Michael J. Daniels FD, 2 Jonathan M. Samet,3 and Scott L. Zeger2. Estimating ... Francesca Dominici AM, Scott L. Zeger, and Jonathan M. Samet. Airborne ...
Accepted Manuscript Receptor model based source apportionment of PM10 in the metropolitan and industrialized areas of Mangalore Gopinath Kalaiarasan, Raj Mohan Balakrishnan, V.V. Khaparde PII: DOI: Reference:
S2352-1864(16)30108-0 http://dx.doi.org/10.1016/j.eti.2016.10.002 ETI 92
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Environmental Technology & Innovation
Received date: 9 March 2016 Revised date: 16 September 2016 Accepted date: 18 October 2016 Please cite this article as: Kalaiarasan, G., Balakrishnan, R.M., Khaparde, V.V., Receptor model based source apportionment of PM10 in the metropolitan and industrialized areas of Mangalore. Environmental Technology & Innovation (2016), http://dx.doi.org/10.1016/j.eti.2016.10.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Receptor Model based Source Apportionment of PM10 in the Metropolitan and
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Industrialized areas of Mangalore
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Gopinath Kalaiarasan1, Raj Mohan Balakrishnan2*, V V Khaparde3 1 Department of Chemical Engineering, National Institute of Technology Karnataka
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Surathkal, India
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2
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Surathkal, India
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3
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(NEERI), Nehru Marg, Nagpur, India.
Department of Chemical Engineering, National Institute of Technology Karnataka
Air Pollution Control Division, National Environmental Engineering Research Institute
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*
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Chemical Engineering, National Institute of Technology Karnataka Surathkal, India.
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Phone Number: +91 824 2473042; Fax: +91 824 2474033.
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Abstract:
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PM10 samples were collected from a traffic site (Town hall) and industrial site (KSPCB) of
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Mangalore, India during 2014. Chemical characterization using ICP-MS proclaimed the
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presence of twelve trace elements (Ca, Cd, Cr, Cu, Fe, Pb, Mg, Mn, Sr, Ti, V, and Zn) from
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traffic site and six trace elements (Cd, Ni, Pb, K, Cr and Zn) from industrial site. Source
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apportionment has been done using Enrichment Factors (EF’s) and Principal Component
19
Analysis (PCA). EF’s outcome using Fe as reference element showed higher enrichment for
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Zn, Pb, Cd, V, Cr, Ti and Cu compared to Sr, Ca, Mg and Mn. Similarly EF’s calculated for
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industrial site using K as a reference element exhibits higher enrichment for Cd, Ni, Pb, Cr and
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Zn. Principal Component Analysis using varimax rotation distinguishes three sources
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(vehicular sources, crustal sources and brake wear emissions) for PM10 particles at traffic and
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two sources (steel and non-ferrous metal industries emissions and Coal/fuel oil combustion
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emission) at industrial site. This is the first known work for source identification of particulate
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matter (PM10) in coastal industrial city Mangalore.
Corresponding author: Dr. Raj Mohan B. Associate Professor & Head, Department of
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Keywords: Emissions, Enrichment Factor, PM10, Principal Component Analysis, Trace
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Elements
30 31 32 33 34
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1. Introduction
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Rapid economic, urban and industrial growth has promoted air pollution as a prime concern in
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the Indian scenario. Among the top 100 world cities with worst PM10 pollution 37 cities from
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India has been featured by the World Health Organization (WHO) [1]. Of the 3 million
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premature deaths in the world that occur each year due to out-door and in-door air pollution,
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the highest number are assessed to occur in India, resulting in high concentration of pollutants
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in the ambient air[2].These increasing apprehensions over air quality have hastened the thrust
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to conduct numerous studies for determining the chemical composition and sources
43
contributing to atmospheric air pollution. One major type of air pollutant that affects the
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environment, human health and overall climate change is the particulate matter (PM). These
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particulate matters are generally referred to as ultrafine, fine and coarse particles, with
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aerodynamic diameters of approximately 1 µm, 2.5 µm and 10 µm, respectively. Among the
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various PM’s, PM10 is considered as an inhalable particle [3] and several studies reported the
48
impact of PM10 on mortality and respiratory diseases. [4, 5, 6, 7, 8]. An association between
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particulate matter and heart diseases was recognized in the last century [9]. These disastrous
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particulates are introduced into the atmosphere due to various natural sources and
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anthropogenic activities such as fossil fuel combustion which emits large quantities of primary
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pollutants and gaseous compounds that convert into particles within hours to days after
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emission [10]. Similarly emissions from vehicles, industries, domestic heating and cooking
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purposes [11] and emissions from iron and steel industries, aerosols derived from coal and fuel
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oil combustion for industrial processes were also considered as other important sources
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contributing the urban and industrial air pollution. Hence, it is necessary to define the sources
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contributing the airborne particulate matter which in turn helps to validate and improve the
58
emissions inventory to structure effective policy, regulatory measures and control strategies to
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reduce air pollution to acceptable levels.
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Generally, receptor modelling technique is involved in identifying the sources, since it is
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recognized as a valuable tool which provides both theoretical and mathematical framework for
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quantifying source contributions at the receptor site [12]. The modelling executes by taking
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speciated concentrations measured at a receptor (or point of impact) and works in reverse to
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determine the contributions of sources to measured concentrations. The relationship between
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measured concentrations and emission sources is inferred, without the need for simulating
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dispersion processes, as the model works directly with concentrations rather than emissions.
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The information required to conduct receptor modelling is simply knowledge of the chemical 2
68
composition of particulate concentrations at the source and receptor. Additional information
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such as meteorology, topography, location and magnitude of sources, while useful for
70
interpretative purposes, is not vital [13].
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Among the various multivariate receptor models used for source apportionment studies,
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Principal Component Analysis (PCA) is recognized as one of the most commonly used receptor
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technique for source identification[14]. Based on this approach, numerous source
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apportionment studies on particulate matter for major Indian cities has been carried out by
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many researchers ([12];[11];[15]; [16];[17]; [18]).
76
In the recent past, Mangalore has been claimed to be one of the growing commercial city in
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southern part of India because of its expanding industries (mainly chemical and fertilizer
78
industries) and developments leaning towards information technology, biotechnology and
79
related industries. The emissions from these industrial and commercial firms in addition to the
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residential complexes subsidize to increased air pollutant loads in the atmosphere. These air
81
pollutants mainly comprise of heavy metals, carbon and its derivatives in particulate and
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gaseous forms and pose a very high threat on the health of the public in this region. Hence a
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continuous monitoring and analysis of the air pollutants level in the atmosphere of Mangalore
84
becomes essential. Certain investigations carried out by Hegde and warrier et al. [19, 20] deals
85
with the chemical characteristics of the general atmospheric aerosols and their ionic
86
concentrations in the ambient atmosphere of Mangalore. However, the above mentioned
87
studies don’t deal with particles with aerodynamic diameter < 10µm and their source
88
contribution in the ambient atmosphere of Mangalore region. Hence the present study bridges
89
this gap by focusing on estimating the mass concentration, characterization and source
90
identification of PM10 particles prevailing in the ambient atmosphere of Mangalore.
91
The study has been carried out by choosing two hot spots, one being a traffic site (Town hall)
92
and other being an industrial site Karnataka State Pollution Control Board (KSPCB) in
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Mangalore region. PM10 particles have been collected from the study areas by using Respirable
94
suspended Particulate Matter (RSPM) sampler and the elemental composition of the particles
95
have been analysed using Inductively Coupled Plasma – Mass Spectrometry (ICP-MS). The
96
obtained elemental data have been used for principal component analysis (PCA) studies to
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identify the likely sources contributing PM10 particles at the receptor site.
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3
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2. Materials and Methods
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2.1 Study area
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Mangalore is located at 12.87°N 74.88°E in the Dakshina Kannada district of Karnataka, India
102
bounded by Arabian Sea in the west and Western Ghats in the east (Figure.1). It has a tropical
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monsoon climate and receives about 95 per cent of its total annual rainfall within a period of
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about six months from end of May to October, and a post monsoon period prevails from
105
November to February, hot and humid from March to May.
106
2.2 Sampling sites
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Particulate matter samples for industrial site were collected from the rooftop of Karnataka State
108
Pollution Control Board (KSPCB) building in Baikampady industrial area. The site is located
109
close to smelting industries, iron and steel industry, fabrication industry, tools and hard metal
110
manufacturing industries which all are coal and fuel oil fired. In the same way samples for
111
traffic site were collected from the roof of Town hall (Mangalore Corporation Building). This
112
site is bounded by hospitals, schools, play grounds, major road junctions, commercial malls
113
and supermarkets, accompanying with heavy traffic of Hampankatta road.
114
115
Figure.1 Location of Sampling Sites at Mangalore City
4
116
2.3 Sampling and analysis
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The air samples were collected as per Guidelines for the measurement of ambient air pollutants
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volume – I [21] for a period of 24 hours (8×8×8) at a flow rate of 1m3/min. The samples were
119
collected every 8 hours (06:00 to 14:00, 14:00 to 22:00, and 22:00 to 06:00) on Glass Fibre
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Filters (Whatman GF/A 20.3×25.4 cm) using High Volume RSPM Samplers (Envirotech
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APM460 BL). The mass concentration of PM10 particles were measured by gravimetric
122
technique using weighing balance (Oahu pioneer with accuracy 0.0001g). The filter papers
123
were kept in a desiccator before and after sampling for 24 hours at a temperature of 27+30C
124
and at a Relative Humidity (RH) of 55 + 2% to remove the moisture present in them.
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A punch of 4.8 cm diameter was taken from the sampled filter paper and digested [22] in a
126
microwave digester (CEM’s MARS 5). The digested samples were then filtered (Whatman
127
No.1) and made up to 50 mL using MilliQ water (Siemens Labostar, TWF water purification
128
System (Type I, III) of resistivity 18MΩ-cm). The digested samples were taken for trace
129
elements detection by using ICP-MS (Perkin Elmer Nexion 300X). The obtained elemental
130
data were then subjected to source categorization and identification using Enrichment Factor
131
analysis and Principal Component Analysis (PCA)
132
2.4 Enrichment Factor
133
Enrichment factors (EF’s) are used to differentiate the sources of elements obtained in the
134
samples from anthropogenic and natural sources. These enrichment factors can be estimated
135
by using the upper continental crust composition data obtained from CRC Handbook of
136
Chemistry and Physics [23]. The general formula used for calculation is
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EF =
(X/Ref)PM (X/Ref)Crust
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Where, ‘X’ is the concentration of an element in both the collected sample and upper
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continental crust; ‘Ref’ is the reference element used for finding the source contribution. The
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elemental sources obtained are categorized into anthropogenic and natural emissions according
141
to the EF values obtained. For EF< 10 it is assumed that the elements have its origin from
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natural sources and for EF values >10 they are derived from anthropogenic sources [16].
143
However some studies have slight changes in EF values and their source categorization[24,
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25]. Generally, Na, K, Al, Mg, Ca, Mn and Fe are used as the reference elements. In the present
145
study the annual mean percent compostion of elements in PM10 samples at both sites exhibited
5
146
higher correlation for K and Fe elements. Thus K and Fe were used as reference elements for
147
industrial site and traffic site respectively[26].
148
2.5 Principal Component Analysis
149
PCA is a statistical tool that uses an orthogonal transformation to convert the original set of
150
observations of possibly inter-correlated variables into a set of values of linearly
151
uncorrelated variables called principal components, which have linear combinations to the
152
original variables[27].The outcomes of PCA are factor loadings which reveal the amount of
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contribution of a variable to a particular principal component and its similarity with other
154
variables. Higher the loading factor of a variable indicates its contribution to the particular
155
principal component. In practice, factor loadings > 0.5 are selected for the principal component
156
interpretation [28] and values closer to 1 indicate a strong correlation with corresponding
157
principal component. The eigenvalues > 1 obtained for the principal components in a PCA are
158
considered to have higher statistical significance [29] . In the present study PCA analysis was
159
performed using (Developer: IBM Analytics) SPSS 16 software.
160
3. Results and Discussion
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3.1 Particulate matter mass concentration
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The particle mass concentration of PM10 samples ranges from 46.30 µg/m3 to 231.48 µg/m3and
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55.67 µg/m3 to 188.20 µg/m3 in traffic and industrial sites (Figure 2 and Figure 3) with an
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annual average of 131.94 µg/m3 and 122.54 µg/m3 respectively (Table 2 and Table 3).The
165
above mentioned values are found to be higher than the PM10 emission standards proposed by
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Central Pollution Control Board (CPCB) Govt. of India (Table 1). These values show a
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prominent difference in particulate loads between the sites. This difference in concentration
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reveals that a large fraction of the total particles emitted from traffic site have an aerodynamic
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diameter of (PM2.5 – PM10) compared to industrial sites. A similar pattern of results in Indian
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scenario for particulate matter emissions has been critically reviewed by P.Pant stating that
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vehicular emissions possess a higher concentration of PM particles compared to Industrial
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emissions [30]. The particulate mass concentration values at both sites during post monsoon
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(November to February) were found to be higher due to temperature inversion during which
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there would have been a significant decrease in mixing height. This situation causes a decrease
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in atmospheric temperature which in turn results with higher mass concentration of particulate
176
matter in the ambient atmosphere. Similarly the particulate concentrations observed during
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March to May showed lower values compared to post monsoon period at both the sites because 6
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of the increase in temperature which would have cause a substantial increase in mixing height.
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Heavy precipitation during monsoon season (June to October) leads to a lower mass
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concentrations in the study area. The statistical summary of PM10 particles and trace elements
181
of both the sites are given in Table2 and Table3.
182 183 184 185 186 187 188 189 190 191 192 193 194 195 196
Figure.2 Graphical plot of PM10 concentration in Traffic site
197 198 199 200 201 202 203 204 205
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Figure.3 Graphical plot of PM10 concentration in Industrial site
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Table.1 Air quality standards proposed by India
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210
Pollutant
Time Weighted Average
Concentration in ambient air (μg/m3)
PM10, μg/m3
Annual*
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*Central Pollution Control Board India (CPCB) Monitoring & Analysis Guidelines Volume-I 2009
211 212
Table.2 Statistical summary of PM10 (µg/m3) and Trace elements (ppm) at Traffic Site
213
Pollutants PM10 Ca Cd Cr Cu Fe Mg Mn Pb Sr Ti V Zn
Mean 131.94 26.22 0.02 0.08 0.05 5.41 4.71 0.14 0.53 0.30 59.75 12.87 30.24
Median 113.75 32.15 0.02 0.05 0.05 5.21 5.81 0.11 0.25 0.31 59.56 17.02 37.12
SD 57.17 12.40 0.02 0.10 0.03 2.80 2.19 0.12 0.75 0.14 33.18 7.45 14.66
214 215 216
Table.3 Statistical summary of PM10 (µg/m3) and Trace elements (ppm) at Industrial site
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Pollutants
Mean
Median
SD
PM10
122.54
104.55
47.01
Cd
0.09
0.08
0.04
Cr
0.15
0.18
0.13
K
3.81
4.22
2.21
Ni
1.82
1.83
0.11
Pb
0.03
0.01
0.53
Zn
0.30
0.30
0.09
218 219
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3.2 Enrichment Factor
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Enrichment Factors were calculated (Figure 4 and Figure 5) using Fe and K as a reference
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element for samples collected from traffic and industrial sites respectively. In the traffic site,
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EF values for Zn, Pb, Cd, V, Cr, Ti and Cu were found to be greater than 10. This phenomenon
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reveals that these elements are enriched due to anthropogenic emissions. However elements
225
like Sr, Ca, Mg and Mn possessing EF values less than 10 reveal their origin from natural
226
sources. In industrial site, EF for Cd, Ni, Pb, Cr and Zn were found to be greater than 10 which
227
divulges that the contribution of these elements are from anthropogenic emissions.
228 229 230 231 232 233 234 235 236 237 238
Figure.4 Enrichment Factors of elements using Fe as reference element from Traffic Site
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Figure.5 Enrichment Factors of elements using K as reference element from Industrial Site
249
9
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3.3 Source identification using Principal Component Analysis
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The results of PCA using varimax rotation for twelve elements are shown in Table 4. Three
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principal components (PC) were yielded namely PC1, PC2 & PC3 and are labelled as crustal
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sources, vehicular emissions and brake wear emissions. The results indicate that PC1 is found
254
to have higher variance of 46.09% compared to PC2 and PC3 with higher loadings for Ca, Fe,
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Mg, Mn and Sr. These trace elements would have been contributed from the playground
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activities, vehicular transport on the unpaved roads, wind driven road dust deposited on the
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shoulders of the road and construction activities adjoining the sampling site. Thus majority of
258
the PM10 particles under PC1 were found to be emitted from crustal sources. Similar kind of
259
observations have been made by Kothai et.al and P. Salvador et al. obtaining higher loadings
260
for crustal elements such as Mg, Fe, Mn, Ca and Sr, exhibiting the soil dust as a main marker
261
for crustal source [16, 31].
262
The factor loadings under PC2 given in Table 4 are found to be higher for Pb, Zn, V, Ti and
263
Cu with 26.03% variance. The elements under PC2 may be contributed from the vehicular
264
emissions, since the sampling site is a major city junction flooded with traffic during the day
265
possessing a higher density of vehicles from all categories (petrol vehicles, diesel vehicles,
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heavy duty vehicles and CNG fuelled auto rickshaw). Thus the vehicular exhaust contributed
267
a larger fraction of the emissions in addition to brake lining dusts and tyre wear emissions.
268
Hence the elements bearing higher factor loadings in PC2 are categorized as vehicular
269
emissions. Also S. Lawrence et al. found that elements like Fe, Cu, Ba, Ni, Pb, Zn, V and Ti
270
showed good correlation with traffic volume[32].
271
Compared to PC1 and PC2, PC3 has a lower variance of 24.90% with higher loadings for
272
elements Cd, Cr and Cu. The contributions of these elements are from brake lining abrasions
273
from passenger cars, brake wear and brake drum emissions. Being a traffic site it is evident that
274
these elements would have been emitted from the wear and tear of brake pads and brake linings.
275
Hence the elements of this PC3 are categorized to be brake wear emissions. Experimental
276
findings by Westerlund & Johansson confirms the presence of these pattern of elements from
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the emission abrasions of brake linings and brake pads [33].
278
The PCA analysis of the six elements estimated from the samples collected at industrial site
279
results with two principal components PC1 & PC2 and their factor loadings are shown in Table
280
5. The observations unveils that PC1 indicates the emission sources are contributed are from
10
281
steel and non-ferrous metal industries emissions, whereas emissions of PC2 are from coal and
282
fuel oil combustion.
283
PC1 acquired a higher variance of 43.27% compared to PC2 with higher loadings for K, Cd
284
and Zn. These elements have been contributed from the emissions of various grades of alloy
285
processing industries, industrial heaters, propellers and spring manufacturing industries located
286
in the vicinity of the sampling site. Thus it is evident from the results that the elements under
287
PC1 are emitted from steel and non-ferrous metal industries those involved in metallurgical
288
production. Similar kind of observations has been made by Owoade et al. from the emissions
289
of a scrap iron and steel smelting industrial area [34]. PC2 with a variance of 28.51% found to
290
exhibit higher loadings for Cr, Ni and Pb. This elemental pattern suggests that the coal and fuel
291
oil combustion carried out for raw material processing from various industries at the sampling
292
site bears the contribution of these emissions. Lee and Hieu investigated and found a similar
293
kind of trace metals contribution to the ambient atmosphere from coal and fuel oil combustion.
294
[25]. Source apportionment of PM10 particles at both traffic and industrial sites are shown in
295
Figure.6 and Figure.7
296
Table.4 PCA factor loadings using varimax rotation for samples from Traffic Site Elements Ca Cd Cr Cu Fe Mg Mn Pb Sr Ti V Zn % variance Probable Sources
PC 1 0.880 0.803 0.855 0.949 0.905 46.09 Crustal sources
PC 2 0.556 0.938 0.985 0.823 0.876 0.937 26.03 Vehicular emissions
297 298
11
PC 3 0.969 0.867 0.755 24.90 Brake wear emissions
299
Table.5 PCA factor loadings using varimax rotation for samples from Industrial Site Elements Cd Cr K Ni Pb Zn % variance Probable sources
300 301 302
PC 1 0.870 0.884 0.824 43.27 SNM industries emissions
PC 2 0.697 0.844 0.650 28.51 CFC emissions
SNM-Steel & Non-Ferrous metal industries; CFC-Coal/Fuel oil combustion
303
Brake wear emissions 33%
304 305
Crustal sources 34%
306 307 308 309 310 311 312
Figure.6 Source apportionment of PM10 particles collected from Traffic Site
313 314 315 316 317 318 319 320 321 322 323 324
Figure.7 Source apportionment of PM10 particles collected from Industrial Site
Vehicular emissions 33%
325 326
4. Conclusions:
327
Air samples collected from Town Hall (traffic site) and KSPCB (industrial site) revealed that
328
the concentration of PM10 particles prevailing in the ambient atmosphere of coastal industrial
329
city Mangalore is relatively high compared to the CPCB standards proposed by Govt. of India.
330
Chemical characterization of these samples were carried out using ICP-MS which revealed 12
331
twelve trace elements for traffic site and six trace elements for industrial site respectively.
332
Enrichment Factor (EF) analysis on traffic site showed higher enrichment for Zn, Pb, Cd, V,
333
Cr, Ti and Cu confirming their contribution from anthropogenic sources, Similarly EF values
334
for Sr, Ca, Mg and Mn were found to be relatively low which endorses their contribution from
335
crustal sources. However EF analysis of industrial site showed higher enrichment for all the
336
trace elements Cd, Cr, K, Ni, Pb and Zn revealing their origin from anthropogenic emissions.
337
PCA studies yielded three components in the traffic site which categorized the major sources
338
of these elements into crustal and soil sources, vehicular sources and brake wear of passenger
339
cars respectively. Subsequently in industrial site two components showed their origin from
340
steel and non-ferrous metal industries and coal/fossil fuel combustion processes. Hence the
341
study reveals that urban and industrial areas of Mangalore are under sustained pollution threat
342
from automobile and industrial emissions. The results of this study can be used to structure
343
effective policy and control measures for particulate concentrations that violated the standards
344
proposed by CPCB Govt. of India in the study area.
345 346
5. Acknowledgement
347
The work is financially supported by Karnataka State pollution Control Board (KSPCB), India.
348
B.O. Sanction Order No. PCB/134/COC/CEPI/2013-14/1965. The authors thankfully
349
acknowledge KSPCB for their financial support for carrying the work effectively.
350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371
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HIGHLIGHTS
First known work for PM10 source identification in coastal industrial Mangalore city.
PM10 concentration found to be relatively higher than the standards proposed by CPCB.
Seasonal changes affect the particle concentration to a greater extent.
PCA identified three and two sources for traffic and industrial sites respectively.
Vehicular and steel industries emissions are dominant in traffic and industrial sites
