Assesment Intra Urban

8/12/2019 Assesment Intra Urban

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Assessment of intra-urban variability in indoor air quality

and its impact on childrens health

B. K. Padhi &Pratap Kumar Padhy &Lokanath Sahu &

V. K. Jain &Rupak Ghosh

Received: 30 June 2008 /Accepted: 5 January 2010 /Published online: 9 March 2010# Springer Science+Business Media B.V. 2010

Abstract The results from a number of studies suggest that

children living close to busy roads may have impairedrespiratory health. The study reported here was designed

specifically to test the hypothesis that exhaust from traffic

has an impact on indoor air quality and childrens

respiratory health. Children living at three different loca-

tions in a suburban area in India were enrolled in the study,

and the concentrations of indoor air quality parameters were

measured at selected households during the period March

2006February 2007 using portable air quality monitors.

Respiratory symptoms were identified by means of a

questionnaire completed by parents and from the results

of a pulmonary function test (PFT) carried out using an

electronic Spiro Meter. The logistic regression model

revealed associations between respiratory symptoms and

traffic-related indoor air pollutants among our study

population. The prevalence of respiratory disorders was

greater among children living in close proximity to traffic

sources than among those living more distant from these

sources, even after the adjustment of confounding factors.

We also found intra-urban variability of indoor air quality

and associated differences in respiratory symptoms. Our

findings support the hypothesis that traffic has an impact onindoor air quality and that it is associated with childrens

health. The findings from this study have important policy

and program implications, including the need for public

information campaigns designed to inform people about the

risks of exposure to traffic exhausts.

Keywords Children . Indoor air pollution . Intra-urban .

Lung function . Respiratory symptoms . Traffic

Introduction

The World Health Organization (WHO) reports that 25% of

all preventable diseases are due to a poor physical

environment (World Health Organization2002). It has also

been reported that over 40% of the global burden of

diseases attributed to environmental factors falls on

children, who account for about 10% of the worlds

population (Murray and Lopez 1996; Tamburlini et al.

2002). Air pollution is the single largest environment-

related cause of ill health among children in most countries.

Motor vehicle emissions are a major source of air pollution

throughout the world (Mage et al. 1996; Mayer 1999;

Samet2007) and there is widespread public concern over

their effect on asthma, particularly among children (Venn et

al. 2001; Janssen et al. 2003). Air pollution from motor

vehicles is one of the most serious and rapidly growing

problems in urban centers of India (UNEP/WHO 1992;

CRRI 1999). The problem of air pollution has assumed

serious proportions in some of the major metropolitan cities

of India, with vehicular emissions having been identified as

one of the major contributors to the deteriorating air quality

(CPCB2001).

B. K. Padhi :V. K. Jain

School of Environmental Sciences,Jawaharlal Nehru University,

New Delhi, India

P. K. Padhy (*) :L. SahuCentre for Environmental Studies,

Visva-Bharati University,

Santiniketan, West Bengal, India

e-mail: [email protected]

R. Ghosh

Suri Sadar Hospital,

West Bengal, India

Air Qual Atmos Health (2010) 3:149158

DOI 10.1007/s11869-010-0063-x


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Numerous epidemiological studies have documented

adverse effects of air pollution on health (Wjst et al.

1993; Oberdorster et al.1995; Kramer et al.2000; Aneja et

al. 2001; Vedal et al. 2003). In sub-urban areas, motor

vehicle emissions are likely to vary substantially within a

small area, and researchers have begun to document

differences in traffic-related pollutants on a neighborhood

scale (Hoek et al. 2002; Lebret et al. 2002; Wilsona et al.2005; Samet 2007). A number of recent epidemiological

studies have found associations between residential prox-

imity to busy roads and a variety of adverse respiratory

health outcomes in children, including respiratory symp-

toms, asthma exacerbations, and decrements in lung

function (Ghose et al. 2005; Kaushik et al. 2006).

Therefore, it is important to evaluate the extent to which

proximity to traffic may affect the indoor air quality. The

indoor environment is especially relevant in such studies

because pollutants, such as, ambient particulate matter

(PM), carbon monoxide (CO), nitrogen oxide (NO), nitrogen

dioxide (NO2), sulphur dioxide (SO2), and ozone (O3) maypenetrate from the outside. The study of air pollution

exposures at the intra-urban scale therefore presents a

challenge in a new era of exposure assessment in epidemi-

ological research and one that has been recently identified as

a preferential area for future work (Brunekreef and Holgate

2002; Sajani et al.2004; Jerrett et al.2005; Padhi and Padhy

2008). The study reported here examined the impact of

traffic on indoor air quality and childrens health at the intra-

urban spatial scale within a suburban area in India.

Materials and methods

Study area

Bolpur is situated between latitude 24N and longitude

87E in the north-western part of West Bengal, a highly

populated state in the eastern part of India. It is one of the

growing urban cities in India. Uncontrolled and mixed

vehicular density on insufficient and badly cared road

space, inadequate parking facilities, low turnover of old

vehicles with too frequent breakdowns, and undisciplined

drivers together with a bad traffic management strategy

have profound effects on the air quality of this area. In

addition to these factors, the widespread use of solid

biomass fuels in food and tea stalls has worsened the

situation and poses a threat to human health. The study area

was divided into three sampling sites, namely, site-I (within

0.5 km of the main road), site-II (within 1.0 km of the main

road), and site-III (within 5.0 km of the main road). The

condition of the terrain in all three sites was comparable.

Site selection was based on vehicle density, road condition,

proximity to other stationary sources, and human habitat.

Indoor Air Quality (IAQ) measurements

The IAQ investigation at the households enrolled in the

study was conducted from June 2006 to July 2007. In each

sample area, the indoor air quality was studied in detail in

20 representative households selected from among the

larger group on which health data were collected. These

households were selected on the basis of fuel use, indoorsmoking, and other potential sources of indoor air pollution.

The selection criteria were the use of LPG (liquefied

petroleum gas) as a cooking fuel and no indoor smoking.

Each investigated household was monitored on at least two

to three, and 24-h average value was taken as the final

result. The air quality parameters measured in this study

were of CO, CO2, NO, NO2, SO2, O3, and suspended

SPM as well as relative humidity (RH) and temperature.

Instruments were positioned at the center of the living

room, 0.5 m above the ground, at least 0.5 m away from the

walls, and 1 m away from potential sources of air

pollutants. All gaseous pollutants as well as temperatureand RH were measured by a portable multigas air quality

monitor (YES Plus, Canada), whereas the, SPM was

monitored by a Handy Sampler (model KDM HS-7;

KDM. Pvt. Ltd., New Delhi, India) using pre-weighed

Whatman-GF/A glass fiber filter paper (USEPA 1971).

Study subjects

In each sampling site, we selected approximately 100

households (site-I:108, site-II: 105, site-III: 110) that used

LPG for cooking for enrollment in this study. The households

were virtually indistinguishable from each other in terms of

socio-economic status, economy, diet, home construction,

and access to health care. They were expressly matched on

these key characteristics in order to minimize the potential for

confounding factors and to provide some control of all major

known and suspected risk factors for indoor air quality and

respiratory diseases. There were 435 children in these

households (site-I 145, site-II 138, site-III 152) aged between

6 and 10 years who were included in this study.

Study of respiratory symptoms of children

A questionnaire developed on the pattern of IUATLD

(International Union Against Tuberculosis and Lung

Disease) (Burney et al. 1989) and ISAAC (International

Study of Asthma and Allergies in Childhood) (Asher et al.

1995), with a few modifications, was used for evaluating

respiratory health. The questionnaire contained questions

on personal characteristics (name, sex, age, height, weight,

parents smoking habit, family income, mothers education,

etc.), respiratory symptoms [history of cough with or

without any expectoration, amount of expectoration/day,

150 Air Qual Atmos Health (2010) 3:149158


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winter exacerbations, history of hemoptysis and amount,

history of any post-nasal drip, history of any wheezing,

history of chest tightness, doctor-diagnosed asthma, and

any systematic complaints (fever, headache, etc.)].

Lung function measurement

Our study was designed specifically to investigate traffic-related indoor air pollution on the lung function of children.

Of the 435 children from the selected households, lung

function measurements were performed on only 240

children (site-I 78, site-II 82, site-III 80); the remaining

children had already had been diagnosed with a systematic

respiratory illnesses. The lung function parameters, such as

peak expiratory flow (PEF), forced vital capacity (FVC),

forced expiratory volume in 1 s (FEV1), forced expiratory

flow (FEF), slow vital capacity (SVC), were measured on

an electronic Spiro Meter (model DT Spiro; Maestros

Medline Systems, Mumbai, India) interfaced to a personal

computer. All pulmonary function tests (PFT) were carriedout at a fixed time of the day (09001300 hours) to minimize

the diurnal variation. The spirometer was calibrated daily and

operated within the ambient temperature range. The lung

function test of our study was based on the operation manual

of the instrument with special reference to the official

statement of the American Thoracic Society of Standardiza-

tion of Spirometry (American Thoracic Society1987). Each

subject was instructed to stand upright in a stable position, to

place the mouthpiece in the mouth while keeping the nose

closed, and then to make a maximal inspiratory effort and to

blow out with a maximal effort. The test was repeated five

times after adequate rest, and the data were transferred from

the spirometer to the computer for analysis.

Anthropometric measurements

Height, weight, and the circumference of the waist (WC) and

hip were recorded using a standard technique (Lohman et al.

1988) by the recorder. Height and weight were measured to

the nearest 0.1 cm and 0.5 kg, respectively. Waist and hip

circumferences were measured with an inelastic tape to the

nearest 0.2 cm. Body mass index (BMI) and waisthip ratio

(WHR) were computed using the following formula:

BMI Weight kg

Heigth2 m2 WHR Waist circumference cm =Hip circumference cm

Statistical analysis

Analysis of the descriptive statistics, such as mean,

standard deviation (SD) of anthropometry, air pollutants,

and respiratory functions of the subjects, was performed.

The General Linear Model (GLM) was applied to study the

relationship of intra-urban variation of indoor air quality. To

investigate the relationship between PFT and air pollutants,

we used multiple regression analysis. Finally, to explore the

relationship between respiratory symptoms and the expo-

sure to pollutants, we used multiple logistic regression

analysis, in which the potential confounding factors were

controlled. The adjusted odds ratios (ORs) and their 95%confidence intervals (CIs) were computed. The adjusted

ORs were calculated for an interquartile range (IQR) of

measured pollutant concentrations (i.e., the OR for a given

health outcome given a pollutant concentration at the 75th

percentile of the distribution relative to that at the 25th

percentile). All statistical analyses were performed using

the SPSS statistical package ver. 10.0 (SPSS, Chicago, IL).

Results

The basic socio-demographic characteristics of the studyhouseholds are given in Table 1. Houses were similar in

terms of type and structure. On average, each house had

three rooms and housed eight people. The majority of the

houses were naturally ventilated.

The summary statistics of indoor air quality along with

micro-meteorological conditions are described in Table 2.

The indoor concentrations of all the pollutants were greater

in houses located at 0.5 km of the main road. The highest

mean levels of indoor air quality parameters were measured

in houses located at 0.5 km of the main road: 240.0, 2525.0,

42.5, 55.0, 48.7, 22.0 ppb for the gaseous components

(CO, CO2, NO, NO2, SO2, a n d O3, respectively) and

185.0 g/m3 for SPM. In comparison, the lowest mean

levels were recorded in houses located 5.0 km away from

the main road and averaged 138.9, 2127.0, 22.3, 25.6, 20.0,

and 10.5 ppb for the gaseous components (CO, CO2, NO,

NO2, SO2, O3, respectively) and 87.5 g/m3 for SPM.

These differences in the levels of the measured pollutants

are highly significant (p < 0.001) (Table 2). We found a

decrease in the concentrations of all indoor air quality

parameters with increasing distance from the main road,

indicating that increases in the distance from automobile

sources is correlated to a decrease in the concentrations of

air quality parameters. The GLM with Scheffes posthoc

test was applied and revealed that there is a statistically

significant difference in the indoor concentrations of air

quality parameters at the different study locations (Table 2).

Table3 shows the anthropometric characteristics of the

study subjects, and Table4 shows the results of the PFT on

the study subjects. A comparison of PFT values among the

three study groups using one-way analysis of variance

(ANOVA) with Turkeys posthoc test revealed statistically

significant differences between the lung function results of the

Air Qual Atmos Health (2010) 3:149158 151


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groups. Children living in the households at 0.5 km of the

main road were found to have lower a lung function than those

who lived in the households located 5.0 km away from the

main road (PEF=3.2, 3.9, and 4.3, FVC=2.0, 3.5, and 3.8,

FEV1=1.5, 2.1, and 2.8, FEF2575%=1.8, 2.5, and 2.9, and

SVC,=2.5, 3.2, and 3.6 l/s for site-I, -II, and III, respectively).Correlations among indoor air quality variables are

reported in Table 5. With the exception of temperature

and RH, all of the air quality variables were found to be

positively correlated, with stronger correlations for CO,

NO, NO2, and SO2. The correlations between potential

confounders and pulmonary function results are presented

in Table 6. Age, BMI, WHR, environmental tobacco

smoke, and living habitat were important factors affecting

the lung function of our study subjects.

Multiple regressions were used to test the association

between indoor air pollutant concentrations and lung

function variability among the study subjects. The resultsof the adjusted pulmonary function tests regressed on

indoor air pollution data are presented in Table 7. These

regressions of adjusted pulmonary function values for

children residing at 0.5 km of the main road showed that

NO2 and SPM were most strongly associated with lower

values of PEF, FVC, FEV1, FEF2575%, and SVC. We

found statistically significant relationships between air

pollutant levels and pulmonary function tests in the three

groups. SPM exposure was associated with statistically

significant decreases in all five measures of pulmonary

function, and theses associations were stronger than those

of the other pollutants measured.

The effects of traffic-related indoor air pollution on the

prevalence of asthma, wheeze, and shortness of breath aresummarized in Table 8. Unlike many other studies, we

observed a significant association between the prevalence

of respiratory symptoms in the study populations and

indoor air pollution exposures. The results of the logistic

regression analysis are shown in Table8. Not all the data on

respiratory symptoms collected during the survey are

presented in this tableonly the significant respiratory

symptoms associated with the exposure to air pollutants.

The results revealed that the observed differences in air

pollutant level had a significant impact on respiratory health

even after these were adjusted for confounding factors. The

children living at 0.5 km of the main road had a higher riskof developing respiratory symptoms or diseases than those

living in 5.0 km away from the main road.

Discussion

We have studied the relationship between traffic-related

indoor air pollution and the development of asthmatic

Table 3 Basic anthropometric characteristics of the study subjects

Variables 0.5km from main road

(n=145)

1.0km from main road

(n=138)

5.0km from main road

(n=152)

Pvalue

Age, years (mean SD) 8.01.0 7.51.5 8.01.0 0.135

Sex (male, %) 61.53 64.63 56.25 0.113

Race/ethnicity (Asian Indian, %) 97.93 96.37 92.10 0.153

BMI kg/m2

(mean SD) 13.50.91 13.60.87 13.30.90 0.231WHR (mean SD) 0.920.23 0.850.15 0.890.17 0.173

Nutritional status (good, %) 44.87 50.0 38.75 0.08

Disease history, including anemia (yes, %) 3.44 5.07 1.97 0.12

BMIBody mass index, WHR waist-to-hip ratio

Table 4 Comparison of pulmonary function test results in children stratified by the distance of the houses from the main road

Variables (l/s) 0.5km from main road(n=78)

1.0km from main road(n=82)

5.0km from main road(n=80)

Pvalue

PEF 3.21.0 3.90.5 4.30.62 0.001

FVC 2.00.7 3.50.35 3.80.78 0.002

FEV1 1.50.8 2.10.3 2.80.25 0.005

FEF2575% 1.80.5 2.50.23 2.90.31 0.001

SVC 2.50.8 3.20.35 3.60.42 0.004

PEFPeak expiratory flow, FVC forced vital capacity FEV1forced expiratory volume in 1 s, FEFforced expiratory flow, SVCslow vital capacity

All values are given as the mean SD



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symptoms and respiratory infections among children living

in a suburban setting in India. To the best of our

knowledge, this is the first epidemiological study in India

to evaluate relationships between traffic-related indoor air

pollution and respiratory symptoms. We found that thechildren in our study who lived near a busy road had a

significantly increased chance of having bronchitis symp-

toms and doctor-diagnosed asthma than those living further

away from a main road. These monitoring results indicate a

significant difference between the indoor air quality of the

roadside-situated households and those situated further

away from roads. Vehicles in major metropolitan cities are

estimated to account for 70% of the CO, 50% of the

hydrocarbons, 3040% of the NOx, 30% of the SPM, and

10% of the SO2 of the total pollution, with two thirds of

this contributed by two wheelers alone. These high levels of

pollutants are the main causal factors of respiratory andother air pollution-related ailments, including lung cancer

and asthma (CPCB2001). The localized vehicular pollution

contributes to about 1.1% of all deaths annually worldwide

and has recently been estimated to be responsible for up to 6%

of all deaths annually in Europe (Knzli et al.2000). Several

studies indicate that traffic on roads is a major source of air

pollutants in urban areas, but relatively few studies have

evaluated the specific effects of traffic-related air pollution

on individuals living close to traffic-intensive roads.

A qualitative comparison of our results can be made with

those from several previous studies. Chattopadhyay et al.

(2007) studied the levels of PM10 and volatile organic

compounds in motor vehicle exhaust and the lung function

of 505 residents of Kolkata (India) and found that changesin lung function were associated with higher traffic loads

and an increased deterioration of respiratory function. In

this study, 3.76% of the subjects had restrictive impairment,

3.17% had obstructive impairment, and 1.98% had both.

Ghose et al. (2005) studied the status of urban air pollution

and its impact on human health in the city of Kolkata and

found that SPM, NOx, and CO levels were associated with

respiratory disorders and had a negative effect on human

health. Sharma et al. (2004) studied the effects of

particulate air pollution on the respiratory health of subjects

who lived in three different areas of Kanpur, India and

found that an increase of 100 g/m

3

in PM10 wasassociated with a reduction of approximately 3.2 l/min in

mean PEF rate for an individual. Nitta et al. (1993)

conducted a cross-sectional study in Tokyo, Japan and

found that exposure to automobile exhaust may be

associated with an increased risk of certain respiratory

symptoms, including chronic cough, chronic phlegm,

chronic wheezing, and chest cold with phlegm. Sekine et

al. (2004) studied the long-term effects of exposure to

automobile exhaust on the pulmonary function of female

Table 5 Correlations among pollutants and weather variables

Variables CO CO2 NO NO2 SO2 O3 SPM Temperature Relative humidity

CO 1.0

CO2 0.79 1.0

NO 0.63 0.25 1.0

NO2 0.61 0.21 0.78 1.0

SO2 0.56 0.13 0.45 0.52 1.0

O3 0.34 0.11 0.63 0.58 0.32 1.0

SPM 0.41 0.09 0.36 0.43 0.49 0.13 1.0

Temp 0.12 0.18 0.24 0.21 0.15 0.35 0.29 1.0

RH 0.31 0.28 0.35 0.20 0.15 0.11 0.38 0.46 1.0

Variable PEF FVC FEV1 FEF2575% SVC

Age 0.13** 0.08* 0.05* 0.04* 0.03

Sex 0.08* 0.05 0.12* 0.07* 0.04

BMI 0.19** 0.11** 0.10** 0.08* 0.09*

WHR 0.15** 0.09* 0.13** 0.10** 0.07*

Nutritional status 0.03 0.02 0.08* 0.01 0.03

Environmental tobacco smoke 0.25*** 0.18*** 0.31*** 0.11** 0.23***

Living habitat 0.09* 0.1** 0.11** 0.08* 0.09*

Parents respiratory diseases 0.10** 0.08* 0.09* 0.09* 0.06*

Table 6 Partial correlations of

lung function measurementswith potential confounders

Significant at: *P


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adults in Tokyo, Japan and found that subjects living in

areas with high concentrations of air pollution showed

higher prevalence rates of respiratory symptoms and a

larger decrease in FEV1than those living in areas with low

concentrations of air pollution. Shima et al. (2003) found

that the prevalence of asthma in girls living in Chiba

prefecture, Japan was higher among those living


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between outdoor NO2concentrations and wheezing. In this

study, girls were more susceptible to indoor NO2than boys.

In a Japanese study, Shima and Adachi (1996) studied theserum immunoglobulin (Ig) E and hyaluronate levels in

children living along major roads and found that serum

hyaluronate concentrations were higher in those who lived


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exert an important influence on this increase in pollutant

concentrations. Future studies that can better characterize

exposures to traffic pollutants and their sources will provide

important information towards gaining a better understand-

ing of the public health impacts of motor vehicle emissions

and to developing an integrated assessment that will be

helpful to the air quality concerns in epidemiological

studies.

Acknowledgments The authors acknowledge the cooperation of the

households and the local communities of the Bolpur-Santiniketan area

during the study. The financial support provided by the UGC, India to

one of the authors (B. K. Padhi) is gratefully acknowledged. The

authors are also grateful to the two anonymous reviewers for their

valuable comments and suggestions which helped in improvement of

the quality of the manuscript.

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