Aldemar Knossos Royal Village Conference Center June 1-6, 2008, Crete, Greece

Transcription

1 First International Conference: From Deserts to Monsoons Aldemar Knossos Royal Village Conference Center June 1-6, 28, Crete, Greece AEROSOL LOADINGS OVER EASTERN MEDITERRANEAN AND THE CONTRIBUTION OF SAHARAN DUST E. GERASOPOULOS 1, N. KALIVITIS 2, G. KOUVARAKIS 2, R. HILLAMO 3 AND N. MIHALOPOULOS 2 1 Institute for Environmental Research and Sustainable Development National Observatory of Athens 2 Environmental Chemical Processes Laboratory, Chemistry Department University of Crete 3 Air Quality Research Finnish Meteorological Institute

2 The National Observatory of Athens (NOA) is the oldest research centre in Greece. It was founded in 1842, as the Royal Observatory of Athens, by Baron Georgios Sinas. It has been operating since 1846, when the first astronomical and meteorological observations started. NOA is operating today five Research Institutes and provides the facilities for graduate student training in collaboration with Greek and foreign Universities. It hosts the UNESCO Chair for Natural Disasters It operates the National Seismological Network It hosts the Greek Focal Point on the Global Earth Observing System of Systems (GEOSS) National Observatory of Athens, Thissio, Founded in 1842 The Meteorological Institute is one of the two constitutional institutes of NOA. The Observatory holds the oldest meteorological database in Greece (since 1858)

3 In the 7 s the Meteorological Institute was the first organization that started the systematic study of air pollution problems in Athens. In 8 s, the first automatic air pollution monitoring station was installed. In 1999, the institute was renamed to Institute of Environmental Research and Sustainable Development (IERSD), the official name up to present. IERSD aims at promoting environmental science and engineering. The Institute is active in the following thematic areas: * Meteorology / Weather Forecast * Climatology and Climate Change * Atmospheric Chemistry and Physics * Atmospheric Pollution monitoring and modelling * Solar and Wind Energy * Resource Management and Sustainable Development * Hydrology / Hydraulics * Water Resources Engineering * Remote Sensing / Radar Meteorology * Energy Planning and Conservation Institute for Environmental Research & Sustainable Development, Penteli, Athens

4 Infrastructure Mobile Air Quality station NO, NO 2, SO 2, O 3, THC, CH 4, PM 1 Analyzers DOAS - Differential Optical Absorption Spectroscopy NO 2, SO 2, O 3, benzene, toluene, p- m- xylene

5 Infrastructure X-band dual-polarization radar 9.37 GHz, 6 kw max power, GPS, wireless,.9 o beam width, up to 12 km range Real-time estimation of precipitation characteristics precipitation rate storm velocity type (rain, hail, snow) Combination with a network of sensors for cloud-to-cloud and cloudto-ground electrical discharge measurements (indicative of convective cores) Spatial representation of the radar signal (proportional to rain intensity)

6 Infrastructure Bound Meteorological Balloon Vertical profiles of meteorological parameters (up to six levels) Temperature Relative Humidity Wind speed and direction Full equipment for performing radiosoundings

7 Infrastructure Actinometric Station Pyranometers (total, diffuse horizontal plane, tilted) Pyrgeometers (IR) Pyrheliometers (direct, total, spectral) UV Radiometers MFR-7 Albedometer

8 Infrastructure Atmospheric Chemistry Lab Aerosol station GC GCMS HPLC PM impactors IC SMPS, CPC Nephelometers

9 AEROSOL LOADINGS OVER EASTERN MEDITERRANEAN AND THE CONTRIBUTION OF SAHARAN DUST

10 Example of a dust event and impact on visibility 17 April 24

11 Major dust producing areas Long-term mean TOMS AI over Africa north of the equator (filled contours) calculated using data from Long-term mean ( ) precipitation (black isoplythes) is derived from a.5 by.5 rainfall data set [New et al., 1999].West African major dust hot spots are indicated as WA1, WA2, and WA3 and the Bodele Depression in Chad as BOD. Engelstaedter and Washington (27, JGR 112, D313)

12 Main pathways of dust transport over E. Mediterranean AOD = Mean columnar aerosol optical 355nm AOD1 = Mean PBL aerosol optical 355nm AOD2 = Mean FT aerosol optical 355nm cluster 3 (7%) AOD = AOD1 = AOD2 =.16.9 cluster 2 (24%) AOD = AOD1 = AOD2 =.12.6 cluster 6 (27%) AOD =.59.2 AOD1 = AOD2 =.15.9 cluster 5 (3%) AOD = AOD1 = AOD2 = cluster 1 (6%) AOD = AOD1 =.4.26 AOD2 = cluster 4 (6%) AOD =.97.7 AOD1 =.6.8 AOD2 =.37.9 Amiridis et al. (25, JGR 11 (D21): Art. No. D2123)

13 Main pathways of dust transport over E. Mediterranean Moulin et al. (1998, JGR 13(D11), )

14 Annual variation of dust particles at the surface Moulin et al. (1998, JGR 13(D11), )

15 Main vertical pathways of dust transport nm 532 nm 355 nm 355 nm 4 Height (m) hpa 85 hpa 1 Boundary Layer 975 hpa. 4.x1-6 8.x1-6 2.x1-4 4.x Backscatter (m -1 sr -1 ) Extinction (m -1 ) Lidar Ratio (sr) Dust layers identification via Raman Lidar: A dust layer is shown between km, and important optical properties like backscatter, extinction and lidar ratio are extracted Balis et al. (24, GRL 31 (13): Art. No. L1314)

16 1 m 3 m (FTT: Free Tropospheric Transport)

17 (VET: Vertically Extended Transport) 1 m 3 m (BLT: Boundary Layer Transport)

18 Main vertical pathways of dust transport Frequency of Occurrence Συχνότητα Εμφάνισης VET FTT BLT ΧΕΙΜΩΝΑΣ ΑΝΟΙΞΗ ΚΑΛΟΚΑΙΡΙ ΦΘΙΝΟΠΩΡΟ Winter Spring Summer Fall All percentages should be regarded with the view that the frequency of potential dust transport trajectories differs seasonally, especially in summer when they are infrequent SPRING: both VET and FTT cases are equally distributed FALL: 57% of the potential dust transport cases are associated with VET and about 37% with FTT WINTER: a similar situation is encountered with VET being more dominant. SUMMER: the reverse situation is observed with FTT dominating (6%) over VET (25%) and BLT (15%) which is indicative of the stability of the atmosphere during summer which blocks the vertical distribution of the dust layers The summer maximum of FTT is in agreement with observations in the Canary Islands while the winter maximum of VET is not observed in that area, where the low-altitude dust outbreaks dominate [Viana et al., 22], a fact that partly differentiates the dust transport patterns from northern Africa toward eastern Mediterranean and the Atlantic. Kalivitis et al. (27, JGR 112, D322)

19 VET type of dust transport

20 VET type of dust transport 45 AOT (5 nm) 1.4 Scattering Coefficient (M m -1 ) PM 1 (μg m -3 ) PM 1 Συντελεστής Scattering Σκέδασης Coeff AOT (5 nm). 24/1 25/1 26/1 27/1 28/1 29/1 3/1 31/1 1/2 25 Kalivitis et al. (27, JGR 112, D322)

21 VET type of dust transport Volume Size Distribution (μm 3 μm -2 ).7 26/1/25 27/1/ /1/25 3/1/ Radius Ακτίνα (μm)

22 VET type of dust transport PM 1 vs AOT PM 1 = (274±13)*AOT - (22±8) (R 2 =.93) PM 1 (μgr m -3 ) PM 1 vs scat. coef. 1 5 PM 1 =(3.9±.1)*Scat - (25±4) 5 (R 2 =.9) Συντελεστής Scattering Coefficient Σκέδασης (Mm -1 ) AOT (87 nm) Kalivitis et al. (27, JGR 112, D322)

23 FTT type of dust transport

24 FTT type of dust transport Scattering Coefficient (M m -1 ) PM 1 (μg m -3 ) AOT (5 nm) PM 1 Συντελεστής Scattering Σκέδασης Coeff. Time delay AOT (5 nm). 11/5 12/5 13/5 14/5 15/5 16/5 24 Kalivitis et al. (27, JGR 112, D322)

25 FTT type of dust transport Volume Size Distribution (μm 3 μm -2 ) /5/24 13/5/24 14/5/24 15/5/ Radius Ακτίνα (μm)

26 FTT type of dust transport Time series were synchronized before the correlation 3 25 PM 1 vs AOT PM 1 = (365±5) *AOT + (7±1) (R 2 =.6) PM 1 (μgr m -3 ) 15 1 PM 1 vs scat. coef. PM 1 =(4.2±.5) *Scat (22±15) (R 2 =.7) Scattering Συντελεστής Coefficient Σκέδασης (M m -1 ) AOT (87 nm) Kalivitis et al. (27, JGR 112, D322)

27 Annual variation of dust particles at the surface PM 1 masses present a prominent peak in spring (April-May) due to the increased frequency of dust transport from northern Africa PM 1 (μg m -3 ) PM 2.5 present similar seasonal features, however the observed peak in April is moderate compared to that of PM1, demonstrating the dominance of coarse particles during the dust events. 2 a) 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec PM 1 presents a summer maximum with monthly values up to 13 μgm 3 (up to almost 3 μgm 3 for individual impactor samplings, which expresses a significant number of submicron particles from regional or long-range transported pollution PM 2.5 (μg m -3 ) b) PM 1 (μg m -3 ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Correlation Coefficients between seasonal cycles Regional or local pollution Fine Particles Long range dust transport Coarse Particles SDI stages cut-off (μm) Increased uncertainty in mass estimation 99% c.l. 95% c.l. up to 1 μm c) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Gerasopoulos et al. (27, ACP 7, )

28 Contribution of dust to surface PM levels PM 1 (μg m -3 ) N NE E SE S SW W NW N Wind flow direction (degrees) Both averages (solid) and medians (dash) of PM 1 show a distinct enhancement when air masses arrive from the SE-S-SW direction. This fact suggests that dust arriving often from deserted areas in N. Africa (e.g. Sahara) should contribute significantly to particulate matter levels. South sector PM 1 = 7 μg m h limit of 5 μg m -3 is exceeded more than half of the time during southerlies. The increased standard deviation (orange area) indicates the potential for different dust loadings over the area depending on the intensity of the dust events that may result in mean daily values over 14 μg m -3. The identification of the wind direction was based on 5- day back-trajectories calculated by the HYSPLIT model (Hybrid Single-Particle Lagrangian Integrated Trajectory Model). NW-N-NE sectors 4-5 μg m -3 Gerasopoulos et al. (26, Atm. Env. 4, )

29 Contribution of dust to surface PM levels December - June Heraklion: limit exceedance ~25-3% of the days. Finokalia: limit exceedance ~2% of the days % Days exceeding 5 μg m Finokalia Heraklion Finokalia Heraklion Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec % of exceedances due to dust July November Heraklion: limit exceedance 4-55% of the days Finokalia: constantly below the limit. The origin of the high particulate matter loading is shown at the upper panel as the percentage of the days exceeding 5 μg m -3 for which back trajectories suggest the occurrence of transported dust from the south (North Africa). Heraklion: ~4% of the limit exceedances due to transported dust throughout the year, excluding summer when the percentage is only 6% indicating that pollution is responsible for most of the PM 1 exceedances. Finokalia: mostly related to air masses coming from the South. Gerasopoulos et al. (26, Atm. Envir. 4, )

30 1/3 4/3 7/3 1/3 1/4 4/4 7/4 1/4 1/5 4/5 7/5 1/ Παράμετρος Angstrom 1/3 4/3 7/3 1/3 1/4 4/4 7/4 1/4 1/5 4/5 7/5 1/ AOT (44 nm) AOT (87 nm) AOT 1/3 4/3 7/3 1/3 1/4 4/4 7/4 1/4 1/5 4/5 7/5 Std. AOT (87 nm) 1/5 Contribution of dust to surface PM levels Anthropogenic particles Dust Transport Events Kalivitis et al. (27, JGR 112, D322)

31 Size-segregated characteristics of particles E F Percentage Frequency a) A B C D Mass Mean Diameter (μm) G A - Aitken 1 (.4-.8) B - Aitken 2 (.8-.25) C - Accumulation 1 ( ) D - Accumulation 2 (.55-1) E - Coarse 1 (1-3) F - Coarse 2 (3-7) G - Extra Coarse (>7) Gerasopoulos et al. (27, ACP 7, )

32 Aitken 1 Aitken 2 Accum1 Accum2 1% 1 13% 33% 1% 7% 43% 95% 19% 19% 5% 19% 16% 52% 1% 28% Mode Average Mass (μg m -3 ) 7% 73% 92% 57% 4% 16% 1% 96% 33% 1% 1% a) Winter Spring Summer Autumn Winter Spring Summer Autumn Mode Average Mass (μg m -3 ) 4% Size-segregated characteristics of particles b) Coarse 1 Coarse 2 Extra Coarse Gerasopoulos et al. (27, ACP 7, )

33 Size-segregated characteristics of particles AERONET vs Impactors A very good agreement is revealed between the AERONET volume size distributions and the mass size distributions derived from the SDI impactor, denoting that the latter can provide significant information on the columnar distribution of the size resolved characteristics of particulate matter in the area. Gerasopoulos et al. (27, ACP 7, )

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36 Size-segregated characteristics of particles Percentage Frequency a) FINE PARTICLES Diff_Diameter (μm) Impactors Fine Fraction Mass a) AERONET Fine Fraction Volume y = 16(±23) x + 4(±1) R 2 =.48.4 COARSE PARTICLES Sp Percentage Frequency Impactors Coarse Fraction Mass Sp Sp Sp Au Au y = 15(±17) x + 6(±1.4) R 2 =.62 Sp b) Diff_Diameter (μm) b) AERONET Coarse Fraction Volume Gerasopoulos et al. (27, ACP 7, )

37 Contribution of dust to surface PM levels PM 1 Heraklion vs Finokalia: PM 1 Heraklion (μg m -3 ) y=1.12x+15 R 2 = PM 1 Finokalia (μg m -3 ) Spring Summer Winter-Fall Slope = 1.12, R 2 =.84 PM 1 variability at Heraklion is controlled by the background levels Intercept = 15 μg m -3 Demonstrates the influence of the city effects. Spring slope =.96±.18, Intercept = 11±4 South sector slope = 1.8 (R 2 =.98) Long range transported aerosols that affect the regional background (spring data enclosed in the dotted curve in Fig. 4 lie near the 1:1 line). Summer and fall slope = 1.41±.14, Intercept = 19±3 Influence of local pollution as also revealed by the frequency distributions. The above results clearly indicate the dominant role of; i) regional background in controlling particulate matter loads at both locations, ii) long-range transported dust. Gerasopoulos et al. (26, Atm. Envir. 4, )