improve
interpolationinterpolation
RecommendationsRecommendations
More monitoring locations for air pollutantsMore monitoring locations for air pollutants Weather conditions Weather conditions Transportation modelingTransportation modeling Improve software for spatial data analysisImprove software for spatial data analysis
AcknowledgementsAcknowledgements
Dr. FalkeDr. Falke Dr. TurnerDr. Turner
Thank You!Thank You!
Any QuestionsAny Questions