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Motivation Materials and Methods Background Results Conclusions and Future Work Data was collected from various instruments. • AOD values come from our ground Radiometer (AERONET) • The Planetary Boundary Layer (PBL) height is collected from direct observations with Light Detection and Ranging (LIDAR). • Other meteorological inputs are readily obtained from weather station data. • Target PM2.5 data is obtained locally at City College using a EPA TEOM instrument. Artificial Neural Networks (ANN’s) are mathematical models that take multiple input data streams, multiply it by specified weights and biases , perform linear superposition as well as non linear stretching to produce an output that should match a given output as closely as possible (in a LSQ way). • Performance is quantified by the magnitude of the correlation of the R value between the target and the Neural Network output. • An R value of 1 means a strong linear relationship. Neural Network analyses were performed using the MATLAB Neural Network Toolbox. • Accurate Neural Networks can be developed by adjusting: • Inputs (which are used, and how they are filtered) • Percent of data that is trained, validated, and trained • Number of hidden neurons • Training algorithms Using Neural Networks to Predict PM2.5 Values Becca Latto, Lina Cordero, and Barry Gross • Aerosols are tiny particles that can have a dramatic impact on the Earth, and aerosols with a diameter of 2.5µm or less (PM 2.5 ) can cause health problems such as heart and lung complications • Direct measurements of fine aerosols are necessary, but expensive, thus estimations from remote sensing instruments are crucial • For this purpose, we plan to use Neural Networks to connect surface PM2.5 values to other variables that are available either by satellite or from meteorological models • By using more detailed AOD from AERONET and PBL height from LIDAR, we can better assess the actual factors needed for PM2.5 estimation without model or satellite bias interference. • Based on the results we expect, we will replicate this approach using MODIS AOD and WRF meteorological parameters to produce regional PM2.5 products. Aerosols are tiny particles that can have a dramatic impact on the earth. Aerosols come from volcanic eruptions, desert dust, and human activities such as the burning of coal and oil as well as tropical forests. Aerosol PM2.5 are aerosols with a diameter of 2. 5µm or less and can cause health problems such as heart and lung complications, upon extended exposure Extinction is a phenomenon in which aerosols scatter and/or absorb sunlight and is proportional to the “amount” of aerosols at a given point Aerosol Optical Depth (AOD) numerically expresses the path integrated extinction caused by aerosols Planetary Boundary Layer (PBL) is where aerosols are generally trapped in the atmosphere. For this reason, we expect Daily and seasonal variations of PBL height dynamics to have an important role in estimating PM2.5- AOD relationship. Acknowledgements: Special Thanks to: The National Oceanic and Atmospheric Administration – Cooperative Remote Sensing Science and Technology Center (NOAA- CREST) and New York City Research Initiative (NYCRI) for supporting this project. NOAA CREST - Cooperative Agreement No: NA11SEC4810004 Figure 3: Line plot showing the R values for each input(s) as well as how stable each NN was for 10 loops. • Neural Network analysis demonstrates that using Fine AOD or Total AOD as an input is not an important factor for the NN and does not dramatically change the output, • PBL height is the most important factor after AOD in improving the PM2.5 Estimator • The NN of Figure 3 also shows that using all of the variables as the inputs gives the highest correlation, with marginal improvement over the NN with PBL height and AOD alone • We note the best improvement in summer which is reasonable since the PBL is deeper and better mixed and satellite AOD is bound to be less biased. • Later on, we will explore if a suitable neural network can be made with 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 N N ESTIM A TIO NS (Fine AO D and LIDA R PBL)sum mer in-situ PM 2.5 EstimatedPM 2.5 R = 0.8974 y = -0.61 + 1.06x N = 165 RM SE = 0.46 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 N N ESTIM ATIONS (TotalAO D and LIDA R PBL)summ er in-situ PM 2.5 EstimatedPM 2.5 R = 0.9028 y = -0.42 + 1.04x N = 165 RM SE = 0.44 Figure 1: Scatter plot showing the accuracy of the NN Estimations with Fine AOD and LIDAR PBL inputs for summer data Figure 2: Scatter plot showing the accuracy of the NN Estimations with Total AOD and LIDAR PBL inputs for summer data Figure 5: Bar graph showing the relation between Total AOD with all variables of the data partitioned by seasons and computed R values 1 2 3 4 5 6 7 8 9 10 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 EffectofTotalA O D and O therV ariables on R V alue N um berofLoops R Values A O D Total A O D Totaland PBL A O D Totaland RelH u A O D Totaland V o/Ext A O D Totaland W ind Speed A O D Totaland W ind D irection A O D Totaland Tem p A O D Totaland A llV ariables Biggest Single Improvement after AOD is PBL Height ANN with 1 hidden layer architecture Winter Spring Sum m er Fall Y ear 0 0.2 0.4 0.6 0.8 1 PM 2.5 Correlation R TotalAO D and LID A R PBL Winter Spring Sum m er Fall Y ear 0 0.2 0.4 0.6 0.8 1 PM 2.5 Correlation R All Variables Figure 4: Bar graph showing the relation between Total AOD and LIDAR PBL inputted as data partitioned by seasons and computed R values
