Integration of Multiple Remote Sensing and In Situ Observations to Assess Regional Air Quality Monitoring Forecasts Sponsors: National Aeronautics and Space Administration (NASA) NASA Goddard Space Flight Center (GSFC) NASA Goddard Institute for Space Studies (GISS) NASA New York City Research Initiative (NYCRI) City College of New York (CCNY) Contributors: Dr. Barry Gross (PI) Arianna Moshary (Undergraduate Student) Chayma Boussayoud (High School Student) What are aerosols? Why study them? Project Goals Equipment PM2.5 Results Abstract Conclusion and Future Work Arianna Moshary and Chayma Boussayoud Ceilometer and Ozone Results In this project, we compare and analyze different kinds of available data on atmospheric aerosols and ozone. By retrieving and processing different kinds of data, we are looking to understand trends in New York and to compare it to a National Centers for Environmental Prediction (NCEP) Model. Aerosols have a large impact on climate and together with ozone can have hazardous effects on human health. Furthermore, aerosols come in so many different physical and chemical types that characterizing their behavior presents a challenge. Therefore, it is important to try to bring together available data to understand their dynamics. For our study, we are drawing data from a number of resources including The New York State Department of Environmental Conservation (DEC), City College of New York, and the NASA Aerosol Robotic Network (AERONET). In addition to more conventional surface measurements, we explore the vertical structure of particulates using an instrument called a ceilometer. To ensure ceilometer backscatter can be used to quantify aerosols, we demonstrate strong correlations between them, indicating that, while they measure different aerosols and aerosol properties, they still trend together. Furthermore, we show diurnal trends for both aerosol and ozone and a strong dependence for aerosols on location in New York State. When compared to data forecasts from the NCEP Model, we have been able to validate some of the predictions but also find some inconsistencies that we hope to be able to understand further. Another resource that we have hope to include are satellite data on Aerosol Optical Depth to explore the large scale aerosol distributions in Megacities in comparison to CMAQ 1)Compare and analyze different kinds of data on atmospheric aerosols and ozone to understand trends in New York. Compare data to the National Centers for Environmental Prediction (NCEP) Community Multi-scale Air Quality (CMAQ) model 2)Try to understand the reasons why the NCEP model does not always agree with measurements. CMAQ Ozone forecasts indicate diurnal trends but seem to underestimate the ozone concentration when compared to the actual measurements. Possible explanations include: underestimates the daily temperatures as an indicator of solar radiation that enhances ozone production. overestimates of particulates that block solar radiation. More statistical comparisons are needed to identify which of the possible explanations are correct. Our future objectives include: 1) To obtain much more data to look at these questions on a more statistical basis, 2) Understand the inconsistencies of the NCEP Model 3) Look at satellite aerosol products to see how the model performs spatially over a larger domain 4) Analyze rural vs urban data over a larger time frame CMAQ forecasts show large diurnal spikes (pre sunrise and post- sunset) in the PM2.5 concentration which are much larger than the small diurnal patterns seen by both the surface samplers. Possible explanations include: model emission inventory is overestimated model does not properly distribute the aerosols An analysis of the regression plots shows that while the correlation coefficient is fairly high (0.5846), and the model loosely depicts diurnal trends, it is still far from perfect. It still tends to overestimate in polluted conditions, and needs to be updated to account for the differences. Correlation Coefficient = X-Y Comparison of NCEP PM2.5 Surface Values and TEOM PM2.5 Data Surface Values Comparison Between TEOM PM2.5 Measurements and NCEP PM2.5 Model Values 7/07/11 6/30/11 Ceilometer and PM2.5 Predictions- 6/30* Ceilometer and PM2.5 Predictions- 7/07* Aerosols are solid or liquid particles in the atmosphere that come from sources like industrial pollution, combustion, wind-blown dust and natural processes. They can affect climate by interacting with cloud and atmospheric properties, thereby effecting the amount of sunlight that reaches Earth. Together with ozone, fine particles with diameters < of 2.5 (PM 2.5). Can negatively affect public health because they are small enough to get into the lungs. For this reason, the EPA publishes Air Quality Standards for both ozone PM2.5 concentrations and uses CMAQ to forecast both Ozone and PM2.5 While ozone diurnal trends are fairly well modeled, the PM2.5 trends seen by CMAQ are often strongly overestimated especially in morning Possible explanations include underestimates of emission inventories in CMAQ model or enhanced distribution of the emissions too near the surface. We hope to understand the possible factors using a combination of ground and vertical measurements. How does NYC compare to outside NYC? We predicted that NYC would have larger amounts of fine aerosol particles in the atmosphere then other New York State locations. We looked to the New York State Department of Environmental Conservation (DEC) data on PM2.5 monitoring to validate this and thus get a better idea of NY aerosol trends. The graphs below show how we looked at comparisons for the same day between different sites. Preliminary comparisons from summer cases seem to show larger PM 2.5 concentrations for the city than upstate. Preliminary measurement comparisons indicate PM2.5 levels are higher in cities than in rural areas but more statistical comparisons are needed to demonstrate this better. These graphs show the PM2.5 concentrations in New York City and in Whiteface base, a rural area outside New York City. These graphs compare the PM2.5 Levels over the course of one day. These graphs show the particle concentrations at different heights at over the course of two different days. The graphs labeled predicted show the fine particle densities predicted by the NCEP model, and the graphs labeled measured show the one hour time averaged backscatter data collected by the ceilometer. *Note: Here we are comparing two different kinds of data. However, they both trend with particle concentrations, and the comparison is thus relevant. Predicted Measured These graphs compare the actual ozone levels measured at CCNY with the ozone concentrations predicted by the NCEP model. The graph on the left is for June 30 th and the graph on the right is for July 7 th. Ozone Comparisons 6/30/11 Ozone Comparisons 7/07/11 These graphs compare the PM2.5 data for CCNY, the Ceilometer Data (scaled for ease of comparison) and the NCEP model predicted PM2.5 concentrations This graph is an X-Y comparison of the NCEP model predicted PM2.5 surface values and actual PM2.5 Data from TEOM. The x-axis contains the NCEP data and the Y axis contains the DEC TEOM data. Vaisala Ceilometer CL31 There are many surface stations that measure aerosol characteristics but location and expenses make these measurements biased near urban centers. Additionally, these sensors only look at surface pollution so if measurements do not agree with model forecasts, we have limited resources to understand what causes the variation. Therefore, it is beneficial to have techniques that can look at the pollution up to several kilometers above the surface. Measurements of this type can come from ground-based, air-based or satellite-based instruments. One of our main pieces of equipment in this study was a ground-based ceilometer located at City College. A ceilometer is able to measure aerosol concentrations at a distance using a laser. The light from the laser is scattered by aerosols at different heights as the light hits them. The sensors in the ceilometer measure the time delay of the laser echo and create a vertical profile that shows the height of the aerosol which scattered the light. One of the main advantages of the ceilometers is that it can be set up to operate continuously, and does not need to be monitored during operation. However, the ceilometer is primarily designed to determine cloud base heights so it does not measure aerosols as well as other instruments might. The other instruments we looked at were a Thermo Electron Corporation (TECO) model 49C to measure ozone. It does this by using ultraviolet light absorption. We also looked at readings from a Rupprecht & Patashnick Tapered Element Oscillating Microbalance (TEOM), which measures PM2.5 Concentration.