assessment of pm2.5 retrievals using a combination of satellite aod and wrf pbl heights in...
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ASSESSMENT OF PM2.5 RETRIEVALS USING A COMBINATION OF SATELLITE AOD AND WRF PBL
HEIGHTS IN COMPARISON TO WRF/CMAQ BIAS CORRECTED OUTPUTS
Lina Corderoa
Nabin Malakara, Barry Grossa, Yonghua Wua, Fred Mosharya, Mike Kub
a NOAA-Cooperative Remote Sensing Science and Technology Centerb New York State Department of Environmental Conservation (NYSDEC)
12th Annual CMAS Conference
Chapel Hill, NC – October 28-30, 2013
2
MOTIVATION
• Fine particulate matter measurements (PM2.5) are essential for air quality monitoring; however, expensive ground-based monitors limits the spatial extent of air quality networks.
• Environmental Protection Agency (EPA) mandates 24-hr PM2.5 standard (35 ug/m3).
• Existing Air Quality models suitable for short range forecasting at different scales exist (GEOS-CHEM, CMAQ) but these models are complex and depend strongly on meteorology and emissions.
• On the other hand, satellites provide column integrated information that can potentially be used to estimate PM2.5. However, a wide range of factors such as aerosols variability, meteorology and the vertical structure of aerosols, affect the PM2.5-AOD relationship.
• Supported by NYSERDA, we explore the relative performance of both approaches for the New York State domain.
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0 20 40 60 80 1000
20
40
60
80
100PM2.5 ESTIMATIONS Winter
in-situ PM2.5
CM
AQ
PM
2.5
(fr
om m
odel
) R = 0.8422y = -3.15 + 1.56xN = 53RMSE = 0.54
0 20 40 60 80 1000
20
40
60
80
100PM2.5 ESTIMATIONS Spring
in-situ PM2.5
CM
AQ
PM
2.5
(fr
om m
odel
) R = 0.7568y = 1.66 + 0.87xN = 232RMSE = 0.48
0 20 40 60 80 1000
20
40
60
80
100PM2.5 ESTIMATIONS Summer
in-situ PM2.5
CM
AQ
PM
2.5
(fr
om m
odel
) R = 0.7825y = 2.17 + 0.60xN = 243RMSE = 0.46
0 20 40 60 80 1000
20
40
60
80
100PM2.5 ESTIMATIONS Fall
in-situ PM2.5
CM
AQ
PM
2.5
(fr
om m
odel
) R = 0.6439y = 2.85 + 1.00xN = 206RMSE = 0.58
0 20 40 60 80 1000
20
40
60
80
100PM2.5 ESTIMATIONS Year
in-situ PM2.5
CM
AQ
PM
2.5
(fr
om m
odel
) R = 0.6646y = 4.19 + 0.65xN = 734RMSE = 0.51
80 W 78
W 76
W 74
W 72 W
40 N
42 N
44 N
46 N
Station Mapping
CURRENT CMAQ PERFORMANCE
• CMAQ 2006-2007 obtained from EPA-RSIG portal.
• NY state region emphasized.• Seasonal bias observed (especially
Fall / Summer).• How does this compare to satellite
based approaches?
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CURRENT SATELLITE APPROACH
• GEOS-CHEM (global model) estimates the spatial relationship between PM2.5 forecast and column path AOD on a daily basis.
• Currently being used by IDEA (Infusing satellite Data into Environmental air quality Applications) providing real time spatial maps of PM2.5 [van Donkelaar (2012)].
• Combines MODIS Terra-AQUA data to generate average AOD.• PM2.5/AOD ratios from the GEOS-CHEM are obtained at low resolution.• Additional processing using inverse distance weighting (IDW) spatial
interpolation to improve satellite coverage and bias corrections against field stations are applied.
12th Annual CMAS Conference
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PROBLEMS WITH CURRENT APPROACH
• GEOS-CHEM low resolution (0.5 x 0.5 degree) misses interesting urban/suburban transitions.
• Clear spatial patterns in CMAQ PM2.5/AOD ratio connected to higher resolution land surface/emission inventories are important.
• Strong differences seem to occur in the magnitude of the PM2.5/AOD ratio for the different approaches.
12th Annual CMAS Conference
74 W
GEOSCHEM PM25/MODIS AOD Summer
0
20
40
60
80
100
74 W
CMAQ PM25/MODIS AOD Summer
0
20
40
60
80
100
6
LOCAL STUDY CASE
• Assessment of PM2.5 estimation in a local study for better understanding critical factors under more controlled conditions.
• Use of neural network (NN) for potential PM2.5-AOD non-linearity.
12th Annual CMAS Conference
80 W 78
W 76
W 74
W 72 W
40 N
42 N
44 N
46 N
Station Mapping• Explore the effect of other factors along with
AOD as NN regressors.• Explore the effect of seasonal variability.• AOD retrievals from AERONET.• PBL from direct Lidar observations.• Meteorology from weather stations. • PM2.5 from TEOM monitor at CCNY.
7
ADDITIONAL FACTORS ASSESSMENT
• PBL height found to be the most relevant variable.
• Temperature shows some sensitivity (most likely indicative of the co-varying nature between temperature and PBL height).
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0 20 40 60 80 1000
20
40
60
80
100NN ESTIMATIONS (TOTALAOD+PBL) Year
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.7033N = 510RMSE = 0.57
0 20 40 60 80 1000
20
40
60
80
100NN ESTIMATIONS (TOTALAOD+RH) Year
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.5451N = 2519RMSE = 0.62
0 20 40 60 80 1000
20
40
60
80
100NN ESTIMATIONS (TOTALAOD+TEMP) Year
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.5914N = 2519RMSE = 0.59
0 20 40 60 80 1000
20
40
60
80
100NN ESTIMATIONS (TOTALAOD+WIND) Year
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.5243N = 2491RMSE = 0.62
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SEASONALITY ASSESSMENT
• Further improvements can be observed if data are restricted by seasons. Biggest improvement in PBL height addition is observed in summer, consistent with stronger convective conditions associated with urban heat island.
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Winter SpringSummer Fall Year0
0.2
0.4
0.6
0.8
1PM2.5 Correlation
R
Winter SpringSummer Fall Year0
0.2
0.4
0.6
0.8
1PM2.5 RMSE
RM
SE
Winter Spring Summer Fall Year0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1PM2.5 RMSE
RMSE
TOTALAODNN
TOTALAOD+PBLNN
TOTALAOD+VOLEXTNN
TOTALAOD+TEMPNN
TOTALAOD+WINDNN
TOTALAOD+WINDDIRNN
TOTALAOD+RHNN
9
REGIONAL STUDY CASE
• Local experiments indicate sensitivity of PM2.5 estimator to PBL height under certain conditions.
• Therefore, it is reasonable to apply satellite AOD and WRF PBL heights into the NN to evaluate PM2.5 estimations.
12th Annual CMAS Conference
80 W 78
W 76
W 74
W 72 W
40 N
42 N
44 N
46 N
Station Mapping
• Combined MODIS Terra and Aqua AOD.• WRF PBL data available from the EPA-
RSIG portal. • AIRNow PM2.5 used to train the NN with
focus on New York state.
10
PRELIMINARY PM2.5 PRODUCTS ASSESSMENT
• NN returns best estimations compared to in-situ measurements against other two approaches compared on the same dataset.
• GEOS-CHEM product returns the largest errors which mainly come from fine PM overestimations (overly strong PM25/AOD ratios).
12th Annual CMAS Conference
0 20 40 60 80 1000
20
40
60
80
100NN ESTIMATIONS (MODISAOD+WRFPBL)Year
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.6774y = 0.58 + 0.94xN = 621RMSE = 0.47
0 20 40 60 80 1000
20
40
60
80
100GEOSCHEM PM2.5 Year
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.5918y = 5.74 + 0.41xN = 621RMSE = 0.80
0 20 40 60 80 1000
20
40
60
80
100CMAQ PM2.5 Year
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.5345y = 6.05 + 0.43xN = 621RMSE = 0.72
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SEASONALITY ASSESSMENT
• Smaller but positive improvements seen when WRF PBL is added to NN across all seasons.
• Issues such as inaccurate WRF PBL heights need to be accounted for.
• Satellite approaches seem to generally outperform CMAQ in study except spring / summer.
• No comparisons in winter because of lack of satellite measurements.
• Satellite approaches are limited to clear sky conditions but spatial processing can be used to improve spatial coverage.
12th Annual CMAS Conference
Winter SpringSummer Fall Year0
0.2
0.4
0.6
0.8
1
R
PM2.5 Correlation
Winter Spring Summer Fall Year0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
RM
SE
PM2.5 RMSE
MODISAODLR
MODISAODNN
MODISAOD+WRFPBLNN
GEOSCHEMCMAQ
12
IMPLEMENTING NN FOR PM2.5 MAPPING
• Use daily AOD and WRF PBL to illustrate our NN performance. However, cloud cover can notably reduce spatial coverage.
• Iteratively apply IDW averages of the data to improve spatial coverage while using a 0.1 degree radial domain.
12th Annual CMAS Conference
80.0 W
MODIS AOD 7-18-2006
0
0.2
0.4
0.6
0.8
1
80.0 W
MODIS AOD 7-18-2006
0
0.2
0.4
0.6
0.8
1
13
IMPLEMENTING NN FOR PM2.5 MAPPING
• Good agreement between station and estimations data with low PM2.5 values observed in the non-urban region while high fine PM values are observed in the metropolitan area.
12th Annual CMAS Conference
0 20 40 60 80 1000
20
40
60
80
100NN ESTIMATED PM2.5 7-18-2006
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.8962y = -0.95 + 1.72xN = 21RMSE = 0.44
0 20 40 60 80 1000
20
40
60
80
100CMAQ PM2.5 7-18-2006
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.7661y = 1.89 + 1.63xN = 21RMSE = 0.49
74 W
NN ESTIMATED PM2.5 7-18-2006
0
5
10
15
20
25
30
35
74 W
CMAQ PM2.5 7-18-2006
0
5
10
15
20
25
30
35
14
IMPROVING CMAQ FORECASTS
• In current comparisons, satellite approach has the potential to improve on existing CMAQ forecasts under clear sky conditions.
• However, CMAQ model approaches are still extremely valuable for all weather retrievals.
• Therefore, CMAQ outputs can be improved regionally by quantifying bias sources and using machine learning tools (such as NN) to adjust for these biases.
• Parameters such as relative humidity (RH), temperature, PBL height, pressure, wind-speed, urban / non-urban are explored.
12th Annual CMAS Conference
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RELEVANT VARIABLES / PERFORMANCE
• Most important meteorological / environmental variables found based on correlations between PM2.5 model bias and variables.
• Top 4 cases used for NN building / testing.
• Results fairly stable to number of NN nodes .
• Training based on 70% of data and remaining 30% for testing (all points included in regressions).
• Strongest improvement when adding temperature into model to correct bias.
R value between variable and bias
Performance for different case runs
Variable R value
Temp (Seasonal) 0.31947
WindSpeed 0.13642
RH 0.097782
PBL 0.052404
Variables R RMSE
** Uncorrected 0.64 0.52
CMAQ, Temp 0.755 0.406
CMAQ, Temp, WS 0.776 0.391
CMAQ, Temp, PBL 0.782 0.386
CMAQ, Temp, RH 0.788 0.382
CMAQ, Temp, PBL, RH 0.797 0.375
CMAQ, Temp, WS, PBL 0.804 0.368
CMAQ, Temp, WS, RH 0.806 0.367
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CMAQ BIAS DEPENDENCE ON METEOROLOGY
• Clear improvements although outliers still observed for some high in-situ values.
• If all variables added, significant reduction in bias and RMSE spread is also observed.
• One issue to assess is the robustness of the NN.
• Does the testing data performance have the same quality?
12th Annual CMAS Conference
0 20 40 60 80 1000
20
40
60
80
100
in-situ PM2.5
CM
AQ
PM
2.5
Before Bias Correction
R = 0.6417y = 4.79 + 0.62xN = 17963RMSE = 0.52
0 20 40 60 80 1000
20
40
60
80
100Bias Corrected
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.8615y = 3.21 + 0.74xN = 17963RMSE = 0.31
0 20 40 60 80 1000
20
40
60
80
100All Data (CMAQ+Tempr)
in-situ PM2.5
Est
imat
ed P
M2.
5
R = 0.7513y = 5.31 + 0.57xN = 17963RMSE = 0.41
0 20 40 60 80 1000
20
40
60
80
100All Data (CMAQ+Tempr+PBL)
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.7811y = 4.80 + 0.61xN = 17963RMSE = 0.39
17
ROBUSTNESS OF NN (2 CASES)
0 20 40 60 80 1000
20
40
60
80
100All Data (CMAQ+Tempr)
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.7513y = 5.31 + 0.57xN = 17963RMSE = 0.41
0 20 40 60 80 1000
20
40
60
80
100All Data (CMAQ+Tempr+PBL)
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.7811y = 4.80 + 0.61xN = 17963RMSE = 0.39
0 20 40 60 80 1000
20
40
60
80
100Training Indices (CMAQ+Tempr)
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.7567y = 5.26 + 0.57xN = 13277RMSE = 0.40
0 20 40 60 80 1000
20
40
60
80
100Training Indices (CMAQ+Tempr+PBL)
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.7830y = 4.77 + 0.61xN = 13277RMSE = 0.38
0 20 40 60 80 1000
20
40
60
80
100Test Indices (CMAQ+Tempr+PBL)
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.7811y = 4.91 + 0.60xN = 4686RMSE = 0.39
0 20 40 60 80 1000
20
40
60
80
100Test Indices (CMAQ+Tempr)
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.7513y = 5.45 + 0.55xN = 4686RMSE = 0.42
12th Annual CMAS ConferenceTesting and training results nearly identical (Robust)
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CONCLUSIONS
• Adding lidar derived PBL improved PM2.5 estimations for local CCNY site.
• Combining satellite AOD and WRF PBL height in a regionally trained NN performed better with much less over-bias at low PM2.5 values compared against GEOS-CHEM ratio approach used in IDEA
• Daily PM2.5 maps based on the NN approach using high resolution AOD and PBL grids for the NY state region (applying IDW) showed reasonable agreement with station data.
• On the other hand, we find that significant improvement can be made to CMAQ PM2.5 by performing bias correction on the model outputs.
• Temperature was the dominant bias factor but additional variables such as RH, wind-speed and PBL height also contributed.
• NN results were shown to be robust by comparing Testing and Training sets which performed very similarly.
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ACKNOWLEDGMENTS
• This project was made possible by the National Oceanic and Atmospheric Administration, Office of Education Educational Partnership Program award NA11SEC4810004 as well as NYSERDA under grant # 22885. Its contents are solely the responsibility of the award recipient and do not necessarily represent the official views of the U.S. Department of Commerce, National Oceanic and Atmospheric Administration (NOAA) or New York State Energy Research and Development Authority (NYSERDA).
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QUESTIONS ??
ADDITIONAL SLIDES
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REFERENCES
• Boyouk, N., J. F. Leon, H. Delbarre, T. Podvin and C. Deroo, 2010: Impact of the mixing boundary layer on the relationship between PM2.5 and aerosol optical thickness. J Atmos. Environ., 44, 271-277.
• Hoff, R.M. and S. A. Christopher, 2009: Remote sensing of particulate pollution from space: have we reached the promised land? J. Air. & Waste Manage. Assoc., 59, 645-675.
• Hogrefe. C., P. Doraiswamy, B. Colle, K. Demerjian, W. Hao, M. Beauharnois, M. Erickson, M. Souders and J.-Y. Ku, 2011: Effects of Grid Resolution and Perturbations in Meteorology and Emissions on Air Quality Simulations Over the Greater New York City Region. 10th Annual CMAS Conference, Chapel Hill, NC.
• Hu, X., J. W. Nielsen-Gammon and F. Zhang, 2010: Evaluation of Three Planetary Boundary Layer Schemes in the WRF Model. J. Appl. Meteor. Climatol., 49, 1831–1844.
• Pope, C. A., III, R. T. Burnett, M. J. Thun, E. E. Calle, D. Krewski, K. Ito, et al. 2002: Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. of the American Medical Association, 287, 1132−1141.
• T.-C. Tsai, et al. 2009: Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008. Atmos. Environ. 45, 4777-4788.
• U. S. Environmental Protection Agency. Air quality criteria for particulate matter. 2004. EPA/600/P-99/002aF, Research Triangle Park, N. C.
• van Donkelaar, A., R. V. Martin, A. N. Pasch, J. J. Szykman, L. Zhang, Y. X. Wang and D. Chen, 2012: Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America. Environ. Science & Technology, 46, 11971-11978.
• Zhang, H., R. M. Hoff and J. A. Engel-Cox, 2009: The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: a geographical comparison by EPA regions. J. Air & Waste Manage. Assoc. 59, 1358-1369.
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ADDITIONAL SATELLITE PRODUCTS
12th Annual CMAS Conference
• NN trained with satellite variables.• Highest correlation with in-situ PM2.5 in
comparison to the other methods.• Further investigations infusing surface as
well as other meteorological information are ongoing
VariableWavelength
(μm)Solar_Zenith --Solar_Azimuth --Sensor_Zenith --Sensor_Azimuth --Scattering_Angle --Optical_Depth_Land_And_Ocean 0.55 Mean_Reflectance_Land_All 2.1
0 20 40 60 80 1000
20
40
60
80
100NN ESTIMATIONS (MODISAOD+WRFPBL)Year
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.6774y = 0.58 + 0.94xN = 621RMSE = 0.47
0 20 40 60 80 1000
20
40
60
80
100NN Estimations
in-situ PM2.5
Est
imat
ed P
M2
.5
R = 0.6817y = 1.08 + 0.92xN = 1110RMSE = 0.46
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VARIOUS COMBINATION OF SATELLITE AND CMAQ
12th Annual CMAS Conference
Variables R RMSEMODIS 0.682 0.465MODIS+WIND 0.716 0.447MODIS+PRESS 0.731 0.433MODIS+PBL 0.755 0.418MODIS+PRESS+WIND 0.763 0.416MODIS+RH 0.776 0.410MODIS+LC+PBL 0.784 0.397MODIS+TEMP 0.802 0.382MODIS+TEMP+WIND 0.809 0.385MODIS+PRESS+RH 0.811 0.381MODIS+RH+WIND 0.811 0.379MODIS+TEMP+PRESS 0.812 0.382MODIS+TEMP, PRESS, WIND 0.830 0.363MODIS+TEMP, RH, WIND 0.832 0.365MODIS+TEMP+RH 0.838 0.354MODIS+TEMP, PRESS, RH 0.838 0.359MODIS+PRESS, RH, WIND 0.840 0.351MODIS+CMQ 0.855 0.340MODIS+CMQ+MO+PBL 0.859 0.341MODIS+CMQ+LC 0.861 0.328All (14) 0.862 0.347MODIS+CMQ+LC+MO 0.866 0.327MODIS+CMQ+PBL 0.873 0.323