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Spatio-temporal variability of desert dust storms in Eastern Mediterranean (Crete, Cyprus, Israel) between 2006 and 2017 using a uniform methodology


Abstract and Figures

The characteristics of desert dust storms (DDS) have been shown to change in response to climate change and land use. There is limited information on the frequency and intensity of DDS over the last decade at a regional scale in the Eastern Mediterranean. An algorithm based on daily ground measurements (PM10, particulate matter ≤10 μm), satellite products (dust aerosol optical depth) and meteorological parameters, was used to identify dust intrusions for three Eastern Mediterranean locations (Crete-Greece, Cyprus, and Israel) between 2006 and 2017. Days with 24-hr average PM10 concentration above ~30 μg/m³ were found to be a significant indicator of DDS for the background sites of Cyprus and Crete. Higher thresholds were found for Israel depending on the season (fall and spring: PM10 > 70 μg/m³, winter and summer: PM10 > 90 μg/m³). We observed a high variability in the frequency and intensity of DDS during the last decade, characterized by a steady trend with sporadic peaks. The years with the highest DDS frequency were not necessarily the years with the most intense episodes. Specifically, the highest dust frequency was observed in 2010 at all three locations, but the highest annual median dust-PM10 level was observed in 2012 in Crete (55.8 μg/m³) and Israel (137.4 μg/m³), and in 2010 in Cyprus (45.3 μg/m³). Crete and Cyprus experienced the same most intense event in 2006, with 24 h-PM10 average of 705.7 μg/m³ and 1254.6 μg/m³, respectively, which originated from Sahara desert. The highest 24 h-PM10 average concentration for Israel was observed in 2010 (3210.9 μg/m³) during a three-day Saharan dust episode. However, a sub-analysis for Cyprus (years 2000–2017) suggests a change in DDS seasonality pattern, intensity, and desert of origin. For more robust conclusions on DDS trends in relation to climate change, future work needs to study data over several decades from different locations.
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1. Introduction
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H<9  / 85=@M @=A=H J5@I9 C:  μ;A7<=@@9CG 9H 5@ 
'CINCIF=89G 9H 5@  5=@M *' @9J9@G 8IF=B; - 9J9BHG 75B
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H=CBG 5F9 9LD97H98 HC 697CA9 ACF9 G9J9F9 !CI8=9 %F5GBCJ 9H
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D9F=C8  =B7F95G98 5H 5B 5J9F5;9 F5H9 C:  85MGM95F F957<
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8IF=B; H<9 K=BH9F 5B8 GDF=B; =B  657?;FCIB8 *' @9J9@G 9L
799898 H<9 85=@M / @=A=H =B  CIH C:  85MG K=H<  C: H<9 =B7=
89BHG @=B?98 HC - !9F5GCDCI@CG 9H 5@  #B 85H5 :FCA 5 ACF9
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=B H<9 D5GH
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2. Material and methods
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GH5H=CB S( S  A 5G@ == =B MDFIG M=5 '5F=B5
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ID HC  C: H<9 85MG !9F5GCDCI@CG 9H 5@  .<9 M=5 '5
F=B5 ' GH5H=CB =G D5FH C: H<9 5=F EI5@=HM ACB=HCF=B; B9HKCF? CD9F5H98
6M H<9 =F +I5@=HM -97H=CB MDFIG 9D5FHA9BH C: &56CF #BGD97H=CB .<9
ACB=HCF=B; G=H9 =G @C75H98 =B H<9 (=7CG=5 8=GHF=7H  ?A GCIH<K9GH C: H<9
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9F5;9/   .<9 7CA6=B5H=CB C: ,.F9;F9GG=CB A9H<C8 =89B
H=V98  -  - 5B8  - :CF ' %& 5B8 - F9GD97
H=J9@M K<9B 5DD@=98 :CF H<9 F9:9F9B79 M95FG CF ' 5B8
%*+,,'/3'4#, %+'.%'/(4*' /4#,.6+2/.-'.4 888 8888 
%&  C: - 5B8  C: (- K9F9 7CFF97H@M 7@5GG=V98 5B8 :CF -
 C: - 5B8  C: (- K9F9 7CFF97H@M 7@5GG=V98
8 CF H<9 VB5@ GH9D C: - 7@5GG=V75H=CB K9 5DD@=98 H<9 7CA6=B98
,.F9;F9GG=CB G7<9A9 HC 5@@ H<9 85MG C: H<9 M95F GHI8M D9F=C8
5B8 7@5GG=V98 85MG 5G - 5B8 (- CF 85MG K=H< A=GG=B; 85H5
A5=B@M *' K9 =BGD97H98 H<9 85MG =B 5 G=A=@5F K5M K=H< H<9 F9:9F
9B79 M95FG 5B8 =89BH=V98 - 65G98 CB IGH ) G5H9@@=H9 =A5;9G
5B8 A9H9CFC@C;=75@ J5F=56@9G *'HC*' F5H=C *' 9@9A9BH5@
7CB79BHF5H=CBG =: K9F9 5J5=@56@9
9 #B 588=H=CB - K9F9 7@5GG=V98 65G98 CB H<9=F GCIF79 C: CF=;=B -5
<5F5B '=88@9 5GH9FB' A=LHIF9 C: -5<5F5B 5B8 ' 89G9FHG 5:H9F
9L5A=B=B; 85M 657?K5F8 5=F HF5>97HCF=9G HF5798 5H   5B8
 A <9=;<H !& IG=B; H<9 "M6F=8 -=B;@9*5FH=7@9 &5;F5B;=5B #B
H9;F5H98 .F5>97HCFM "3-*&#. AC89@ -H9=B 9H 5@ ,C@D< 9H
5@ 
 2'.&3 #.#,93+3
19 5DD@=98 H<9 .<9=@-9B 9GH=A5HCF 5B8 '5BB%9B85@@ H9GH C: G=;B=:
=75B79 5 BCBD5F5A9HF=7 A9H<C8 HC H9GH 5B8 AC89@ HF9B8G HC 5GG9GG H<9
@=B95F HF9B8 C: - 19 5@GC 9L5A=B98 H<9 BCB@=B95F HF9B8 C: *' 7CB
79BHF5H=CBG 5B8 IGH) 8IF=B; 8IGH 85MG IG=B; 5 !9B9F5@=N98 88=H=J9
'C89@ !' K=H< 5 D9B5@=N98 F9;F9GG=CB GD@=B9 :IB7H=CB CB H=A9 -
K<9F9 μ==G H<9 A95B 85=@M *' 7CB79BHF5H=CB CF IGH) :CF - 85M
= β=G H<9 F9;F9GG=CB =BH9F79DH 5B8 : F9DF9G9BHG 5 D9B5@=N98 F9;F9GG=CB
GD@=B9 :IB7H=CB K<9F9 H<9 89;F99G C: :F998CA K9F9 89H9FA=B98 6M 7<CCG
=B; H<9 GACCH<=B; D5F5A9H9F 6M !9B9F5@=N98 FCGG 05@=85H=CB 7F=H9F=CB
CF H<9 HF9B8 5B5@MG9G K9 IG98 H<9 HF9B8%9B85@@GD@=B9G 5B8
A;7JD57?5;9G =B , GH5H=GH=75@ GC:HK5F9 , CF9 .95A 
3. Results and discussion
  %*#2#%4'2+34+%3
.<9 85=@M *' 7CB79BHF5H=CB 5B8 IGH) H=A9 G9F=9G CJ9F H<9
GHI8M D9F=C8  5F9 DF9G9BH98 =B =;  5B8 H<9 <CIF@M *'
@9J9@G 5F9 =@@IGHF5H98 =B =; - .<9 85=@M 5J9F5;9 *' J5@I9G F5B;98
:FCA  HC  μ;A=B ' :FCA  HC  μ;A=B %&
5B8 :FCA  HC  μ;A=B - IGH) K5G K=H<=B H<9 F5B;9 C:
 5B8  5H 5@@ G=H9G
.<9 ,. 5B5@MG=G =89BH=V98 HKC 7@5GG=V75H=CB FI@9G :CF ' 5B8
H<F99 :CF %& 5B8 - .<9 FI@9G 5F9 89G7F=698 =B .56@9  IGH)
5B8 *' K9F9 H<9 ACGH G=;B=:=75BH J5F=56@9G =B 7@5GG=:M=B; - :CF 5@@
H<F99 G=H9G =::9F9BH - 7F=H9F=5 J=G=6=@=HM ) 59FCGC@ G75HH9F=B;
H9FBG 9H7 <5J9 699B DF9J=CIG@M IG98 =B G9J9F5@ GHI8=9G '=7<59@=89G
9H 5@ !5BCF 9H 5@ 5@C 9H 5@ <I8BCJG?M 9H
5@ =5DCI@= 9H 5@  @CF9G 9H 5@ '=7<59@=89G
9H 5@  "CK9J9F H<9 IG9 C: 7F=H9F=5 65G98 CB ;FCIB8 5B8 7C@IA
B5F 59FCGC@ @C58 5B8 H<9=F 7CA6=B5H=CB <5G 699B G<CKB HC 69 5 F9@=
56@9 HCC@ :CF - 7@5GG=V75H=CB G=B79 =H 5@@CKG H<9 =89BH=V75H=CB C: @CK
A98=IA 5B8 <=;< =BH9BG=HM 9J9BHG %5@=J=H=G 9H 5@ 57<CFFC
9H 5@  )H<9F 9LD@5B5HCFM J5F=56@9G K9F9 H9AD9F5HIF9 :CF %&
5B8 K=B8 GD998 5B8 G95GCB5@=HM :CF - .<9G9 FI@9G 5@CB; K=H< H<9 F9
;F9GG=CB 7C9:V7=9BHG .56@9 - K9F9 IG98 HC =89BH=:M - :CF 5@@ M95FG
C: H<9 GHI8M D9F=C8
19 =89BH=V98  '   %& 5B8  - - =B H<9 5GH
9FB '98=H9FF5B95B F9;=CB 8IF=B; H<9 HK9@J9M95F GHI8M D9F=C8 .<9 A5
>CF=HM  C: H<9 =89BH=V98 - :FCA ' GH5H=CB K9F9 89G7F=698
6M 85=@M *' 5J9F5;9 ;F95H9F CF 9EI5@ HC  μ;A',.FI@9
 5B8 5@ACGH <5@:  C: H<9 %& - K9F9 89G7F=698 6M 85=@M
IGH) 5J9F5;9 ;F95H9F CF 9EI5@ HC  5B8 *' 5J9F5;9 9EI5@
56CJ9  μ;A %&,.FI@9  -=A=@5F@M =B - O C: H<9
- K9F9 =89BH=V98 8IF=B; :5@@ 5B8 GDF=B; ACBH<G K=H< *' 5J9F5;9
;F95H9F CF 9EI5@ HC  μ;A-,.FI@9  5B8  8IF=B; K=B
H9F 5B8 GIAA9F ACBH<G K=H< *' 5J9F5;9 ;F95H9F CF 9EI5@ HC  μ;
A-,.FI@9  .<9G9 D9F79BH5;9G 5@GC 89D=7H H<9 BIA69F C: -
H<5H K9F9 C6G9FJ98 5H ;FCIB8 @9J9@ 5B8 5::97H <IA5B <95@H< .<9 F9GH
  5B8  :CF ' %& 5B8 - F9GD97H=J9@M F9DF9G9BH -
K<=7< C77IFF98 5H <=;<9F 5HACGD<9F=7 @9J9@G
.56@9  DF9G9BHG GIAA5FM GH5H=GH=7G :CF *' @9J9@G IGH) 5B8
A9H9CFC@C;M 5H H<9 H<F99 G=H9G *' 7CB79BHF5H=CB 5B8 IGH) K9F9
AI7< <=;<9F =B - H<5B =B ' 5B8 %& - GH5H=CB =G G9H K=H<=B H<9 7=HM
5G CDDCG98 HC H<9 CH<9F HKC GH5H=CBG K<=7< 5F9 @C75H98 =B FIF5@ 5F95G
(9J9FH<9@9GG 99F -<9J5 =G 5 7=HM BCH =AD57H98 6M <95JM HF5:V7 CF =B8IG
HFM #H =G GIFFCIB898 6M @5F;9 5F=8 5F95G 5B8 5G F9GI@H H<9 7CBHF=6IH=CB
C: 5BH<FCDC;9B=7 GCIF79G HC *' 9J9B =B BCB8IGH 85MG =G CB@M O
%F5GBCJ 9H 5@ 6
Fig. 2. J9F5;9 85=@M 7CB79BHF5H=CB C: *' μ;A 5B8 IGH) CJ9F H<9 M95F GHI8M D9F=C8 =B ' %& 5B8 -
%*+,,'/3'4#, %+'.%'/(4*' /4#,.6+2/.-'.4 888 8888 
Table 1
,. 7@5GG=V75H=CB FI@9G:CF -
,I@9L ,I@9L ,I@9L
' *'    *'  
IGH) 
%& IGH) 
*' 
IGH) 
 *'  
.9AD9F5HIF9 
IGH)  
*' 
- *' 
-95GCB 5:5@@GDF=B;
*' 
-95GCB 5GIAA9FK=BH9F
*'  
IGH) 
1=B8GD998  
5:5@@ -9DH9A69F(CJ9A69F K=BH9F 979A69F 96FI5FM GDF=B; '5F7<'5M GIA
A9F $IB9I;IGH
.<9 H<F99 5GH '98=H9FF5B95B G=H9G 9LD9F=9B798  7CAACB -
'*' U  μ;A %&*' U  μ;A
-*' U  μ;A K<=7< 7CFF9GDCB8G HC  C: - :CF
'  :CF %& 5B8  :CF - 'CGH C: H<9G9 - CF=;=B5H98
:FCA -5<5F5B 89G9FH B' B %& B- K=H< - 699B =A
D57H98 ACGH =BH9BG9@M '*' U  μ;A
%&*' U  μ;A -*' U  μ;A .<9 F9GH
C: H<9 7CAACB 8IGH 85MG K9F9 =BWI9B798 :FCA 9=H<9F ' 89G9FH
B' B %& B- CF :FCA 6CH< -5<5F5B 5B8 ' 89G9FHG
B' B %& B- #B 588=H=CB ' 5B8 - 9LD9F=9B798 5BCH<9F
 7CAACB 8IGH 85MG  C: - :CF ' K=H< *' U  μ;
A  C: - :CF - K=H< *' U  μ;A %& 5B8 -
  C: - :CF %& K=H< *'  U  μ;A  C: -
:CF - K=H< *'  U  μ;A 5B8 ' 5B8 %&  7CAACB
8IGH 85MG  C: - :CF ' K=H< *' U  μ;A  C:
- :CF %& K=H< *' U  μ;A
 '4'/2/,/)+%#, %/.&+4+/.3
%& <58 H<9 <=;<9GH 85=@M 5J9F5;9 C: F9@5H=J9 <IA=8=HM 5B8 K=B8
GD998 6IH @CK9GH H9AD9F5HIF9 .56@9  - 5@H<CI;< =G GIFFCIB898 6M
@5F;9 89G9FHG 9LD9F=9B798 ACF9 DF97=D=H5H=CB H<5B ' IF=B; - H<9
A95B *' @9J9@ K5G ID HC H<F99 ' %& CF VJ9 - H=A9G <=;<9F
H<5B H<9 BCB8IGH 85MG 5J9F5;9  :CIF HC VJ9:C@8 =B7F95G9 K5G C6G9FJ98
:CF IGH) 8IF=B; - 5G K9@@ - K9F9 5GGC7=5H98 K=H< <=;<9F H9A
D9F5HIF9G 5B8 8F=9F 7CB8=H=CBG 5H 5@@ H<F99 G=H9G - K9F9 K=B8=9F =B -
6IH H<9 CDDCG=H9 K5G C6G9FJ98 =B %&
.<9 CF=;=B C: H<9 A5>CF *' GCIF79G :CF H<9 K<C@9 GHI8M D9F=C8
K5G GHI8=98 IG=B; H<9 6=J5F=5H9 DC@5F D@CHG :CF 957< G=H9  =;  =
J5F=5H9 DC@5F D@CHG GH5H=GH=75@@M F9DF9G9BH H<9 *' 7CB79BHF5H=CBG =B DC
@5F 7CCF8=B5H9G H5?=B; =BHC 577CIBH H<9 K=B8 GD998 5B8 8=F97H=CB .KC
8=::9F9BH J9FG=CBG C: H<9 D@CHG 5F9 G<CKB =; 58 G<CK <CK H<9
*' 7CB79BHF5H=CBG J5FM D9F G95GCB 89D9B8=B; CB @C75@ K=B8 GD998
5B8 8=F97H=CB 5B8 H<9F9:CF9 H<9 8=F97H=CB C: <=;< *' 7CB79BHF5H=CBG
F98 7C@CF7C898 F9DF9G9BH H<9 89G9FH 8IGH GCIF79 CF =; 9 H<9
7CB79BHF5H=CBG =B H<9 K=B8 GD9988=F97H=CB 6=BG79@@G K9F9 AI@H=D@=98
6M :F9EI9B7M C: C77IFF9B79 5B8 F9DF9G9BH H<9 8CA=B5BH GCIF79G C:
*' CJ9F H<9 K<C@9 GHI8M D9F=C8 5B8 @9GG H<9 - 7CBHF=6IH=CB .<9
K9=;<H98 7CB79BHF5H=CBG 6=J5F=5H9 DC@5F D@CHG  =; 9 G<CK 5 69HH9F
F9DF9G9BH5H=CB C: H<9 5=F A5GG GCIF79G H<5H 8CA=B5H9 H<9 A95B 7CB79B
HF5H=CBG F97CF898 =B 5 DF9J=CIG GHI8M 'CINCIF=89G 9H 5@ 
=; 58 ' G<CK H<5H *' @9J9@G =B MDFIG K9F9 GHFCB;@M =BWI
9B798 6M K=B8G CF=;=B5H=B; :FCA -- F9;=CBG IB89F A98=IA @9J9@ K=B8
GD998 7CB8=H=CBG K=H< GCA9 G95GCB5@ J5F=5H=CBG -D97=:=75@@M -CIH<
(CFH< :F=75 K=B8G <58 5 GHFCB;9F 9::97H =B H<9 K=BH9F 5B8 GDF=B; -
F56=5B *9B=BGI@5 5B8 -1 (CFH< :F=75 K=B8G <58 5 GHFCB;9F 9::97H
=B H<9 GIAA9F 5B8 -1-- (CFH< :F=75F56=5B *9B=BGI@5 K=B8G =B
H<9 :5@@ "CK9J9F =B H<9 56G9B79 C: G9J9F9 - 9J9BHG  =; 9' H<9
*' 7CB79BHF5H=CBG K9F9 ACGH@M 5::97H98 6M H<9 1-1 IB89F J9FM @CK
K=B8 GD998 7CB8=H=CBG 6IH 5=F A5GG9G :FCA ( HC (1 K9F9 BCH :CIB8 HC
69 7CAACB :CF H<9 =G@5B8 K<=7< =G =B 5;F99A9BH K=H< H<9 K=B8 7@=A5
HC@C;M C: H<9 =G@5B8 $57CJ=89G 9H 5@ 
%& K5G <=;<@M 5::97H98 6M K9GH9F@M K=B8G IB89F A98=IA K=B8
GD998 7CB8=H=CBG  =; 9 %& .5?=B; =BHC 577CIBH H<9 G95GCB5@ 9::97H
*' GCIF79G K9F9 :FCA GCIH< =B H<9 :5@@ 5B8 GDF=B; 95GH 5B8 -- =B
H<9 K=BH9F 5B8 :FCA K9GH ID HC H<9 95GH =B H<9 GIAA9F %& =G <=;<@M
5::97H98 6M (CFH< :F=75 8IGH 9GD97=5@@M =B H<9 GIAA9F
#B - H<9 CF=;=B C: *' 8CA=B5BH GCIF79G J5F=98 G=;B=:=75BH@M 6M
G95GCB  =; 58 "=;< *' @9J9@G =B K=BH9F K9F9 5::97H98 6M -1 DF9
J5=@=B; K=B8G BCFH< K=B8G =B H<9 GDF=B; A5=B@M K9GH 5B8 =B GCA9 75G9G
GCIH< 5B8 95GH K=B8G =B H<9 GIAA9F K<=@9 =B :5@@ H<9 95GH9F@M K=B8G
7@95F@M HF5BGDCFH98 <=;< *' 7CB79BHF5H=CBG IF=B; GDF=B; H<9 DF9
J5=@=B; K=B8G =B H<9 F9;=CB 5F9 9=H<9F K9GH CF 95GH "CK9J9F H<9 K=B8G
H<5H 5F9 HF5BGDCFH=B; 89G9FH 8IGH 8IF=B; H<=G G95GCB F957< H<9 7=HM C:
99F -<9J5 A5=B@M :FCA H<9 BCFH< K=H<  C: H<9 8IGH CF=;=B5H=B; :FCA
H<9 -5<5F5 9G9FH #B 7CB8=H=CBG @9GG 5::97H98 6M - 9J9BHG =B -  =;
9- K9 C6G9FJ98 HKC DF9J5=@=B; 7CB8=H=CBG C: *' @9J9@G BCFH<K9GH
K=B8G C: A98=IA K=B8 =BH9BG=HM 5B8 G97CB85F=@M H<9 9::97H C: 95GH9FB
K=B8G K=H< 5@GC A98=IA K=B8 =BH9BG=HM
 ..5#, 6#2+#$+,+49 /( &'3'24 &534 34/2-3 %*#2#%4'2+34+%3
  (2'15'.%9
.56@9  G<CKG H<9 5BBI5@ =B7=89B79 C: - D9F G=H9 J9B H<CI;<
' <58 H<9 <=;<9GH BIA69F C: =89BH=V98 - CB@M  C: H<9A K9F9
9L7998=B; H<9 / *' 85=@M @=A=H C:  μ;A DFC656@M 8I9 HC H<9 :F9
EI9B7M C: @CK =BH9BG=HM 9J9BHG CF 8I9 HC H<9 :57H H<5H 8IGH 59FCGC@ K5G
HF5J9@@=B; =B H<9 IDD9F 5HACGD<9F=7 @9J9@G 56CJ9 MDFIG :CF G9J9F5@ C:
H<9 - #B %& 5B8 -  5B8  C: H<9 HCH5@ - =89BH=V98 K9F9
9L7998=B; H<9 / *' 85=@M @=A=H F9GD97H=J9@M
19 C6G9FJ98 5 <=;< J5F=56=@=HM =B H<9 :F9EI9B7M C: - 8IF=B; H<9
@5GH 897589 "CK9J9F H<9 M95F  K5G H<9 M95F K=H< H<9 <=;<9GH
BIA69F C: - C77IFF9B79 5H 5@@ H<F99 G=H9G #B ' H<9 :F9EI9B7M
Table 2
-IAA5FM GH5H=GH=7G A95B U G8 C: *' IGH) 5B8 A9H9CFC@C;=75@ J5F=56@9G :CF H<9 GHI8M D9F=C8  =B ' %& 5B8 - GH5H=CBG @@ 5@@ 85MG (- (CB9G9FH IGH -HCFA
85MG - 9G9FH IGH -HCFA 85MG
05F=56@9 ' %& -
B  
B  
B  
B  
B  
B  
B  
B   -B  
*' μ;
 U   U  = U  = U   U  = U  = U   U  = U  =
IGH)  U   U  = U  = U   U  = U  = U   U  = U  =
.9ADS  U   U  = U  = U   U  = U  = U   U   U 
,"  U   U  = U  = U   U  = U  = U   U  = U  =
1-AG  U   U  = U  = U   U  = U  = U   U  = U  =
=D05@I9 C: 4H9GH 
%*+,,'/3'4#, %+'.%'/(4*' /4#,.6+2/.-'.4 888 8888 
Fig. 3. =J5F=5H9 DC@5F D@CHG C: *' 7CB79BHF5H=CBG μ;A D9F G95GCB :CF ' %& 5B8 - IB89F 8=::9F9BH G95GCBG
Table 3
BBI5@ BIA69F C: - =89BH=V98 6M H<9 ,.F9;F9GG=CB 7CA6=B5H=CB G7<9A9
            .CH5@
-             
-K=H<*'   μ;A             
'5BI5@@M 5 
-             
-K=H<*'   μ;A            
'5BI5@@M 5           
-             
-K=H<*'   μ;A             
'5BI5@@M 5            
55MG K=H< A=GG=B; 85H5 C: *' CF CH<9F J5F=56@9G C: H<9 FI@9G K9F9 9L5A=B98 65G98 CB IGH) 5B8 G5H9@@=H9 =A5;9G
C: - K=H< *' 9L7998=B; H<9  μ;AK5G 5@GC <=;< 69:CF9 
K=H< 5 G97CB8 D95? =B  :H9F  H<9 :F9EI9B7M K5G @CK9F 5B8
F9A5=B98 F9@5H=J9@M GH56@9 #B %& K9 C6G9FJ98 5 F9@5H=J9@M 7CBGH5BH
BIA69F C: - K=H< GDCF58=7 D95?G =B 79FH5=B M95FG 9:CF9  H<9
- 56CJ9  μ;AK9F9 CB@M :9K 6IH H<5H =G DFC656@M 6975IG9 C:
H<9 <=;< D9F79BH5;9 C: A=GG=B; 85H5 #B - H<9 :F9EI9B7M G99AG HC 69
<=;<9F 69HK99B M95FG  K=H< H<9 9L79DH=CB C:  5B8 
:CF K<=7< K9 <58  A=GG=B; 85H5 :CF *' 5B8 @CK9GH =B 
#B CF89F HC =89BH=:M 5BM GH5H=GH=75@@M G=;B=:=75BH @=B95F HF9B8 C: - :F9
EI9B7M K9 5DD@=98 H<9 .<9=@-9B 9GH=A5HCF 5B8 '5BB%9B85@@ H9GH C:
G=;B=:=75B79 .<9 CB@M GH5H=GH=75@@M G=;B=:=75BH @=B95F HF9B8 K9 :CIB8 K5G
5 8CKBHF9B8 :CF ' - :F9EI9B7M  -M95F DJ5@I9  
6IH H<9 HF9B8 K5G BC @CB;9F GH5H=GH=75@@M G=;B=:=
75BH K<9B H<9 5B5@MG=G :C7IG98 CB - K=H< *' 85=@M 5J9F5;9 56CJ9
 μ;A
*F9J=CIG GHI8=9G =B H<9 F9;=CB :CIB8 5B =B7F95G=B; HF9B8 CJ9F '
8IF=B;  =B 5GGC7=5H=CB K=H< 7<5B;=B; GMBCDH=7 7CB8=H=CBG
!5BCF 9H 5@  5B8 5B =B7F95G=B; HF9B8 CB - :F9EI9B7M 8IF
=B;  5B8  =B MDFIG 7<=@@9CG 9H 5@  6IH 5 897F95G9
=B H<9 - 5BBI5@ BIA69F =B H<9 '98=H9FF5B95B 69HK99B M95FG 
5B8  !?=?5G 9H 5@  8IF=B;  %F5GBCJ 9H 5@
5 =B #GF59@ 5B8 69HK99B  5B8  =B H<9 BCFH< 79BHF5@ 5F95
C: H<9 #69F=5B *9B=BGI@5 57<CFFC 9H 5@ 
.<9 G95GCB5@ =B7=89B79 C: - =B D9F79BH5;9 C: 85MG D9F M95F =G
G<CKB =B =;  .<9F9 =G 5 G=;B=:=75BH G95GCB5@ J5F=5H=CB C: - 57FCGG
H<9 M95FG -DF=B; =G H<9 <=;< 8IGH G95GCB :CF 5@@ G=H9G ' 
%*+,,'/3'4#, %+'.%'/(4*' /4#,.6+2/.-'.4 888 8888 
Fig. 4. BBI5@ =B7=89B79 C: -  D9F G95GCB 5B8 M95F 5H H<9 H<F99 G=H9G
%&  -  #B ' O C: - =B   5B8
 C77IFF98 8IF=B; GDF=B; "CK9J9F =B M95FG 69:CF9   C:
- C77IFF98 =B H<9 GIAA9F G9BG G@CD9GIAA9F  DJ5@I9  
#B %& - K9F9 C6G9FJ98 ACF9 C:H9B =B H<9 :5@@ O C: -
CJ9F H<9 @5GH :CIF 7CBG97IH=J9 M95FG  "CK9J9F BC GH5H=GH=
75@@M G=;B=:=75BH G95GCB5@ HF9B8 K5G :CIB8 #B - H<9F9 K9F9 5@GC ACF9
- 8IF=B; :5@@  :CF H<9 @5GH VJ9 M95FG  6IH @9GG
8IF=B; H<9 K=BH9F  CJ9F  G9BG G@CD9K=BH9F 
DJ5@I9   DF9J=CIG 5B5@MG=G :CF #GF59@ 8IF=B; 
!5BCF 9H 5@  5B8  %F5GBCJ 9H 5@ 5 M95FG
:CIB8 5B =B7F95G9 =B K=BH9F 8IGH 9J9BHG
  +.4'.3+49
J9B H<CI;<  K5G H<9 M95F K=H< H<9 <=;<9GH BIA69F C: -
=B H<9 F9;=CB H<=G K5G BCH 5@GC H<9 M95F K=H< H<9 GHFCB;9GH 9J9BHG #B
' H<9 5BBI5@ A98=5B -*' 7CB79BHF5H=CB @9J9@G K9F9 O μ;
A K=H< 5 A5L=AIA J5@I9 =B   μ;A  =;  "CK9J9F =B
 5B8  H<9F9 K9F9 ACF9 - 57H=B; 5G CIH@=9FG=B H<9 5BBI5@
- 8=GHF=6IH=CB O μ;A 5B8 H<9 A5L=AIA <F -*'
5J9F5;9 C:  μ;AK5G C6G9FJ98 8IF=B; 5 -5<5F5B 8IGH 9D=GC89
=B   <CIF@M*'  μ;A .<9 IGH)
5BBI5@ A98=5B J5@I9G F5B;98 :FCA  M95F  HC  M95F 
.<9 <=;<9GH IGH) J5@I9G K9F9 C6G9FJ98 =B  5B8  K=H< 5
<F A5L=AIA =B   <F IGH) 
#B %& H<9 5BBI5@ A98=5B -*' 7CB79BHF5H=CBG K9F9 69HK99B
 M95F  5B8  M95F  μ;A .<9 M95F K=H< ACGH
<=;< =BH9BG=HM 9J9BHG K9F9 C6G9FJ98 =B  5B8 H<9 A5L=AIA <F
*' 5J9F5;9 C:  μ;A=B   <CIF@M*'
 μ;A K<=7< 7CFF9GDCB8G HC H<9 G5A9 -5<5F5B 8IGH
9D=GC89 H<5H ' 9LD9F=9B798 .<9 <=;<9GH IGH) J5@I9G :CF %& K9F9
C6G9FJ98 =B   5B8  K=H< 5 <F A5L=AIA =B  
 <F IGH) 
#B - *' 5BBI5@ A98=5B @9J9@G 8IF=B; - J5F=98 69HK99B 
M95F  5B8  M95F M95F K=H<  A=GG=B; 85H5 μ;A
#B  5 M95F K=H< G5H=G:57HCFM 5J5=@56=@=HM C: 85H5 A98=5B -*'
K5G  μ;A .<9 M95FG  5B8  9LD9F=9B798 H<9 ACGH :F9
EI9BH =BH9BG9 9J9BHG .<9 ACGH 9LHF9A9 9J9BH K5G BCH C6G9FJ98 =B 
5G =H K5G =B H<9 75G9 C: ' 5B8 %& 9J9B - K5G =BWI9B798 6M H<5H
8IGH 9J9BH   < *'  μ;A .<9 A5L=AIA 85=@M
*' 7CB79BHF5H=CB :CF - K5G C6G9FJ98 =B  K=H< 5 <F 5J9F
5;9 C:  μ;A <CIF@M*'  8IF=B; 5
H<F9985M -5<5F5B 8IGH 9D=GC89 .<9 <CIF@M *' 7CB79BHF5H=CB H<CI;<
F957<98 H<9 A5L=AIA  μ;A =B   8IF=B; 5B
O<CIF 8IGH 9D=GC89 IGH) F5B;98 :FCA  =B  HC  =B
 K=H< H<9 <=;<9GH 85=@M IGH) 5J9F5;9 J5@I9 =B  
 <F IGH)  )B CJ9F5@@ =H G99AG H<5H :CF -  K5G H<9
M95F K=H< H<9 ACGH :F9EI9BH 5B8 ACGH =BH9BG9 8IGH 9D=GC89G )B H<9 G5A9
H=A9 =H K5G H<9 8F=9GH M95F C: H<9 897589 K=H< HCH5@ 5BBI5@ DF97=D=H5H=CB
C:  AA  AA =B  AA =B 
.=A9 G9F=9G 5B5@MG=G C: - =BH9BG=HM *' IGH) F9J95@98
H<5H H<9 HF9B8 =G BCH @=B95F =; G<CKG H<9 89J=5H=CB :FCA H<9 *'
5B8 IGH) 85=@M A95B CJ9F H=A9 75@9B85F 85H9 C: - 19 C6
G9FJ98 5 BCBACBCHCB=7 HF9B8 CJ9F H=A9 #B ' -*' @9J9@G K9F9
56CJ9 H<9 5J9F5;9 95F@M =B  DFC656@M 8I9 HC H<9 ACGH G9J9F9
9J9BH C: H<9 897589 F957<=B; 5 A=B=AIA 8IF=B; @5H9  HC 95F@M
 5B8 :C@@CK=B; 5 F9@5H=J9@M GH56@9 *' HF9B8 5:H9FK5F8G K=H< GCA9
%*+,,'/3'4#, %+'.%'/(4*' /4#,.6+2/.-'.4 888 8888 
Fig. 5. CLD@CHG C: *' 7CB79BHF5H=CBG μ;A 5B8 IGH) 8IF=B; - D9F M95F :CF ' %& 5B8 -
Fig. 6. .=A9 HF9B8G C: -*' 5B8 -IGH) :CF ' %& 5B8 - GH5H=CB 5G<98 @=B9G F9DF9G9BH H<9  7CBV89B79 =BH9FJ5@G :CF H<9 9GH=A5H9 5B8 A5F?G CB L5L=G F9DF9G9BH H<9
9B8 C: H<9 M95F
GA5@@9F GDCF58=7 D95?G G=A=@5F D5HH9FB K5G C6G9FJ98 :CF %& GH5H=CB
IGH) :C@@CK98 5 8=::9F9BH D5HH9FB H<5B H<9 CB9 C: *' 5H 6CH< GH5
H=CBG #B ' IGH) G99A98 HC 69 =B7F95G=B; :FCA  IBH=@ 
K<9B =H F957<9G 5 D@5H95I IGH) =B %& :C@@CKG 5 <=;< J5F=56=@=HM
K=H< 5 6=; 8FCD =B M95FG  HC  #B - *' @9J9@G G99A98 HC =B
7F95G9 :FCA  H<FCI;<  5B8 897F95G98 5:H9F H<5H K=H< 5 G97CB8
GA5@@9F D95? =B  #B  5 D95? :CF IGH) K5G 5@GC C6G9FJ98
-=A=@5F D5HH9FBG K9F9 C6G9FJ98 :CF <CIF@M *' 8IF=B; -  =; -
.<9 VB8=B;G :FCA ' GHI8=9G CB 8IGH =BH9BG=HM J5FM 6M @C75H=CB
GHI8M D9F=C8 5B8 A9H<C8C@C;M *9M 9H 5@  :CIB8 BC G=;B=:=
75BH HF9B8 C: :F=75B 8IGH 7CBHF=6IH=CB HC *' CJ9F 79BHF5@ 5B8 GCIH<
9FB '98=H9FF5B95B 5F95G =B7@I8=B; !F9979 5B8 MDFIG :FCA  HC
 K=H< GDCF58=7 5BBI5@ D95?G  897F95G=B; HF9B8 CB *' 7CB
HF=6IH=CB K5G C6G9FJ98 :FCA  CF  H<FCI;<  =B K9GH9FB
5B8 79BHF5@ '98=H9FF5B95B 5F95G %F5GBCJ 9H 5@ 5 9L5A=B98
- 9J9BHG =B H<F99 7=H=9G =B #GF59@ :CF H<9 M95F D9F=C8 
%*+,,'/3'4#, %+'.%'/(4*' /4#,.6+2/.-'.4 888 8888 
5B8 :CIB8 H<5H G=B79  - 9J9BHG 6975A9 ACF9 G9J9F9 19 8=8 BCH
=89BH=:M H<9 G5A9 D5HH9FB =B CIF 5B5@MG9G .<=G =G DFC656@M 8I9 HC H<9 :57H
H<5H H<9 D9F=C8 C: =BJ9GH=;5H=CB =G :FCA  CBK5F8G 5G K9@@ 5G H<9 :57H
H<5H 5 8=::9F9BH GH5H=GH=75@ 5DDFC57< K5G 58CDH98 J5B 9H 5@ 
G<CK98 H<5H GIF:579 K=B8 D5HH9FBG 5F9 F9GDCBG=6@9 :CF H<9 J5F=5H=CB C:
(CFH< :F=75 8IGH 9A=GG=CBG  <=GHCF=7 F95B5@MG=G :FCA  HC 
:FCA H<9 G5A9 5IH<CFG ;5J9 5 GH5H=GH=75@@M G=;B=:=75BH 8CKBHF9B8 =B H<9
8IGH 9A=GG=CBG 5GGC7=5H98 K=H< 5B =B7F95G9 =B ;F99B<CIG9 ;5G 9A=GG=CBG
#B 19GH :F=75 -5<9@ 5B =B7F95G9 C: 8IGH 9A=GG=CBG K5G C6G9FJ98 CJ9F
H<9 @5GH 897589G GH5FH=B; :FCA @5H9 G 5B8 =H K5G F9@5H98 HC 8FCI;<HG
5B8 HC H<9 =B7F95G9 =B H<9 H<F9G<C@8 K=B8 J9@C7=HM :CF 8IGH 9BHF5=BA9BH
!CI8=9  'C89@ DFC>97H=CBG GI;;9GH 5B =B7F95G9 =B - 9J9BHG
=BH9BG=HM &9@=9J9@8 9H 5@  <CK9J9F H<9 :IHIF9 C: - K=@@ 89
D9B8 CB 5BH<FCDC;9B=7 DF9GGIF9 CB 89G9FH GIF:579G B5HIF5@ 7@=A5H=7 J5F=
56=@=HM 9; =B H<9 @ (=RC -CIH<9FB )G7=@@5H=CB CF H<9 (CFH< H@5BH=7
)G7=@@5H=CB 5B8 7@=A5H9 7<5B;9 !CI8=9 
  42'.& /6'2 4*' ,#34 47/&'%#&'3 
!FCIB8 *' 85H5 :FCA MDFIG 5F9 5J5=@56@9 G=B79  5B8 H<9M
K9F9 DF9J=CIG@M IG98 HC GHI8M *' @9J9@G 5B8 - HF9B8G =B MDFIG
:CF H<9 D9F=C8  7<=@@9CG 9H 5@  19 H<9F9:CF9 9L
D5B898 CIF HF9B8G 5B5@MG=G 5B8 9L5A=B9 H<9 - =B7=89B79 CJ9F H<9
@5GH  M95FG :CF MDFIG "CK9J9F IGH) =G 5J5=@56@9 :FCA M95F
 5B8 CBK5F8G 5B8 H<9F9:CF9 K9 K9F9 BCH 56@9 HC IG9 =H :CF H<=G
5B5@MG=G #B CF89F HC 9BGIF9 H<5H - K9F9 =89BH=V98 =B H<9 G5A9 K5M
:CF 5@@ H<9 M95FG K9 IG98 H<9 G5A9 HCC@G :FCA H<9 DF9J=CIG 5B5@MG=G
19 VFGH =89BH=V98 GIGD97H98 85MG C: <=;< D5FH=7@9 @9J9@G 65G98 CB =
H<9 H< D9F79BH=@9 C: *' 85H5 :FCA (=7CG=5 IF65B *'   μ;
A 5B8 M=5'5F=B5 2M@=5HCG 657?;FCIB8 *'   μ;A GH5H=CB
5B8 == H<9 7CA6=B5H=CB C: <=;< )  5B8 @CK B;GHFCA 9L
DCB9BH  H<5H G=;B=V9G <=;< @C58 C: 7C5FG9 D5FH=7@9G =B H<9 5=F
5FB565 5B8 !C66= !?=?5G 9H 5@  .<9 ) 5B8
B;GHFCA 9LDCB9BH 85H5 K9F9 C6H5=B98 :FCA ')#- 'C89F5H9 ,9GC@I
H=CB #A5;=B; -D97HFCF58=CA9H9F .,, 99D @I9 D@5H:CFA 5H 5 GD5
H=5@ F9GC@IH=CB C: S Y S O ?A Y  ?A 5J5=@56@9 G=B79 
.<9 85H5 K9F9 8CKB@C5898 :FCA H<9 !=CJ5BB= CB@=B9 85H5 GMGH9A 89
J9@CD98 5B8 A5=BH5=B98 6M H<9 (- !- #- <HHDG;=CJ5BB=;G:7
B5G5;CJ;=CJ5BB= - K9F9 H<9B 7CBVFA98 :FCA "3-*&#. 657?HF5
>97HCF=9G -H9=B 9H 5@ ,C@D< 9H 5@  5B8 G5H9@@=H9 =A5;9G
:FCA (- 1CF@8J=9K .<=G A9H<C8C@C;M K5G 5DD@=98 HC MDFIG 85H5
:CF H<9 D9F=C8  H<9 M95F K<=7< G5H9@@=H9 85H5 GH5FH98 HC 69 5J5=@56@9
H<FCI;< 
19 =89BH=V98  - =B MDFIG 8IF=B;  5B8  M95FG
K<9F9 :CF ACF9 H<5B <5@: C: 9J9BHG B   *' @9J9@G 9L799898
H<9 85=@M / @=A=H C:  μ;A5H H<9 IF65B G=H9 .<9 5BBI5@ =B7=89B79
K5G K=H<=B H<9 GA5@@ F5B;9 C:  - D9F M95F K=H< HKC D95?G =B
 B   5B8  B   :C@@CK98 6M HKC M95FG C: @CK =B7=
89B79 C: - =B  B   5B8  B    =;  "CK9J9F
9J9B H<9F9 K5G 5 G=;B=:=75BH =B7F95G=B; HF9B8 :FCA  HC  G9BG
G@CD9   DJ5@I9   BC GH5H=GH=75@@M G=;B=:=75BH HF9B8 K5G C6
G9FJ98 :FCA  HC  G9BG G@CD9   DJ5@I9   19 8=8 BCH
VB8 5BM GH5H=GH=75@@M G=;B=:=75BH HF9B8 CB G95GCB5@ J5F=56=@=HM C: - :F9
EI9B7M 5B8 =BH9BG=HM  =; -
#B MDFIG H<9 8IGH *' 5BBI5@ A98=5B F5B;98 :FCA  μ;A
 HC  μ;A μ;A5H H<9 (=7CG=5 IF65B G=H9 5B8 :FCA
 μ;A HC  μ;A μ;A5H H<9 FIF5@ 5F95  =;
 .<9 <=;<9GH <F A5L=AIA 8IGH *' @9J9@ K5G C6G9FJ98 =B 
IF65B  μ;A FIF5@  μ;A 5B8 H<9 @CK9GH =B  IF
65B  μ;A 5B8  FIF5@  μ;A
IF=B; H<9 G5A9 H=A9 H<9 5BBI5@ HCH5@ DF97=D=H5H=CB 5H H<5@5GG5 '9
H9CFC@C;=75@ -H5H=CB =B (=7CG=5 F5B;98 :FCA  AA =B  HC  AA
=B  HCH5@ DF97=D=H5H=CB =G 75@7I@5H98 :CF 75@9B85F M95FG .<9 8F=9GH
M95F :FCA H<9 @5GH  M95FG K5G 5@GC H<9 M95F K=H< H<9 <=;<9GH :F9EI9B7M
C: - 9L7998=B; H<9 / 85=@M @=A=H 5B8 <=;<9GH 5BBI5@ -*' 7CB
79BHF5H=CB :CF H<9 IF65B 5F95 C: (=7CG=5
.<9 8IGH *' 5BBI5@ 5J9F5;9 A95GIF98 5H H<9 IF65B 5B8 FIF5@
GH5H=CB =B H<9 5F95 C: (=7CG=5 G<CK98 5 GH5H=GH=75@@M G=;B=:=75BH 89
7F95G9 *'IF65B G9BG G@CD9   DJ5@I9   *'FIF5@ G9BG
G@CD9   DJ5@I9   6IH :CF H<9 IF65B G=H9 H<9 897F95G9 K5G
BCH ACBCHCB=7  =;  .<9 897F95G9 K5G C6G9FJ98 8IF=B; H<9 GDF=B;
G95GCB 5H 6CH< G=H9G *' IF65B G9BG G@CD9   DJ5@I9  
*'FIF5@ G9BG G@CD9   DJ5@I9   =; -
  3/52%' /( /2+)+.
.<9 =BWI9B79 C: H<9 HKC A5>CF 89G9FH 8IGH GCIF79G C: 5GH9FB
'98=H9FF5B95B CB H<9 5BBI5@ @9J9@G K5G 9L5A=B98 :FCA "3-*&#. HF5
>97HCF=9G .56@9  'CGH C: - CF=;=B5H98 :FCA -5<5F5B 89G9FH 5B8
G97CB8@M :FCA H<9 89G9FHG =B H<9 '=88@9 5GH B-5<5F5B 
B'  B'-5<5F5B  %& =G =BWI9B798 5@ACGH 9L7@I
G=J9@M :FCA -5<5F5B 89G9FH B-5<5F5B   B'   B'-5<5
F5B   5B8 =H =G H<9F9:CF9 BCH :IFH<9F 8=G7IGG98 =B H<=G G97H=CB  GA5@@
BIA69F C: - K5G =BWI9B798 6M 5=F A5GG9G CF=;=B5H=B; 6M 6CH< -5<5
F5B 5B8 '=88@9 5GH9FB 89G9FHG G=AI@H5B9CIG@M "CK9J9F 8IF=B; M95FG
 5B8  =B MDFIG 5B8 M95F  =B #GF59@ H<9 BIA69F C: -
CF=;=B5H=B; :FCA ' 89G9FHG K5G =B7F95G98 )B 5J9F5;9 - CF=;=B5H=B;
:FCA (CFH< :F=75 5B8 '=88@9 5GH K9F9 C: <=;<9F =BH9BG=HM =B ' K<=@9
H<=G K5G H<9 75G9 :CF 9D=GC89G CF=;=B5H=B; :FCA H<9 -5<5F5B 89G9FH =B -
&=B95F HF9B8G 5B5@MG=G G<CK98 H<5H - CF=;=B5H=B; GC@9@M :FCA -5
<5F5 G9BG G@CD9   DJ5@I9   CF :FCA 6CH< -5<5F5B
Fig. 7. (IA69F C: =89BH=V98 89G9FH 8IGH 85MG 6@I9 5B8 89G9FH 8IGH 85MG K=H< *' 7CB79BHF5H=CB 56CJ9  μ;A5H H<9 IF65B G=H9 F98 =B MDFIG D9F M95F  CF =BH9FDF9H5H=CB C: H<9
F9:9F9B79G HC 7C@CF =B H<=G V;IF9 @9;9B8 H<9 F9589F =G F9:9FF98 HC H<9 K96 J9FG=CB C: H<=G 5FH=7@9
 %*+,,'/3'4#, %+'.%'/(4*' /4#,.6+2/.-'.4 888 8888 
Fig. 8. CLD@CHG C: *' 7CB79BHF5H=CBG μ;A 8IF=B; - 5H H<9 MDFIG IF65B 5B8 FIF5@ 5F95G D9F M95F
Fig. 9. .=A9 HF9B8G C: -*' :CF MDFIG IF65B GH5H=CB 5G<98 @=B9G F9DF9G9BH H<9
 7CBV89B79 =BH9FJ5@G :CF H<9 9GH=A5H9 5B8 A5F?G CB L5L=G F9DF9G9BH H<9 9B8 C: H<9
5B8 ' 89G9FHG HC;9H<9F -5<5F5B' G9BG G@CD9  
DJ5@I9   =B MDFIG 5B8 :FCA DIF9 -5<5F5B 8IGH =B #GF59@ G9BG
G@CD9   DJ5@I9   897F95G98 CJ9F M95FG (C G=;B=:=75BH 5B
BI5@ HF9B8 K5G :CIB8 :CF H<9=F =BH9BG=HM  =; -
4. Conclusions
#B H<=G GHI8M K9 9L5A=B98 - 9J9BHG =B H<F99 5GH9FB '98=H9F
F5B95B 5F95G MDFIG F9H9!F9979 #GF59@ CJ9F 5 M95F D9F=C8 
H<FCI;<  .<9 IG9 C: ;FCIB8 5B8 7C@IAB5F 59FCGC@ 85H5 A589 DCG
G=6@9 H<9 =89BH=V75H=CB 5B8 7<5F57H9F=N5H=CB C: @CK 5B8 <=;< =BH9BG=HM
9J9BHG 5MG K=H< <F 5J9F5;9 *' 7CB79BHF5H=CB 56CJ9 O μ;A
K9F9 :CIB8 HC 69 5 G=;B=:=75BH =B8=75HCF C: - :CF H<9 657?;FCIB8
G=H9G C: MDFIG  μ;A 5B8 F9H9  μ;A "=;<9F H<F9G<C@8G
K9F9 :CIB8 :CF 99F -<9J5 89D9B8=B; CB H<9 G95GCB :5@@ 5B8 GDF=B;
*'   μ;A K=BH9F 5B8 GIAA9F *'   μ;A
#BH9F5BBI5@ J5F=56=@=HM 5B8 GD5H=5@ 8=::9F9B79G K9F9 C6G9FJ98 :CF
8=::9F9BH 9J9BH 7<5F57H9F=GH=7G 'CF9 :F9EI9BH 9J9BHG K9F9 C6G9FJ98 =B
MDFIG 6IH #GF59@ 9LD9F=9B798 <=;<9F =BH9BG=HM 9J9BHG @@ H<F99 5F95G
K9F9 5H H=A9G G=AI@H5B9CIG@M 5::97H98 6M - 5B8 H<=G C77IFF98 :CF
 C: H<9 - 5H 957< G=H9 #GF59@ 5B8 MDFIG <58 ACF9 7CA
ACB - 5G H<9M 5F9 6CH< =BWI9B798 :FCA (CFH< :F=75 5B8 F56=5B
*9B=BGI@5 89G9FHG F9H9 =G F5F9@M =AD57H98 6M 89G9FHG :FCA H<9 5GH #B
H9F5BBI5@ J5F=56=@=HM C: - :F9EI9B7M 5B8 =BH9BG=HM :C@@CK98 5 F9@5
H=J9@M GH958M HF9B8 .<9 5BBI5@ D5HH9FB C: - :F9EI9B7M K5G G=A=@5F :CF
%*+,,'/3'4#, %+'.%'/(4*' /4#,.6+2/.-'.4 888 8888  
Table 4
BBI5@ BIA69F C: - *' μ;A  5B8 IGH) @9J9@G 6M 89G9FH 5F95 C: CF=;=B
            .CH5@B *' μ;A IGH)
-5<5F5B               U   U 
'              U   U 
-5<5F5B'               U   U 
-5<5F5B               U   U 
'              U   U 
-5<5F5B'               U   U 
5@@ H<F99 G=H9G F957<=B; 5 D95? =B  "CK9J9F D95?G C: CH<9F -
7<5F57H9F=GH=7G *'  <5J9F5;9 IGH) <CIF@M 5B8 85=@M *'
A5L=A5 G95GCB5@=HM K9F9 C6G9FJ98 =B 8=::9F9BH M95FG A5=B@M 8I9 HC H<9
=BH9F5BBI5@ J5F=56=@=HM C: @C75@ F9;=CB5@ 5B8 ;@C65@ 7@=A5H=7 7CB8=H=CBG
#B #GF59@ 5B8 MDFIG H<CI;< H<9 M95F K=H< H<9 <=;<9GH - :F9EI9B7M
5B8 =BH9BG=HM K5G H<9 M95F K=H< H<9 @CK9GH DF97=D=H5H=CB 5H5 5J5=@56=@
=HM F9GHF=7H98 CIF >C=BH 5B5@MG=G :CF H<9 H<F99 G=H9G HC H<9 @5GH 897589 CB@M
"CK9J9F 5B5@MG=G :CF MDFIG 69B9VH98 :FCA 5J5=@56@9 85H5 :CF  M95FG
H H<=G G=H9 - :F9EI9B7M :C@@CK98 5 F9@5H=J9@M GH958M HF9B8 CJ9F H<9
M95FG 6IH G95GCB5@=HM <5G 7<5B;98 89ACBGHF5H=B; 5 897F95G9 C: 8IGH =B
.F9B8 5B5@MG9G :CF - 7<5F57H9F=GH=7G 8=::9F 57FCGG GHI8=9G @C75H=CBG
5B8 H=A9 D9F=C8G G K9 <5J9 G99B =B H<=G KCF? H<9 7<C=79 C: H<9 GHI8M
D9F=C8 IB89F 9L5A=B5H=CB 75B =AD57H H<9 F9GI@HG IHIF9 GHI8=9G 5F9
B99898 CB H<9 7<5B;9G C: - 7<5F57H9F=GH=7G CJ9F G9J9F5@ 897589G 5B8
=B F9@5H=CB HC 7@=A5H9 7<5B;9 =B7@I8=B; GMBCDH=7G75@9 7CB8=H=CBG
Declaration of competing interest
.<9 5IH<CFG 897@5F9 H<5H H<9M <5J9 BC ?BCKB 7CAD9H=B; VB5B7=5@ =B
H9F9GHG CF D9FGCB5@ F9@5H=CBG<=DG H<5H 7CI@8 <5J9 5DD95F98 HC =BWI9B79
H<9 KCF? F9DCFH98 =B H<=G D5D9F
.<=G KCF? K5G GIDDCFH98 6M H<9 IFCD95B /B=CB K=H<=B H<9 :F5A9
KCF? C: H<9 &#  ' *FC;F5A IB89F H<9 !F5BH ;F99A9BH &# 
Appendix A. Supplementary data
-IDD@9A9BH5FM 85H5 HC H<=G 5FH=7@9 75B 69 :CIB8 CB@=B9 5H <HHDG8C=
7<=@@9CG - J5BG $- 3=5@@CIFCG *% %@95BH<CIG - -7<K5FHN $ %CIHF5?=G * 
*' 7CB79BHF5H=CB @9J9@G 5H 5B IF65B 5B8 657?;FCIB8 G=H9 =B MDFIG H<9 =AD57H C:
IF65B GCIF79G 5B8 8IGH GHCFAG $ =F 15GH9 '5B5;9 GGC7  
7<=@@9CG - @)N5=F=  @5<A58  !5FG<=7?  (9CD<MHCI ' CI<5AF5 1
35GG=B '  %CIHF5?=G *  7IH9 9::97HG C: 5=F DC@@IH=CB CB ACFH5@=HM 5 M95F
5B5@MG=G =B %IK5=H BJ=FCB #BH  
5FB565  !C66= !*  9FCGC@ G95GCB5@ J5F=56=@=HM CJ9F H<9 '98=H9FF5B95B F9;=CB
5B8 F9@5H=J9 =AD57H C: A5F=H=A9 7CBH=B9BH5@ 5B8 -5<5F5B 8IGH D5FH=7@9G CJ9F H<9 65G=B
:FCA ')#- 85H5 =B H<9 M95F  HACG <9A *<MG  
57<CFFC 0 IF;CG ' '5H9CG  .C@985BC  9BBCIB5 3 .CFF9G  89 FIHCG
' "9F;I985G   #BJ9BHCFM C: :F=75B 89G9FH 8IGH 9J9BHG =B H<9 BCFH<79B
HF5@ #69F=5B *9B=BGI@5 =B  65G98 CB GIBD<CHCA9H9F,)(. 5B8 D5FH=7I
@5H9A5GG'* 85H5 HACG <9A *<MG  
<I8BCJG?M  %CIHF5?=G * %CGH=BG?=  *FC7HCF -* !5FG<=7?   -D5
H=5@ 5B8 H9ADCF5@ J5F=56=@=HM =B 89G9FH 8IGH 5B8 5BH<FCDC;9B=7 DC@@IH=CB =B #F5E
 $ =F 15GH9 '5B5;9 GGC7  
=5DCI@=  '5BCIG5?5G '# 0F5HC@=G - 05G=@5HCI 0 *5H9F5?= - 5=F57<H5F= %
+I9FC@ 2 A5HC  @5GHI9M  %5F5B5G=CI  &I75F9@@=  (5J5 - 5@NC@5=
! !=5B9@@9 0& C@CA6=  @J9G  IGHC8=C  *=C  -DMFCI  %5@@CG !
@9:H<9F=58=G %  #,/-&#  D@IG 9GH=A5H=CB C: B5HIF5@ GCIF79 7CBHF=6IH=CBG
HC IF65B 5A6=9BH 5=F *' 5B8 *' 7CB79BHF5H=CBG =B GCIH<9FB IFCD9  =AD@=75
H=CBG HC 7CAD@=5B79 K=H< @=A=H J5@I9G HACG <9A *<MG  
5@C ' @5GHI9M  ,=DC@@  *9F9N ( '=B;I=@@CB ' +I9FC@ 2 *5B8C@V '
 9H97H=CB C: -5<5F5B 8IGH 5B8 6=CA5GG 6IFB=B; 9J9BHG IG=B; B95FF95@H=A9 =B
H9BG=J9 59FCGC@ CDH=75@ DFCD9FH=9G =B H<9 BCFH<K9GH9FB '98=H9FF5B95B HACG <9A
*<MG  
J5B . @5A5BH  !59H5B= ' !I=7<5F8   .<9 D5GH DF9G9BH 5B8 :IHIF9 C:
:F=75B 8IGH (5HIF9  
@CF9G ,' %5M5 ( G9F ) -5@H5B -  .<9 9::97H C: A=B9F5@ 8IGH HF5BGDCFH CB
*' 7CB79BHF5H=CBG 5B8 D<MG=75@ DFCD9FH=9G =B #GH5B6I@ 8IF=B;  HACG
,9G  
!5BCF  -HIDD  @D9FH *   A9H<C8 HC 89H9FA=B9 H<9 9::97H C: A=B9F5@ 8IGH
59FCGC@G CB 5=F EI5@=HM HACG BJ=FCB  
!5BCF  )G9H=BG?M # -HIDD  @D9FH *  #B7F95G=B; HF9B8 C: :F=75B 8IGH CJ9F
 M95FG =B H<9 95GH9FB '98=H9FF5B95B $ !9CD<MG ,9G HACG 
!9F5GCDCI@CG  %CIJ5F5?=G ! 565G5?5@=G * 0F9?CIGG=G ' *IH5I8 $* '=<5@CDCI
@CG (  )F=;=B 5B8 J5F=56=@=HM C: D5FH=7I@5H9 A5HH9F *' A5GG 7CB79BHF5H=CBG
CJ9F H<9 5GH9FB '98=H9FF5B95B HACG BJ=FCB  
!?=?5G  "5HN=5B5GH5GG=CI ( '=<5@CDCI@CG ( %5HGCI@=G 0 %5N58N=G - *9M $
+I9FC@ 2 .CFF9G )  .<9 F9;=A9 C: =BH9BG9 89G9FH 8IGH 9D=GC89G =B H<9 '98=H9F
F5B95B 65G98 CB 7CBH9ADCF5FM G5H9@@=H9 C6G9FJ5H=CBG 5B8 ;FCIB8 A95GIF9A9BHG H
ACG <9A *<MG  
!CI8=9 -  9G9FH 8IGH 5B8 <IA5B <95@H< 8=GCF89FG BJ=FCB #BH  
#BB9GG  89G ' ;IGHQ*5B5F985  5FFP $ 9B98=7HCK  @97<G7<A=8H '
CA=B;I9N $$ B;9@9B , G?9G " @9AA=B; $ "I=>B9B 0 $CB9G & %=D@=B;
4 '5GG5FH - *5FF=B;HCB ' *9I7< 0" ,5N=B;9F ' ,9AM - -7<I@N ' -IHH=9
'  .<9 '- F95B5@MG=G C: 5HACGD<9F=7 7CADCG=H=CB HACG <9A *<MG 
$57CJ=89G * .<9CD<=@CI  .MAJ=CG - *5G<=5F89G -  1=B8 GH5H=GH=7G :CF
7C5GH5@ GH5H=CBG =B MDFIG .<9CF DD@ @=A5HC@  
%5@=J=H=G ( !9F5GCDCI@CG  0F9?CIGG=G ' %CIJ5F5?=G ! %I6=@5M ( "5HN=5B5GH5G
G=CI ( 05F85J5G # '=<5@CDCI@CG (  IGH HF5BGDCFH CJ9F H<9 95GH9FB '98=H9F
F5B95B 89F=J98 :FCA .CH5@ )NCB9 '5DD=B; -D97HFCA9H9F 9FCGC@ ,C6CH=7 (9HKCF?
5B8 GIF:579 A95GIF9A9BHG $ !9CD<MG ,9GHACG 
%F5GBCJ " %5HF5 # %CIHF5?=G * F=;9F '  CBHF=6IH=CB C: 8IGH GHCFAG HC
*' @9J9@G =B 5B IF65B 5F=8 9BJ=FCBA9BH $ =F 15GH9 '5B5;9 GGC7  
%F5GBCJ " %5HF5 # F=;9F '  #B7F95G9 =B 8IGH GHCFA F9@5H98 *' 7CB79BHF5
H=CBG 5 H=A9 G9F=9G 5B5@MG=G C:  BJ=FCB *C@@IH  
%F5GBCJ " %@CC; # F=;9F ' %5HF5 #  .<9 GD5H=CH9ADCF5@ 8=GHF=6IH=CB C:
D5FH=7I@5H9 A5HH9F 8IF=B; B5HIF5@ 8IGH 9D=GC89G 5H 5B IF65B G75@9 *&C- )B9 
&9@=9J9@8 $ "58>=B=7C@5CI * %CGHCDCI@CI  !=5BB5?CDCI@CG  *CNN9F  .5B5F<H9
' .MF@=G   'C89@ DFC>97H98 <95H 9LHF9A9G 5B8 5=F DC@@IH=CB =B H<9 95GH9FB
'98=H9FF5B95B 5B8 '=88@9 5GH =B H<9 HK9BHMVFGH 79BHIFM ,9; BJ=FCB <5B; 
'=7<59@=89G - JF=D=8CI * %5@@CG !  'CB=HCF=B; 5B8 DF98=7H=B; -5<5F5B 9G9FH
8IGH 9J9BHG =B H<9 95GH9FB '98=H9FF5B95B 195H<9F  
'=7<59@=89G - *5FCB=G  ,9H5@=G  .MAJ=CG   'CB=HCF=B; 5B8 :CF975GH=B; 5=F
DC@@IH=CB @9J9@G 6M 9LD@C=H=B; G5H9@@=H9 ;FCIB865G98 5B8 GMBCDH=7 85H5 9@56CF5H98
K=H< F9;F9GG=CB AC89@G 8J '9H9CFC@
'=88@9HCB ( 3=5@@CIFCG * %@95BH<CIG - %C@C?CHFCB= ) -7<K5FHN $ C7?9FM 1
9AC?F=HCI * %CIHF5?=G *   M95F H=A9G9F=9G 5B5@MG=G C: F9GD=F5HCFM 5B8
75F8=CJ5G7I@5F ACF6=8=HM =B (=7CG=5 MDFIG H<9 9::97H C: G<CFHH9FA 7<5B;9G =B 5=F
DC@@IH=CB 5B8 8IGH GHCFAG BJ=FCB "95@H< 
'CINCIF=89G * %IA5F * (9CD<MHCI '%  GG9GGA9BH C: @CB;H9FA A95GIF9
A9BHG C: D5FH=7I@5H9 A5HH9F 5B8 ;5G9CIG DC@@IH5BHG =B -CIH<5GH '98=H9FF5B95B H
ACG BJ=FCB  
(9CD<MHCI ' 3=5@@CIFCG * CI@@  %@95BH<CIG - *5J@CI * *5G<=5F8=G -
C7?9FM 1 %CIHF5?=G * &589B   *5FH=7I@5H9 A5HH9F 7CB79BHF5H=CBG 8IF
=B; 89G9FH 8IGH CIH6F95?G 5B8 85=@M ACFH5@=HM =B (=7CG=5 MDFIG $ LDC -7= BJ=FCB
D=89A=C@  
*9M $ +I9FC@ 2 @5GHI9M  CF5GH=9F9  -H5:C;;=5 '  :F=75B 8IGH CIH6F95?G
CJ9F H<9 '98=H9FF5B95B 5G=B 8IF=B;  *' 7CB79BHF5H=CBG D<9BCA9BC@
C;M 5B8 HF9B8G 5B8 =HG F9@5H=CB K=H< GMBCDH=7 5B8 A9GCG75@9 A9H9CFC@C;M HACG
<9A *<MG  
*=?F=85G ' 0F9?CIGG=G ' -7=5F9 $ %@95BH<CIG - 05G=@=58CI  %=N5G  -5JJ=89G
 '=<5@CDCI@CG (  -D5H=5@ 5B8 H9ADCF5@ G<CFH 5B8 @CB;H9FA J5F=56=@=HM C:
GI6A=7FCB VB9 5B8 GI6 μA D5FH=7I@5H9 A5HH9F *' *' *' =B MDFIG H
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Observation-based and modelling studies have identified the Eastern Mediterranean and Middle East (EMME) region as a prominent climate change hotspot. While several initiatives have addressed the impacts of climate change in parts of the EMME, here we present an updated assessment, covering a wide range of timescales, phenomena and future pathways. Our assessment is based on a revised analysis of recent observations and projections and an extensive overview of the recent scientific literature on the causes and effects of regional climate change. Greenhouse gas emissions in the EMME are growing rapidly, surpassing those of the European Union, hence contributing significantly to climate change. Over the past half-century and especially during recent decades, the EMME has warmed significantly faster than other inhabited regions. At the same time, changes in the hydrological cycle have become evident. The observed recent temperature increase of about 0.45°C per decade is projected to continue, although strong global greenhouse gas emission reductions could moderate this trend. In addition to projected changes in mean climate conditions, we call attention to extreme weather events with potentially disruptive societal impacts. These include the strongly increasing severity and duration of heatwaves, droughts and dust storms, as well as torrential rain events that can trigger flash floods. Our review is complemented by a discussion of atmospheric pollution and land-use change in the region, including urbanization, desertification and forest fires. Finally, we identify sectors that may be critically affected and formulate adaptation and research recommendations towards greater resilience of the EMME to climate change.
This study aims to better characterize the location of the strong regional emitters of aerosols affecting the Eastern Mediterranean basin, through the use of a recently developed three-dimensional (3D) version of a Concentration Weighted Trajectory (CWT) model which can identify the prevalent transport pattern of polluted air masses as a function of their altitude (0 m ≤ Layer 1 < 1000 m (near ground layer), 1000 m ≤ Layer 2 < 2000m (intermediate layer), 2000m ≤ Layer 3 (upper layer)). Daily PM10 and PM1 chemical composition measurements from a regional background station on the island of Cyprus were incorporated in the model. Intrusions of PM1 constituents of an anthropogenic origin (Secondary Inorganic Aerosols (SIA) (SO42− and NH4+), K+, EC and OC), were mainly associated with Eastern continental airflows (Middle East regions) in Layers 1 and 2 during the cold periods (16 October to 15 April). During the warm periods (16 April to 15 October) the prevalence of Northern airflows in all layers mainly through Turkey enhanced the levels of SIA and K+ in PM1. Sea salt constituents in PM10 were particularly carried by West – Northwest Mediterranean airflows in all layers during both cold and warm periods. Dust intrusions from Northeast Africa and the Middle East were highlighted by episodes of PM10 crustal species and were most evident in Layer 1 during cold seasons. Therefore, this study reveals the spatial and vertical distribution of air masses transporting the main aerosol components at the Eastern Mediterranean.
The Sistan Basin in southeast Iran is one of the windiest and dustiest arid environments over the globe. The present study analyses, for the first time, long-term (April 2012 to March 2020) PM10 concentrations taken in Zabol (31.0324° N, 61.4902° E), the main city in Sistan, aiming to investigate the pollution trends, to assess the seasonality and the contribution of dust, to study the effect of wind and other meteorological parameters and to associate the extreme dust-related PM10 episodes with synoptic meteorological conditions. The annual-mean PM10 concentration was 429 μg m−3, which is one of the highest over the globe, while in summer, PM10 increased up to 693 μg m−3, with most turbid months July and August. The mean PM10 concentrations were much higher than the median ones, driven by severe dust storms throughout the year (193 days with PM10 > 1000 μg m−3), but with higher frequency and intensity in summer. A statistically-significant increasing trend was found in PM10 concentrations from 2012 to 2020, associated with a pronounced increase in wind speed, especially after 2017. PM10 concentrations were significantly correlated with mean and maximum wind speeds (r = 0.46, 0.41), while the visibility was highly reduced by increasing wind speed and PM10 levels. Dust contributes most (∼64%) to AOD and consequently to PM10, while the highest (>90th percentile) PM10 concentrations, both in summer and winter, were linked to intensification of the Caspian Sea high-pressure system that modulates the wind regime and meteorological patterns in central and southwest Asia.
In this work, number size distributions of particles between 0.2 and 34 μm were measured with a laser spectrometer. Temporal and spatial variability in size distributions were investigated. Measurements were carried out at two stations; the Mediterranean coast of Turkey (Marmaris) and central Anatolia (Ankara). PM1, PM2.5, and PM10 mass concentrations were measured. Flow climatology indicated that air masses spent more time in the south of Mediterranean basin in winter and in the North of the basin during summer months. Median PM10 concentrations measured in Marmaris and Ankara stations were 17 and 38 μg m⁻³, respectively. North African dust transport was not the main PM10 source in the suburban station, but it was significant in the rural station. The median PM2.5 concentration was higher at Ankara due to anthropogenic emissions which was not observed in Marmaris. Median PM1 concentrations were comparable in both stations. 90–95% of the particles were between 0.26 and 0.42 μm size ranges. Coarse particle concentrations were higher at the suburban station, whereas fine particles were higher at the rural station. Concentrations depicted a well-defined seasonal pattern in the rural station, with higher concentrations in summer season. Although concentrations of PM size fractions were also higher in the summer season in Ankara station, seasonal differences were not as well-defined as in the rural station. PM1 and PM2.5 concentrations did not show any difference between weekends and weekdays, but PM10 concentrations, particularly the 2.5 μm < D < 10 μm, were higher on weekdays due to contribution of resuspended road dust.
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Turkey is located in the heart of complex transition geography between Eurasia and the Middle East. In the grand scheme, the so-called eastern Mediterranean Basin is located almost in the middle of the dusty belt, and is a hot spot of climate change. The downstream location of dust-carrying winds from close desert sources reveals Turkey as an open plane to particulate matter exposure throughout the year. In order to clarify this phenomenon, this paper aims to determine the desert dust climatology of Turkey via CALIPSO onboard Lidar. This prominent instrument enables us to understand clouds, aerosols and their types, and related climatic systems, with its valuable products. In this study, a 9-year CALIPSO-derived pure dust product dataset was formed to explain horizontal and vertical distributions, transport heights and case incidences. The results indicated that the pure dust extinction coefficient increased as the location shifted from west to east. Moreover, in the same direction of west to east, the dominant spring months changed to summer and autumn. Mountain range systems surrounding Anatolia were the main obstacles against lofted and buoyant dust particles travelling to northern latitudes. Even if high ridges accumulated mass load on the southern slopes, they also enabled elevated particles to reach the ground level of the inner cities.
Objectives Although studies collectively examining the traffic and residential heat pollutant emissions are abundant, research investigations dedicated to Cyprus are scarce. This investigation has simulated the levels of air pollutants, namely, CO, NOx, PM2.5, and PM10 and reconciled them with actual air quality measurements in Nicosia, Cyprus, during a 9-month period at an hourly resolution. To this end, several scenarios and cases were formulated to tackle emissions and minimise human mortality risks in the city. Methods The GRAL dispersion model was used to project pollution levels. Nine different traffic scenarios were devised to estimate variations in concentration of PM2.5 and NOx under various policies, such as banning diesel passenger vehicles (PV), light duty vehicles (LDV), non-Euro 6 standards vehicles, stringent speed limits and a ubiquitous roll-over of electric passenger vehicles. Moreover, 4 distinct cases were analysed to year 2030 considering a fluctuation in traffic of ±20% whereas all vehicles conform to Euro 6 standards. Three additional policies examined the prohibition of diesel PV and LDV, 80% electric PV and outlawing fireplaces. Drawing on the findings of these scenarios and cases, the total cardiovascular and respiratory mortality rates at the capital of Cyprus, Nicosia, were deduced. Results The most promising scenario in terms of curbing emissions was to ban non-Euro 6 vehicles and diesel PV and LDV which could contain average NOx concentration, in Nicosia, from 52.9 μg/m³ to 15.0 μg/m³. If this policy were to be implemented, it could have saved 70% of the premature deaths tied to NOx emissions. For particulate matter, banning fireplaces and abandoning non-Euro 6 vehicles could lower average concentrations from 18.3 μg/m³ to 13.1 μg/m³, saving at least 30% of the people poised to lose their lives from particulate matter risks. Conclusion Traffic and residential heat policies are not easy to implement. However, our study has demonstrated that the most effective policies for curbing NOx emissions would be to ensure that all vehicles abide with the Euro 6 standards and, concurrently, ban diesel passenger and light duty vehicles. Lastly, phasing out domestic fireplaces appears to be the most promising solution for containing particulate matter, in 2030.
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The state of the thermal environment can affect human health and well-being. Heat stress is associated with a wide range of health outcomes increasing morbidity and mortality and is recognized as an important health risk posed by climate change. This study aims at examining the effect of thermal conditions on the daily number of hospital admissions in Cyprus. Data from eight public hospitals located in five districts of Cyprus were analyzed from 2009 to 2018. Meteorological hourly gridded data were extracted by the ERA-5 Land reanalysis database with a spatial horizontal resolution of 0.1° × 0.1°. The Physiologically Equivalent Temperature (PET) and the Universal Thermal Climate Index (UTCI) were calculated as measures of the integrated effect of meteorological variables. Negative binomial regression was fitted to examine associations between the daily number of hospital admissions and meteorological variables, PET, and UTCI. The results showed that the mean daily temperature (Tair) was positively associated with hospital admissions from any cause. Hospital admissions increased by 0.6% (p < 0.001) for each 1 °C increase of Tair and by 0.4% (p < 0.001) for each 1 °C increase of PET and UTCI. Ozone and nitrogen oxides act as confounding factors. An effect of particulate matter (less than 10 μm in diameter) was observed when the analysis focused on April to August. Thresholds above which hospital admissions are likely to increase include daily mean Tair = 26.1 °C, PET = 29 °C, and UTCI = 26 °C. Studies on heat-related health effects are necessary to monitor health patterns, raise awareness, and design adaptation and mitigation measures.
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The purpose of this study is to assess the impact of the lockdown measures in 2020 on the urban air quality in Nicosia capital city, in Cyprus—an island-country in the East Mediterranean—which is often affected by transboundary dust pollution. The study focuses on three criteria pollutants, nitrogen dioxide (NO2), carbon monoxide (CO) and Particulate Matter (PM10), taken from three Air Quality Monitoring Stations; two urban stations and one reference-background. The results of this study show that the decrease in traffic, which is the main source of high concentrations of pollutants in the urban area, reached up to 66.5% during the lockdown. At the beginning of the lockdown period, it exhibited a downward trend of 29% for CO concentration, and downward trend 43% for NO2 and PM10 concentrations. The NO2 concentration exhibited an upward trend towards the end of the lockdown; with the indication that this was due to meteorological conditions relevant to the monitoring stations and the transport of NO2 concentrations from sources that cannot be tracked. PM10 concentrations exhibited a varying behaviour as observed in the trends, where the decreasing trend was followed by an increasing trend due to transboundary air pollution episodes occurring in the same period.
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Airborne microbial communities directly impact the health of humans, animals, plants, and receiving ecosystems. While airborne bacterial and fungal communities have been studied by both cultivation-based methods and metabarcoding surveys targeting specific molecular markers, fewer studies have used shotgun metagenomics to study the airborne mycobiome. We analyzed the diversity and relative abundance of fungi in nine airborne metagenomes collected on clear days (“background”) and during dust storms in the Eastern Mediterranean. The negative correlation between the relative abundance of fungal reads and the concentrations of atmospheric particulate matter having an aerodynamic diameter smaller than 10 μm (PM10) indicate that dust storms lower the proportion of fungi in the airborne microbiome, possibly due to the lower relative abundance of fungi in the dust storm source regions and/or more effective transport of bacteria by the dust. Airborne fungal community composition was altered by the dust storms, particularly those originated from Syria, which was enriched with xerophilic fungi. We reconstructed a high-quality fungal metagenome-assembled genome (MAG) from the order Cladosporiales, which include fungi known to adapt to environmental extremes commonly faced by airborne microbes. The negative correlation between the relative abundance of Cladosporiales MAG and PM10 concentrations indicate that its origin is dominated by local sources and likely includes the indoor environments found in the city.
This study analyzes the spectral characteristics of in situ measured aerosol properties, namely scattering, absorption and single scattering albedo (SSA), explicitly examining the curvature characteristics of their wavelength dependence. Measurements were performed at an urban background site in Athens, Greece, during December 2017 and March 2018 characterized by contrasting aerosol sources and types, i.e. residential wood burning (RWB) and desert dust, respectively, which modulate the urban conditions, that are typically dominated by fossil-fuel combustion. Aerosol types were classified via the Scattering Ångström Exponent (SAE) vs. Absorption Ångström Exponent (AAE) matrix, with the black carbon (BC)-dominated type displaying the highest fraction (~40%) in both periods. The spectral optical properties were examined for each aerosol type and month, revealing notable differences, which are closely linked to the contrasting emission sources, atmospheric dynamics and mixing processes. BC from fossil-fuel emissions and regional background aerosols mostly presented similar characteristics in both months. Emphasis was given on the spectral curvature effect, since the different aerosol types display notable changes in the spectral dependence of scattering and absorption in logarithmic coordinates. The enhanced presence of brown carbon resulted in negative curvature for scattering (concave curves) and highly positive for absorption (convex curves), while the presence of BC was mostly translated in a better fit of the Ångström formula and small curvature effects. Intense dust events resulted in positive curvature for absorption and mostly negative for scattering. A scatterplot between the wavelength dependence of SSA and the scattering curvature may further differentiate key aerosol types i.e. BC, brown carbon and dust. This study highlights the curvature effects of the scattering, absorption and SSA, which have not been adequately addressed yet. The approach provides new insights in the differentiation of source-related aerosol types, although more analysis is needed to examine if findings are reproduced in other environments.
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The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.
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Background The health burden from exposure to air pollution has been studied in many parts of the world. However, there is limited research on the health effects of air quality in arid areas where sand dust is the primary particulate pollution source. Objective Study the risk of mortality from exposure to poor air quality days in Kuwait. Methods We conducted a time-series analysis using daily visibility as a measure of particulate pollution and non-accidental total mortality from January 2000 through December 2016. A generalized additive Poisson model was used adjusting for time trends, day of week, and temperature. Low visibility (yes/no), defined as visibility lower than the 25th percentile, was used as an indicator of poor air quality days. Dust storm events were also examined. Finally, we examined these associations after stratifying by gender, age group, and nationality (Kuwaitis/non-Kuwaitis). Results There were 73,748 deaths from natural causes in Kuwait during the study period. The rate ratio comparing the mortality rate on low visibility days to high visibility days was 1.01 (95% CI: 0.99–1.03). Similar estimates were observed for dust storms (1.02, 95% CI: 1.00–1.04). Higher and statistically significant estimates were observed among non-Kuwaiti men and non-Kuwaiti adolescents and adults. Conclusion We observed a higher risk of mortality during days with poor air quality in Kuwait from 2000 through 2016.
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This paper presents some of the results of a project that aimed at the design and implementation of a system for the spatial mapping and forecasting the temporal evolution of air pollution from dust transport from the Sahara Desert into the eastern Mediterranean and secondarily from anthropogenic sources, focusing over Cyprus. Monitoring air pollution (aerosols) in near real-time is accomplished by using spaceborne and in situ platforms. The results of the development of a system for forecasting pollution levels in terms of particulate matter concentrations are presented. The aim of the present study is to utilize the recorded PM 10 (particulate matter with aerodynamic diameter less than 10 í µí¼‡m) ground measurements, Aerosol Optical Depth retrievals from satellite, and the prevailing synoptic conditions established by Artificial Neural Networks, in order to develop regression models that will be able to predict the spatial and temporal variability of PM 10 in Cyprus. The core of the forecasting system comprises an appropriately designed neural classification system which clusters synoptic maps, Aerosol Optical Depth data from the Aqua satellite, and ground measurements of particulate matter. By exploiting the above resources, statistical models for forecasting pollution levels were developed.
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Natural sources' contribution to ambient air particulate matter (PM) concentrations is often not considered; however, it may be significant for certain areas and during specific periods of the year. In the framework of the AIRUSE-LIFE+ project, state-of-the-art methods have been employed for assessing the contribution of major natural sources (African dust, sea salt and forest fires) to PM concentrations, in Southern European urban areas. 24 h measurements of PM10 and PM2.5 mass and chemical 20 composition were performed over the course of a year in five cities: Porto, Barcelona, Milan, Florence and Athens. Net African dust and sea salt concentrations were calculated based on the methodologies proposed by EC (SEC 2011/208). The contribution of uncontrolled forest fires was calculated through receptor modelling. Sensitivity analysis with respect to the calculation of African dust was also performed, in order to identify major parameters affecting the estimated net dust concentrations. African dust contribution to PM concentrations was more pronounced in Eastern Mediterranean, with the mean annual relative 25 contribution to PM10 decreasing from 21 % in Athens, to 5 % in Florence, and around 2 % in Milan, Barcelona and Porto. The respective contribution to PM2.5 was calculated equal to 14 % in Athens and from 1.3 to 2.4 % in all other cities. High seasonal variability of contributions was observed, with dust transport events occurring at different periods in the Western and Eastern Mediterranean basin. Sea salt was mostly related to the coarse mode and also exhibited significant seasonal variability. Sea salt concentrations were highest in Porto, with average relative contributions equal to 12.3 % for PM10. Contributions from 30 uncontrolled forest fires were quantified only for Porto and were low on an annual basis (1.4 % and 1.9 % to PM10 and PM2.5, Atmos. Chem. Phys. Discuss., 2 respectively); nevertheless, contributions were greatly increased during events, reaching 20 and 22 % of 24 h PM10 and PM2.5 concentrations, respectively.
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The study of Saharan dust events (SDEs) and biomass burning (BB) emissions are both topics of great scientific interest since they are frequent and important polluting scenarios affecting air quality and climate. The main aim of this work is evaluating the feasibility of using near-real-time in situ aerosol optical measurements for the detection of these atmospheric events in the western Mediterranean Basin (WMB). With this aim, intensive aerosol optical properties (SAE: scattering Ångström exponent, AAE: absorption Ångström exponent, SSAAE: single scattering albedo Ångström exponent and g: asymmetry parameter) were derived from multi-wavelength aerosol light scattering, hemispheric backscattering and absorption measurements performed at regional (Montseny; MSY, 720 m a.s.l.) and continental (Montsec; MSA, 1570 m a.s.l.) background sites in the WMB. A sensitivity study aiming at calibrating the measured intensive optical properties for SDEs and BB detection is presented and discussed. The detection of SDEs by means of the SSAAE parameter and Ångström matrix (made up by SAE and AAE) depended on the altitude of the measurement station and on SDE intensity. At MSA (mountain-top site) SSAAE detected around 85 % of SDEs compared with 50 % at the MSY station, where pollution episodes dominated by fine anthropogenic particles frequently masked the effect of mineral dust on optical properties during less intense SDEs. Furthermore, an interesting feature of SSAAE was its capability to detect the presence of mineral dust after the end of SDEs. Thus, resuspension processes driven by summer regional atmospheric circulations and dry conditions after SDEs favoured the accumulation of mineral dust at regional level having important consequences for air quality. On average, SAE, AAE and g ranged between −0.7 and 1, 1.3 and 2.5 and 0.5 and 0.75 respectively during SDEs. Based on the aethalometer model, BB contribution to equivalent black carbon (BC) accounted for 36 and 40 % at MSY and MSA respectively. Linear relationships were found between AAE and %BCbb, with AAE values reaching around 1.5 when %BCbb was higher than 50 %. BB contribution to organic matter (OM) at MSY was around 30 %. Thus fossil fuel (FF) combustion sources showed important contributions to both BC and OM in the region under study. Results for OM source apportionment showed good agreement with simultaneous biomass burning organic aerosol (BBOA) and hydrocarbon-like organic aerosol (HOA) obtained by applying a positive matrix factorization model (PMF) to simultaneous Aerosol Chemical Speciation Monitor (ACSM) measurements. A wildfire episode was identified at MSY, showing AAE values up to 2 when daily BB contributions to BC and OM were 73 and 78 % respectively.
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Dust storms are a common phenomenon in arid and semi-arid areas, and their impacts on both physical and human environments are of great interest. Number of studies have associated atmospheric PM pollution in urban environments with origin in natural soil/dust, but less evaluated the dust spatial patterns over a city. We aimed to analyze the spatial-temporal behavior of PM concentrations over the city of Beer Sheva, in southern Israel, where dust storms are quite frequent. PM data were recorded during the peak of each dust episode simultaneously in 23 predetermined fixed points around the city. Data were analyzed for both dust days and non-dust days (background). The database was constructed using Geographic Information System and includes distributions of PM that were derived using inverse distance weighted (IDW) interpolation. The results show that the daily averages of atmospheric PM10 concentrations during the background period are within a narrow range of 31 to 48 μg m-3 with low variations. During dust days however, the temporal variations are significant and can range from an hourly PM10 concentration of 100 μg m-3 to more than 1280 μg m-3 during strong storms. IDW analysis demonstrates that during the peak time of the storm the spatial variations in PM between locations in the city can reach 400 μg m-3. An analysis of site and storm contribution to total PM concentration revealed that higher concentrations are found in parts of the city that are proximal to dust sources. The results improve the understanding of the dynamics of natural PM and the dependence on wind direction. This may have implications for environmental and health outcomes.
Long-term particulate matter (PM) mass concentration measurements have been performed in Cyprus at three major cities, one industrial area and two remote stations covering the entire southern part of the island in an effort to assess; i) the spatial and temporal variability of sub-10 μm (PM10), fine (PM2.5) and submicron (PM1) particulate matter in the eastern Mediterranean, ii) the main source areas contributing to their levels and iii) the relative contribution of regional and local anthropogenic and natural sources to PM levels. It was found that dust is responsible for the 33.6 ± 5.2% or about 10 μg m⁻³ of the annual PM10 levels reported in background stations; the latter underlines the significant contribution of natural sources on the ambient PM10 amounts in the eastern Mediterranean region. A significant (p < 0.001) decreasing trend of 0.7 ± 0.1 μg m⁻³ y⁻¹ was observed when both PM10 and PM2.5 annual values are considered, indicating contribution from both natural and anthropogenic sources to this tendency. By considering the PMx (with x = 1, 2.5 and 10) mass concentrations obtained at the background station of Agia Marina as representative of the regional influence, the local influence of the urban and industrial sites on the measured PMx levels can be estimated. On average, 36–44% of the observed PM10 levels at the urban and industrial locations is estimated to originate from local anthropogenic and/or natural emissions including vehicle, biomass-burning, shipping emissions (in Limassol), airport related emissions (in Larnaca), resuspension of dust and sea-salt (in coastal locations). These local emissions are almost equally distributed in the fine and coarse fractions as 40–50% of the local PM10 amounts are due to fine particles emissions. The above results highlight significant emissions from both fine mode (e.g. residential heating and traffic) and coarse mode urban emissions (e.g. dust resuspension, wear and tear in brakes and tires, respectively) in urban and industrial locations in Cyprus.
Mineral dust is the most significant source of natural particulate matter. In urban regions, where > 50% of the world population is currently living, local emissions of particulate matter are further aggravated by mineral dust loadings from deserts. The megacity of Istanbul is located in an area sensitive to local pollution due to transportation (i.e., private cars, public transportation, aircrafts, ships, heavy diesel trucks, etc.), industrial emissions, residential heating, and long-range transport from Europe, Asia, and deserts. In this work, the effect of desert dust transport on PM10 concentrations and physical properties was investigated for the period of 2007–2014 in the touristic area of Aksaray, Istanbul. The Dust Regional Atmospheric Model (DREAM8b) was used to predict dust loading in Istanbul during dust transport events. Variations on surface PM10 concentrations were investigated according to seasons and during dust transport events. Cluster analysis of air mass backward trajectories was useful to understand frequency analysis and air mass trajectory dependence of PM10 concentrations on dust loadings. The effect of desert dust transport on aerosol optical depths was also investigated. It was observed that PM10 concentrations exceeded the air quality standard of 50 μg m− 3 50% of the time during the study period. The largest number of exceedances in air quality standard occurred during the spring and winter seasons. Approximately 40–60% of the dust loading occurs during the spring. Desert dust and non-desert dust sources contribute to 22–72% and 48–81% of the ground-level PM10 concentrations in Aksaray, Istanbul during the study period. Averaged AOD observed during dust transport events in spring and summer ranged 0.35–0.55. Cluster analysis resolved over 82% the variability of individual air mass backward trajectories into 5 clusters. Overall, air masses arriving to Istanbul at 500 m are equally distributed into northern (52%) and southern (48%). Frequency analysis of PM10 concentrations with mean air mass backward trajectories showed that PM10 from local anthropogenic sources may be enhanced by long-range transport from the African Desert, Asian Desert, Arabian Peninsula, Russia, and Ukraine. The work presented here provides the first integrated assessment for evaluation of occurrence and quantification of the effect of dust transport to ground-level PM10 concentrations in Istanbul, which is helpful for human health prevention and implementation of air quality control measures.
Satellite imaging has emerged as a method for monitoring regional air pollution and detecting areas of high dust concentrations. Unlike ground observations, continuous data monitoring is available with global coverage of terrestrial and atmospheric components. In this study we test the utility of different sources of satellite data to assess air pollution concentrations in Iraq. SeaWiFS and MODIS Deep Blue (DB) aerosol optical depth (AOD) products were evaluated and used to characterize the spatial and temporal pollution levels from the late 1990s through 2010. The AOD and Ångström exponent (an indicator of particle size, since smaller Ångström exponent values reflect a source that includes larger particles) were correlated on 50 × 50 km spatial resolution. Generally, AOD and Ångström exponent were inversely correlated, suggesting a significant contribution of coarse particles from dust storms to AOD maxima. Although the majority of grid cells exhibited this trend, a weaker relationship in other locations suggested an additional contribution of fine particles from anthropogenic sources. Tropospheric NO2 densities from the OMI satellite were elevated over cities, also consistent with a contribution from anthropogenic sources. Our analysis demonstrates the use of satellite imaging data to estimate relative pollution levels and source contributions in areas of the world where direct measurements are not available. Implications: The authors demonstrated how satellite data can be used to characterize exposures to dust and to anthropogenic pollution for future health related studies. This approach is of a great potential to investigate the associations between subject-specific exposures to different pollution sources and their health effects in inaccessible regions and areas where ground monitoring is unavailable.