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Atmospheric Environment 39 (2005) 6015–6026

www.elsevier.com/locate/atmosenv

Assessment of ambient air PM10 and PM2.5 andcharacterization of PM10 in the city of Kanpur, India

Mukesh Sharma�, Shaily Maloo

Environmental Engineering and Management Program, Department of Civil Engineering, Indian Institute of Technology Kanpur,

Kanpur 208016, India

Received 17 September 2004; received in revised form 5 December 2004; accepted 8 April 2005

Abstract

This research was initiated to study the air quality in the city of Kanpur, India in terms of PM10 and PM2.5 and

chemical composition in terms of heavy metals and benzene-soluble organic fraction (BSOF) for PM10. Three sampling

locations, Indian Institute of Technology (IIT) (control site), Vikas Nagar (VN) (commercial site) and Juhi Colony (JC)

(residential site) were selected. Total forty-seven 24-h samples were collected for PM2.5 and PM10 during October

2002–February 2003 at these locations. The collected PM10 samples were subjected to chemical analysis for

determination of heavy metals and toxic organic fraction by measuring BSOF. PM10 (45–589mgm�3), PM2.5

(25–200mgm�3), BSOF (1–170mgm�3) and heavy metals were highest at VN followed by JC and IIT. The study

concluded that the overall air quality in the city of Kanpur was much inferior to other cities in India and abroad.

Similar to PM10 and PM2.5, heavy metals were almost 5–10 times higher than levels in European cities. The study

concluded that there was a need to address the issue of PM2.5 monitoring and control. Because regular PM2.5

monitoring may take some time, a linear model for predicting PM2.5 using routinely monitored parameters PM10 and

BSOF was suggested for preliminary assessment. The model was checked for its adequacy and it was validated.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: PM10; PM2.5; Fine particulate; Benzene-soluble fraction; Heavy metals; India

1. Introduction

Particulate matter (PM) has been widely studied in

recent years due to its potential health impact and need

for its control. Studies indicate that finer PM has the

strongest health effects (Schwartz et al., 1996; Borja-

Aburto et al., 1998). The sources, characteristics, and

potential health effects of PM10 (particles with aero-

dynamic diameter less than 10 mm) and PM2.5 (particles

with aerodynamic diameter less than 2.5 mm or fine

particles) are very different; the latter can more readily

e front matter r 2005 Elsevier Ltd. All rights reserve

mosenv.2005.04.041

ing author.

ess: mukesh@iitk.ac.in (M. Sharma).

penetrate into the lungs and are therefore more likely to

have short- and long-term effects such as premature

death, increased respiratory symptoms and disease,

decreased lung functions and alterations in lung tissues.

Various health effects of PM, from less serious to very

serious ones, are associated with its specific chemical and

physical (but mostly chemical) components (Dockery

et al., 1993). The particle size is very important both in

terms of deeper penetration into the lungs and fine

particles are carriers of toxic air pollutants including

heavy metals and organic compounds. Exposure to

heavy metals can cause adverse health effects including

metal toxicity. Many organic pollutants like poly-

cyclic aromatic hydrocarbons (PAH) are carcinogenic,

d.

ARTICLE IN PRESSM. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–60266016

mutagenic and genotoxic in small concentrations. It is,

therefore, important to study both particle size distribu-

tion and their chemical composition.

In view of the adverse health effects of finer

particulate fraction, the US Environmental Protection

Agency (USEPA) discontinued monitoring of total

suspended particulates (TSP) in 1987 in favor of PM10

and later achieved its first year of nation-wide monitor-

ing of PM2.5 in 1999. Most of the developed countries

are now targeting PM2.5 for monitoring and control.

However, there has been no attempt in India or in

developing countries in general to study PM2.5. It has

been discussed in various meetings at governmental

levels (in India) as to what possibly could be done to

assess PM2.5 levels in the situation where it will take

some time to garner the resources including finances,

equipment, revision in sampling protocols, etc. for

regular sampling of PM2.5. An acceptable procedure to

infer PM2.5 levels from existing monitoring setup can be

useful in assessing levels of PM2.5.

The main components of PM2.5 are organic matter

(30–60%), metals (o1%), nitrates and sulfates (25–35%),

elemental carbon (5%) and rest others (USEPA, 1995). In

order to represent PM2.5 in an indirect way, one needs to

choose parameters to account for organic and inorganic

fractions of PM2.5. As regards organic component,

benzene-soluble organic fraction (BSOF) is an indicator

of aromatic and neutral compounds (see section on

literature review). As regards inorganic fraction, it can

possibly be represented by a fraction of PM10. Therefore,

there can be a way to represent PM2.5 based on levels of

PM10 if the matter is investigated in detail.

The present study was designed and completed to

answer some of the questions that what can be done

until the entire system gears up for the sampling of

PM2.5. The objectives of the study were as follows:

(i)

To assess the air quality in the study area, city of

Kanpur, India, in terms of

(a) inhalable PM10 and respirable PM2.5;

(b) heavy metals and BSOF contents in PM10.

(ii)

To explore the possibility of using PM10 and BSOF

levels as an indicator of PM2.5 levels.

2. Literature review

TERI (2001) has reviewed the air-quality data

available in India for the past 10 years and found a

large gap in data. It was found that only a few studies

have done speciation of PM10 for chemical composition

(in India). One study has reported PAH levels in

Mumbai (Venkataraman and Kulkarni, 2000) and other

two studies have reported heavy metals, one in Delhi

(Balachandran et al., 2000) and other in Mumbai

(Kumar et al., 2001) in PM. There has been no attempt

to study the total air quality in terms of fine and coarse

fractions and their speciation. The possible reasons for

lack of studies on finer fraction and organic speciation

are one-time large investment (in instruments), opera-

tional cost and the required quality control. For

example, PAH analysis for one sample may cost up to

US$ 500 (Sharma, 1994).

In Western Europe, North America and Western

Pacific, except China, annual mean TSP concentrations

range between 20 and 80mgm�3(Sivertsen, 2002), and

PM10 levels are between 10 and 55mgm�3. High TSP and

PM10 annual mean concentrations are found in South

East Asia (Sivertsen, 2002) ranging between 100 and

400mgm�3 for TSP and 100–300mgm�3 for PM10. High

annual TSP concentrations of 300–500mgmm�3 are

observed in the large cities of China. In Lahore (Pakistan),

TSP mean annual values were 607–678mgm�3 (Smith

et al., 1996). Similarly, the PM10 levels in Indian cities

have been found to be in the range of 100–400mgm�3

(Sharma et al., 2003). This indicates that the pollution

load in south and southeast Asian countries is several

times higher than European countries in terms of TSP and

PM10. The PM2.5 (in mgm�3) levels in some developed

countries were: 6 in Brickenes, Norway (NILU, 2002a); 19

in Bern, Switzerland (NILU, 2002a) and 52 in Taiwan

(Fung and Wong, 1995). There is no data of PM2.5 in

developing countries. But going by the trends of TSP and

PM10 levels, one would expect levels of PM2.5 to be

considerably high in south and southeast Asian countries.

2.1. Chemical speciation of particulates

Chemical speciation is essential for establishing more

specific relationships between particle concentrations

and measures of public health (Chow and Watson,

1998). Chemical speciation also facilitates understanding

of PM temporal and spatial variations, source/receptor

relationships, and the effectiveness of emissions reduc-

tion strategies. It is essential that the chemical speciation

of PM be undertaken even in developing countries.

2.1.1. Heavy metals

Schroeder et al. (1987) has reported 30–35 heavy

metals in atmospheric PM. Manganese, copper, zinc,

cadmium, chromium, iron, nickel, potassium, calcium,

vanadium, barium, arsenic, selenium and strontium are

the most commonly found metals in the pollution

sources and have been studied widely. Metals associated

with the finer fraction mostly originate from the

incomplete combustion of carbon-containing materials

from motor vehicles, power plants, smelters, incinera-

tors, cement kilns and home furnaces.

The metals derived from natural sources are usually

present in the coarse fraction. Re-suspension of roadside

dust and soil is another potential source of heavy metals.

ARTICLE IN PRESS

Table 1

Particulate heavy metal concentrations

Heavy metals Danisha (ngm�3) Delhib (ng m�3) Argentinac (ngm�3) Taiwand (ngm�3) Italye (ngm�3)

Pb 15.7 660 64 133 72

Ni 2.7 420 3.2 — 5

Cd 0.6 80 0.41 — 18

Cr 4.4 280 4.3 656 11

Zn 47.4 — 273 251 56

Fe 640 15 000 (nearly) 1183 6990 11

Mn 19.8 — 26 31 23

Cu 20.4 — 30 49 9

Ca — — 5343 4450 —

aKamp (2002).bBalachandran et al. (2000).cBilos et al. (2001).dFung and Wong (1995).eRastoga et al. (2002).

M. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–6026 6017

Iron is a metal present in significant concentration in

most emission sources of air pollution particles (Schroe-

der et al., 1987). Prior to banning of lead in gasoline,

vehicles were the major source of lead. In addition, the

other important sources of lead are re-suspended soil and

oil burning. Fly ash from coal-fired power plants is rich

in mineral content. It has high concentrations of iron,

zinc, lead, vanadium, manganese, chromium, copper,

nickel, arsenic, cobalt and cadmium (Schroeder et al.,

1987). Table 1 presents typical levels of metals in the

atmosphere in developed countries and in Delhi, India.

The average lead level in Delhi in the year 1998 (i.e.

after introduction of unleaded gasoline in 1995)

(Balachandran et al., 2000) was 660 ngm�3, whereas

at other places, lead levels were below 100 ngm�3

(Table 1). Not only lead, but the levels of other metals

like nickel, cadmium and chromium were also high in

Delhi. Chromium has been found to be almost absent at

all places except in Taiwan and in Delhi. The level of

iron in the residential area of Delhi has been reported to

be about 15mgm�3, which is twice the levels in Taiwan,

and several times higher than that in other places. In

summary, similar to PM levels, the levels of toxic metals

in air can be possibly higher in south and southeast

Asian cities than European and other cities.

2.1.2. Toxic organic compounds

The carbonaceous fraction of ambient PM consists of

elemental carbon and a variety of organic compounds

(organic carbon). As per USEPA (1995), organic carbon

forms a major fraction of PM2.5 (30–60%). The major

organic compounds identified in the ambient aerosol

include alkanes, alkenes, fatty acids, alcohols, aromatics,

aliphatics, ketones, sugars, etc. (Rogge et al., 1993).

The concentration of organics associated with parti-

culates is usually determined by organic solvent extrac-

tion of samples collected on glass fiber filters. The

solvent extract can either be analyzed on various

instruments for detailed analysis for speciation or

subjected to gravimetric analysis for bulk measurements.

The choice of solvent also depends on the compounds to

be studied (i.e. polar, non-polar, etc.). Benzene has been

widely used as the solvent (Cukor et al., 1972; Sawicki

et al., 1965; Ciaccio et al., 1974) and aerosol organics

concentrations expressed as benzene-soluble organic

fraction (BSOF).

Crebelli et al. (1991) established mutagenecity of

BSOF of diesel particulate matter. In their work

(Crebelli et al., 1991), the mutagenicity spectra of the

organic extracts (in benzene) of both air-borne and

diesel gasoline soot particles were determined using a

battery of nine bacterial strains of different genetic

specificity. The assays with crude extracts and with

fractioned acidic, neutral and basic components revealed

striking difference in the pattern of mutagenic responses

by each of the complex mixtures. The mutagenicity of

air-borne PM was shown to depend mainly on neutral

and aromatic compounds. Fukino et al. (1982) has

reported that the mutagenic activity of BSOF from air-

borne particles was more in Ames Salmonella system.

The study also revealed that the major portion (about

95%) of the BSOF of air filter samples is neutral and

aromatic hydrocarbon. BSOF in coke oven emissions

have been studied extensively to represent the aromatic

fraction (large fraction being PAH). In fact, for BSOF in

PM in coke oven areas, a regulatory limit of 0.2mgm�3

has been fixed (Mastrangelo et al., 1996). BSOF of total

particulate has been generally accepted as an index of

the health hazard.

The literature unambiguously suggests that BSOF is

an indicator of toxic organic fraction. Therefore, in

developing countries like India, where routine detailed

ARTICLE IN PRESSM. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–60266018

organic speciation is too expensive and difficult to

perform, BSOF can indicate toxicity of PM arising out

of organic compounds in PM.

We learn the following from the literature review:

(i) one should expect high levels of PM2.5 in

developing countries of south Asia, (ii) there is a need

to speciate PM in terms of heavy metals and organic

compounds through bulk measurements as BSOF and

(iii) evolve a procedure from current air sampling

facilities that may indicate PM2.5 levels in a reasonable

way for an initial assessment. To specifically address

these issues, a study was undertaken in Kanpur, India

involving measurements of PM10 and PM2.5, speciation

of PM10 and interpretation of results.

3. Study area

As stated in the introduction, study area for this

research was the city of Kanpur, India. The city of

Kanpur has a population of about 3 million and is

situated in north-central part of India (longitude

881220E and latitude 261260N) in Gangetic Plane. The

Fig. 1. Location of air-quality m

overall study comprised: (i) selection of sampling

location and sample collection, (ii) laboratory analysis

of the samples and (iii) interpretation of results. The

choice of sampling locations was aimed to select a

control site, an urban commercial site and an urban

residential site. It was not possible to completely isolate

any area as only control, residential, or commercial but

the predominant land-use was considered while selecting

the sampling location; Indian Institute of Technology

(IIT) (control), Vikas Nagar (VN) (commercial) and

Juhi Colony (JC) (residential) (Fig. 1). The laboratory

work consisted of measurement of PM10, PM2.5, heavy

metal (in PM10) and BSOF (in PM10).

Indian Institute of Technology (IIT) is an educational

institute having residential campus with no commercial

or industrial activities. The campus lies at about 15 km

north of city with minimum emissions. Within the

campus, vehicular population mainly comprises of

two- wheelers and cars. The heavy-duty vehicle popula-

tion is negligible. For most part of the year campus

lies on the upwind side and receives no air pollution

from Kanpur city. This site can ideally be taken as a

control site.

onitoring site at Kanpur.

ARTICLE IN PRESSM. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–6026 6019

Vikas Nagar (VN) is a commercial cum residential

area. The area lies about 600m away from a National

Highway and experiences heavy traffic load of heavy-

duty diesel vehicles, two-stroke vehicles and diesel-

driven three-wheelers (Vikram tempos) throughout the

day. Numerous commercial activities and other local

sources are also found in and around the area typical of

urban city in India. Some of the roads in close proximity

of the sampling site were not paved properly and re-

suspension of soil is also likely to affect the air quality.

Juhi Colony (JC) is although not purely a residential

area, but at least in its immediate proximity, there was

no major road/traffic. However, within about 1 km

radius there are markets and sizeable traffic. This site

can be taken as typical residential area in an urban area.

PM emission inventory is not available for the city of

Kanpur. The major particulate emissions sources

include industries (using heavy oil and coal), vehicles

(registered number of vehicles in city: 350,000), dis-

orderly mixed traffic causing congestion, construction

activities, use of captive diesel generator sets (power

(electricity) failure is common), use of soft coal for

domestic cooking and refuse (leaves) burning.

4. Materials and methods

PM10 and PM2.5 sampling was carried out simulta-

neously at each location; at least 12 samples were

collected at each location (Table 2). Details of particu-

late sampler and filter papers are given in Table 3

Table 2

Schedule for collection of air samples

Sampling location PM10 and PM2.5

Sampling months Number of samples

IIT October, 2002 14

VN November, 2002 4

December, 2002 14

January, 2003 3

JC January, 2003 6

February, 2003 6

Table 3

Instruments used for sampling and their specifications

Sampler type Model Particle size

Hi-volume sampler (for

PM10)

APM 450, Envirotech,

New Delhi

10mm and less

Wins-Anderson

impactor (for PM2.5)

APM550, Envirotech,

New Delhi

2.5mm and less

All initial and final weighing (using 440 Metler

balance with sensitivity 0.00001 g) of filter papers were

done in humidity-controlled room and filters were

conditioned in desicator for 24 hours before and after

the sampling.

4.1. Sample collection and storage

The desiccated filter papers were weighed twice on the

balance (APM 440, Metler). The conditioned and

weighed filter papers were placed in filter holder

(PM2.5) and cloth-lined envelope (PM10) and taken to

the field for sampling to avoid contamination of the

filter papers on the way.

Before starting the sampling, initial volume and timer

readings were noted for PM2.5 and the manometer reading

for PM10 sampler in field monitoring sheet. The pre-

weighed and coded filter papers were placed in the filter

holder of the respective samplers and screwed properly

before starting the samplers. Both the PM10 and PM2.5

samplers were operated for 24-h sampling period. Before

and after each set of sampling, data were entered in the

field data sheet in the pre-defined format and concentra-

tions of PM10 and PM2.5 were calculated gravimetrically.

After sampling, the PM2.5 filter papers were removed

with forceps and placed in the cassette and the cassette

was wrapped with aluminum foil. Similarly, the PM10

filter paper was wrapped in aluminum foil and placed in

envelope and the both the filter papers were brought

back to the laboratory. The samples were stored in

aluminum foil to prevent the degradation of organic

compounds due to photo-oxidation. The weighed filter

papers were preserved in freezer until further chemical

analysis for heavy metals and BSOF was undertaken.

4.1.1. Quality control in sampling

(1)

The PM2.5 sampler is designed to work at a flow rate

of 16.6770.83 lmin�1 (Chow and Watson, 1998).

Daily flow rate calculations (gas meter reading/timer

reading) were made to make sure that the fluctua-

tions in flow rate were within range.

(2)

Similarly, the PM10 sampling is to be performed at

the flow rate of 1m3min�1. The manometer reading

of PM10 sampler was taken 3–4 times in a day to

ensure that the flow rate variations were within

Flow rate Filter paper

0.9–1.2m3min�1 Whatman GF/A of

800 � 1000size

16.67 lmin�1 or 1m3 h�1 Millipore filter of 47mm

diameter

ARTICLE IN PRESSM. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–60266020

0.9–1.1m3min�1. Average flow rate was used for

calculating the volume.

(3)

The replacement filter in the Wins-impactor needs to

be changed after 72 h of sampling (Chow and

Watson, 1998) or when the filter appears clogged

as per the operator’s judgement. Also, the filter

should always be kept immersed in 3–4 drops of

silicon oil. Based on this, the filter paper in the

impactor was either replaced or oiled at regular

intervals as per the need.

4.2. Estimation of heavy metals

After particulate collection, chemical speciation is the

next step in air-quality assessment. For this purpose,

heavy metals (Pb, Zn, Fe, Ni, Cd and Cr) were analyzed

in PM10. The extraction and analysis of heavy metals

was carried out as per the USEPA method IO-3.2

(USEPA, 1999). As per the method, PM10 samples

collected on glass fiber filters may be digested either

by hot-acid digestion or by microwave-assisted digestion

system (USEPA, 1999). The reference suggests micro-

wave digestion process. One-fourth portion of the

filter papers were digested using 15ml hydrochloric

and nitric acid mixture (3:1) by laboratory micro-

wave digestion system (Ethos, Milestone, Italy) for

23min at about 1801C. The digested sample was filtered,

made up to the required volume and stored in plastic

bottles.

All heavy metals were analyzed on Atomic Absorp-

tion Spectrophotometer (AAS)(GBC Avanta S, Aus-

tralia). Before analyzing the samples, instrument was

calibrated for Pb, Fe, Zn, Cr, Cd and Zn. As per the

USEPA method, stock solutions (of 1000 ppm) were

prepared and diluted to the range of working standards

for individual metal. The calibration graphs were

prepared using these working standards in the linear

range of the optical density (0.04–0.8). The instrument

was calibrated at three different levels for each metal.

In order to examine the background heavy metal

contents of blank filter paper, exactly same extraction

and analysis procedure was employed for PM10 filter

papers. As suggested in the method, 5% of the total

number of samples were taken as blank and analyzed for

presence of specific metals to verify reproducibility and

low background metal concentrations.

4.2.1. Quality control in the heavy metal analysis

To avoid contamination from various sources, the

following procedure was followed:

(1)

All the glassware and filter assembly were acid

washed and oven dried to avoid contamination

among samples.

(2)

Three blanks were analyzed for all the heavy metals

to check the interference from filter papers in the

sample. The filter blanks were found to have all the

metals higher than the minimum detectable limit.

The concentration of metals in sampled filter papers

was found to be higher than in blank filters.

(3)

Every third sample was analyzed twice to check the

repeatability.

(4)

For one of the filter papers, three sets of extractions

were performed and the samples were analyzed

to check the difference in concentrations to ensure

that metals were uniformly distributed on the filter

paper.

4.3. Benzene-soluble organic fraction (BSOF)

To determine the toxic organic fraction in terms of

BSOF in PM, ASTM test method 4600-87 (ASTM,

1990) was used. It is a gravimetric method. The method

has been recommended by National Institute of

Occupational Safety and Health, USA to represent

organic compounds in ambient air.

For PM10 air samples, one-fourth of the PM10 filter

paper was taken in cleaned and oven-dried glass vessels/

bottles and 20ml of HPLC-grade benzene was added.

The vessels/bottles were sealed with glass caps and sealer

to avoid loss of organic fraction during ultrasonication.

The samples were subjected to ultrasonication for 20min

at room temperature. The extracted samples were

vacuum-filtered through 0.54 mm glass fiber filter. The

extract was transferred to cleaned, oven dried and pre-

weighed 50ml beakers; each sample was extracted twice

through ultrasonication. Mouth of the beakers was

covered with perforated aluminum foil to avoid

contamination. The benzene extract was evaporated to

dryness (in 15–20 h) in the oven at 401C. On drying, the

beaker was weighed on a 5-digit balance (APM440,

Metler). The difference in weight is the fraction

dissolved in benzene that can be translated into mgm–3

of BSOF.

Four filter blanks of glass fiber filter paper were

analyzed for BSOF. The BSOF in filter blank was found

to be varying between 1.5% and 2%. As a part of

quality control, the difference in initial and final weights

of the beaker should not be less than 0.001 g and in all

samples, the difference in weight was greater than

0.001 g.

5. Results and discussion

5.1. Particulate matter

Figs. 2 and 3 present the PM10 and PM2.5 levels at the

three locations where sampling was done. The average

PM10 concentration at IIT was found to be 80mgm�3.

However, average PM10 levels at VN and at JC were

exceeding the Indian national air-quality standard

ARTICLE IN PRESS

IIT VN JC

Con

cent

ratio

n in

µg

m-3

0

100

200

300

400

500

600

700

Fig. 2. PM10 levels in Kanpur.

IIT

Con

cent

ratio

n in

µg

m-3

0

50

100

150

200

250

VN JC

Fig. 3. PM2.5 levels in Kanpur.

Table 4

PM2.5 to PM10 ratio

Location IIT VN JC

PM2.5/PM10 0.74 0.56 0.45

M. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–6026 6021

(100mgm�3) and were found to be similar, 272 and

281 mgm�3, respectively. However, variability in PM10

at JC was much more.

The PM2.5 levels were the lowest at IIT with an

average of 61mgm�3. The average PM2.5 concentration

at VN was found to be 146mgm�3, which is about two

and a half times higher than the levels at IIT. At JC

average PM2.5 was 95mgm�3. When result of PM2.5 is

compared among the sampling sites (Fig. 3), it depicts

an interesting picture. While average PM10 concentra-

tion at VN and JC were similar, the PM2.5 at VN is

much higher (146mgm�3) compared to JC (95 mgm�3).

It brings up a significant point that although PM10 is a

better indicator of TSP, it may not necessarily represent

true picture of more hazardous fine particulates (PM2.5).

This situation is particularly important in Indian

context, where significant portion of PM10 may be

locally generated wind-blown dust in the coarse fraction

(PM10–PM2.5) which may not be as harmful as PM2.5.

This point that PM2.5 may not be represented by PM10

levels also becomes clear when one looks at the PM2.5/

PM10 ratios (Table 4). Although the average PM10 level

at VN is less or similar to the levels at JC, the ratio of

PM2.5/PM10 is much higher at VN indicating a larger

fine fraction in PM10 at this location. The sampling

location at VN was close to a minor road and within

1 km from a major national highway characterized by

movement of heavy-duty trucks and these high PM2.5

levels can be attributed to emissions from these sources.

PM10 levels in Kanpur urban locations (272.70764.64mgm�3 (VN) and 281.977170.57mgm�3 (JC)) are

higher than levels in metro cities like Kolkata, Mumbai

and comparable with levels in Delhi (Sharma et al,

2003). Although the objective of this work was not to

find the reasons as to why the levels are high in Kanpur,

it obviously reflects on large emissions in Kanpur if one

considers the meteorological conditions in Kanpur and

Delhi (aerial distance between two cities is about

250 km) to be similar. As regards PM2.5 levels, there is

no study which has measured PM2.5 in India. However,

if one compares the PM2.5 levels in Kanpur with cities in

the US and European countries (see section on literature

review), the PM2.5 levels are almost 10 times higher. The

high levels of PM2.5 in Kanpur suggest that there is a

definite need to measure and control PM2.5.

5.2. Heavy metals

In this study, PM10 air samples were analyzed for

heavy metals: Pb, Fe, Zn, Ni, Cd and Cr (Fig. 4). It can

be observed from Fig. 4 that levels of heavy metals are

highest at VN followed by JC and IIT. The trend in

variation of PM2.5 levels and metal contents is similar

suggesting least pollution at IIT followed by JC and VN.

This is in accordance with the fact that most of the

heavy metals are associated with fine particulates

making them more toxic. The other interesting point is

in spite of introduction of unleaded gasoline, lead

continues to be present in ambient air and may still

pose a health risk.

The heavy metal levels found in the present study in

Kanpur were compared with the studies conducted at

some other places (Table 5). It was found that the levels

of all the metals were 5–10 times higher than the levels in

European countries like Spain and Norway. The Pb and

Zn levels at Taiwan (133 ngm�3and 251 ngm�3) are

close to the levels found at control site IIT (150 and

320 ngm�3, respectively). Fe levels in the present study

were found comparable with the Fe levels at Taiwan and

Spain, but the levels at Delhi were reported to be very

high. In Delhi, the concentration of all metals was found

to be higher than at Kanpur but the difference in

ARTICLE IN PRESS

IIT

Lead Nickel Cadmium Chromium Zinc

Hea

vy m

etal

Con

cent

ratio

n in

ng

m-3

0

100

200

300

400

500

600

700

VN

Lead Nickel Cadmium Chromium Zinc

Con

cent

ratio

n of

Hea

vy M

etal

in n

g/m

3 m-3

0

500

1000

1500

2000

2500

JC

Lead Nickel Cadmium Chromium Zinc

Con

cent

atio

n of

Hea

vy m

etal

in n

g m

-3

0

500

1000

1500

2000

2500

IRON

IIT Vikas Nagar Juhi Colony

Con

cent

ratio

n of

iron

in n

g/m

3 m-3

0

2000

4000

6000

8000

Fig. 4. Heavy metal levels in the ambient air of Kanpur (ngm�3).

Table 5

Comparison of heavy metal levels at various locations

Location Pb (ngm�3) Zn (ngm�3) Ni (ngm�3) Cd (ngm�3) Cr (ngm�3) Fe (mgm�3)

Spaina 8–698 28–479 0.1–21 0.1–4 0.1–22 0.20–10

Taiwanb 133 251 — — 656 6.99

Norwayc 0.36–10.36 0.96–46.68 0.09–5.71 0.01–0.28 0.21–1.56 —

Delhid 600–1900 400–800 — 20–150 300–700 5–20

Mumbaie 10607300 — 160740 — 150760 —

Present study 70–1030 200–1630 40–270 2–43 32–400 0.30–6.17

aQuerol et al. (2002).bFung and Wong (1995).cNILU, 2002b.dBalachandran et al. (2000).eKumar et al. (2001).

M. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–60266022

average concentration was not much except for Fe.

From this discussion, it can be concluded that the

pollutant load in terms of heavy metals in Kanpur is

much higher than in other countries but probably less

than the levels at Delhi.

5.3. Benzene-soluble organic fraction (BSOF)

Two solvents, benzene and ether were tried to assess

the organic content of PM. Results indicated that ether-

soluble organic fraction was much smaller (less than

ARTICLE IN PRESS

Table 6

BSOF in PM10

Location IIT VN JC

(% by w/w) (mgm�3) (% by w/w) (mgm�3) (% by w/w) (mgm�3)

BSOF 9.8774.79 9.1377.03 40.00719.96 106.71762.38 10.3277.99 48.48742.35

BSOF

IIT

Con

cent

ratio

ns in

the

air

(m-3

)0

50

100

150

200

250

VN JC Delhi

Fig. 5. Benzene-extractable organic fraction in PM10 (mgm�3).

M. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–6026 6023

50%) than that of BSOF for the same samples. In the

following text, results of BSOF only are discussed.

Results of BSOF are presented in Table 6 and Fig. 5.

Although the work place standard for BSOF has been

reported as 200 mgm�3 (Mastrangelo et al., 1996),

ambient air-quality standard should be much lower. If

one takes a safety factor of 10 (Asante-Duah (1998) has

suggested a factor of safety of 10 or higher), it gives an

acceptable level of 20mgm�3 for BSOF. The BSOF at

various locations can be examined against a value of

20mgm�3. Similar to PM2.5 and heavy metals, BSOF is

least at IIT and well within the acceptable limit of

20mgm�3. However, level of BSOF is very high at VN

(106762mgm�3) indicating high levels of organic

compounds including PAHs. BSOF levels are also high

at JC (48742 mgm�3) in comparison to the levels at IIT

(977mgm�3) and exceeds the tentative value of

20mgm�3.

The BSOF measured at Kanpur has been compared

with the BSOF levels at Delhi (from unpublished data

obtained for December 2002–January 2003 from Central

Pollution Control Board, Delhi) (Fig. 5). The average

BSOF levels at the busy traffic junction in Delhi

(48mgm�3) have been found to be lower than the BSOF

(104mgm�3) at VN. The levels at JC are comparable to

those at Delhi but the levels at IIT are very low.

Comparison of BSOF levels between Kanpur and Delhi

suggests that Kanpur is more polluted than Delhi in

terms of toxic organic pollution adsorbed on PM. One

should bear in mind that all buses (state and private),

taxis and three-wheelers have been converted to use

CNG in Delhi, a much cleaner fuel than diesel, which

has resulted in lower BSOF in Delhi.

5.4. PM10 and BSOF levels an indicator of PM2.5

The results and discussion so far have indicated that

PM2.5 levels and metals and BSOF in PM10 are very

high. It suggests that air in terms of fine particulate and

its chemical composition is hazardous and quick actions

are required. A similar situation may be prevailing in

other cities in India.

PM2.5 monitoring and chemical speciation requires

modifications in laboratory arrangements including

change in equipment and sampling protocol. In India,

the number of PM10 monitoring stations is very large

(about 300). It is not easy to substitute entire PM10

monitoring by PM2.5 monitoring at all locations, at

least, not in immediate future. PM2.5 monitoring will

require additional infrastructure (change in sampling

equipment, change in filter paper (Quartz/Teflon filter),

more precise balance, skilled manpower and modifica-

tions in quality assurance and quality control. Never-

theless, there is a need to assess PM2.5 pollution in

some way until actual PM2.5 sampling can be taken up

in a big way.

Organic matter, heavy metals, nitrates, sulfates and

elemental carbon are the main components of particu-

late (PM2.5). In order to represent PM2.5 in an indirect

way, one needs to choose parameters to account for

organic and inorganic fractions of PM2.5. As regards

organic component, BSOF is an indicator of aromatic

and neutral compounds. It can therefore be argued that

BSOF can possibly represent the organic fraction of

PM2.5. As regards inorganic fraction of PM2.5, it can

possibly be represented by a fraction of PM10.

In this study, measurements of PM10, PM2.5 and

BSOF have been done. This available concurrent data

provide an opportunity to model PM2.5 as a function of

PM10, BSOF and other independent variables. Preli-

minary data analysis indicated correlation between (i)

PM2.5 and PM10, (0.72) and (ii) PM2.5 and BSOF (0.82).

In succeeding section, attempt has been made to develop

a statistical model for PM2.5 using the information on

PM10 and its BSOF contents.

ARTICLE IN PRESS

R 2 = 0.6113

0

50

100

150

200

0 50 100 150 200

Mod

el-c

ompu

ted

PM2.

5 le

vels

(µg

m-3

)

Measured PM2.5 Levels (µg m-3)

Fig. 6. Model performance for measured and model computed

PM2.5.

R2 = 0.6575

200

250

µg m

-3)

M. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–60266024

5.4.1. PM2.5 modeling

A set of 43 data points was available with concurrent

measurements of PM2.5, PM10 and BSOF. Out of this,

23 data points were chosen randomly for developing the

model and the remaining were used for validation of

model.

The method of ordinary least squares was employed

to estimate the parameters of simple linear model using

statistical package SYSTAT (Wilkinson, 1990). The

estimated statistical model is

PM2:5 ¼ 45:53þ 0:17PM10 þ 0:38BSOF: (1)

All variables in Eq. (1) are in mgm�3. In India,

although there is no measurement of PM2.5, but back-

ground level of PM10 is around 60mgm�3, as suggested

by Eq. (1), a background level of PM2.5 around 45 may

not be unreasonable.

5.4.2. Model performance

1502.

5 (

(

(i)

50

100

led

PM

The value of R2 for the present regression analysis

was 0.611 (correlation coefficient ¼ 0.78); signifi-

cant in a statistical sense at 5% level of significance.

ode

(ii)

0

0 50 100 150 200

M

All three estimated coefficients (constant and that

of PM10 and BSOF) were statistically significant at

5% level of significance.

Measured PM2.5 (µg m-3) (iii)

Fig. 7. Linear plot of model computed and measured PM2.5

levels.

The assumptions of normality and constant var-

iance in errors was checked by plotting residuals

(errors) and the computed value of the dependent

variable. A randomly distributed plot (not shown

here) suggested constant variance and normal

distribution of errors.

(iv)

Analysis of variance and F-statistic: the analysis of

variance (ANOVA) and the value of F-ratio are

used for assessing the significance of regression.

When the F-ratio is statistically significant, it

implies that a significantly large amount of the

variation in the data about the mean has been

taken up by the regression equation. The calculated

F-ratio (15.73) was much higher than the critical

F-ratio (3.49) at 5% level of significance indicating

significant regression.

The model performance was judged by the visual

examination of the linear plot of measured and model

computed PM2.5 (Fig. 6).

5.4.3. Model validation

The developed model has been validated against an

independent set of data consisting of 20 data points

(data points those were not included in estimating model

coefficients). The model computed PM2.5 levels are

compared with the actual measurements (Fig. 7). The

model computed PM2.5 levels compare favorably with

the observed values. It was found that the independent

set (Fig. 7) performed better in terms of R2 than the set

used for development of the model.

5.4.4. Model applicability

Although the model is found statistically significant

and validated against another data set, model has

limited utility. Model is based on limited data from

one city and one season. The partitioning of PM2.5 in

organic and inorganic phases is a function of season and

type of PM2.5 sources prevailing in the area including

long-range transportation. In addition, to completely

describe PM2.5, further speciation in terms of elemental

and organic carbon, sulfates, nitrates and other ions are

desirable. However, the model can be used for broad

reconnaissance as a first step to identify the locations

and areas of concern for PM2.5 through simple analysis

of routinely measured PM10 and its BSOF content.

6. Conclusion

The study concluded that the overall air quality in the

city of Kanpur was much inferior to other cities in India

and abroad. Similar to PM10 and PM2.5, heavy metals

ARTICLE IN PRESSM. Sharma, S. Maloo / Atmospheric Environment 39 (2005) 6015–6026 6025

were almost 5–10 times higher than levels in European

cities. The organic content as indicated by benzene-

soluble fraction was also high at urban locations

(106762 and 48742mgm�3). The study concluded that

there was a need to address the issue of PM2.5

monitoring and control. A possible approach for a

preliminary assessment of PM2.5 pollution levels could

be through modeling PM2.5 based on PM10 level and

its organic content measured by benzene-soluble

organic fraction.

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