Spatial Variability and Vertical Distribution of Aerosols Over an - PowerPoint PPT Presentation

Spatial Variability and Vertical Distribution of Aerosols Over an Eastern Mediterranean Region Using LIDAR and MODIS/MAIAC-Measurements. Haifa as a Case Study Alexandra Chudnovsky 1 , Albert Ansmann 2 , Irina Rogozovski 1 , Holger Baars 2 , Alexei Lyapustin 3 , and Yujie Wang 3 1 Porter School of Environment and Earth Sciences, Air-O Lab, Tel-Aviv University 2 Leibniz Institute for Tropospheric Research, Leipzig, Germany 3 GEST / UMBC, NASA Goddard Space Flight Center, Baltimore, MD, USA 10th International Conference on Urban Climate / 14th Symposium on the Urban Environment August 6-10, NY

Strength of Remote Sensing for air quality studies  Existing PM 2.5 ground-monitoring networks encompass a relatively small number of stations per urban area, which is not sufficient to characterize spatial variability of particles within a large metropolitan area  Expanding spatial coverage and “ filling gaps ” in areas where there are no ground sensors (much of the third globe)  A cost effective way of obtaining global information about the earth-atmosphere system  Event and emission identification and transport, atmospheric composition determination, monitoring of long range transport events (forest fires, volcanic plumes, and stagnant haze masses over highly industrialized areas) influencing local air quality

Objectives  To describe the aerosol pollution state in the vertical column in the PBL  Studying the long-term regional variability of different natural and anthropogenic aerosol components  Buildup of an aerosol lidar center at TAU in the framework of a long-term collaboration with TROPOS and its integration into the world-leading community

Previous 1 km 10 km work Boston Boston A June 17, 2003 June 17, 2003 A AOD 0.8 A A 1 km 10 km Boston Boston 0 June 10, 2003 B B June 25, 2003 June25, 2003 1 MAIAC 1 km (left column) and MOD04 10 km (right column) , representing low pollution (A) and 10 km moderate pollution (B) days as measured by PM 2.5 . Note the loss of AOD variability on conventional images (right column). Consider left column only next. Image A and B show dynamic variation for a given region, regardless of pollution. Chudnovsky, A., Kostinski, Lyapustin, A., and Koutrakis, P. (2013). Spatial scales of pollution from variable resolution satellite imaging. Environmental Pollution, Vol. 172, pp. 131-138. C

Merging different data sets Detailed correlation study: MAIAC (AOD) vs surface observation (PM), and AERONET (AOD) Statistical modelling of the correlation. What parameters and conditions impact the most satellite retrieval error? Collaboration with TROPOS and NASA Building 3D profile: understanding better the origin of aerosols

Satellite retrievals Basic Idea: Desert regions are darker at shorter wavelengths so aerosol show up better.

Technion, LiDAR site

Towards a more Generalizable Linkage New Version of Existing Satellite Data 2000- current Spatial Resolution Lyapustin, A., Martonchik, J., Wang, Y., Laszlo, I., and Korkin, S (2011a). Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables. J. Geophys. Res ., 116(D03210).

Methodology Data collection Ground Monitoring Models Satellite data AERONET MAIAC Polly LiDAR Ground Stations HYSPLIT (AOD 440 nm) (AOD 470 nm) (PM 2.5 , PM 10 ) Synoptic patterns Poor MAIAC/AERONET Data processing and screening (Based on correlation Osetisnsky, Comparison to the vertical profile of the Alpert) atmosphere Good MAIAC/AERONET correlation Establishment of the spatial-temporal Data merging with different sources aerosol distribution over Gush Dan area Establishment of a 3D spatial-temporal aerosol distribution over Haifa Flow chart that illustrates the overall methodology of this study.

Data availability and data selection for the analyses A. Missing data B. Cloudy days C. Included data: polarization 0.00 - 0.05 0.00 - 0.05 0.00 - 0.05 0.05 - 0.10 0.05 - 0.10 0.05 - 0.10 0.10 - 0.15 0.10 - 0.15 0.10 - 0.15 Panel A (03.08.2012) represents missing data were the white 0.15 - 0.20 0.15 - 0.20 0.15 - 0.20 MODIS MAIAC AOD 0.20 - 0.25 0.20 - 0.25 0.20 - 0.25 0.25 - 0.30 0.25 - 0.30 0.25 - 0.30 0.30 - 0.35 0.30 - 0.35 0.30 - 0.35 circle highlights the area with no retrieved data; Panel B 0.35 - 0.40 0.35 - 0.40 0.35 - 0.40 (30.08.2012) demonstrates a cloudy day and where the algorithm cloud mask was applied on MAIAC retrieval data and blocked a significant area of the image. Note high AOD values in the vicinity of the cloud mask (highlighted by white arrows); Panel C (08.08.2013) shows residue calibration errors in the original MODIS L1B data, related to the MODIS mirror-side difference at 10km (polarization effects). Note that data presented in Panel C was included in the analyses. Marked in a black box is the research area. MODIS RGB Sever et al. 2017, Atmospheric Environment

Long term analyses of data: Correlation between MODIS MAIAC and AERONET (arid) Sede Boker station (2001-2014) Panel A Correlation (R) between MODIS MAIAC Month Terra Aqua 1.0 Terra Aqua (470nm) and AERONET (extrapolated Jan 0.60 0.49 0.8 Feb 0.68 0.71 470nm). AERONET AOD values represent a Mar 0.82 0.82 one-hour time interval, from half an hour 0.6 Apr 0.71 0.86 Percent before the satellite pass-time to half an hour May 0.76 0.64 afterwards. MAIAC ’ s data represent an 0.4 Jun 0.48 0.62 average AOD for an area of 3x3 pixels, Jul 0.65 0.68 0.2 whereby the Sede Boker AERONET station in Aug 0.70 0.63 located in the central pixel. The monthly Sep 0.95 0.95 0.0 (panel A) and thereafter the seasonally (panel Oct 0.70 0.66 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec B) correlations are for the years 2000-2015 for Nov 0.59 0.47 Month Dec 0.58 0.56 Terra and 2002-2015 for AQUA. All presented Total 0.81 0.82 correlations are based on daily data Panel B comparisons between MAIAC and 1.0 0.91 0.91 Terra Aqua AERONET. The values of the monthly 0.79 0.77 correlations are presented in the adjacent 0.8 0.64 0.64 table. 0.62 0.62 0.6 Percent 0.4 0.2 Sever et al. Submitted 0.0 Winter (DJF) Spring (MAM) Summer (JJA) Fall (SON) Month 12

Nes Ziona- April 2013 Nes Ziona- July 2013 Nes Ziona- August 2013 Nes Ziona- November 2013 Monthly trend of AOD for Nes Ziona: comparison between AERONET and MAIAC AOD retrievals

Study area Study area. Left: a map of Israel, with the Haifa region highlighted by the dashed line (major cities and the AERONET station marked); Right: an enlargement of the study area (AERONET station and PM2.5/PM10 stations marked).

Average AOD spatial variability for selected months: August and October 2016 Terra and Aqua

AQUA TERRA 0.6 0.6 Underestimation Underestimation 0.5 0.5 y = 1.121x + 0.021 y = 0.89x + 0.048 R ² = 0.74 AERONET AOD (470 nm) AERONET AOD (470 nm) R ² = 0.72 0.4 0.4 0.3 0.3 0.2 0.2 Overestimation Overestimation 0.1 0.1 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0 0.1 0.2 0.3 0.4 0.5 0.6 MODIS AOD (470 nm) MODIS AOD (470 nm)

440-675 Angstrom 0.45 -1.0 1.01 - 1.35 1.35 - 1.7 PM 2.5 concentrations (µg/m 3 ) 5 -12.5 MODIS MAIAC AOD Anthropogenic 3 October pollution Bad 12.5 -36.5 Ground pollution + lofted dust Good correspondence Days: August 13, October 1 25-26 October, AERONET AOD 29 September

MODIS MAIAC AOD Wind direction 25-26 October AERONET AOD

Polly lidar, Range-corrected signal, 532 nm 100.0 4 4000 Technion, Haifa, Israel 25 Oct, 07-08 UTC 3500 Altitude, km 50.0 3 3000 532 nm 2500 0.0 2 2000 1500 Residual pollution layer 1 1000 500 Mixing layer 0 0 07:00:00 07:10:00 07:20:00 07:30:00 07:40:00 07:50:00 08:00:22 Time 10/25/2016 10/25/2016 10/25/2016 10/25/2016 10/25/2016 10/25/2016 10/25/2016 Range-corrected signal, 532 nm Time (UTC) 100.0 4000 4 3500 25 Oct, 11-12 UTC Altitude, km 50.0 3 3000 2500 0.0 2 2000 1500 1 Residual pollution layer 1000 500 Mixing layer 0 0 11:00:00 11:10:00 11:20:00 11:30:00 11:40:00 11:50:00 12:00:00 Time 10/25/2016 10/25/2016 10/25/2016 10/25/2016 10/25/2016 10/25/2016 10/25/2016 Time (UTC)

Range-corrected signal, 532 nm Polly lidar, 100.0 4 4000 Technion, Haifa, Israel 3500 26 Oct, 10-12 UTC Altitude, km 50.0 3 3000 2500 Lofted layers of 0.0 2 2000 532 nm dust and pollution 1500 1 1000 Residual pollution layer 500 Marine PBL 0 0 10:00:00 10:15:00 10:30:00 10:45:00 11:00:00 11:15:00 11:30:00 11:45:00 12:00:00 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 Time Time (UTC) Range-corrected signal, 532 nm 100.0 5 5000 4500 26 Oct, 12:30-14:30 UTC 4 4000 Altitude, km 50.0 3500 3 3000 0.0 2500 Lofted layers of 2 2000 dust and pollution 1500 1 1000 Residual pollution layer 500 Marine PBL 0 0 12:30:00 12:45:00 13:00:00 13:15:00 13:30:00 13:45:00 14:00:00 14:15:00 14:29:12 Time 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 10/26/2016 Time (UTC)

October 26, 2016 AOD Fine Fraction AOD 0 0.44 1 0