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Air quality assessment and its relation to potential health impacts in Delhi, India

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The main objective of the air quality index (AQI) system is to interpret air quality in a standardized indicator to enable the public to understand the likely health and environmental impacts of air pollutant concentration levels monitored on any given day. The daily averaged concentration data of air pollutants of monitoring sites under the National Ambient Air Quality Monitoring Programme of Delhi were analysed for the period 2001–2010 using the AQI system. This study was undertaken to (i) evaluate the trends of air quality for the past 10 years, (ii) ascertain the association of air quality with mortality and respiratory morbidity rate of Delhi, and (iii) examine the seasonal variation of air quality. The air quality status was found to be varying from 'moderate' to 'unhealthy for sensitive group' category from the health impact point of view. Non-trauma mortality (r = 0.877, P < 0.01) as well as respiratory morbidity were found to be significantly correlated with AQI values. The present study increases public awareness of the health implications of air pollution and helps assess pollution trends in a more meaningful way.
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RESEARCH ARTICLES
CURRENT SCIENCE, VOL. 109 , NO. 5, 10 SEPTEMBER 2015 902
*For corre spondence. (e-mail: si raju ahmed@redi ffmail.com)
Air quality assessment and its relation to
potential health impacts in Delhi, India
Sanjoy Maji1, Sirajuddin Ahmed2,* and Weqar Ahmad Siddiqui1
1Department of Applied Sciences and Humanit ies, and
2Department of Civil Engineering, Jamia Millia Islamia ( Central Univers ity) , Ja mia Nagar , New Delhi 11 0 02 5, India
The main objective of the air quality index (AQI)
system is to interpret air quality in a standardized i n-
dicator to enable the public to understand the likely
health and environmental impacts of air pollutant
concentration levels monitored on any given day. The
daily averaged concentration data of air pollutants
of monitoring sites under the National Ambient Air
Quality Monitoring Programme of Delhi were ana-
lysed for the period 2001–2010 using the AQI system.
This study was undertaken to (i) evaluate the trends of
air quality for the past 10 years, (ii) ascertain the
association of air quality with mortality and respira-
tory morbidity rate of Delhi, and (iii) examine the sea-
sonal variation of air quality. The air quality status
was found to be varying from ‘moderate’ to ‘unhealthy
for sensitive group’ category from the health impact
point of view. Non-trauma mortality (r = 0.877,
P < 0.01) as well as respiratory morbidity were found
to be significantly correlated with AQI values. The
present study increases public awareness of the health
implications of air pollution and helps assess pollution
trends in a more meaningful way.
Keywords: Air quality index, health impacts, mortality,
respiratory morbidity.
OVER the last few decades, several studies have been un-
dertaken in various parts of the world to assess the rela-
tionship between air quality and health1–3. Evidence from
different studies has shown that respiratory and cardio-
pulmonary disease is strongly associated with air quality3–5.
Many studies in the western countries have reported
increase in daily mortality rate, hospital admission and
emergency visits to hospitals with fluctuation of daily air
pollution level2,6–8 . However, few studies have been con-
ducted for the Asian region9. According to World Health
Organization (WHO)10, urban air pollution is responsible
for approximately 800,000 deaths and 4.6 million lost
life-years annually around the globe. The problem of air
pollution has assumed serious proportions in Delhi,
which is also reflected by an increase in the respiratory
and cardiovascular mortality11 . A report published by the
Directorate of Economics and Statistics, Government of
National Capital Territory (NCT) of Delhi (New Delhi,
India) found a higher percentage of certified death
(24.9% in 2009 compared to 16.4% in 2005) due to dis-
ease of respiratory and circulatory system12; both of which
are believed to have direct linkages with air pollution.
Man y cities in India are considered to be among the
polluted megacities of the world. Although the available
national statistics on air quality provides a gloomy pic-
ture, studies documenting the health impact due to dete-
riorating air quality ar e only a handful. There have been
few studies in Delhi, th e capital city of India, document-
ing the association of air pollution with adverse health
effects1 3–16 as well as other cities like Mumbai17 and Kol-
kata18, linking adverse health effects due to prevailing air
pollution levels.
Delhi is considered among the most polluted megaci-
ties of the world19 and offers a first-hand choice to study
air pollution problems. The air quality report published by
the Central Pollution Control Board (CPCB), Government
of India (GoI) reported that Delhi has exceeded the
annual average respirable particulate matter (RSPM) con-
centration limit by more than four times the national
annual standards20. Of late, the air quality of Delhi has
undergone man y changes in terms of the level of pollut-
ants and control measures taken to r educe them. Under
the supervision of the Supreme Court of India, the Gov-
ernment of NCT of Delhi has taken several steps to
reduce air pollution levels in the city during the past
years. Significant among them are the following rulings:
(1) switching over to CNG in case of public transport,
(2) introduction of Bharat IV stage (equivalent to Euro-
IV) fuel, (3) closure of hazardous industries in the city,
(4) introduction of metro system, etc. In spite of all these
measures, population growth coupled with rapid urbani-
zation have contributed to an increase in air pollution in
Delhi. The city itself accounts for about 8% of the total
registered motor vehicles in India, which is more than
three other megapolitan cities (Mumbai, Kolkata and
Chennai) taken together21. Currently, Delhi adds over
1000 new personal vehicles each day on its roads.
The annual report on registration of births and deaths
in Delhi11 also shows an increasing trend in respiratory
mortality in certified deaths for the period 2004–2010
(Figure 1).
doi: 10.18520 /v109 /i5/ 902-909
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CURRENT SCIENCE, VOL. 109 , NO. 5, 10 SEPTEMBER 2015 903
In the past, some studies were undertaken for air qua-
lity assessment of Delhi22 –26. The analysis was based on
annual or monthly averages of air quality data to deter-
mine the trend of air quality in comparison to ambient air
quality standards. Sharma et al.2 7 proposed an air quality
index (AQI) system and interpreted the air quality of
Delhi. However, the present study interprets the daily
averaged concentration data of air pollutants for the
period 2001–2010 based on AQI values proposed by the
US Environmental Protection Agency (USEPA)28 and
correlates AQI with primary hospital admission data and
mortality data collected. Daily AQI values allow interpr e-
tation of air quality data from health significance levels.
Further, categorization of AQI in various health signifi-
cance levels allow more in-depth analysis of air pollution
problem in Delhi.
Air quality index
Air quality index (also known as the air pollution index
(API)) is a number used by government agencies to char-
acterize the daily air quality. The main objective of the
AQI system is to interpret air quality in a standardized
indicator to enable the public to understand the likely
health and environmental impacts of air pollutant concen-
tration levels monitored. As on any given day AQI
increases, an increasingly large percentage of th e popula-
tion is likely to experience increasingly severe adverse
health effects. The pollutant concentrations are divided
into index range 0–500 and the overall range is sub-
divided into six sub-indices which correspond to six cate-
gories of air quality based on their potential health and
environmental impacts (Table 1).
Materials and methods
Study area
Delhi city is located in North India, at 282417 and
285300N lat., 774530 and 772130E long., and
Figure 1. Trend of respir ator y mortality in certi fied deaths in Delhi
for the per iod 2004–201 0.
approximately 216 m amsl. The city is spread over
1483 sq. km (47% urban, 53% rural) of area. Delhi is lo-
cated in the subtropical belt. Th e climate is mainly influ-
enced by its inland position and the prevalence of
continental type of climate during major part of the year.
The climate is characterized by extr eme dryness with an
intensely hot summer and cold winter. Delhi experiences
four well-defined seasons: winter (December to Febru-
ary), summer (March to June), monsoon (July to Septem-
ber) and post-monsoon (October and November). At
present the total population of Delhi is approximately
16.79 million (Census 2011) and is constantly increasing
due to migration pressure from all over the India. The
density of population per square kilometre is about
11,320 (the national average is 382 per sq. km). There
has been a significant increase in environmental pollution
over the past decade.
Air quality data
CPCB, GoI continuously monitors (twice a week) the
level of pollutants in different parts of Delhi under the
National Ambient Air Quality Monitoring Programme
(NAAQMP). The daily data on air pollution levels (24 h
average RSPM (particulate matter with an aerodynamic
diameter less than 10 m, i.e. PM1 0) concentration,
oxides of nitrogen (NOx) and sulphur dioxide (SOx) were
obtained directly from the CPCB for monitoring stations
Ashok Vihar/Pitampura (note 1), Janakpuri, Siri Fort,
Nizamuddin, Sahazada Bagh and Sahadara for the period
2001–2010. Figure 2 shows the location of the monitor-
ing stations.
Population and health data
Population and mortality data were obtained from the
Delhi Statistical Handbook21 and ‘Report on medical
certification of cause of deaths in Delhi12. The data on
yearly counts of out patient department (OPD) patients
with respiratory diseases were collected directly from the
medical records of Safdar jung Hospital and Dr Ram
Manohar Lohia Hospital, New Delhi. Both are among the
four largest hospitals under the Ministry of Health and
Table 1. Air quali ty index (AQI) values a nd descriptors
AQI valu e Level s of health co ncern
0–50 Good
51–10 0 Modera te
101–1 50 Unhea lthy for sensitive grou ps
151–2 00 Unhea lthy
201–3 00 Very u nhealthy
301–4 00 Haza rdou s
401–5 00 Haza rdou s
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Figure 2. Map of Delhi s howi ng air qu ality monitor ing stations.
Family Welfare, GoI, with free beds. Safdarjung Hospital
is located in the South Delhi and has approximately 1500
beds. Dr Ram Manohar Lohia Hospital is located in Cen-
tral Delhi and has 937 beds. Safdarjung Hospital receives
patient from all parts of Delhi and its neighbourhood,
whereas Dr Ram Manohar Lohia Hospital receives
patients mainly from nearby areas.
Development of AQI
The US EPA AQI is based on daily concentration of five
of the criteria pollutants (viz. particulate matter (PM10/
PM2.5 ), sulphur dioxide, ozone, nitrogen dioxide and car-
bon monoxide). However, as all the pollutants are not
measured under NAAQMP, our calculation is based on
three criteria pollutants (RSPM, SOx, NOx) only. Th e
daily sub-index values are computed based on maximum
operator concept like that of the US EPA AQI. The
maximum value of sub-indices for each pollutant was
taken to represent overall AQI of the location. The fol-
lowing mathematical equation was used for calculating
the sub-indices
high low
low low
high low
( ) ,
I I C C I
C C
I
 
(1)
where I is the (air pollution) index, C the pollutant con-
centration, Clow the concentration breakpoint that is C,
Chigh the concentration breakpoint that is C, Ilow the
index breakpoint corresponding to Clo w, and Ih igh the
index breakpoint corresponding to Chigh.
Data analysis
Daily averaged concentration data of air pollutants were
interpr eted into AQI values for different air quality moni-
toring stations for the period 2001–2010 based on the
US EPA method28 . Air quality monitoring stations were
compared based on yearly percentage trend in each of the
health categories (AQI code frequency). For studying the
seasonal variation of the AQI values, the whole year was
divided into the following seasons: rainy season (July–
September), summer (March–June), post-monsoon (Octo-
ber and November) and winter (December–February).
Daily AQI values calculated based on concentration of
criteria air pollutants at each of th e air quality monitoring
stations were used to obtain the seasonal distribution of
AQI code frequency percentage.
The degree of association of AQI values with all n on-
trauma mortality (all causes, excluding accident and
suicide) rate and yearly respiratory OPD patient count
was determined through correlation study. It is difficult to
certify the exact cause of death; therefore, the relation-
ship between air quality and premature mortality is most
often studied using variations of all non-trauma deaths
with pollution levels. Since there are relative changes in
different air quality classes over the years, it is difficult to
study the effects of individual AQI classes on mortality
rate. Higher AQI value denotes poor air quality and an
increasingly large percentage of the population is likely
to experience increasingly severe adverse health effects.
To study the strength of association of AQI values on
mortality rate, weighting factor (e.g. 1 for AQI category
‘Good’, 2 for ‘Moderate’, 3 for Unhealthy for sensitive
groups’, 4 for ‘Unhealthy’, 5 for ‘Very unhealthy’, 6 for
‘hazardous’, and 7 for ‘most hazardous’) was used for
aggregating the frequency percentage of differ ent AQI
classes. Th e weighted aggregated AQI (WAAQI) values
were corr elated with all non-trauma mortality rate and
respiratory morbidity rate to study the association of AQI
with health implications.
The respiratory OPD patient count for Safdarjung Hos-
pital was correlated with frequency percentage of com-
posite average AQI values of Delhi, whereas for Dr Ram
Manohar Lohia Hospital the correlation study was per-
formed with AQI value of Nizamuddin monitoring station
which is the n earest residential ambient air quality moni-
toring station with air quality similar to that in and
around the hospital ar ea.
Results and discussion
The air quality trends were compared on annual basis for
all the monitoring stations keeping in mind the number of
days with unhealthy conditions. For inter-annual and
inter-spatial comparison, percentage distribution in each
AQI category was multiplied with the AQI category
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Table 2. Variabili ty of AQI sub-index values of Pita mpura for 2001–2010
AQI class/year 0–50 51–10 0 101–150 151–200 201–300 301–4 00 401–5 00
2001 2 68 26 2 0 2 0
2002 6 51 35 6 2 0 0
2003 7 56 34 3 0 0 0
2004 7 74 9 3 3 4 0
2005 9 72 14 5 0 0 0
2006 4 69 16 11 0 0 0
2007 16 30 36 5 3 8 2
2008 0 63 24 9 3 1 0
2009 0 34 53 13 0 0 0
2010 2 26 36 28 3 4 1
Table 3. Variabili ty of AQI sub-index values of Janakpuri for 2001–2010
AQI class/year 0–50 51–10 0 101–150 151–200 2 01–300 3 01–4 00 401–5 00
2001 10 63 22 5 0 0 0
2002 6 54 33 5 2 0 0
2003 3 66 27 2 2 0 0
2004 5 71 17 5 2 0 0
2005 3 64 30 3 0 0 0
2006 7 44 40 9 0 0 0
2007 8 44 37 7 3 0 1
2008 0 35 27 2 7 5 3 3
2009 0 8 33 2 5 13 13 8
2010 0 14 27 1 9 22 12 6
Table 4. Variabili ty of AQI sub-index values of Siri Fort for 2001–2 010
AQI class/year 0–50 51–10 0 101–150 151–200 2 01–300 3 01–4 00 401–5 00
2001 18 60 19 3 0 0 0
2002 5 65 21 7 2 0 0
2003 14 69 10 7 0 0 0
2004 8 65 25 1 1 0 0
2005 13 68 15 4 0 0 0
2006 17 50 20 1 1 2 0 0
2007 4 38 32 2 1 4 1 0
2008 3 26 41 1 8 8 4 0
2009 0 11 36 3 4 14 4 1
2010 5 21 38 2 4 8 3 1
number and added. These values were compared to ana-
lyse the severity trends of air quality.
AQI analysis of different air quality monitoring
stations
Tables 2–7 show the frequency percentage of AQI values
for different ambient air quality monitoring stations dur-
ing 2001–2010. The severity trends are found to increase
for all the ambient air quality monitoring stations during
the study period and are most significant from the year
2008 onwards. In the analysis it was also observed that
AQI values vary widely among different stations. Inter-
annual variability of AQI values for the differ ent air qua l-
ity monitoring stations are discussed below:
Pitampura monitoring station: For this station (Table
2), air quality up to 2006, mostly falls in the ‘moderate’
to ‘unhealthy for sensitive group’ category. However,
from 2007 onwards the frequency of ‘unhealthy’ category
increases from 5% to 28%. But the air quality in 2010 is
the worst among all years; it reaches th e ‘hazardous’
category on several occasions.
Janakpuri monitoring station: Air quality at this station
(Table 3) is found to be most severe amongst all the mon-
itoring stations where the air quality reaches the ‘hazard-
ous’ and ‘severe’ category 13% and 8% of the time in
2009 and 12% and 6% of time in 2010 r espectively. The
air quality gradually deteriorates from 2005 onwards,
with 2009 being the worst among all the years.
Siri Fort monitoring station: In this station (Table 4),
from 2006 onwards there is gradual increase in severity
trends of air quality. Number of days with air quality
under ‘moderate’ category decreases gradually, whereas
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Table 5. Variabili ty of AQI sub-index values of Niza muddin for 2 001–2010
AQI class/year 0–50 51–10 0 101–150 151–200 2 01–300 3 01–4 00 401–5 00
2001 17 68 12 3 0 0 0
2002 4 60 32 4 0 0 0
2003 6 67 24 3 0 0 0
2004 5 69 21 4 1 0 0
2005 11 77 12 0 0 0 0
2006 14 59 22 5 0 0 0
2007 22 58 17 3 0 0 0
2008 7 42 20 1 6 5 5 5
2009 1 10 24 3 5 19 7 4
2010 0 20 33 3 5 7 3 2
Table 6. Variabili ty of AQI sub-index values of Sahazada Ba gh for 2001–2010
AQI class/year 0–50 51–10 0 101–150 151–200 2 01–300 3 01–4 00 401–5 00
2001 0 33 53 1 3 1 0 0
2002 2 38 48 8 2 2 0
2003 2 57 35 6 0 0 0
2004 3 69 23 5 0 0 0
2005 2 71 22 5 0 0 0
2006 10 40 45 3 2 0 0
2007 6 56 22 1 4 1 1 0
2008 0 52 24 1 0 6 4 4
2009 0 20 54 2 1 1 4 0
2010 4 16 42 2 5 7 4 2
Table 7. Variabili ty of AQI sub-index values of Sahadar a for 2001–2010
AQI class/year 0–50 51–10 0 101–150 151–200 2 01–300 3 01–4 00 401–5 00
2001 19 66 14 1 0 0 0
2002 6 46 41 5 2 0 0
2003 6 60 29 5 0 0 0
2004 9 64 23 4 0 0 0
2005 3 70 22 4 0 0 1
2006 4 59 25 1 0 1 1 0
2007 4 41 33 1 6 4 2 0
2008 1 34 38 2 1 4 1 1
2009 0 21 59 2 0 0 0 0
2010 1 18 28 3 2 15 5 1
days un der ‘unhealthy for sensitive group’ and ‘un-
healthy’ categories. In 2009 and 2010, air quality reaches
the ‘hazardous’ and ‘most hazardous’ categories as well.
Nizamuddin monitoring station: There is a predomi-
nantly ‘moderate’ air quality in the Nizamuddin area
(Table 5) up to 2007. Year 2008 onwards, air quality
touches to ‘unhealthy’, ‘hazardous’ and ‘most hazardous’
category on a few occasions. Year 2001 is found to be
best amongst all the year s, with air quality being within
NAAQS standards about 85% of the time.
Sahazada Bagh monitoring station: Air quality at this
station (Table 6) shows a pattern where there is a gradual
improvement up to 2005, and gradual deterioration from
2006 onwards. Air quality at this station touched
‘hazardous’ category 4% of the time during the years
2008, 2009 and 2010.
Sahadara monitoring station: This station (Table 7)
shows a pattern similar to the Sahazada Bagh monitoring
station. There is a gradual improvement in air quality
from 2002 to 2005 (2001 is the best amongst all the
years) and the air quality deteriorates gradually from
2006 onwards. The air quality in 2010 is worst amongst
all the years for the station; air quality reaches ‘hazard-
ous’ category 5% of the time.
Analysis of AQI trend for different seasons
Figure 3 shows the monthly variations of frequency per-
centage of AQI values greater than 100 (the ‘safe’ limit)
for different air quality monitoring stations for the period
2001–2010. Th e general trend of AQI for all the monitor-
ing stations during winter (December–February) is found
to be the highest. On the other hand, the minimum values
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Figure 3. Monthly vari ation of air qua lity index (AQI ) frequency (>10 0) at different air quality monitoring stations for the period 2001–2010:
a, Pitampur a; b, Janak puri; c, Siri Fort; d, Niza muddin; e, Sahazada Bagh; f, Sahadara.
Figure 4. Season-wise percent age distribution of poor air qu ality
(AQI >100 ) in Delhi during 2001–2010.
for AQI are obtained for the rainy season (July–September).
Summer (March–June) and post-monsoon (October and
November) also show higher degree of pollution loads.
Figure 4 shows the season-wise percentage distribution of
worst air quality for Delhi. Th e reason for this discrep-
ancy can be explain ed from the fact that high pollution
load during winter is due to reduced dispersion on
account of low wind velocity and frequent temperature
inversion, whereas high particulate pollution load in
summer can be attributed to dust storms, greater wind
velocity and the n orthwesterly winds bringing additional
burden of particulates from th e neighbouring state of
Rajasthan. During the monsoon period, because of large
precipitation high wind velocities and changes in general
wind direction, low level of pollution is observed.
Analysis of association of AQI with respect to
mortality values
AQI is widely used to report to the public an overall
assessment of air quality on a given day with respect to
its health significance. As AQI increases, an increasingly
large percentage of the population is likely to experience
severe adverse health effects. The relationship between
air quality and premature mortality is most often studied
using time-series analysis of daily obser vations of th e
number of deaths and pollution levels. These studies ca p-
ture the short-term association of air pollution exposure
with probability of death. The underlying assumption is
that there is a distribution of susceptibility to the effects
of air pollution in any population. People who are in a
weakened physical state or who have a history of chronic-
obstructive pulmonary disease (COPD) or cardiopulmon-
ary problems are believed to be the most vulnerable. In
the case of a sharp rise in pollution, the most vuln erable
individuals are more likely to die. As an individual’s
sensitivity to pollutant exposure increases, so does the
severity of the response for a given pollutant exposure.
Such deaths have presumably been advanced (i.e. ‘prema-
ture’) to some degree, due to exposure of higher pollution
load. It is expected that the increase in premature deaths
will be reflected in yearly count of all non-trauma mortal-
ity. Figure 5 shows the all non-trauma mortality trend per
lakh population against the r elative changes in AQI
values greater than 100 for the years 2001 through 2010.
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The plot clearly shows that mortality rate follows the
trends of change in AQI sub-indices values. This associa-
tion of AQI tr ends with yearly all non-trauma mortality
rate is more prominent from 2005 onwards; with an
increase in the frequency percentage of higher index val-
ues (i.e. index value >100), there is an increase in all non-
trauma mortality rate. However, male mortality rate is
found to be higher than female mortality rate. A signifi-
cant correlation was found between all non-trauma mor-
tality rate (per lakh population) and weighted aggregated
AQI values (WAAQI; r = 0.877, P < 0.01; Figure 6).
Analysis of association of respiratory morbidity with
AQI values
The health damages associated with air pollution are gener-
ally studied by the changes in respiratory morbidity rate as-
sociated with fluctuation in pollution level. A significant
Figure 5. Association of yea rly morta lity ra te with relative changes
in AQI su b-index valu es among the Delhi popu lation for the period
2001– 2010.
Figure 6. Correlation ana lysis between all non -trauma morta lity rate
(per la kh popula tion) and weighted a ggr egated air qual ity index
(WAAQI) of Delhi for 20 01–2010 .
correlation was observed between respiratory morbidity
rate and weighted aggregated frequency percentage of
different AQI classes for the period 2001–2010. Since,
Safdarjung Hospital receives patient from all parts of
Delhi and Dr Ram Manohar Lohia Hospital receives
patients mainly from hereby areas, weighted aggregated
frequency percentage of different AQI classes of all air
quality monitoring stations and weighted aggregated fre-
quency percentage of differ ent AQI classes of Nizamud-
din air quality monitoring stations (nearest to Dr Ram
Manohar Lohia Hospital) for the period 2001–2010 were
correlated with yearly count of respiratory OPD patients
in the two hospitals, respectively. A significant correla-
tion was observed between yearly count of respiratory
OPD patients at Safdarjung Hospital with weighted com-
posite WAAQI of all air quality monitoring stations
(r = 0.766; P < 0.01; Figure 7). Yearly count of respira-
tory OPD patients at Dr Ram Manohar Lohia Hospital
was found to be significantly correlated with WAAQI of
Nizamuddin air quality monitoring station (r = 0.631,
P < 0.05; Figure 8).
Conclusion
This study shows a significant relationship of AQI values
with mortality rate as well as respiratory morbidity rate.
Figure 7. Correlation ana lysis between respira tory OPD count at
Safda rjung Hospita l and WAAQI of Delhi for 20 01–2010.
Figure 8. Correlation ana lysis between respira tory OPD count at
Dr R. M. Lohia H ospital and WAAQI of Niza muddin air qu ality moni-
toring sta tion for 2001 –2010.
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CURRENT SCIENCE, VOL. 109 , NO. 5, 10 SEPTEMBER 2015 909
The air quality data interpretation through AQI system
shows more in-depth analysis of air quality in comparison
to interpretation based on ambient air quality standards.
The study shows that AQI values give a proper represen-
tation of the air quality interpreting data for a whole year
with respect to different health categories. The analysis of
air quality in Delhi shows a gradual deterioration in with
respect to the AQI values from 2005 onwards. Statistical
analysis shows a significant association of the AQI values
in relation to the all non-trauma mortality rate (r = 0.877,
P < 0.01) and respiratory morbidity rate prevailing
among the Delhi population.
Note
1. In the year 2006, Ashok Vihar monitoring stations was shift ed to
the adja cent Pitampura area. For the sake of AQ I calcu lat ions, they
are consid ered the same.
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ACKNOWLED GEME NTS. We thank Medica l Records Depart ment
of Sa fdarjung Hospital, New Delhi and Dr Ram Ma nohar Lohia Hospi-
tal and Centra l Pollution Contr ol Boa rd, N ew Delhi for providin g data
on res pira tory pat ients and pollu tants conc entration.
Receiv ed 2 9 Ja nuary 201 5; revised accepted 26 May 2015
... Most states, and 76·8% of the population of India, were exposed to annual population-weighted mean PM2·5 greater than 40 μg/m³, which is the limit recommended by the National Ambient Air Quality Standards in India (Balakrishnan et al., 2019b). Many cities in India are considered to be among the polluted megacities of the world (Maji et al., 2015). Delhi is considered among the most polluted megacities of the world (Gurjar et al., 2010) and offers a first-hand choice to study air pollution problems. ...
... Recent studies (Maji et al., 2015) in Dehli reported on air pollution and health risks but there is still a knowledge gap on how meteorological conditions affect the air pollutants concentration. In this perspective, the present study comes therefore to establish a relationship between meteorological parameters (temperature, humidity, wind speed and wind direction) and criteria air pollutants concentration levels in Delhi, one of the most polluted megacities on the globe (Maji et al., 2015). ...
... Recent studies (Maji et al., 2015) in Dehli reported on air pollution and health risks but there is still a knowledge gap on how meteorological conditions affect the air pollutants concentration. In this perspective, the present study comes therefore to establish a relationship between meteorological parameters (temperature, humidity, wind speed and wind direction) and criteria air pollutants concentration levels in Delhi, one of the most polluted megacities on the globe (Maji et al., 2015). The proper understanding of mechanisms that produce air pollution, enhances the forecast accuracy of air pollution along with adequate mitigation system. ...
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Meteorological parameters (temperature, humidity, wind speed and wind direction) can be linked to air pollutants concentration levels in any area. The present study purposed to establish the relationship between meteorological parameters and selected criteria air pollutants concentration levels in Delhi. To infer spatial and temporal pattern of air pollutants and relationship with meteorological parameters, the secondary data for criteria pollutants as well as meteorological data (temperature, humidity, wind speed and wind direction) of selected sites were procured from a governmental agency "System of Air quality and weather Forecasting And Research (SAFAR)" for the period of four years (2013 to 2016). Microsoft excel was used for graphical representation purpose. Pearson correlation to establish pollutants relation with meteorological parameters was done by using SPSS. The study found that 2013 was the most polluted year and 2016 was the least in Delhi. It also showed that site 1 which is located in south Delhi is most polluted with regards of gaseous pollutants however; site 6 which is located in north Delhi is the most polluted site in terms of particulate matter. Some meteorological parameters have great influence on the spatial and temporal pattern of criteria pollutants in the selected sites. PM has negative correlation with temperature and humidity while in most sites NOX (NO & NO2) have positive relation with temperature. O3 also have positive relation with temperature because temperature accelerates formation of ozone. However, O3 has negative relationship with relative humidity because precipitation washes out the pollutant concentration. Generally, wind speed has no effect on concentration of air pollutants.
... Moreover, BB and open burning of agriculture residuals are also causing ABCs, cloud condensation nuclei (CNN), haze, and other air pollution phenomena. The Indian sub-continent, in particular, the Indo-Gangetic Basin (IGB), is one of the most polluted and populated regions in northern India, usually considered PM hotspots globally, and has expressed great concern about the harmful effects of adverse air quality Shyamsundar et al., 2019), and human health (Maji et al., 2015). ...
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The Himalayas, which include delicate and unspoiled ecosystems, have the third-largest glacier ice store in the world. Recent research reveals that anthropogenic and natural factors contribute to the deteriorating air quality in the region. Rising particulate matter (PM) levels might have devastating effects on the regional climate, hydrologic cycles, and ecology. Given the scarcity of studies (the majority of which are of short duration and focus on a single pollutant and satellite-based observation), unique topography, meteorological characteristics, monsoon dynamics, temperature inversion, and mixing of pollution emission from local and distant sources, it is difficult to understand the general pollution trend in the Himalaya. Nonetheless, past studies indicate that local biomass burning, long-distance transport, especially from the Indo-Gangetic Plain (IGPs), dust storms, and tourist activities are the primary drivers to rising PM pollution in the Himalayas region. Emissions from these sources develop exponentially and encompass severe pollution episodes because of the IGP's complicated hilly terrain, cloud condensation nucleation process, atmospheric brown clouds (haze), dust storm, and transport of PM from crop residue burning (especially during the post-monsoon season). In light of this, the current work outlines the sources, factors, and variables that contribute to the Himalayan region's rising pollution levels and sheds light on significant areas of recent research. The present study examines in depth the consequences of the monsoon, the dynamics of pollution in IGP, and the movement of PM from IGP to the Himalayan region. This review aims to highlight research gaps and limitations in the existing literature for a better understanding of the current PM pollution in the Himalayas and surrounding sites, which is essential for understanding climate change and health consequences in this region, and to provide significant theoretical and practical implications for assessing particulate pollution in the Himalayas region.
... Nitrogen oxide is one of the most frequent pollutants in the atmosphere [23]. Nitrogen oxide is a yellowish-brown liquid or a reddish-brown gas that is compressed. ...
... Multiple air pollutants prevailing at ISBT Flyover and Wazirabad Road in Delhi recorded a maximum number of morbidity cases related to COPD hospital admission (Kumar and Mishra, 2018). A report by GNCTD found that 24.9% of people died due to respiratory and circulatory failure in 2009 compared to 16.4% in 2005; both failures are believed to have linkage with air pollution (Maji et al., 2015). To assess the respiratory health of industrial workers and their families, the data of the number of entries in OPD and IPD in ESIC dispensaries and hospitals in Delhi was scrutinized and it was found that there was a positive association between air pollution (PM 2.5 , NO 2 & CO) and respiratory diseases (viz., respiratory tuberculosis, acute pharyngitis, other acute upper respiratory infections, acute bronchitis & acute bronchiolitis, other diseases of the nose and nasal sinuses, bronchitis emphysema and other chronic obstructive pulmonary diseases, asthma and other diseases of the upper respiratory tract) but respiratory diseases not found statistically associated with PM 10 . ...
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Industrialization has been contributing to the economic development of countries all over the globe but on the other side of the coin, it is also causing deterioration of the environment as well as human health. Air pollution in industrial areas has emerged as a hasten issue in recent years due to its aggravated effects on health and wellbeing. The objectives of the study are to analyze the concentrations of air pollutants (PM10, PM2.5, NO2 & CO) in industrial areas of Delhi and to assess the association among metrological variables, air pollutants, and respiratory diseases of industrial workers and their family members. To map these pollutants concentration, Kriging and Inverse Distance Weighted (IDW) interpolation techniques were employed. The results showed that several industrial areas were cloaked with all these four pollutants in varied ranges and different seasons. The increase in the number of registered and operating industries in Delhi, and the consequential rise in the air pollutants' concentrations followed the quadratic polynomial trend. Spearman’s rho correlation technique revealed that the respiratory disease entries of workers and their families depicted a statistically strong negative correlation with temperature, rainfall, and wind speeds while a strong positive association with PM2.5, NO2, and CO.
... The overall reduction in the pandemic periods (January-August 2020) was reported by up to 33.67% over India. Maji et al. (2015) estimate the trends of air quality from 2001 to 2010 and also explained the seasonal and monthly variation of AQI over Delhi based on CPCB datasets. (Garg & Content courtesy of Springer Nature, terms of use apply. ...
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The present study aims to highlight the contrast relationship between COVID-19 (Coronavirus Disease-2019) infections and air pollutants for the Indian region. The COVID-19 data (cumulative, confirmed cases and deaths), air pollutants (PM10, PM2.5, NO2 and SO2) and meteorological data (temperature and relative humidity) were collected from January 2020 to August 2020 for all 28 states and the union territory of India during the pandemic. Now, to understand the relationship between air pollutant concentration, meteorological factor, and COVID-19 cases, the nonparametric Spearman's and Kendall's rank correlation were used. The COVID-19 shows a favourable temperature (0.55–0.79) and humidity (0.14–0.52) over the Indian region. The PM2.5 and PM10 gave a strong and negative correlation with COVID-19 cases in the range of 0.64–0.98. Similarly, the NO2 shows a strong and negative correlation in the range of 0.64–0.98. Before the lockdown, the concentration of pollution parameters is high due to the shallow boundary layer height. But after lockdown, the overall reduction was reported up to 33.67% in air quality index (AQI). The background metrological parameters showed a crucial role in the variation of pollutant parameters (SO2, NO2, PM10 and PM2.5) and the COVID-19 infection with the economic aspects. The European Centre for Medium-Range Weather Forecasts derived monthly average wind speed was also plotted. It can see that January and February of 2020 show the least variation of air mass in the range of 1–2 m/s. The highest wind speed was reported during July and August 2020. India's western and southern parts experienced an air mass in the range of 4–8 m/s. The precipitation/wet deposition of atmospheric aerosols further improves the AQI over India. According to a study, the impact of relative humidity among all other metrological parameters is positively correlated with Cases and death. Outcomes of the proposed work had the aim of supporting national and state governance for healthcare policymakers.
... The Indian subcontinent, especially the Indo-Gangetic Basin (IGB) in north India is one of the most populous and heavily polluted regions, which has been considered as one of the global aerosol pollution hotspots, and this region has shown great concern for the adverse effects of aerosols on air quality (Kishore et al., 2019;Shyamsundar et al., 2019;Ojha et al., 2020) and human health (Maji et al., 2015;Chowdhury et al., 2018). The enhanced aerosol loading over the region, which is attributed to the various anthropogenic activities, shows strong spatio-temporal variability and large seasonal heterogeneity in their characteristics due to variety of emission sources, different synoptic meteorology and unique topography of the region (Srivastava et al., 2012a(Srivastava et al., , 2012bTiwari et al., 2013Tiwari et al., , 2015aKumar et al., 2018;Bikkina et al., 2019). ...
Article
The particulate matters less than 10 μm sizes (PM10) were measured at a highly polluted urban environment of Delhi, and simultaneously at a downwind semi-urban site, Gual Pahari, in the north-west Indo-Gangetic Basin (IGB). The measurements were conducted during January-December 2008 to compare and contrast aerosol emissions, compositions and related optical and radiative properties for two different environments. While the total PM10 was about 30% lower at Gual Pahari as compared to Delhi, the total mass of water-soluble inorganic species (WSIs) was significantly enhanced (∼23%) at Gual Pahari. Amongst WSIs, the major secondary inorganic species (SIs) were about 52% higher at Gual Pahari as compared to Delhi, with elevated levels in the post-monsoon/winter seasons. In contrast, the major crustal species were about 76% higher at Delhi as compared to Gual Pahari, with elevated levels in the summer/monsoon seasons. This indicates a strong inter-seasonal spatial variability in aerosol sources and compositions at these sites. Further, the simulations using aerosol optical model had resulted mean absorption coefficient (babs) and single scattering albedo (SSA) higher by about 50% and lower by about 18%, respectively, at Delhi (babs, 147±63 M m⁻¹ and SSA, 0.65±0.12) as compared to Gual Pahari (babs, 73±26 M m⁻¹ and SSA, 0.79±0.07). This reveals relative abundance of absorbing particles at Delhi compared to Gual Pahari. As a result, the estimated mean atmsopehric forcing was 91±23 W m⁻² at Delhi, which was ∼55% higher as compared to Gual Pahari. This is further corroborated with observed higher heating rate at Delhi (2.5±0.7 K day⁻¹) as compared to Gual Pahari (1.1±0.4 K day⁻¹). Our results highlight that diverse near-surface emissions together with atmospheric processing leads to strong inter-seasonal spatial heterogeneity in aerosol chemical, optical and radiative properties between the adjacent distinct sites. This has an important implication for a city-scale air pollution modeling.
... liquefied petroleum gas (LPG)/ piped natural gas (PNG)) and very recently traffic intervention odd-even policy (Goyal and Sidhartha, 2003;Narain and Krupnick, 2007;Saxena et al., 2012;CPCB, 2016;Chowdhury et al., 2017;Khanna and Sharma, 2020). Despite all the above efforts, the concentration of PM 2.5 and other pollutants remain at their alarming levels in Delhi and its surrounding regions, which led to major health concerns in the recent times (Maji et al., 2015;Chowdhury and Dey, 2016). Recently, a National Clean Air Programme (NCAP) was launched aiming to reduce the air pollutants, mainly PM 2.5 concentrations up to ~20-30% by 2024 in the non-attainment cities identified as the most polluted cities in India and currently the pollution levels in these cities are significantly higher than the prescribed national standards (NCAP, 2019). ...
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The enforced lockdown amid COVID-19 pandemic eased anthropogenic activities across India. The satellite-derived aerosol optical depth (AOD) and absorption AOD showed a significant reduction of ~30% over the Indo-Gangetic Basin (IGB) in north India during the lockdown period in 2020 with respect to the previous year 2019, when no such lockdown was in effect. Further, near-surface air pollutants were investigated at an urban megacity Delhi during 01 March to 31 May 2020. Except O3, a drastic reduction in PM10, PM2.5, NO, NO2 and CO concentrations were observed by ~58%, 47%, 76%, 68% and 58%, respectively during the lockdown period of 2020 as compared to 2019. While, O3 was low in the initial phase and gradually increased with progression of lockdown phases, the mean O3 during the entire lockdown period was nearly similar in both the years. Though, all the measured pollutants showed significant reduction during the entire lockdown, a phase-wise enhancement, associated with the conditional relaxations was observed in their concentrations. Thus, the present results may help, not only to assess the impact of outbreak on air quality, but also in designing the mitigation policies in urban megacities in more efficient ways to combat the air pollution problems.
... Few past studies (Prasad et al., 2013;Rizwan et al., 2013;Maji et al., 2015) also analysed the air quality and associated health impacts for Lucknow and Delhi. They found that air quality is deteriorating in these cities due to air pollution. ...
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There has been a series of articles and publications since decades dealing with the question, “What is air pollution and how does it matter?”. According to the World Health Organisation (WHO, 2016), more than 80% of the people living in urban areas are exposed to much poorer air quality than the what WHO limits. These accounts for the regions which monitor air quality on a regular basis and the actual percentage can be even worse. As urban air quality declines, the risk of health problems such as stroke, heart disease, lung cancer, chronic and acute respiratory diseases including asthma can increase in people who live in them. This article is a compilation of the results from publications on air pollution and its impacts on human health. This includes articles relating to atmospheric pollution and human health, how air pollution affects the ecosystem, impact of air pollution on climate change and the policies on air pollution.
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The Indian Institute of Tropical Meteorology (IITM), in partnership with the National Center for Atmospheric Research (NCAR), has developed a high resolution (400 m) air-quality Early Warning System (EWS) for Delhi using an advanced approach of assimilating aerosol optical depth, fire emissions from a space-borne platform, and real-time aerosol observations from in-situ network in WRF-Chem model to produce a 72-h forecast. The present study summarizes the performance of EWS forecast and prevailing meteorological conditions for winter months of 2020-2021. We examined the performance of model-simulated meteorology by comparing against the in-situ observations. The model shows a positive bias in downwelling shortwave radiation (≈ 34 Wm⁻²) and warm bias in near-surface temperature (≈ 3 K). The simulated winds while having realistic magnitudes depict more southwesterly behavior compared to the observations. The model struggles to accurately predict Planetary Boundary Layer Height. The model realistically simulates the relationship between wind and air quality parameters. The air quality forecast from the model is found to be skillful in three different AQI categories, with accuracy > 88% for critical category events. The model also exhibits good statistical performance in predicting AQI, with low mean bias (0.68), and low variance (NMSE < 0.09).
Chapter
There have been a series of articles published since decades, which deal with air pollution, its causes, and possible impacts on climate and health. Exposure to air pollution is a leading global risk factor. According to the World Health Organization (WHO), more than 80% of the people living in the urban areas are exposed to poor air quality as compared to what WHO recommends. As the urban air quality declines, the risk of health problems such as stroke, heart disease, lung cancer, chronic and acute respiratory diseases, including asthma, can increase in the people living in such areas. This chapter presents an overview of air pollution, air quality, and its impacts on our climate system, human health, and ecosystem. Also, the recent policies implemented by the Government of India on air pollution in reduction/mitigation are discussed along with the potential impacts due to the current lockdown amid the COVID-19 pandemic.
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There is increasing interest in evaluating the association between specific fine-particle (particles with aerodynamic diameters less than 2.5 µm; PM2.5) constituents and adverse health outcomes rather than focusing solely on the impact of total PM2.5. Because PM2.5 may be related to both constituent concentration and health outcomes, constituents that are more strongly correlated with PM2.5 may appear more closely related to adverse health outcomes than other constituents even if they are not inherently more toxic. Therefore, it is important to properly account for potential confounding by PM2.5 in these analyses. Usually, confounding is due to a factor that is distinct from the exposure and outcome. However, because constituents are a component of PM2.5, standard covariate adjustment is not appropriate. Similar considerations apply to source-apportioned concentrations and studies assessing either short-term or long-term impacts of constituents. Using data on 18 constituents and data from 1,060 patients admitted to a Boston medical center with ischemic stroke in 2003–2008, the authors illustrate several options for modeling the association between constituents and health outcomes that account for the impact of PM2.5. Although the different methods yield results with different interpretations, the relative rankings of the association between constituents and ischemic stroke were fairly consistent across models.
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China is one of the few countries with some of the highest particulate matter levels in the world. However, only a small number of particulate matter health studies have been conducted in China. The study objective was to examine the association of particulate matter with an aerodynamic diameter of less than 10 μm (PM10) with daily mortality in 16 Chinese cities between 1996 and 2008. Two-stage Bayesian hierarchical models were applied to obtain city-specific and national average estimates. Poisson regression models incorporating natural spline smoothing functions were used to adjust for long-term and seasonal trends of mortality, as well as other time-varying covariates. The averaged daily concentrations of PM10 in the 16 Chinese cities ranged from 52 μg/m3 to 156 μg/m3. The 16-city combined analysis showed significant associations of PM10 with mortality: A 10-μg/m3 increase in 2-day moving-average PM10 was associated with a 0.35% (95% posterior interval (PI): 0.18, 0.52) increase of total mortality, 0.44% (95% PI: 0.23, 0.64) increase of cardiovascular mortality, and 0.56% (95% PI: 0.31, 0.81) increase of respiratory mortality. Females, older people, and residents with low educational attainment appeared to be more vulnerable to PM10 exposure. Conclusively, this largest epidemiologic study of particulate air pollution in China suggests that short-term exposure to PM10 is associated with increased mortality risk.
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Environmental health risk assessment is increasingly being used in the development of environmental health policies, public health decision making, the establishment of environmental regulations, and research planning. The credibility of risk assessment depends, to a large extent, on the strength of the scientific evidence on which it is based. It is, therefore, imperative that the processes and methods used to evaluate the evidence and estimate health risks are clear, explicit, and based on valid epidemiological theory and practice. Epidemiological Evidence for Environmental Health Risk Assessment is a World Health Organization (WHO) guideline document. The primary target audiences of the guidelines are expert review groups that WHO (or other organizations) might convene in the future to evaluate epidemiological evidence on the health effects of environmental factors. These guidelines identify a set of processes and general approaches to assess available epidemiological information in a clear, consistent, and explicit manner. The guidelines should also help in the evaluation of epidemiological studies with respect to their ability to support risk assessment and, consequently, risk management. Conducting expert reviews according to such explicit guidelines would make health risk assessment and subsequent risk management and risk communication processes more readily understood and likely to be accepted by policymakers and the public. It would also make the conclusions reached by reviews more readily acceptable as a basis for future WHO guidelines and other recommendations, and would provide a more rational basis for setting priorities for future research.
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Particulate matter (PM) in all the four Metropolitan cities in India are higher than the prescribed standards of Central Pollution Control Board, India as well as WHO guidelines. Over last 10 years various changes in fuel quality, vehicle technologies, industrial fuel mix and domestic fuel mix have taken place resulting in changes in air quality in these cities. A set of time series analysis methods viz. t-test adjusted for seasonality, Seasonal Kendall test and Intervention analysis have been applied to identify and estimate the trend in PM10 and total suspended particles (TSP) levels monitored for about 10 years at three monitoring sites at each of the four cities in India. These tests have indicated that overall PM10 levels in all four metro cities have been decreasing or stationary. The distinct trends for the monthly averages of PM10 concentrations at Parel, Kalbadevi in Mumbai and Thiruvattiyar in Chennai for the period 1993–2003 were declining by 10%, 6% and 5% per annum, respectively. This is ascribed to a shift in the magnitude and spatial distribution of emissions in the city. However, the monthly averages of TSP do not have a clear trend over the period 1991–2003.
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A comprehensive emission inventory for megacity Delhi, India, for the period 1990–2000 has been developed in support of air quality, atmospheric chemistry and climate studies. It appears that SO2 and total suspended particles (TSP) are largely emitted by thermal power plants (∼68% and ∼80%, respectively), while the transport sector contributes most to NOx, CO and non-methane volatile organic compound (NMVOC) emissions (>80%). Further, while CO2 has been largely emitted by power plants in the past (about 60% in 1990, and 48% in 2000), the contribution by the transport sector is increasing (27% in 1990 and 39% in 2000). NH3 and N2O are largely emitted from agriculture (∼70% and ∼50%, respectively), and solid waste disposal is the main source of CH4 (∼80%). In the past TSP abatement to improve air quality has largely focused on traffic emissions; however, our results suggest that it would be most efficient to also reduce TSP emissions by power plants. We also assessed the potential large-scale transport of the Delhi emissions based on 10-day forward trajectory calculations. The relatively strong growth of NOx emissions indicates that photochemical O3 formation in the regional environment may be increasing substantially, in particular in the dry season. During the summer, on the other hand, convective mixing of air pollutants may reduce regional but increase large-scale, i.e. hemispheric effects.
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The present study discusses the ambient air quality of Delhi from the point of view of change of diesel by Compressed Natural Gas (CNG) in transportation in Delhi. Several initiatives were taken to reduce extremely high levels of pollutants present in the ambient air of urban city. One of the initiatives was to move public transport to CNG, which has been implemented in Delhi since April 2001. Delhi boasted CNG in nearly 2200 buses, 25,000 three wheelers, 6000 taxis and 10,000 cars. However, more than half of the vehicles are yet to be changed to CNG.A relative comparison of ambient air concentration of pollutants, e.g. carbon monoxide (CO), sulphur dioxide (SO2), suspended particulate matter (SPM) and oxides of nitrogen (NOX), emitted from transport sector, during the years 1995–2000 (without CNG) and the year 2001 (with CNG) has been made in order to assess the impact of CNG vehicles on ambient air quality in Delhi. It has been found that concentration contribution of above pollutants has been reduced considerably.The annual average concentration of SPM came down to 347 from 405 μg m−3, which is still beyond the permissible limits. The concentration of annual averages of CO, SO2 and NOX decreased to 4197 from 4681 μg m−3, 14 from 18 μg m−3 and 34 from 36 μg m−3, respectively, and are well within the permissible limits. An analysis of SO2/NOX and CO/NOX concentrations, whose correlation coefficient r2 has the values 0.7613 and 0.7903, respectively, indicates that point sources are contributing to SO2 and mobile sources are contributing to NOX concentrations.
Article
This study evaluates the health risks in megacities in terms of mortality and morbidity due to air pollution. A new spreadsheet model, Risk of Mortality/Morbidity due to Air Pollution (Ri-MAP), is used to estimate the excess numbers of deaths and illnesses. By adopting the World Health Organization (WHO) guideline concentrations for the air pollutants SO2, NO2 and total suspended particles (TSP), concentration-response relationships and a population attributable-risk proportion concept are employed. Results suggest that some megacities like Los Angeles, New York, Osaka Kobe, Sao Paulo and Tokyo have very low excess cases in total mortality from these pollutants. In contrast, the approximate numbers of cases is highest in Karachi (15,000/yr) characterized by a very high concentration of total TSP (∼670μgm−3). Dhaka (7000/yr), Beijing (5500/yr), Karachi (5200/yr), Cairo (5000/yr) and Delhi (3500/yr) rank highest with cardiovascular mortality. The morbidity (hospital admissions) due to Chronic Obstructive Pulmonary Disease (COPD) follows the tendency of cardiovascular mortality. Dhaka and Karachi lead the rankings, having about 2100/yr excess cases, while Osaka-Kobe (∼20/yr) and Sao Paulo (∼50/yr) are at the low end of all megacities considered. Since air pollution is increasing in many megacities, and our database of measured pollutants is limited to the period up to 2000 and does not include all relevant components (e.g. O3), these numbers should be interpreted as lower limits. South Asian megacities most urgently need improvement of air quality to prevent excess mortality and morbidity due to exceptionally high levels of air pollution. The risk estimates obtained from Ri-MAP present a realistic baseline evaluation for the consequences of ambient air pollution in comparison to simple air quality indices, and can be expanded and improved in parallel with the development of air pollution monitoring networks.