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CREATING A REGIONAL PM2.5 MAP 
BY FUSING SATELLITE AND 
KRIGING ESTIMATES 
Daniel Vidal 
Faculty Mentors: 
Dr. Barry Gross 
Dr. Nabin Malakar 
Dr. Lina Cordero
Motivations 
• We evaluate the measurements derived from the Air Quality System (AQS) 
repository to estimate ground-level concentrations of fine particulate matter 
(PM2.5) in northeast USA. 
• The study PM2.5 is important due to their effect on climate change and health 
conditions. In urban areas, these particles are produced from vehicle 
combustion and industrial facilities. 
• Direct measurement of PM2.5 is expensive, making the use of remote sensing 
instruments crucial. We approach this through an optimal spatial interpolation 
method, Kriging, which is based on a regression against observed values of 
surrounding data points, weighted according to spatial covariance values. 
• Unlike most interpolating methods, Kriging assigns weights according to a 
data-driven weighting function. Through this method we would obtain an 
interpolated estimation of the PM2.5 with the covariance. 
• We then fuse the Kriging with the satellite remote sensing estimates of PM2.5 
to obtain better and more reliable coverage map of PM2.5 for northeast.
Station locations and PM frequency 
• The station information obtained from the EPA provided for a very 
well distributed dataset. 
• This information is crucial since the remote sensing data alone 
cannot provide for adequate coverage over the northeast. 
• For the month of August, we use 138 stations for our estimations.
Kriging Estimation/ Spherical Variogram 
 Kriging aims to optimize interpolation based on a regression and 
weighting based on spatial covariance between the data points and 
estimation points. 
 Using a Spherical variogram model, we are able to obtain a more 
reasonable Kriging estimation, due to the high-levels of short-range 
variability in our data. 
Spherical Model 
Used for Variogram 
푔 ℎ = 퐶 ∗ 1.5 
ℎ 
푎 
− .5 
ℎ 
푎 
3 
퐶 표푡ℎ푒푟푤푖푠푒 
푖푓 ℎ ≤ 푎
Kriging Estimation 
Error 
 Most other interpolation methods, such as IDW (Inverse Distance 
Weighting) are referred to as deterministic methods of interpolation. 
Kriging is a geostatistical method. 
 Kriging provides for a statistical measurement of the relationship 
between known points and unknown points. 
 In our estimation of PM2.5, based on the variance, we are confident in 
our estimations.
Fusion Results of Remote Sensing 
PM and Kriging Results 
 Fusion of the Kriging and Neural 
Network results gives us a more 
accurate estimation of the 
surface PM. 
 We see a more reasonable 
agreement with the station data 
than our results for Kriging alone. 
 The results are improved due to 
Kriging putting more confidence 
for points near stations. 
Fusion
Other Successful Fusion Days 
Fusion August 2nd, 2006 Fusion August 5th, 2006 
Fusion August 22nd, 2006 Fusion March 30th, 2006
Correlation between Stations and Fusion 
Estimations 
 Initial results show promising correlations between the station data and the 
fused PM2.5 product. 
 Some of the days still have less correlation, which need to be further 
investigated.
Future Research 
 The NN estimation is being developed at CCNY, we are working on to 
improve upon the existing air quality models by using neural network and 
other available methods. 
 Some of the days in the fused PM2.5 product need to be further investigated 
for improving the low correlation between the estimation and ground station. 
 Develop a web based alert system for sensitive group in northeast, and extend 
the domain in the future.
Contributions 
 Daniel’s contributions to this research include: 
 Writing the paper 
 Preparation of this PowerPoint 
 Creation of his own poster 
 Plotting the daily correlation coefficients for August 2006 
 Rewriting the code that produces the Kriging product using the 
spherical model. 
 Writing the code that produces the daily Kriging product for 
2005 to 2007. 
 Writing the MATLAB code that produces the fused product.
Acknowledgments 
 1-This project was made possible by the Research Experiences for Undergraduates in 
Satellite and Ground-Based Remote Sensing at CREST_2 program funded by the 
National Science Foundation under grant AGS-1062934. Its contents are solely the 
responsibility of the award recipient and do not necessarily represent the official views 
of the National Science Foundation. 
 2-This research is supported by the National Science Foundation's Research 
Experiences for Undergraduates (NSF REU) Grant No. AGS-1062934 under the 
leadership of Dr. Reginald Blake, Dr. Janet Liou-Mark, Ms. Laura Yuen-Lau 
 3- The National Oceanic and Atmospheric Administration – Cooperative Remote 
Sensing Science and Technology Center (NOAA-CREST) for supporting this project. 
NOAA CREST - Cooperative Agreement No: NA11SEC4810004. 
 4- My mentors Dr. Barry Gross, Dr. Nabin Malakar and Dr. Lina Cordero for their 
patience and hard work guiding me through this research.
References 
 L Cordero, N Malakar, D Vidal, R Latto, B Gross, F Moshary, S Ahmed, “A 
Regional NN estimator of PM2.5 using satellite AOD and WRF meteorology 
measurements”, AMS 2014, Atlanta, GA, USA 
 N Malakar, L Cordero, Y Wu, B Gross, M Ku “INJECTION OF METEOROLOGICAL 
FACTORS INTO SATELLITE ESTIMATES OF SURFACE PM2.5” 
2013 EMEP Conference 
 N Malakar, L Cordero, Y Wu, B Gross, M Fred, “Assessing Surface PM2.5 
Estimates Using Data Fusion of Active and Passive Remote Sensing Methods”, 
British Journal of Environment and Climate Change 3 (4), 547-565 
 Pope, C. A., III, Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., 
et al. (2002), Lung cancer, cardiopulmonary mortality, and long-term 
exposure to fine particulate air pollution. J. of the American Medical 
Association, 287(9), 1132−1141. 
 U. S. Environmental Protection Agency (2004), Air quality criteria for 
particulate matter, EPA/600/P-99/002aF, Research Triangle Park, N. C.
Thank you! 
Any Questions?
AQI 
Category 
Scale/Concentration 
(ug/m3) 
Sensitive Groups Health Effects Statements Cautionary Statements 
Good AQI Index: 0 – 50 
Concentration: 0 - 12 
People with respiratory 
or heart disease, the 
elderly and children are 
the groups most at risk 
None None 
Moderate AQI Index: 51 - 100 
Concentration: 
12.1 – 35.4 
People with respiratory 
or heart disease, the 
elderly and children are 
the groups most at risk 
Unusually sensitive people should 
consider reducing prolonged or heavy 
exertion. 
Unusually sensitive people should 
consider reducing prolonged or 
heavy exertion. 
Unhealthy 
for 
Sensitive 
Groups 
AQI Index: 101 - 150 
Concentration: 
35.5 – 55.4 
People with respiratory 
or heart disease, the 
elderly and children are 
the groups most at risk. 
Increasing likelihood of respiratory 
symptoms in sensitive individuals, 
aggravation of heart or lung disease and 
premature mortality in persons with 
cardiopulmonary disease and the elderly. 
People with respiratory or heart 
disease, the elderly and children 
should limit prolonged exertion. 
Unhealthy AQI Index: 151 - 200 
Concentration: 
55.5 – 150.4 
People with respiratory 
or heart disease, the 
elderly and children are 
the groups most at risk. 
Increased aggravation of heart or lung 
disease and premature mortality in 
persons with cardiopulmonary disease 
and the elderly; increased respiratory 
effects in general population. 
People with respiratory or heart 
disease, the elderly and children 
should avoid prolonged exertion; 
everyone else should limit 
prolonged exertion. 
Very 
Unhealthy 
AQI Index: 201 - 300 
Concentration: 
150.5 – 250.4 
People with respiratory 
or heart disease, the 
elderly and children are 
the groups most at risk. 
Significant aggravation of heart or lung 
disease and premature mortality in 
persons with cardiopulmonary disease 
and the elderly; significant increase in 
respiratory effects in general population. 
People with respiratory or heart 
disease, the elderly and children 
should avoid any outdoor activity; 
everyone else should avoid 
prolonged exertion. 
Hazardous AQI Index: 301 - 500 
Concentration: 
250.5 – 500.4 
People with respiratory 
or heart disease, the 
elderly and children are 
the groups most at risk. 
Serious aggravation of heart or lung 
disease and premature mortality in 
persons with cardiopulmonary disease 
and the elderly; serious risk of 
respiratory effects in general population. 
Everyone should avoid any outdoor 
exertion; people with respiratory or 
heart disease, the elderly and 
children should remain indoors. 
Air Quality 
Index

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CREATING A REGIONAL PM2.5 MAP BY FUSING SATELLITE AND KRIGING ESTIMATES

  • 1. CREATING A REGIONAL PM2.5 MAP BY FUSING SATELLITE AND KRIGING ESTIMATES Daniel Vidal Faculty Mentors: Dr. Barry Gross Dr. Nabin Malakar Dr. Lina Cordero
  • 2. Motivations • We evaluate the measurements derived from the Air Quality System (AQS) repository to estimate ground-level concentrations of fine particulate matter (PM2.5) in northeast USA. • The study PM2.5 is important due to their effect on climate change and health conditions. In urban areas, these particles are produced from vehicle combustion and industrial facilities. • Direct measurement of PM2.5 is expensive, making the use of remote sensing instruments crucial. We approach this through an optimal spatial interpolation method, Kriging, which is based on a regression against observed values of surrounding data points, weighted according to spatial covariance values. • Unlike most interpolating methods, Kriging assigns weights according to a data-driven weighting function. Through this method we would obtain an interpolated estimation of the PM2.5 with the covariance. • We then fuse the Kriging with the satellite remote sensing estimates of PM2.5 to obtain better and more reliable coverage map of PM2.5 for northeast.
  • 3. Station locations and PM frequency • The station information obtained from the EPA provided for a very well distributed dataset. • This information is crucial since the remote sensing data alone cannot provide for adequate coverage over the northeast. • For the month of August, we use 138 stations for our estimations.
  • 4. Kriging Estimation/ Spherical Variogram  Kriging aims to optimize interpolation based on a regression and weighting based on spatial covariance between the data points and estimation points.  Using a Spherical variogram model, we are able to obtain a more reasonable Kriging estimation, due to the high-levels of short-range variability in our data. Spherical Model Used for Variogram 푔 ℎ = 퐶 ∗ 1.5 ℎ 푎 − .5 ℎ 푎 3 퐶 표푡ℎ푒푟푤푖푠푒 푖푓 ℎ ≤ 푎
  • 5. Kriging Estimation Error  Most other interpolation methods, such as IDW (Inverse Distance Weighting) are referred to as deterministic methods of interpolation. Kriging is a geostatistical method.  Kriging provides for a statistical measurement of the relationship between known points and unknown points.  In our estimation of PM2.5, based on the variance, we are confident in our estimations.
  • 6. Fusion Results of Remote Sensing PM and Kriging Results  Fusion of the Kriging and Neural Network results gives us a more accurate estimation of the surface PM.  We see a more reasonable agreement with the station data than our results for Kriging alone.  The results are improved due to Kriging putting more confidence for points near stations. Fusion
  • 7. Other Successful Fusion Days Fusion August 2nd, 2006 Fusion August 5th, 2006 Fusion August 22nd, 2006 Fusion March 30th, 2006
  • 8. Correlation between Stations and Fusion Estimations  Initial results show promising correlations between the station data and the fused PM2.5 product.  Some of the days still have less correlation, which need to be further investigated.
  • 9. Future Research  The NN estimation is being developed at CCNY, we are working on to improve upon the existing air quality models by using neural network and other available methods.  Some of the days in the fused PM2.5 product need to be further investigated for improving the low correlation between the estimation and ground station.  Develop a web based alert system for sensitive group in northeast, and extend the domain in the future.
  • 10. Contributions  Daniel’s contributions to this research include:  Writing the paper  Preparation of this PowerPoint  Creation of his own poster  Plotting the daily correlation coefficients for August 2006  Rewriting the code that produces the Kriging product using the spherical model.  Writing the code that produces the daily Kriging product for 2005 to 2007.  Writing the MATLAB code that produces the fused product.
  • 11. Acknowledgments  1-This project was made possible by the Research Experiences for Undergraduates in Satellite and Ground-Based Remote Sensing at CREST_2 program funded by the National Science Foundation under grant AGS-1062934. Its contents are solely the responsibility of the award recipient and do not necessarily represent the official views of the National Science Foundation.  2-This research is supported by the National Science Foundation's Research Experiences for Undergraduates (NSF REU) Grant No. AGS-1062934 under the leadership of Dr. Reginald Blake, Dr. Janet Liou-Mark, Ms. Laura Yuen-Lau  3- The National Oceanic and Atmospheric Administration – Cooperative Remote Sensing Science and Technology Center (NOAA-CREST) for supporting this project. NOAA CREST - Cooperative Agreement No: NA11SEC4810004.  4- My mentors Dr. Barry Gross, Dr. Nabin Malakar and Dr. Lina Cordero for their patience and hard work guiding me through this research.
  • 12. References  L Cordero, N Malakar, D Vidal, R Latto, B Gross, F Moshary, S Ahmed, “A Regional NN estimator of PM2.5 using satellite AOD and WRF meteorology measurements”, AMS 2014, Atlanta, GA, USA  N Malakar, L Cordero, Y Wu, B Gross, M Ku “INJECTION OF METEOROLOGICAL FACTORS INTO SATELLITE ESTIMATES OF SURFACE PM2.5” 2013 EMEP Conference  N Malakar, L Cordero, Y Wu, B Gross, M Fred, “Assessing Surface PM2.5 Estimates Using Data Fusion of Active and Passive Remote Sensing Methods”, British Journal of Environment and Climate Change 3 (4), 547-565  Pope, C. A., III, Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., et al. (2002), Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. of the American Medical Association, 287(9), 1132−1141.  U. S. Environmental Protection Agency (2004), Air quality criteria for particulate matter, EPA/600/P-99/002aF, Research Triangle Park, N. C.
  • 13. Thank you! Any Questions?
  • 14. AQI Category Scale/Concentration (ug/m3) Sensitive Groups Health Effects Statements Cautionary Statements Good AQI Index: 0 – 50 Concentration: 0 - 12 People with respiratory or heart disease, the elderly and children are the groups most at risk None None Moderate AQI Index: 51 - 100 Concentration: 12.1 – 35.4 People with respiratory or heart disease, the elderly and children are the groups most at risk Unusually sensitive people should consider reducing prolonged or heavy exertion. Unusually sensitive people should consider reducing prolonged or heavy exertion. Unhealthy for Sensitive Groups AQI Index: 101 - 150 Concentration: 35.5 – 55.4 People with respiratory or heart disease, the elderly and children are the groups most at risk. Increasing likelihood of respiratory symptoms in sensitive individuals, aggravation of heart or lung disease and premature mortality in persons with cardiopulmonary disease and the elderly. People with respiratory or heart disease, the elderly and children should limit prolonged exertion. Unhealthy AQI Index: 151 - 200 Concentration: 55.5 – 150.4 People with respiratory or heart disease, the elderly and children are the groups most at risk. Increased aggravation of heart or lung disease and premature mortality in persons with cardiopulmonary disease and the elderly; increased respiratory effects in general population. People with respiratory or heart disease, the elderly and children should avoid prolonged exertion; everyone else should limit prolonged exertion. Very Unhealthy AQI Index: 201 - 300 Concentration: 150.5 – 250.4 People with respiratory or heart disease, the elderly and children are the groups most at risk. Significant aggravation of heart or lung disease and premature mortality in persons with cardiopulmonary disease and the elderly; significant increase in respiratory effects in general population. People with respiratory or heart disease, the elderly and children should avoid any outdoor activity; everyone else should avoid prolonged exertion. Hazardous AQI Index: 301 - 500 Concentration: 250.5 – 500.4 People with respiratory or heart disease, the elderly and children are the groups most at risk. Serious aggravation of heart or lung disease and premature mortality in persons with cardiopulmonary disease and the elderly; serious risk of respiratory effects in general population. Everyone should avoid any outdoor exertion; people with respiratory or heart disease, the elderly and children should remain indoors. Air Quality Index