Environ Monit Assess DOI 10.1007/s10661-007-9651-0
Air quality and respiratory health in Delhi Nidhi & Girija Jayaraman
Received: 17 May 2006 / Accepted: 12 February 2007 # Springer Science + Business Media B.V. 2007
Abstract Delhi is an instructive location for studying the impact of air pollution since it is a rapidly expanding centre of government, trade commerce and industry. We have made an attempt to (1) determine the association between environmental pollution and respiratory morbidity in Delhi for the period 1998–2004, (2) assess the impact on hospital admission of the implementation of recent governmental regulations and (3) calculate the relative risk of hospitalization due to respiratory ailments caused by air pollutants. Seven hospitals from different parts of Delhi were selected. The pollution profiles of these areas were assessed and subsequently Poisson regression model was performed for the patient population. There was a remarkable decrease in monthly average concentration of sulphur dioxide (from 17.9 to 11.1 μg m−3) and increase in monthly average concentration of nitrogen dioxide (from 34.2 to 49.1 μg m−3) after the newly introduced regulations. Particulates were observed to have marginal fall in their concentration but still remained above the permissible limits. Gaseous pollutants, in spite of being at a level lower than the permissible level, showed more consistent significant association with respiratory
admissions. The relative risks of hospitalization due to respiratory diseases were in the range of 1.07–2.82 in residential cum commercial areas. Comparative study of pre and post new stringent governmental regulation showed significant positive association of NO2 with respiratory disorders in southern (RR: 1.10; CI: 1.09– 1.12) and northern regions (RR: 1.33; CI: 1.27–1.39), both mixed use areas. In spite of the improvement in the air quality, the associated health effects were found to be substantial. Keywords Air pollution . Hospital admission . Pre and post legislations . Relative risk List of notations m μ g °C km
meter micron gram degree Celsius kilometer
Introduction Nidhi : G. Jayaraman (*) Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi 110016, India e-mail:
[email protected] Nidhi e-mail:
[email protected]
There is a growing awareness that the existing knowledge and database in weather and environmental monitoring can be effectively incorporated into public health programs and in framing environmental policies. The adverse health effects of air pollution have been widely documented in several reports based on
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studies in large cities in the west (Anderson et al. 1996; Katsouyanni et al. 1997; Pope et al. 1995; WHO 2000 Guidelines for Air Quality). Numerous studies on relating hospital admissions or emergency room visits with air pollution indicate adverse effect on the respiratory health of the population exposed to air pollution (Cho et al. 2000; Wong et al. 2002). In India, studies documenting the association of air pollution with respiratory diseases are relatively new. According to the estimates of WHO and World Bank, from 1992 to 1995 there is an increase in premature deaths due to air pollution in Indian Cities. There have been a few studies in Delhi (Chhabra et al. 2001; Cropper et al. 1997; Joshi 1998; Pande et al. 2001) and other cities like Mumbai and Bangalore indicating adverse health effects due to prevailing air pollution levels. The Indian capital Delhi is an instructive location for impact of air pollution in an urban area since it is a rapidly expanding centre of government, trade commerce and industry. There are wide fluctuations in seasonal conditions due to diverse physical and climatic conditions affecting the pollution level. In recent years, Delhi’s air pollution levels have shown dramatic changes primarily due to a series of rulings by the Indian Supreme Court. Significant among them were the following two rulings: (1) The government of the National Capital Region (NCR) which includes Delhi, was directed to regulate conversion of all commercial vehicles form diesel/ gasoline to clean fuel technology, particularly compressed natural gas (CNG) by March 31, 2000, which was further extended until September 30, 2001 and then to January 31, 2002 by another ruling. (2) In September 2000, the Indian Supreme Court directed the closure of polluting industries and in residential areas in the so-called “non conforming areas” as per the Delhi Development Authorities (DDA) guidelines of land-use classification. The first ruling was enforced strictly and the second, which was met with substantial resistance, is being implemented slowly. It is important to understand the implications of these rulings in terms of their impact on the air quality of Delhi and subsequently its impact on human health. In our earlier study (Agarwal et al. 2006), the status of respiratory morbidity of Delhi was assessed over a 4 years period from 2000 to 2003 by investigating the role of important pollutants and meteorological factors responsible for hospital admissions on account of COPD (Chronic Obstructive
Pulmonary Disease), asthma and emphysema. Stepwise multiple regression analysis explained 33% variability in respiratory morbidity associated with increase in SPM (Suspended Particulate Matter) and relative humidity. The present study is an attempt to assess the disease burden due to ambient air pollution in Delhi in different physical/social environments and under different conditions with inclusion of more hospitals from different parts of the city. The focus is not only to quantify but also to allay/increase the fears generated by media statements, which, at times tend to exaggerate the actual situation. The overall objective of the present paper is (1) to analyze the meteorological data along with air quality data of pollutants i.e., SO2 (Sulphur Dioxide), NO2 (Nitrogen Dioxide), SPM (Suspended Particulate Matter) and RSPM (Respiratory Suspended Particulate matter) for the past 7 years (1998–2004), (2) to correlate the data with hospital admission data in the corresponding period and (3) to compare the air quality as well as hospital admissions of pre and post rulings related to enforcement of land use zoning regulations and policies on vehicular emissions in Delhi. The data collection methodology is described in section “Data collection,” followed by description of methods used for data analysis in section “Data analysis.” Section “Results” and section “Discussion” deal with the results and discussion respectively followed by conclusions in section “Conclusion.”
Data collection In this section, we describe (1) the study area, Delhi metropolis, (2) the location of the seven hospitals along with the type of patients using the facilities, (3) air pollution data (monthly averages) in the neighborhood of the seven hospitals obtained from Central Pollution control Board (CPCB) and (4) meteorological data of Delhi obtained from India Meteorology Department (IMD) for the study period (January 1998–December 2004). Study area New Delhi, the capital of India (28°35 N, 77°17 E) is located in the subtropical belt having extreme type of continental climate. The temperature varies from 45 degrees in summers to 4 degrees in winters. The
Environ Monit Assess Table 1 Profile of different hospitals Name of the hospital with respective air pollution sites
Area of location
(A) Safdarjang (average of Siri Fort & Nizamuddin)
South Delhi (R) 1,500 (6,000 patients visit the out patient department) Central West 500 Delhi (TI) East Delhi (I) 230
(B) Sir Ram Ganga (ITO) (C) Swami Dyanand (Shahadra) (D) Hindu Rao (Ashok Vihar) (E) Dr. Ram Mohan Lohia (ITO) (F) Lok Nayak Jai Prakash (ITO) (G) ESI, Jhilmil Colony (Shahadra) R residential. TI traffic intersection. I industrial.
Fig. 1 Map of Delhi showing hospitals selected and respective air quality monitoring stations
No. of beds
North Delhi (R) 980 Central Delhi (TI) Central Delhi (TI) East Delhi (I)
937 1,200 260
Type of patients
Economic status of the patients
From any part of Delhi and its neighborhood
All strata of society with a small fragment belonging to affluent group Mainly low & middle income group Mainly low & middle income group Mainly low & middle income group All strata of society
Nearby areas of west Delhi Nearby areas Residing in and around the area Residing in and around the area Residing in and around the area Residing in and around the area
Mainly low & middle income group All strata of society
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population of Delhi at present is approximately 12.8 million (2001 census) and is continuously increasing partly due to the migration of people from rural areas which has resulted in significant increase in environmental pollution. Health data The data on monthly count of patients with respiratory diseases was obtained from medical records of seven different hospitals viz., (A) Safdarjung (south), (B) Sir Ganga Ram (west), (C) Swami Dayanand , (D) Hindu Rao (north) , (E) Lok Nayak Jayprakash, (F) Ram Manohar Lohia (central) and (G) Employees State Insurance (ESI), Jhilmil colony (east) for a period of 7 years i.e., from 1998 to 2004 except Ganga Ram, where data was available for 6 years only i.e., from 1998 to 2003 and for LNJP, data was available for 1999–2004. The details about these hospitals, their location, bed capacity, number of patients and respec-
Table 2 Summary statistics of all the variables in the period (1998–2004)
Air quality/meteorological data Data for five pollutants of interest viz. SO2, NO2, SPM, RSPM and Ozone were obtained from CPCB for the monitoring sites near the hospitals. Monthly averages of these pollutants were obtained to be used as air quality indicator in the analysis. However, as ozone concentration is measured by CPCB at only one site, the same data for ozone is used for all the hospitals.
Variables (units)
Hospitals
Admissions/month
A B C D E F G A B, E & C&G D A B, E & C&G D A B, E & C&G D A B, E & C&G D
SO2 (μg m−3)
NO2 (μg m−3)
SPM (μg m−3)
RSPM (μg m−3) B, E & F are grouped together in the table since the pollution data used for all of them is from ITO. C & G are grouped together since pollution data used for them is from Shahadra monitoring station.
tive air pollution monitoring sites are given in Table 1 and locations are shown in Fig. 1. Only hospital A had record of admissions organized in terms of specific diagnosis of respiratory diseases according to ICD (International Classification of Diseases). So it was possible to analyze the monthly count of COPD (chronic obstructive pulmonary diseases) and asthma admissions for a period of 6 years from January 1998 to December 2003 for this hospital only.
Ozone (μg m−3) Temperature (°C) Relative humidity (%) Wind speed (km/hr)
F
F
F
F
Mean±S.D. 121±29 87±17 90±26 85±42 112±42 551±123 108±27 15.20±3.6 16.94±7.17 16.7±6.4 9.97±3.81 31.84±4.82 69.86±17.89 28.51±7.64 24.64±8.20 336.51±91.15 473.24±133.78 366.74±122.4 362.37±116.96 121.5±46.27 221.04±83.19 131.2±52.24 146.03±64.3 22.71±10.9 25.15±6.96 72.16±14.4 6.67±2.25
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Information on meteorological variables, namely, temperature, relative humidity and wind speed measured at Safdarjung Airport were obtained from India Meteorological Department (IMD). Seasons for Delhi were well defined as three distinct seasons viz., monsoon (July to September), winter (October to February) and summer (March to June).
Data analysis Missing data for some of the pollutants in certain months were generated by linear interpolation. Summary statistics were calculated (Table 2) and examined regionally and seasonally for admission counts and atmospheric variables. All the pollutants were observed to be higher at the monitoring site near the hospitals situated near a busy traffic intersection as compared to those situated near other sites. The mean temperature varied from 12 to 35°C in the study period whereas relative humidity varied from 9 to 93% and wind speed from 0.77 to 14.2 km/h. The hospital admissions were widely dispersed around the mean value. In principle, the counts are Poisson distributed which is determined solely by the mean, say, 1 which must be positive. Because of this restriction, loglinear models, a family of generalized linear models (GLMs), are used to model Poisson distributed count data. GLMs use a link function that relates a linear predictor x to the expected value of the dependent variable, i.e., 1. In the case of Poisson model, the link function is log1 and is given as log l ¼ β0 þ β1 x1 þ β2 x2 þ :::::::::::: þ βn xn where β0, β1,.... βn are the regression coefficients and x1, x2.........xn are the predictor variables. Initially, to test the influence of an individual pollutant on respiratory morbidity, a single pollutant model was constructed for each of the five pollutants. The relative risk (RR) and its 95% confidence intervals (95% C.I.s) for the ith predictor were computed as RRi ¼ exp½Δci βi and C.I.=[exp {Δci * (βi −1.96 * S.Ei)}; exp {Δci * (βi +1.96 * S.E.i)}]; (i=1,2,......n) and Δci is an increment in the ith pollutant concentration and S.E. is the standard error. Here Δc for both SO2 and NO2 is
10 μg m−3whereas for SPM and RSPM, these are 100 and 50 μg m−3 respectively. RR is a measure of how much a particular risk factor (say, increase in particulates level) influences the risk of a specified outcome (say, increase in number of hospital admissions). To study the combined effects of the pollutants, multi pollutant models were constructed. Further analyses were performed to determine seasonal variation in the impact of pollution on the respiratory morbidity. Also the model was applied to investigate the effect of pollution on health by comparing the results corresponding to pre and post Governmental legislations.
Results General variations Atmospheric variables Figure 2 shows the annual average of the pollutants near all hospitals. A declining trend over the years was observed for SO2, SPM and RSPM concentration at all the sites whereas NO2 showed a reverse trend i.e., an increase in concentration at all the sites and this pattern was found more pronounced at the site near the hospitals B, E and F. Figure 3 shows average concentration by region and season for all the pollutants. As compared to gaseous pollutants except ozone, particulate matters showed marked seasonal variation. SO2 was highest in winter near all the hospitals except hospital C and G. NO2 concentration was also highest in winter at all sites. SPM concentration was lowest in monsoon at all the sites whereas summer and winter were characterized by nearly same concentration. The seasonal variation for SPM was relatively less than that for RSPM. The RSPM concentration showed a significant increase in winter at all the sites and more specifically at the site near hospitals B, E and F. Ozone concentration was highest in summer whereas winter and monsoon were observed to have almost same average concentration. Figure 4 shows the plot of monthly averages of all the pollutants for 7 years. Almost all the data plots except that for ozone reveals a general trend of wintertime high concentration and summer time low concentration. These results may be due to meteorological conditions and photochemical activity at the site. The general meteorology of the region during winter
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SO2 (µ m-3)
Annual average of SO2 conc.(1998-2004) 30 25 20 15 10 5 0 A
B
C
D
NO2 (µ m-3)
Annual average of NO2 conc. (1998-2004) 100 80 60 40 20 0 A
B
C
D
SPM (µ m-3)
Annual average of SPM conc. (1998-2004) 600 500 400 300 200 100 0 A
B
C
D
RSPM (µ m-3)
Annual average of RSPM conc. (2000-2004) 300
is dominated by high pressure causing increased atmospheric stability i.e., low general circulation and thus more stagnant air masses leading to more accumulation of pollutants. However, during summer the average planetary boundary layer height is typically at its greatest, resulting in increased mixing through a greater volume of the troposphere and hence lower pollutant concentrations. Also during the monsoon period, because of large precipitations, high wind velocities and changes in general wind direction; low level of pollution is observed. The high O3 concentration in summer was observed because of long sunshine hours and high solar radiation. There has been a perceptible change in Delhi’s atmospheric conditions after the stringent regulations being enforced by the government. The CPCB has reported striking decline in many pollutants, especially SO2 and CO. Figure 5 shows the comparable average pollutants concentration pre and post legislations. The average concentration of SO2 was observed to have undergone a marked decline in all the parts of Delhi whereas NO2 concentration was observed to be slightly increasing. SPM levels showed marginal fall in their concentration in all the regions except western region, a busy traffic intersection.
200
Hospital admission
100 0 A
B
C
Fig. 2 Air quality near all the hospitals (1998–2004)
Fig. 3 Air quality near seven hospitals in different seasons. Note: bar, seasonal mean; flag, seasonal SD
D
Figure 6 shows that on an average, monthly count of admissions was higher in summer and winter as compared to monsoon in the general hospitals except the two MCD hospitals, i.e., C and D where admissions
Environ Monit Assess SPM
20
0
0
400
600
300
400
200
200
100
19 98 19 99 20 00 20 01 20 02 20 03 20 04
0 1998 1999 2000 2001 2002 20032004
20
80
10
40 0
0 1998 1999 2000 2001 2002 2003 2004
D
800 700 600 500 400 300 200 100 0 1998 1999 2000 2001 2002 2003 2004
SPM (µg m-3)
0
300 200 100 0
800
400
600
300
400
200
200
100
0 01 20 02 20 03 20 04
20
99
00 20
98
0 19
19
99 20 00 20 01 20 02 20 03 20 04
19
Pre legislation
400
D
60
0 19
SPM (µg m-3)
0
20
400 350 300 250 200 150 100 50 0
C&G NO2 (µg m-3)
20
10
98
SO2 (µg m-3)
40
40
25
Post legislation 80
20
NO2 (µg m-3)
SO2 (µg m-3)
Fig. 5 Average concentration of pollutants in pre and post legislations
800 700 600 500 400 300 200 100 0 1998 1999 2000 2001 2002 2003 2004
60
20
15 10 5 0
60 40 20 0
A
B, E & F
C&G
D
A
B, E & F
Hospitals
C&G
Hospitals
500
SPM (µg m-3)
SO2 (µg m-3)
C
40 35 30 25 20 15 10 5 0 1998 1999 2000 2001 2002 2003 2004 30
SPM (µg m-3)
120
NO2 (µg m-3)
B
30
NO2 (µg m-3)
SO2 (µg m-3)
B
0
400 300 200 100 0 A
RSPM (µg m-3)
10
800
RSPM (µg m-3)
40
RSPM (µg m-3)
60
20
RSPM
A SPM (µg m-3)
30
SO2 (µg m-3)
NO2
B, E & F
C&G
Hospitals
D
D
RSPM (µg m-3)
SO2
A
NO2 (µg m-3)
Fig. 4 Trend of the pollutants during 1998–2004
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A
B 200
No. of patients
200
No. of patients
Fig. 6 Trend of monthly respiratory admissions in the study period
150 100 50
150 100 50
0 1998 1999 2000 2001 2002 2003 2004
0 1998 1999 2000 2001 2002 2003
D No. of patients
No. of patients
C 200 150 100 50
150 100 50
0 1998 1999 2000 2001 2002 2003 2004
0 1998 1999 2000 2001 2002 2003 2004
E
300 250 200 150 100 50 0 1998 1999 2000 2001 2002 2003 2004
1000
No. of patients
No. of patients
200
F
800 600 400 200 0 1998 1999 2000 2001 2002 2003 2004
G No. of patients
200 150 100 50 0 1998 1999 2000 2001 2002 2003 2004 2005
were relatively higher in monsoon. The disease specific analysis in hospital A revealed the average ratio of patients with COPD to those with asthma was 40:13. Also the admissions were highest in winter for both the diseases. Correlation Table 3 shows pair wise correlations of pollutant concentrations and different meteorological variables within each region. NO2 showed positive correlation with the particulate matters especially with RSPM near all the hospitals, however, all of them were not statistically significant. SPM and RSPM showed high correlation among them but it was statistically significant only near hospital D. Ozone was observed to be significantly correlated with SPM
concentration near the hospitals located near traffic intersection. High temperatures as well as higher humidity and wind speed were associated with lower pollutant levels in most of the cases. Ozone was positively correlated with temperature and wind speed and negatively correlated with humidity and all these correlations were statistically significant. Relative Risk (RR) Whole study period Table 4 presents the results from single pollutant as well as multi pollutant Poisson model including all the meteorological variables relating monthly average concentrations with the monthly admission counts in
Environ Monit Assess Table 3 Pair wise correlation coefficients for atmospheric factors Factors
Hospitals
NO2 (μg m−3)
SPM (μg m−3)
RSPM (μg m−3)
Ozone (μg m−3)
Temp (oC)
Humidity (%)
Wind speed (km h−1)
SO2 (μg m−3)
A B, E & C&G D A B, E & C&G D A B, E & C&G D A B, E & C&G D
−0.317 −0.435* 0.234 0.023
0.214 −0.131 0.203 0.096 0.06 0.502* 0.223 0.364
0.234 0.092 0.132 0.652 0.399 * 0.517* 0.502 0.456 0.842 0.791* 0.876 0.858
0.052 0.172 0.047 0.040 −0.12 −0.001 −0.129 −0.199 0.194 0.255* 0.224 0.106 −0.067 0.117 0.095 0.049
−0.22 −0.144 0.053 −0.157 −0.295 −0.113 −0.198 −0.235 0.018 −0.151 0.128 −0.054 −0.415 −0.281 −0.283 −0.381 0.327*
0.025 −0.008 −0.073 0.192 0.067 −0.006 −0.207 0.042 −0.438 −0.319* −0.4 −0.246 −0.138 −0.334 −0.251 −0.064 −0.562*
−0.016 −0.023 0.16 −0.052 −0.24 −0.333* −0.173 −0.414 0.044 −0.075 0.268 −0.227 −0.273 −0.189 0.021 −0.465 0.316*
NO2 (μg m−3)
SPM (μg m−3)
RSPM (μg m−3)
F
F
F
F
Ozone (μg m−3) *P
