Aerosols: Aerosols are mixtures of solid particles and liquid droplets in a gaseous form in the atmosphere. Aerosols can vary from clouds to air pollution, such as smoke and smog. They occur in a variety of places from urban ecosystems, to less inhabited areas such as deserts.Aerosol Optical Depth (AOD): Aerosols have the property of scattering and/or absorbing sunlight, a phenomenon known as extinction. Extinction caused by aerosols exclusively is numerically expressed by the aerosol optical depth (AOD). AOD is a critical measurement in assessing the impact that aerosols have on climate. PM2.5 and Its Effects on Health: The U.S. Environmental Protection Agency has categorized aerosols into two groups:

PM10 – aerosols whose particle diameter size is less than or equal to 10µm.PM2.5 – diameter size less than or equal to 2.5 µm.

PM2.5 has been the subject of intense study due to its particularly harmful effects to health due to its small particle size. PM2.5 is released through the process of combustion from anthropogenic sources like power plants, vehicles, and wood-burning stoves, and natural sources such as wildfires. It has been linked to heart and lung disease as well as to heart attacks, asthma, and bronchitis. The EPA has also set strong ambient guidelines for PM2.5.

The purpose of this this study was to explore models that relate a variety of possible input variables, including AOD, PBL, and other atmospheric conditions, to surface PM2.5 data (target). This is unlike previous studies, which have used standard polynomial regression to analyze and correlate these variables. The goal was to use these networks to help predict future PM2.5 levels based on AOD and PBL information from satellite and meteorological model sources.

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Figure 1: The location of the PBL height for a given day.

State of Alaska Department of Environmental Conservation. Division of Air Quality: Particulate Matter-Background Information. http://dec.alaska.gov/air/anpms/pm/pm_bckgrd.htm (Accessed July 11, 2012).

Union of Concerned Scientists. Global Warming: Global Warming Science and Impacts. http://www.ucsusa.org/global_warming/science_and_impacts/science/aerosols-and-global-warming-faq.html (Accessed July 23, 2012).

United States Environmental Protection Agency. Fine Particle (PM2.5) Designations: Basic Information. http://www.epa.gov/airquality/particlepollution/designations/basicinfo.htm (Accessed July 10, 2012).

Figure 1 obtained from: http://upload.wikimedia.org/wikipedia/commons/5/5f/PBLimage.jpg

Using Neural Network Techniques to Predict Surface PM2.5 Levels from Optical and Meteorological DataNkosi Alleyne and Michael Hirschberger

Figure 2: A table showing the R values and RMSE values for each experiment. Notice the distinct increase in R value and decrease in RMSE that occurs when PBL height is used as an input variable.

Figure 3: A bar graph showing the R values for all 12 experiments.

Figure 4: A bar graph showing the RMSE for all 12 experiments.Figure 5: A histogram showing the variation of the Fine AOD, PBL height experiment for 200 runs. This shows that any disparity in running this experiment repeatedly is statistically insignificant.

•Neural network analysis generally showed stronger correlation (increased R values) as more input variables were added.

• PBL height had the greatest effect on increasing the correlation.• Upon doing the same analysis repeatedly, slightly different R values resulted, but the importance of PBL height was still

apparent.

•Neural networks could be a very useful alternative to standard correlation techniques.•There is a strong need to obtain estimates of the PBL height that can be ingested into the NN estimators together with satellite estimates of AOD. •Weather forecast models such as WRF can predict PBL height but these outputs should be matched up against different measurements to assess accuracy.

Abstract

Introduction

Objectives

References

Results

Discussion/Conclusions and Future Work

Materials and Methods

Planetary Boundary Layer (PBL): Aerosols are generally located in the lowest part of the atmosphere, the Planetary Boundary Layer (PBL). The PBL is characterized by strong turbulence and a high degree of mixing, especially when convective heating is dominant and aerosols mix well. Low-lying aerosols exist in the PBL for a few days, while those higher in the PBL can last for a few years. If aerosols are well mixed in the PBL, then the height of the PBL is important in relating surface PM2.5 to the full column AOD.

Neural Networks: A Neural Network (NN) is a data analysis tool that relates possible input data streams to “target” output data streams. This is done by feeding inputs into the NN and determining the weights of the NN layers that make the model best fit the output target. A statistical ensemble of Input-Target combinations are used to (70%) train, (15%) validate, and (15%) test the network. Once this is complete, a regression value can be calculated which compares the target values to their corresponding outputs. A high regression value corresponds to a reliable neural network.Data: All data was obtained on an hourly basis from 1/1/08 to 6/19/12. PM2.5 data was obtained from two stations: City College and P.S. 154. The mean of these two datasets served as the target variable. 12 different experiments were performed using 7 different inputs. Neural Network analyses were performed using the MATLAB Neural Network Toolbox. For each experiment, MATLAB found a correlation coefficient showing the linear regression between the output and target. The root-mean-square error (RMSE) was calculated according to the accompanying equation where x1 represents the target data and x2 represents the NN output data.

Biggest improvement due to inputting PBL height

Biggest improvement due to inputting PBL height

Atmospheric aerosols have the property of scattering and absorbing sunlight, a process known as extinction. The degree to which extinction occurs is of great importance to studying the earth’s climate because it can help indicate the amount of sunlight, and therefore energy, that is transmitted to the surface. Furthermore, PM2.5 (fine-mode aerosols), has been linked to health problems such as heart and lung disease as well as to heart attacks, asthma, and bronchitis. This study uses neural network techniques to predict surface PM2.5 levels based on different optical and meteorological “input” data and surface PM2.5 “target” data obtained locally. Our results show a general increase in R-value and decrease in root-mean-square error (RMSE) as more inputs were incorporated into the experiments. The addition of the planetary boundary layer (PBL) height caused the greatest improvement in these values. For example, the fine-mode aerosol optical depth (AOD) input experiment yielded an R-value of 0.51 and an RMSE of 6.518. The fine-mode AOD and PBL height input experiment yielded an R-value of 0.747 and an RMSE of 4.973. This supports the idea that if aerosols are well mixed in the PBL, then the height of the PBL is important in relating surface PM2.5 to the full column AOD. Based on this study, neural networks could be a very useful alternative to standard correlation techniques in relating optical and meteorological data to surface PM2.5 data to help predict future surface PM2.5 levels.

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CCNY Contributors:Gary Bouton (MS)Lina Cordero (PhD)

Mentor: Dr. Barry Gross

NYCRI Contributors:Nkosi Alleyne (HSS)Michael Hirschberger (UG)Christopher Widi (HST)

Wu

Sponsors:National Aeronautics and Space Administration (NASA)Goddard Space Flight Center (GSFC)Goddard Institute for Space Studies (GISS)New York City Research Initiative (NYCRI)United States Department of Education The Alliance for Continuous Innovative Learning Environments in STEM (CILES) CILES Grant #P031C110158NOAA CREST-SHIPCUNY: The City College of New York