CREATING A REGIONAL PM2.5 MAP

BY FUSING SATELLITE AND

KRIGING ESTIMATESDaniel 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 Variogram𝑔 ℎ = 𝐶 ∗ 1.5

ℎ

𝑎− .5

ℎ

𝑎

3

𝐶 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

𝑖𝑓 ℎ ≤ 𝑎

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