ASSESSMENT OF PM2.5 RETRIEVALS USING A COMBINATION OF SATELLITE AOD AND WRF PBL HEIGHTS IN COMPARISON TO WRF/CMAQ BIAS CORRECTED OUTPUTS Lina Cordero a Nabin Malakar a, Barry Gross a, Yonghua Wu a, Fred Moshary a, Mike Ku b a NOAA-Cooperative Remote Sensing Science and Technology Center b New York State Department of Environmental Conservation (NYSDEC) 12th Annual CMAS Conference Chapel Hill, NC – October 28-30, 2013

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/m 3 ). 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. 12th Annual CMAS Conference 2

CURRENT CMAQ PERFORMANCE CMAQ obtained from EPA-RSIG portal. NY state region emphasized. Seasonal bias observed (especially Fall / Summer). How does this compare to satellite based approaches? 12th Annual CMAS Conference 3

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 4

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 5

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 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. 6

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). 12th Annual CMAS Conference 7

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. 12th Annual CMAS Conference 8

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 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. 9

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 10

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 11

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 12

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 13

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 14

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 VariableR value Temp (Seasonal) WindSpeed RH PBL VariablesRRMSE ** Uncorrected CMAQ, Temp CMAQ, Temp, WS CMAQ, Temp, PBL CMAQ, Temp, RH CMAQ, Temp, PBL, RH CMAQ, Temp, WS, PBL CMAQ, Temp, WS, RH th Annual CMAS Conference 15

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 16

ROBUSTNESS OF NN (2 CASES) 12th Annual CMAS Conference 17 Testing and training results nearly identical (Robust)

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. 12th Annual CMAS Conference 18

ACKNOWLEDGMENTS This project was made possible by the National Oceanic and Atmospheric Administration, Office of Education Educational Partnership Program award NA11SEC as well as NYSERDA under grant # 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). 12th Annual CMAS Conference 19

QUESTIONS ?? 12th Annual CMAS Conference 20

ADDITIONAL SLIDES

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, 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, 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 Atmos. Environ. 45, U. S. Environmental Protection Agency. Air quality criteria for particulate matter 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, 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, th Annual CMAS Conference 22

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 Variable Wavelength (μm) Solar_Zenith-- Solar_Azimuth-- Sensor_Zenith-- Sensor_Azimuth-- Scattering_Angle-- Optical_Depth_Land_And_Ocean0.55 Mean_Reflectance_Land_All2.1 23

VARIOUS COMBINATION OF SATELLITE AND CMAQ 12th Annual CMAS Conference VariablesRRMSE MODIS MODIS+WIND MODIS+PRESS MODIS+PBL MODIS+PRESS+WIND MODIS+RH MODIS+LC+PBL MODIS+TEMP MODIS+TEMP+WIND MODIS+PRESS+RH MODIS+RH+WIND MODIS+TEMP+PRESS MODIS+TEMP, PRESS, WIND MODIS+TEMP, RH, WIND MODIS+TEMP+RH MODIS+TEMP, PRESS, RH MODIS+PRESS, RH, WIND MODIS+CMQ MODIS+CMQ+MO+PBL MODIS+CMQ+LC All (14) MODIS+CMQ+LC+MO MODIS+CMQ+PBL