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INTRODUCTION Particulate matter, PM10 and PM2.5 is a serious problem for the
urban air pollution in many Bulgarian cities [1,2]. PM10 harmful
health effect is well known and depends on the concentration,
size and chemical composition.
In this study air particulate PM10 mass concentration and more
than 20 elements, measured by EDXRF technique, in the daily
quartz filter samples are obtained during winter and summer
experimental campaigns at NIMH observatory in Sofia. These
data and SO2 and NO2 concentrations from air quality urban
background stations are used to perform source apportionment
study. Multivariate techniques as Factor analyses (FA) and
Principle Components Analyses (PCA) by STATISTICA.6
software are applied to identify sources of PM10.
Application of PCA and FA models for source apportionment of PM10 in Sofia
Blagorodka Veleva1), Elena Hristova1), Maria Kolarova1), Emilia Nikolova2),
Raliza Valcheva2), Orlin Gueorguiev1)
1)National Institute of Meteorology and Hydrology, NIMH-BAS, Tsarigradsko sh. 66, Sofia, e-mail: elena.hristova@meteo.bg 2)Institute of Nuclear Research and Nuclear Engineering, INRNE-BAS, Tsarigradsko sh. 72, Sofia, e-mail: eminik@inrne.bas.bg
SUMMARY The ED-XRF technique was applied to determine more than 20 macro and micro elements in the PM10
filter samples in Sofia. The higher PM10 daily concentrations are related to the higher concentrations for
most of the elements, excluding Ag, Ca and Fe higher values in July.
The factors derived by PCA after varimax rotation are 3 to 6 depending of the selection of the set of
elements and should be considered as preliminary. More and enlarged data set is needed.
Further work is planned to determine water soluble ions in PM10 samples. EDXRF data from next
sampling campaigns will be utilized in further source apportionment study.
Map of Sofia and sampling site
15th EMS Annual Meeting & 12th European Conference on Applications of Meteorology (ECAM) , 07–11 September 2015, Sofia, Bulgaria
The whole data set (85 daily values) is divided in summer and winter subsets. The contribution of 5
factors for each group with different elemental components is derived. 85% of the PM10
concentration in summer is due to the impact of K, Ca, Ti, Fe source. In separate group with no
significant contribution are Zn and Cu.
When we use the entire data set, 51% of the contribution to PM10 is from sources related to the
traffic and road dust resuspension (NO2, Zn, Sr, Ti). The source of S and Mn (grouped in one factor)
contribute with 18% to the PM10 and the crustal elements source (K, Ca, Fe) with about 4.5%.
Median
25%-75%
Min-Max NO2 SO2 PM10
0E-01
2E+04
4E+04
6E+04
8E+04
1E+05
1E+05
1E+05
2E+05
2E+05
ng
.m-3
Factor analyses
The macro elements are found in the majority of the PM10 samples. The number of elements with
concentrations below Detection Limit (DL) is higher in summer. For example, Cd, I, Br, Zr and Ba are not
detected in the July 2012 samples when Pb, Rb and Ni were detected once. The As was detected only in
3 days in the summer of 2012 with maximum on 13 of July. Sn and Sb are below DL approximately in half
of the winter samples. Ni is detected in about 25% of total samples: measured once in July 2012 and in ~
50% of days in July 2013 . The macro elements K and Fe are in the range from less than 50 to 1000 ng.m-
3 in winter and from 100 to 1500 ng.m-3 in summer. In July, the daily Cu concentrations are several times
lower than in February, when the Cu concentrations are above 100 ng.m-3 for certain days. Similar are the
variations of Ti, Zn, Mn. Phosphorous shows high winter concentration, with average value of 534.1 - 590
ng.m-3 in 2012 and 2013 correspondingly, and of 31 – 500 ng.m-3 in July 2012 - 2013.
Win
ter
S
um
mer
The correlations between some elements and PM10 mass concentration are presented in scatter
plot figures for winter and summer experiments. Weak correlation between PM10 and Fe, K and Ti is
observed in winter periods. No correlation (r<0.3) is found between PM10and P, Ca, Mn, Sb and Ba.
Correlation coefficient > 0.6 is obtained between PM10 and NO2, Ti and Sr. The temporal variations
of these elements are influenced probably by different sources contribution and source profiles.
High seasonal variability of PM10/NO2 correlation coefficient is registered (0.85 in winter versus 0.18
in summer). Significant correlation (r>0.57) is obtained between elements pairs: K-Fe, K-Ca, K-Ba,
K-Ti, K-Cl, Ca-Fe, Ca-Ba, Sr-Ti, S-Mn.
PM10 mass concentration
Sulfur content in PM10 in winter is related to the same factor as Mn. It does not correlate significantly with most of the other elements in PM10 and NO2 and SO2 24-average
concentrations. High S concentrations and high S to PM10 ratio is registered in the PM10 samples of 6, 13, 21,22, 23 February 2012 and in 5 and 6 February 2013. HYSPLIT Trajectory
Model [3] is applied in cases with high sulfur concentration to trace back the origin of air masses. HYSPLIT dispersion model is run in case when back trajectories indicate the regional
transport from major industrial SO2 sources. In winter there is a local sulfur background because of the domestic heating.
Median
25%-75%
Min-Max P S Cl K Ca Fe
0
500
1000
1500
2000
2500
3000
ng
.m-3
Median
25%-75%
Min-Max Ti Mn Cu Zn Rb Sr Sn Sb Ba
0
20
40
60
80
100
120
140
160
180
200
ng
.m-3
NO2 vs. PM10
PM10 = -8766, + 1,2398 * NO2
Correlation: r = ,85525
0 20000 40000 60000 80000 1E5 1,2E5 1,4E5
NO2
-2E+04
0E-01
2E+04
4E+04
6E+04
8E+04
1E+05
1E+05
1E+05
2E+05
2E+05
PM
10
95% confidence
SO2 vs. PM10
PM10 = 23975, + 1,2328 * SO2
Correlation: r = ,47956
-10000 0 10000 20000 30000 40000 50000 60000
SO2
0,00E-01
2,00E+04
4,00E+04
6,00E+04
8,00E+04
1,00E+05
1,20E+05
1,40E+05
1,60E+05
1,80E+05
PM
10
95% confidence
Fe vs. PM10
PM10 = 18290, + 84,935 * Fe
Correlation: r = ,63027
-200 0 200 400 600 800 1000 1200
Fe
0E-01
2E+04
4E+04
6E+04
8E+04
1E+05
1E+05
1E+05
2E+05
2E+05
PM
10
95% confidence
Median
25%-75%
Min-Max PM10 NO2 SO2
0E-01
5E+03
1E+04
2E+04
2E+04
3E+04
3E+04
4E+04
4E+04
5E+04
ng
.m-3
Median
25%-75%
Min-Max P S Cl K Ca Fe
0
200
400
600
800
1000
1200
1400
1600
1800
ng
.m-3
NO2 vs. PM10
PM10 = 31969, - ,8256 * NO2
Correlation: r = -,1832
4E+03 5E+03 6E+03 7E+03 8E+03 9E+03 1E+04 1E+04 1E+04 1E+04
NO2
1E+04
2E+04
2E+04
3E+04
3E+04
4E+04
4E+04
5E+04
PM
10
95% confidence
SO2 vs. PM10
PM10 = 21890, + ,46350 * SO2
Correlation: r = ,21238
0E-01
2E+03
4E+03
6E+03
8E+03
1E+04
1E+04
1E+04
2E+04
2E+04
2E+04
SO2
1E+04
2E+04
2E+04
3E+04
3E+04
4E+04
4E+04
5E+04
PM
10
95% confidence
Fe vs. PM10
PM10 = 13035, + 21,424 * Fe
Correlation: r = ,84194
0 200 400 600 800 1000 1200 1400 1600
Fe
1E+04
2E+04
2E+04
3E+04
3E+04
4E+04
4E+04
5E+04
5E+04
PM
10
95% confidence
Su
mm
er
W
inte
r
Stagnant weather conditions and inversions are the major factor for the high PM10 values, registered
during the cold period. Higher PM10 daily concentrations correlate with higher concentrations of the S, K,
Ti, Sr. The PM10 mass concentration, the daily sum of precipitation and mixing height determined from 12
UTC aerological soundings is presented below.
Factor Loadings (Varimax normalized)_All Data set
(Marked loadings are >,700)
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
PM10 0,771 0,262 0,400 0,096 0,070
NO2 0,852 0,005 0,177 0,378 0,078
SO2 0,523 -0,096 0,563 0,171 -0,164
P 0,491 -0,224 -0,157 0,086 -0,630
S 0,142 0,156 0,844 -0,123 0,081
Cl 0,402 0,602 -0,231 0,504 -0,009
K 0,262 0,720 0,478 0,280 -0,006
Ca -0,055 0,915 -0,067 -0,011 -0,047
Ti 0,570 0,435 0,346 -0,083 -0,371
Mn -0,049 -0,036 0,792 0,236 0,234
Fe 0,103 0,909 0,048 -0,096 0,152
Cu 0,584 0,279 -0,197 0,017 -0,077
Zn 0,753 0,044 -0,009 0,129 0,384
Rb -0,120 -0,059 -0,258 -0,694 0,205
Sr 0,761 0,016 0,218 -0,419 -0,117
Sn 0,256 -0,058 0,153 0,095 0,822
Sb -0,016 0,091 -0,037 0,778 0,384
Ba 0,113 0,606 0,122 0,415 -0,019
Factor Loadings (Varimax normalized) _winter data set
(Marked loadings are >,700)
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
PM10 0,316 0,362 0,342 0,629 0,013 0,362
NO2 0,296 0,120 0,007 0,747 -0,079 0,433
SO2 -0,064 0,478 0,467 0,027 -0,180 0,505
P -0,042 0,113 -0,310 0,038 0,768 0,424
S 0,035 0,188 0,903 0,219 0,117 0,062
Cl 0,908 -0,007 -0,216 0,202 0,042 -0,031
K 0,781 0,112 0,528 0,131 0,117 0,086
Ca 0,883 -0,032 -0,033 0,046 0,201 0,018
Ti 0,324 0,252 0,097 0,029 0,065 0,866
Mn 0,028 -0,338 0,881 -0,082 -0,071 0,141
Fe 0,830 0,147 0,121 0,423 -0,012 0,049
Cu 0,354 0,686 -0,189 0,271 0,017 0,058
Zn 0,086 0,011 -0,043 0,856 0,073 0,071
Rb -0,161 -0,075 -0,391 0,042 -0,813 0,126
Sr -0,102 -0,005 0,136 0,464 0,255 0,839
Sn -0,067 -0,134 0,295 0,635 -0,477 -0,258
Sb 0,305 -0,758 -0,056 0,125 -0,298 -0,256
Ba 0,741 -0,067 0,096 -0,143 -0,167 0,175
Backward trajectories for
13.02.2012 sources from Serbia
could contribute to the elevated S
concentration. Dispersion model is
run for TPP “Nikola Tesla A” and
Mining and Smelting Basin Bor
High S concentration in PM10 during
the period 21-23 February 2012
most probably is due to Bulgarian
sources (TPP Maritza Iztok, Arubis
and TPP Republika, Pernik)
Factor Loadings (Varimax normalized) summer
data set, (Marked loadings are >,700)
Factor 1 Factor 2 Factor 3 Factor 4
PM10 0,958 0,064 0,000 -0,173
NO2 -0,230 -0,447 0,595 -0,126
SO2 0,254 0,191 0,650 0,332
S 0,640 -0,243 -0,160 -0,381
K 0,925 0,198 0,105 0,025
Ca 0,856 0,047 0,329 0,097
Ti 0,897 0,200 0,032 0,078
Mn 0,026 -0,257 -0,111 0,823
Fe 0,905 0,102 0,203 0,113
Cu -0,054 -0,822 0,133 0,026
Zn -0,144 -0,827 -0,146 0,250
Rb -0,292 0,038 -0,851 0,323
Element concentrations
TPP “Maritza Iztok” Aurubis - Copper and Copper Recycling TPP “Republika”, Pernik TPP “Nikola Tesla A” Mining and Smelting Basin Bor
Median
25%-75%
Min-Max Ti Mn Cu Zn Rb Sr Sn Sb Ba
0
20
40
60
80
100
120
ng
.m-3
0
100
200
300
400
500
600
700
0
20
40
60
80
100
120
140
160
180
06
.2.1
2
07
.2.1
2
08
.2.1
2
09
.2.1
2
10
.2.1
2
11
.2.1
2
12
.2.1
2
13
.2.1
2
14
.2.1
2
15
.2.1
2
16
.2.1
2
17
.2.1
2
18
.2.1
2
19
.2.1
2
20
.2.1
2
21
.2.1
2
22
.2.1
2
23
.2.1
2
24
.2.1
2
25
.2.1
2
H(m)PM10
mg
.m-3
, m
m
PM10 Precip. Hmix
0
100
200
300
400
500
600
700
0
10
20
30
40
50
60
70
80
90
21
.1.1
3
22
.1.1
3
23
.1.1
3
24
.1.1
3
25
.1.1
3
26
.1.1
3
27
.1.1
3
28
.1.1
3
29
.1.1
3
30
.1.1
3
31
.1.1
3
01
.2.1
3
02
.2.1
3
03
.2.1
3
04
.2.1
3
05
.2.1
3
06
.2.1
3
07
.2.1
3
08
.2.1
3
09
.2.1
3
10
.2.1
3
11
.2.1
3
H(m)PM10
mg
.m-3
, m
m
PM10 Precip. Hmix
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0
5
10
15
20
25
30
35
40
45
02
.7.1
2
03
.7.1
2
04
.7.1
2
05
.7.1
2
06
.7.1
2
07
.7.1
2
08
.7.1
2
09
.7.1
2
10
.7.1
2
11
.7.1
2
12
.7.1
2
13
.7.1
2
14
.7.1
2
15
.7.1
2
16
.7.1
2
17
.7.1
2
18
.7.1
2
19
.7.1
2
20
.7.1
2
21
.7.1
2
22
.7.1
2
23
.7.1
2
24
.7.1
2
25
.7.1
2
26
.7.1
2
H(m)PM10
mg
.m-3
, m
m
PM10 Precip. Hmix
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0
5
10
15
20
25
30
35
40
45
01
.7.1
3
02
.7.1
3
03
.7.1
3
04
.7.1
3
05
.7.1
3
06
.7.1
3
07
.7.1
3
08
.7.1
3
09
.7.1
3
10
.7.1
3
11
.7.1
3
12
.7.1
3
13
.7.1
3
14
.7.1
3
15
.7.1
3
16
.7.1
3
17
.7.1
3
18
.7.1
3
19
.7.1
3
20
.7.1
3
21
.7.1
3
22
.7.1
3
23
.7.1
3
H(m)PM10
mg
.m-3
, m
m
PM10 Precip. Hmix
SAMPLING AND METHODS •PM10 sampling was carried out at the site of Central
Meteorological Station (CMS) of NIMH
•Sampling periods – 6.02–25.02.2012, 2.07-
26.07.2012, 21.01-11.02.2013, 1-23.07.2013.
•Certified PM10 sampler TECORA Echo PM is used.
•Sampling duration is 24h (at 8:00 LST).
•47mm quartz fiber filters (Whatman ). Filters were
conditioned for 48h before and after sampling in a
temperature and humidity controlled room (T=
20±2°C, RH=50±5%).
•Energy Dispersive X-Ray Fluorescence (EDXRF)
technic in INRNE is used .
ACKNOWLEDGEMENTS
This presentation was supported by the Project
BG051PO001-3.3.06-0063 funded by European
Operational Program “Human Resources
Development”.
References:
[1] Nat. Rep. of MoEW, (2013) National Report on the State and Protection of The Environment, MoEW, Sofia,
http://eea.government.bg/bg/soer/2013 ;
[2] Hristova, E., Veleva, B. (2013) ‘Variation of air particulate concentration in Sofia, 2005-2012’, Bulgarian J. of Met. and
Hydr., Vol. 18, No1-2, pp 47-56.;
[3] Draxler, R.R. and Rolph, G.D. (2013) ‘HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model
access via NOAA ARL READY Website (http://www.arl.noaa.gov/HYSPLIT.php). NOAA Air Resources Laboratory,
College Park, MD.
Statistical analysis
Recommended