source apportionment of human exposure to particulates in mumbai

advertisement
SOURCE APPORTIONMENT OF HUMAN
EXPOSURE TO PARTICULATES IN MUMBAI,
INDIA
Paper # 1035
Milind M. Kulkarni
Civil Engineering Department,
Sardar Patel College of Engineering, Andheri(West)
Mumbai 400058 INDIA
Phone: +91 022 26289777, 26241451 Extn. 221
Fax.: +91 022 26237819
Email: milind.kulkarni@vsnl.com
ABSTRACT
Mumbai is considered as the commercial capital of India and is one of the important
cities in the world. The previous studies have shown that particulate matter is the major
air pollutant for Mumbai. In this study, which was funded by AICTE, Government of
India, human exposure to Respirable Particulate Matter(PM5) was assessed for
respondents from the selected population subgroups viz. Low Income Group (LIG) and
Middle Income Group (MIG). The respondents were monitored for 48 hour integrated
exposure with the help of personal samplers (SKC make). Activity diary was recorded
and questionnaire survey was carried out to collect information about workplace and
home characteristics. The sampling period was October 2002 to January 2003. Each
respondent was sampled for once or twice a week. For LIG respondent, the average
personal exposure to RPM was 186 µg/m3 (n=8, Std.Dev.=184) whereas for MIG
respondent, it was 73 (n=14, Std. Dev.=39). The Indian National Ambient Air Quality
(NAAQ) standard was exceeded by 86% for LIG respondent. The respirable dust
collected on polycarbonate membrane filter was digested and further analyzed for metals
Copper, Manganese, Lead, Sodium, Chromium, Calcium, Potassium, Zinc, Iron and
Arsenic using Atomic Absorption Spectrophotometer. The average concentration of Lead
of 0.284 µg/m3 is much below Indian NAAQ standard of 1. The data on concentration of
these metals were used for Source Apportionment using Factor Analysis technique. The
analysis resulted in three factors (represented by groups of metals associated with the
source) related to industrial, residential and crustal sources for all samples combined.
Separate factor analysis resulted in two and three sources for MIG and LIG respondents
respectively. The third source for the LIG respondent represented metals due to
residential sources such as burning of low quality fuel. The ambient air concentration for
PM10 for the area was 291 µg/m3 (n=10).
Keywords: Personal exposure, Factor analysis, Respirable Particulate Matter, LIG, MIG
INTRODUCTION
Of late deteriorating air quality in urban areas has become a major public concern in most
of the countries. The majority of South Asian cities suffer from extremely high levels of
urban air pollution, particularly in the form of small particles. Region-wide, urban air
pollution is estimated to cause 250,000 deaths and billions of cases of respiratory
illnesses every year. 1 A study indicated that approximately 50,000 pre-mature deaths
occur annually due to PM10 (particles smaller than 10 microns) pollution in India.2
Ambient air quality is routinely monitored by regulatory authorities in the major urban
areas. However, there is a little data available on the personal exposure to air pollution
which is closely associated with the health risk than ambient air quality.
Assuming that the air pollution concentrations are homogeneous both in space and in
time, within each microenvironment, the integrated exposure for an individual (i) is
computed as the sum of the product of the pollutant level in each microenvironment(j)
and the time the individual spent in the microenvironment.
J
Ei = ∑ Cj tij ………….. (1)
1
Where Ei = integrated exposure of an individual(i) over the time period
Cj = pollutant level experienced in microenvironment (j)
tij = time spent by individual (i) in microenvironment (j)
J = total number of microenvironments occupied by individual(i) over the time
period
Average exposure of a person is closely related to the integrated exposure and can be
computed by dividing it by the averaging time. Average exposure is also called as the
time weighted concentration and is measured in µg/m3. Different average times are used
in the calculation of exposure to air pollution. For example, total daily exposure often
refers to 24-hours exposure.
Author of this paper conducted one of the first studies of its kind in India in which
personal exposure to various population subgroups in urban areas was measured.3 Source
apportionment identifies different sources of air pollution and their relative contribution.
There have been a few studies on source apportionment of ambient particulates.4
Recently, a study5 was conducted by the Georgia Institute of Technology, United States
in collaboration with National Physical Laboratory, the Indian Institute of Technology,
Mumbai and the National Environmental Engineering Research Institute, India in which
analysis of ambient PM2.5 (particles smaller than 2.5 microns) was carried out using
chemical mass balance receptor modeling. The results of this study indicate that there is
no single dominant source, but sources differ by location and season among the three
Indian cities examined. However, no study has been carried out in India on the source
apportionment of personal exposure to particulates which makes this study first of its
kind. Source apportionment will quantify the contributions of various indoor and outdoor
sources to the personal exposure by statistical analysis and this is a major input for policy
makers.
In this study, personal or human exposure to Respirable Particulate Matter(PM5) was
assessed for respondents from the selected population subgroups viz. Low Income Group
(LIG) and Middle Income Group (MIG). The respondents were monitored for 48 hour
integrated exposure with the help of personal samplers (SKC make). Activity diary was
recorded and questionnaire survey was carried out to collect information about workplace
and home characteristics. The sampling period was October 2002 to January 2003. Each
respondent was sampled for once or twice a week. The respirable dust collected on
polycarbonate membrane filter was digested and further analyzed for various metals
using Atomic Absorption Spectrophotometer. The data on concentration of these metals
were used for Source Apportionment using Factor Analysis which is a statistical
technique. However, the respirable dust was not analyzed for nonmetals and organic
components in this study. To summarize objectives of this study are as follows:
1. To assess the human exposure to particulates for the selected population
subgroups in Mumbai.
2. To assess the human exposure to toxic/nontoxic metals.
3. Source apportionment of Human exposure to particulates using factor analysis.
4. To identify the risk factors and propose guidelines for policy making.
STUDY DESIGN
Personal exposure monitoring studies involve considerable expenditure in terms of
manpower and equipment. Therefore, a careful study design is imperative.
Area of study
The study is conducted in Mumbai, the commercial capital of India, a city with a total
population exceeding 14 million and located on the west coast of India. In this study,
Andheri, a western suburb of Mumbai has been selected as the area of study because with
a typical mix of roads with high traffic density, industrial estates and residential areas, it
represents the city.
The study was conducted in two phases. In the first phase, the concentration of respirable
particulate matter RPM (PM5) was measured with the help of personal samplers at
breathing level at following locations selected on the basis of sources:1. Campus of Sardar Patel College of Engineering: To represent the background air
quality.
2. Apana Bazar Traffic Junction, Andheri(West): To represent air pollution due to
automobile exhaust and resuspension of dust from roads.
3. Hotel Sea View, Juhu Beach: To represent natural/marine sources
4. Indoor home: To represent indoor residential sources
5. Personal Exposure
The possibility of locating a station at an industrial area was also explored. However,
most of the industries are small scale and they are interspersed in the residential areas. In
one area demarcated for industries, most of the establishments were of commercial nature
without any specific air pollution source. Therefore, no location could be set up to
represent industrial sources.
In the second phase, personal exposure was monitored for the selected respondents in the
study area.
Selection of air pollutant
Respirable particulate matter (RPM), Suspended Particulate Matter (SPM), Sulphur
dioxide (SO2) and Nitrogen dioxide (NO2) are the criteria air pollutants which are
routinely monitored by the regulatory authorities in India. However, in this study RPM is
selected due to its high levels in Mumbai 6 and its adverse impact on health as discussed
above. In RPM, sampling can be done for three different sizes: viz. 2.5, 5 and 10
microns. In this study, particles less than 5 micron size or PM5 are sampled. Apart from
being respirable, comparatively easy availability of PM5 samplers is also one of the
reasons. They are widely used in measuring the occupational exposure to RPM all over
the world. A study has developed correlation between concentration of different sizes of
particulates. 3
The respirable dust was digested and analyzed for metals like Copper, Manganese, Lead,
Sodium, Chromium, Calcium, Potassium, Zinc, Iron, Arsenic, Cadmium and Nickel
Selection of respondents
The study was carried out with respondents from middle and low income groups. The
respondents were employees working with the organization conducting study viz. Sardar
Patel College of Engineering (SPCE). The personal exposure to RPM was monitored for
following two respondents representing middle and low-income groups:
1. Dr. Milind M. Kulkarni
Designation: Professor
Residence: Dhake Park, Jogeshwari (East), Mumbai
Work-place: SPCE, Andheri(West), Mumbai
Distance from workplace = 3 km
Mode of commuting: Car
Approximate commuting time = 40 minutes
Middle Income Group = 40
2. Shankar Anant Pujari
Designation: Lab Attendant
Residence: School Committee Chawl, Andheri(E), Mumbai
Work-place: SPCE, Andheri(West), Mumbai
Distance from workplace = 2 km
Mode of commuting: Walk
Approximate commuting time = 40 minutes
Low Income Group
Sampling time
The sampling time for each respondent was 48 hours or two consecutive days. Ideally 24
hour sampling period is enough to measure daily integrated exposure which is a suitable
indicator of health risk. However, to get more mass of dust collected for better accuracy
48 hour sampling period is selected. This will also help in the detection of metals many
of which are below detection limits.
Monitoring period
For the first phase (source location monitoring), the monitoring period was from March
21 to June 02, 2002. On an average each location was monitored once in a week.
Monitoring period for second phase(respondent personal exposure monitoring) was from
October 23, 2002 to January 23, 2003. Each respondent was monitored for once a week
on an average.
Questionnaire Survey
A questionnaire survey was carried out to collect other data such as housing
characteristics, fuel use, socio-economic status etc. An activity diary was also
maintained. The correlation between this data and the personal exposure is explored.
MATERIALS AND METHODS
Sampling and Analysis Method
Personal exposure to RPM is measured by personal sampler (SKC, USA make). The
personal sampler consists of a diaphragm pump and is operated on rechargeable batteries.
It is a compact device and can be worn by the respondent. The personal sampler is fitted
to the waist belt of the respondent and the cyclone can be clipped to the shirt collar. The
inlet of the cyclone is kept near the breathing level of the respondent. The Aluminum
cyclone is a lightweight respirable dust sampler that is used with a filter loaded into a
three piece filter cassette. The respirable particles are collected on the filter for analysis
while larger particles fall into the grit pot and are discarded. In this study 37 mm cyclone
with 37 mm three piece cassette are used. So, the size of filter is also 37 mm.
Polycarbonate membrane filters with a pore size of 0.8 µm are used. Before and after
sampling, the filter is kept in airtight cassette to avoid absorption of moisture.
The flow rate is maintained at1.9 lit/min. The flow rate is calibrated before and after
sampling along with cyclone loaded with filter. If the flow rate differs by more than 5%
after sampling, the concerned observation is considered as void.
Sampling protocol
The sampling was started at the workplace in the morning of the first day. After duty
hours, both the respondents carried the sampler to their residences with the charged pump
and the sampling was carried out in the night either in the living room or the bedroom.
The cycle was repeated for the next day. The sampler should not be kept near the window
or the stove. Polycarbonate filter papers were used for the sampling and the rate of air
flow through the cycle was set at 1.9 lit/min.
The personal sampler pump used for finding RPM concentration works at a stretch for
about 10 to 12 hours. After this, its battery gets exhausted and recharging is necessary
before further usage. Each respondent conducted sampling for two whole days (48
hours). On the first day the cyclone is attached to the pump which runs for 12 hours after
which it is kept for charging during night time. The same cyclone is then attached to
another freshly charged pump. The procedure is repeated for the second day and thus 48
hours of sampling is done.
Digestion of respirable dust
In order to determine the concentration of metals, the respirable dust collected on the
Plolycarbonate filter is digested for further analysis. The procedure used for digestion is
as follows:
The 37 mm diameter filter was kept in a Teflon beaker of 200 ml capacity. It gets
dissolved in 5 ml of Chloroform. To digest the dust refluxing was carried out by filling
the beaker with nitric acid (5 ml), hydrochloric acid (5 ml), perchloric acid (2 ml) at 80 to
90 0 C till the dust is completely digested to incipient dryness. To the cooled mass are
added 2-3 drops of concentrated nitric acid and clear solution was made up to volume by
N/10 hydrochloric acid. A blank was prepared following the same procedure using
identical quantity of unexposed filter and acid.
Factor Analysis for Source Apportionment
Factor analysis attempts to identify underlying variables, or factors, that explain the
pattern of correlations within a set of observed variables. Factor analysis is often used in
data reduction to identify a small number of factors that explain most of the variance
observed in a much larger number of manifest variables. Factor analysis can also be used
to generate hypotheses regarding causal mechanisms or to screen variables for
subsequent analysis.
Principal component and common factor analysis are often placed under the heading
Factor Analysis. Although they are based on different mathematical models, they can be
used on the same data and both produce similar results. These procedures are often used
in exploratory data analysis to
 Study the correlation among a large number of inter-related quantitative
variables by grouping the variables into a few factors; after grouping, the
variables within each factor are more highly correlated with variables in that
factor with variable in other factor.
 Interpret each factor according to the meaning of the variables.
 To find Outliers i.e. Data not conforming to the general trend.
Steps in Factor Analysis
There are three main steps in factor analysis:
1. The correlation matrix is computed. If the variable has very small correlations
with others, you may consider eliminating it from the list of variables, as it is not
significant.
2. The factor loadings are estimated. Here principal component analysis is used as a
method for factor extraction.
3. The loadings are rotated to make them more interpretable. Rotation methods make
the loading of each factor either large or small, not in between. Here, Varimax
rotation is used.
Data used in Factor Analysis
In this study two respondents are monitored for personal exposure to RPM for the period
of three months. Each respondent was monitored for once a week on an average. In all 22
valid samples were generated. Each sample filter was digested and analyzed for 12
metals. This data is used in the factor analysis.
RESULTS AND DISCUSSION
As discussed in the study design, the air monitoring was carried out in two phases. In the
Phase I, the personal exposure is monitored at selected sites representing different
sources such as traffic, natural, indoor, background etc along with the personal level
monitoring. In Phase II, personal exposure to RPM was monitored for two respondents.
One of the respondent was from the middle income group, whereas the other was from
the low income group.
Personal Exposure Monitoring at Selected Source Sites (Phase I)
In this phase, though personal exposure to respondents was not measured, the term
personal exposure monitoring is used to indicate that at selected sites, RPM (PM5) was
monitored using personal samplers and hence the sample will represent the daily personal
exposure at the particular site. The monitoring period was from March 21 to June 02,
2002 and the sampling period was 48 hours. On an average each location was monitored
once in a week. Seven valid readings were obtained for each site. The results are
summarized in Table 1.
Table1. Average RPM (PM5) concentration at Selected Source Sites (n=7)
Site
Personal
Indoor-home
Traffic Junction
Background
Sea shore
RPM (PM5) concentration in
µg/m3
149.71 ± 83.68
125.68 ± 73.16
254.37 ± 73.85
150.62 ± 78.58
111.65 ± 33.73
The traffic junction site recorded the maximum whereas the sea shore site recorded the
minimum RPM concentration. The indoor-home and the background concentration falls
in between. Apparently, the air quality at the background site is affected by the traffic at
the adjoining roads. Hence, the sea-shore site better represents the background air quality.
The personal exposure falls between the indoor-home and traffic junction air quality. The
excess exposure than indoor-home may be due to outdoor sources such as traffic. The
Indian NAAQ for RPM(PM10) is 100 µg/m3. This exceeds at all the stations.
The respirable dust samples collected at the source stations were digested and analyzed
for metals by the Atomic Absorption Spectrophotometer. The results are as given in
Table 2.
Table 2. Average concentration of metals at selected source stations (n=7)
Site
Mn
(µg/m3)
(Mean ± std
dev).
Traffic
0.6213(1)
Cd
(µg/m3)
(Mean ± std
dev).
0.0033
±
0.0020(3)
Cr
(µg/m3)
(Mean ± std
dev).
1.4299
±
1.6352(3)
Cu
(µg/m3)
(Mean ± std
dev).
0.1355
±
0.0717(3)
Fe
(µg/m3)
(Mean ± std
dev).
5.9302
±
7.8116(7)
Na
(µg/m3)
(Mean ± std
dev).
1.4418
±
0.8892(5)
Pb
(µg/m3)
(Mean ± std
dev).
0.5025
±
0.3264(5)
Zn
(µg/m3)
(Mean ± std
dev).
0.2723
±
0.1669(5)
Background
Not
detected
0.0114
±
0.0079(4)
1.5476
±
1.1775(4)
0.0441
±
0.0184(3)
4.5804
±
6.2993(7)
1.3870
±
0.5247(5)
0.9000
±
0.1102(4)
0.1484
±
0.0846(4)
2.5310(1)
1.7177
±
0.0323(2)
0.1256
±
0.0356(2)
0.0525
±
0.0355(2)
Not detected
0.0319(1)
2.6315
±
1.4224(7)
10.8707
±
11.5185(5)
4.8055
±
5.1964(6)
1.0979
±
0.8825(3)
1.5652
±
1.1452(6)
1.5983
±
1.3759(7)
1.0576
±
0.4749(2)
0.5013
±
0.2906(4)
0.2033
±
0.2681(2)
0.1507
±
0.1551(2)
0.1850
±
0.1551(4)
0.2786
±
0.3747(2)
Personal
0.0091(1)
Indoor-Home
0.0462(1)
Sea shore
0.0785(1)
0.0061
±
0.0026(3)
0.0045
±
0(3)
0.0069
±
0.0046(4)
The results show some new trends. For example, Lead is supposed to be the tracer
element for traffic. However, since last five years, unleaded petrol is served in India at all
petrol pumps. Due to this, the traffic site is not associated with high levels of Lead. Apart
from Na and Zn, the concentration of all other metals at sea-shore site is lower than other
that of the Background site. Thus, the result of PM5 concentrations, that sea-shore site
better represents background air quality, is confirmed by the results of metal analysis.
It was found that manganese was rarely detected at any of the sites. Though
cadmium was detected at all the sites, the concentration was very low and also there
wasn’t much difference in the concentration to credit the cadmium concentration to one
particular type of source. The concentration of chromium was also almost the same at
every site but what is significant is the absence of chromium from the sea-shore site. This
indicates that chromium contribution may be solely due to anthropogenic sources. The
concentration of copper was the highest at the traffic junction, which may indicate its
origin from auto exhaust. The concentration of iron was the same almost everywhere but
it was also found that in couple of homes the concentration was very high. The
concentration of sodium was almost the same everywhere but was slightly greater at the
sea-shore site confirming the expectation that sodium has got a marine origin. The
concentration of lead was almost the same at almost all the sites but was significantly low
at the marine front underlining its anthropogenic origin.
Amongst the metals, the Indian NAAQ standard is set only for lead, which is 1.0
3
µg/m . It has been observed that the personal exposure to lead (1.0576 µg/m3) is slightly
exceeding the set standard and at all the other sites the concentration of lead conforms to
the standards. The higher personal exposure to lead compared to other sites needs to be
investigated.
Personal Exposure Monitoring of Selected Respondents (Phase II)
As discussed in the study design, personal exposure to RPM(PM5) was monitored for two
respondents – one from middle and the other from lower income group. Both the
respondents were working in the same organization. However, as the questionnaire
survey revealed their housing characteristics were different. The MIG respondent was
living in an apartment like building where the ventilation was better and LPG was used as
the cooking fuel. On the other hand, the LIG respondent was residing in a chawl or slum
like locality where ventilation was relatively poor and where along with LPG, kerosene
and other low grade fuels were used for cooking. Table 3 shows the data-log of the
gravimetric analysis for the determination of personal exposure to RPM.
Table 3. Determination of personal exposure to Respirable Particulate Matter (PM5)
SR.NO.
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
M9_12
M20_1
S10_12
M19_12
M6_1
S6_1
S23_10
S2_12
S20_1
M16_12
M14_1
M22_1
S19_12
S24_12
M31_12
M30_10
M23_12
M29_11
S28_10
M23_10
M8_1
M12_12
W1
Gms
0.0384
0.03839
0.03827
0.03834
0.03864
0.03876
0.03769
0.03845
0.03804
0.03774
0.03848
0.03815
0.03844
0.03888
0.03823
0.03725
0.03854
0.03804
0.038
0.03416
0.03807
0.0385
W2
gms
0.03866
0.03874
0.03968
0.03883
0.03895
0.03995
0.03816
0.04063
0.0386
0.03791
0.03869
0.03832
0.03903
0.03909
0.03848
0.03762
0.03905
0.03853
0.03921
0.03498
0.03837
0.03869
W
gms
0.00026
0.00035
0.00141
0.00049
0.00031
0.00119
0.00047
0.00218
0.00056
0.00017
0.00021
0.00017
0.00059
0.00021
0.00025
0.00037
0.00051
0.00049
0.00121
0.00082
0.0003
0.00019
T1
min
2803
2000
2641
2992
2080
1935
3040
1851
2158
2531
2716
2018
2395
2228
2644
2519
2552
2823
2042
2349
2615
2751
Q1
litre/min
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
1.912
Volume
m3
5.360
3.824
5.050
5.720
3.977
3.699
5.812
3.540
4.126
4.840
5.193
3.858
4.580
4.260
5.055
4.816
4.880
5.397
3.904
4.492
5.000
5.260
Concentration
µg/m3
48.51
91.53
279.21
85.66
77.95
321.71
80.87
615.82
135.72
35.12
40.44
44.06
128.82
49.30
49.46
76.83
104.51
90.79
309.94
182.55
60.00
36.12
In the sample column, M represents the sample corresponding to the MIG respondent
whereas S represents the LIG respondents. The average personal exposure to RPM (PM5)
is 110.91 µg/m3 . This exceeds the Indian NAAQS(PM10) of 100 µg/m3 by a factor of
1.11. If PM5 concentration is converted to corresponding PM10 concentration, this factor
will increase further. However, in the case of MIG respondent, the average RPM (PM5)
exposure was 73.11 µg/m3 and in the case of LIG respondent it was 240.17 µg/m3. This
shows that due to their poor residential microenvironment as discussed above, the LIG
respondents are exposed to higher dose of air pollutants.
The filters with respirable dust were digested and analyzed for metals viz. Cu, Mn, Pb,
Na, Cr, Ca, K, Zn, Fe, As, Ni and Cd. Out of these Nikel (Ni) and Cadmium (Cd) could
not be detected. The results are shown in Table 4.
Table 4. Personal exposure to metals through Respirable Particulate Matter in µg/m3
Sample
M9_12
M20_1
S10_12
Cu
µg/m3
0.252
0.999
0.501
Mn
µg/m3
1.996
7.296
2.436
Pb
µg/m3
0.000
0.549
0.475
Na
µg/m3
102.332
202.694
107.525
Cr
µg/m3
19.030
37.918
22.554
Ca
µg/m3
71.866
41.056
51.465
K
µg/m3
10.373
26.281
13.010
Zn
µg/m3
4.030
6.172
4.851
Fe
µg/m3
9.869
60.277
18.495
As
µg/m3
5.466
14.540
35.683
M19_12
M6_1
S6_1
S23_10
S2_12
S20_1
M16_12
M14_1
M22_1
S19_12
S24_12
M31_12
M30_10
M23_12
M29_11
S28_10
M23_10
M8_1
M12_12
0.663
1.262
1.357
2.400
1.083
0.288
0.426
0.609
0.783
0.819
0.895
0.364
0.297
1.891
0.174
0.207
0.265
0.218
0.241
3.829
6.512
7.000
12.543
4.016
2.375
5.310
2.869
4.821
3.734
6.253
2.552
2.907
11.475
1.445
1.972
1.581
1.460
3.517
0.350
1.056
1.135
0.275
0.551
0.000
0.000
0.039
0.000
0.044
0.094
0.000
0.561
0.369
0.074
0.077
0.378
0.080
0.133
119.423
166.960
179.459
120.578
156.063
86.573
70.145
101.232
139.036
115.721
125.316
78.556
89.950
85.717
56.142
73.105
63.780
61.740
49.791
23.829
35.253
37.892
41.758
39.016
24.358
27.521
23.975
31.234
28.537
30.234
3.383
24.232
40.594
21.290
24.923
23.353
22.300
24.696
72.867
82.449
88.622
62.681
33.740
102.327
69.979
94.724
109.772
78.122
88.618
41.622
16.819
154.283
112.859
156.557
103.384
108.420
104.981
16.731
20.769
22.324
18.273
27.362
13.257
12.397
19.969
23.613
23.406
25.480
12.226
13.933
22.029
11.525
17.649
15.361
14.160
12.414
3.969
5.507
5.919
4.026
9.213
5.162
4.649
4.410
5.884
5.022
5.504
2.591
4.506
4.693
4.039
5.712
5.298
4.780
4.430
39.668
60.171
64.676
128.407
35.079
19.874
50.537
29.944
48.937
39.061
59.368
22.433
30.959
109.283
13.619
19.749
15.093
14.560
33.479
4.003
20.216
21.730
0.000
4.528
10.834
2.128
7.587
3.447
12.227
6.019
0.000
5.233
0.000
0.000
13.653
2.004
0.000
0.000
Mean
SD
0.727
0.588
4.450
3.047
0.284
0.331
106.902
41.687
27.631
8.804
83.964
35.392
17.843
5.356
5.017
1.249
41.979
30.261
7.695
9.061
Factor Analysis
SPSS software is used for factor analysis.
Descriptive Statistics
The table below shows the descriptive statistics for the variables, which are the metal
concentrations in the respirable dust.
Table 5: Descriptive statistics for metal concentration in µg/m3 (SPSS output)
De scri ptive Statistics
CU
MN
PB
NA
CR
CA
K
ZN
FE
AS
Mean
.72697
4.44998
.28362
106.90170
27.63083
83.96424
17.84282
5.01660
41.97903
7.69533
St d. Deviat ion Analys is N
.587872
22
3.047261
22
.331283
22
41.687424
22
8.804080
22
35.391805
22
5.355661
22
1.248900
22
30.261390
22
9.060756
22
For the 22 samples in this study, mean concentrations of Cu is 0.726μg/m3, Na is
106.9µg/m3 and so on. The standard deviation of Na is 41.687 while that of Cu is 0.587.
Correlation Matrix
The values in the Table 6 below show the correlations between the variables
Table 6. Correlation matrix for variables (output)
Correlation Matrix
CU
MN
PB
NA
CR
CA
K
ZN
FE
AS
CU
1.000
.941
.440
.546
.787
-.027
.588
.193
.941
.044
MN
.941
1.000
.349
.454
.765
.025
.501
.081
.991
-.049
PB
.440
.349
1.000
.637
.517
-.260
.361
.389
.311
.523
NA
.546
.454
.637
1.000
.606
-.387
.747
.533
.398
.480
CR
.787
.765
.517
.606
1.000
.084
.714
.609
.751
.148
CA
-.027
.025
-.260
-.387
.084
1.000
-.015
-.062
.049
-.160
K
.588
.501
.361
.747
.714
-.015
1.000
.687
.486
.162
ZN
.193
.081
.389
.533
.609
-.062
.687
1.000
.046
.250
FE
.941
.991
.311
.398
.751
.049
.486
.046
1.000
-.098
AS
.044
-.049
.523
.480
.148
-.160
.162
.250
-.098
1.000
These correlations indicate degree and nature of relation in the behavior of the
variables. The absolute value indicates the degree of the correlation while the positive or
negative sign indicates the nature of the correlation. Negative correlation between Cu and
Ca indicates that high values of Cu are associated with low values of Ca, while positive
correlation between Cu and Mn indicates that high values of Cu are associated with high
values of Mn.
Communalities
For each variable, the communality is the proportion of the variance of that variable
that can be explained by the common factors–the squared multiple correlation of the
variable with the factors. In the principal components solution, all initial communalities
are 1. After extraction, the estimate for each variable is the multiple R2 from regressing
all other variables on that variable. Table 7 below gives the communalities for the
variables.
Table 7. Communalities for the variables
Communalities
CU
MN
PB
NA
CR
CA
K
ZN
FE
AS
Initial
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Extraction
.949
.980
.678
.849
.900
.628
.816
.876
.988
.578
Extraction Method: Principal Component Analysis.
Communalities range from 0 to 1, with 0 indicating that common factors explain none
of the variance of the variable and 1 that they explain all the variance.
Total Variance Explained
The Table 8, in the form of SPSS software output, shows statistics for each factor
before and after the components are extracted. For principal components, the initial and
extraction statistics are always the same. After rotation the percentage of total variance
accounted for by all the factors does not change. However, the percentage accounted for
by each factor does change. The eigenvalues for the multivariate space of the original
variables are ordered by size. Each value is the total variance explained by a factor.
Table 8
Total Variance Explained
Component
1
2
3
4
5
6
7
8
9
10
Total
5.019
2.049
1.174
.843
.437
.262
.108
6.466E-02
4.014E-02
4.212E-03
Initial Eigenvalues
% of Variance Cumulative %
50.191
50.191
20.487
70.679
11.736
82.415
8.430
90.845
4.368
95.212
2.617
97.830
1.080
98.910
.647
99.556
.401
99.958
4.212E-02
100.000
Extraction Sums of Squared Loadings
Total
% of Variance Cumulative %
5.019
50.191
50.191
2.049
20.487
70.679
1.174
11.736
82.415
Rotation Sums of Squared Loadings
Total
% of Variance Cumulative %
3.808
38.080
38.080
2.465
24.654
62.734
1.968
19.681
82.415
Extraction Method: Principal Component Analysis.
By default, SPSS computes as many components as there are eigenvalues greater than
1. One criterion for determining the number of useful factors for extraction is to exclude
factors with variance less than 1 because they do no better than a single independent
variable.
Scree Plot
As shown in Figure 1, Screen Plot is the display of eigenvalues in the above table
plotted against their order (associated component). This is used to identify useful number
of factors to be retained by looking for large values that separate well from smaller
eigenvalues.
Figure 1
Scree Plot
6
5
4
3
Eigenvalue
2
1
0
1
2
3
4
5
6
7
8
9
10
Component Number
Component Matrix
The Table 9 displays the coefficients (loadings) that relate the variables to the
unrotated factors (components). Unrotated loadings and orthogonally rotated loadings are
the correlations of the variables with the factors. Generally loadings less than 0.2 are
suppressed.
Table 9
Component Matrixa
CR
CU
MN
FE
K
NA
PB
AS
CA
ZN
1
.915
.888
.834
.808
.801
.795
.640
.262
.530
Component
2
3
.233
-.366
-.499
-.550
.386
.452
.443
.687
-.449
.505
-.267
.643
.583
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
Rotated Component Matrix
The objective of rotation is to make larger loadings larger and smaller loadings
smaller than their unrotated values. This helps remove the loadings which do not
contribute significantly to the solution. This enables us to name the factors more
accurately. The results of the statistical analysis are shown in Table 10 below:
Table 10
a
Rotate d Com ponent Matrix
FE
MN
CU
CR
ZN
K
CA
AS
PB
NA
1
.991
.984
.944
.710
Component
2
.440
.310
.388
.205
.625
.925
.781
.211
.299
.345
.546
3
-.763
.684
.680
.633
Ex trac tion Met hod: Principal Component Analy sis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation c onverged in 5 iterations.
Factor analysis on the combined sample of MIG and LIG respondents brings out
three principal sources, which are as follows:
Source 1: Fe, Mn, Cu and Cr
Source 2: Zn, K
Source 3: As, Pb, Na and Ca
With reference to Table 2, Source 1 represents anthropogenic sources such as traffic and
industry. This source is the most dominant source. Source 3 represents group of metals
from natural sources such as marine, crustal and road dust. Source 2 represents metals
emanating from residential sources such as burning low grade cooking fuel.
Factor analysis on the MIG respondent samples resulted in two components as shown in
Table 11.
Table 11. Rotated Component Matrix for samples of MIG respondent
Cu
Mn
Pb
Component
1
0.902
0.906
0.292
2
0.264
0.205
0.736
Na
Cr
Ca
K
Zn
Fe
As
0.301
0.836
0.606
0.704
0.505
0.923
0.139
0.874
0.374
-0.573
0.539
0.532
0.155
0.932
The two sources brought out by the rotated component matrix are:
Source 1: Fe, Mn, Cu, Cr
Source 2: As, Na, Pb
Source 1 represents anthropogenic sources such as traffic and industry whereas Source 2
represents natural sources such as marine, crustal and road dust.
Factor analysis on the LIG respondent samples resulted in three components as shown in
Table 12.
Table 12. Rotated Component Matrix for samples of LIG respondent
1
Cu
Mn
Pb
Na
Cr
Ca
K
Zn
Fe
As
0.941
0.982
0.302
0.761
-0.245
0.201
-0.339
0.992
-0.588
Component
2
0.304
0.157
0.939
0.826
0.410
-0.590
0.205
0.327
0.410
0.458
-0.207
0.862
0.83
0.487
-0.617
3
Source 1: Fe, Mn, Cu, Cr
Source 2: Pb, Na
Source 3: K, Zn
In the case of LIG respondent, first two sources are same as in the case of MIG
respondents. However, there is the additional third source which may be linked to the
residential environment of the LIG respondent and to the use of low grade cooking fuels.
CONCLUSIONS
1. Air monitoring at the selected source sites revealed that RPM(PM5) concentration
is high and it exceeds the Indian NAAQ standard. The sea-shore site better
represented the background air quality. Background site was apparently affected
by anthropogenic sources.
Site
RPM (PM5) concentration in
Personal
Indoor-home
Traffic Junction
Background
Sea shore
µg/m3
149.71 ± 83.68
125.68 ± 73.16
254.37 ± 73.85
150.62 ± 78.58
111.65 ± 33.73
2. Metals’ concentrations were also lower at the sea-shore site. Analysis of metals’
concentrations in the respirable dust provided some guidelines for the source
apportionment. Lead was not associated with the traffic site. Manganese,
Chromium, Copper and Iron were linked with the anthropogenic sources such as
traffic. Sodium was associated with marine source. Zinc can be associated with
traffic as well as well as indoor residential site
3. The concentration of elements in the Respirable Particulate Matter (PM5) in
µg/m3 is as follows:
Element
Cu
Mn
Pb
Na
Cr
Ca
K
Zn
Fe
As
MIG Samples
(n =14)
Average
Std. Dev.
0.701
0.51
4.694
2.90
0.234
0.33
107.057
46.82
26.339
10.26
86.547
32.86
17.360
5.42
4.647
0.99
43.560
27.71
5.217
6.64
LIG Samples
(n = 8)
Average
Std. Dev.
0.738
0.21
4.141
1.94
0.204
0.24
116.19
8.91
27.109
4.03
72.74
19.15
20.632
6.68
5.126
0.34
38.975
20.44
17.976
15.65
Combined Samples
(n=22)
Average
Std. Dev.
0.727
0.59
4.450
3.05
0.284
0.33
106.90
41.69
27.631
8.80
83.96
35.39
17.843
5.36
5.017
1.25
41.979
30.26
7.695
9.06
The level of average Personal exposure to Lead is 0.284 µg/m3 which is below the
standard of 1 µg/m3.
4. The microenvironment of a person has a significant effect on personal exposure to
particulates. Factor analysis on the combined sample of MIG and LIG
respondents brings out three principle sources. Source 1 corresponds to group of
metals of industrial or anthropogenic(traffic) origin. Source 2 apparently
correspond to group of metals emanating from residential sources such as heating,
burning of low grade cooking fuel etc. Source 3 consists of metals which are
crustal elements(Ca), elements from natural sources(Na) and which may also
present in the resuspended road dust.
Factor analysis for samples of MIG respondent separately brings out two principal
sources. Source 1 corresponds to group of metals of anthropogenic origin such as
traffic and industry. Source 2 corresponds to group of metals from natural sources
such as marine, crustal and road dust.
Factor analysis for samples of LIG respondent separately brings out three
principal sources. Source 1 and 2 are the same as in the case of MIG respondent.
However, there is an additional third source which corresponds to the residential
environment of the LIG respondent. This source may be traced to burning of low
grade cooking fuel.
5. The air pollution due to anthropogenic sources such as traffic, industries is the
major source contributing to the personal exposure to respirable particulates.
Therefore, efforts should be made to reduce air pollution due to these sources. In
India, there are many old vehicles (more than 15 years old) still running on the
road. These should be gradually phased out. Transportation system should be
improved to reduce idling time of vehicles at the traffic signals. There are many
small scale industries of unauthorized nature. They should be identified and
brought under the purview of the regulatory authorities. Low income group
population is exposed to additional dose of air pollutants due to their poor
residential environment. Efforts should be made to spread the use of higher
quality fuels such as LPG in these localities. Use of low grade cooking fuel such
as kerosene, bio-fuel such as wood should be discouraged. Awareness about
necessity of good ventilation should be increased.
ACKNOWLEDGEMENT
The financial support provided by the All India Council for Technical Education, New
Delhi under Thrust Area Programme in Technical Educaion (File No. :
8018/RDII/TAP(289)/99-2000) is greatly acknowledged. The support provided by my
students Mr. Zaheer Shaikh, Mr. Lokesh Padhye, Mr. A. Krishna Mohan, Miss Kavita
Deshmukh, Mr. Hariharan Iyer, Mr. Anand Kulkarni and Miss Prashanti Shetty in the
sampling and analysis work is also acknowledged.
REFERENCES
1. The World Bank – South Asia Urban Air Quality Management Home Page. See
http://www.worldbank.org/sarubanair (Accessed January 2005).
2. Brandon, C.; Homman, K. The cost of inaction: valuing economy-wide cost of
environmental degradation in India. Paper presented at the United Nations
University Conference on the Sustainable Future of the Global System, Tokyo,
16-18 October 1995
3. Kulkarni, M.M. Ph.D. Thesis, IIT Bombay, Mumbai, November, 1998
4. Sharma,V.K.; Patil, R.S. Monitoring and Modeling of atmoshpheric aerosols in an
industrial region of Bombay, Environmental Technology, 13, 1043-1052
5. South Asia Urban Air Quality Management Briefing Note No. 14. The World
Bank, August 2004
6. Kulkarni, M.M.; Patil R.S. Monitoring of daily integrated exposure of outdoor
workers to respirable particulate matter in an urban region of India. J.
Environmental Monitoring and Assessment, 1999, 56, 129-146.
Download