A Review on Air Quality Indexing System

Abstract

Air quality index (AQI) or air pollution index (API) is commonly used to report the level of severity of air pollution to public. A number of methods were developed in the past by various researchers/environmental agencies for determination of AQI or API but there is no universally accepted method exists, which is appropriate for all situations. Different method uses different aggregation function in calculating AQI or API and also considers different types and numbers of pollutants. The intended uses of AQI or API are to identify the poor air quality zones and public reporting for severity of exposure of poor air quality. Most of the AQI or API indices can be broadly classify as single pollutant index or multi-pollutant index with different aggregation method. Every indexing method has its own characteristic strengths and weaknesses that affect its suitability for particular applications. This paper attempt to present a review of all the major air quality indices developed worldwide.

Full text

ABSTRACT
Air quality index (AQI) or air pollution index (API) is
commonly used to report the level of severity of air
pollution to public. A number of methods were dev-
eloped in the past by various researchers/environ-
mental agencies for determination of AQI or API but
there is no universally accepted method exists, which
is appropriate for all situations. Different method
uses different aggregation function in calculating
AQI or API and also considers different types and
numbers of pollutants. The intended uses of AQI or
API are to identify the poor air quality zones and
public reporting for severity of exposure of poor air
quality. Most of the AQI or API indices can be broad-
ly classify as single pollutant index or multi-pollut-
ant index with different aggregation method. Every
indexing method has its own characteristic strengths
and weaknesses that affect its suitability for particu-
lar applications. This paper attempt to present a
review of all the major air quality indices developed
worldwide.
Key words: Air pollution, Air quality index, Health,
Environmental factors, Literature review
1. INTRODUCTION
Air pollution is global environmental problem that
influences mostly health of urban population. Over the
past few decades, epidemiological studies have dem-
onstrated adverse health effects due to higher ambient
levels of air pollution. Studies have indicated that rep-
eated exposures to ambient air pollutants over a pro-
longed period of time increases the risk of being sus-
ceptible to air borne diseases such as cardiovascular
disease, respiratory disease, and lung cancer (WHO,
2009). Air pollution has been consistently linked to
substantial burdens of ill-health in developed and devel-
oping countries (Gorai et al., 2014; Bruce et al., 2000;
Smith et al., 2000; WHO, 1999; Schwartz, 1994).
Globally, many cities continuously assess air quality
using monitoring networks designed to measure and
record air pollutant concentrations at several points
deemed to represent exposure of the population to
these pollutants. Current research indicates that guide-
lines of recommended pollution values cannot be regar-
ded as threshold values below which a zero adverse
response may be expected. Therefore, the simplistic
comparison of observed values against guidelines may
mislead unless suitably quantified. In recent years, air
quality information are provided by governments to the
public comes in a number of forms like annual reports,
environment reviews, and site or subject specific anal-
yses/report. These are generally having available or
access to limited audiences and also require time, inter-
est and necessary background to digest its contents.
Presently, governments throughout the world have also
started to use real-time access to sophisticated database
management programs to provide their citizens with
access to site-specific air quality index/air pollution
index and its probable health consequences. Thus, a
more sophisticated tool has been developed to commu-
nicate the health risk of ambient concentrations using
air pollution index (API) or air quality index (AQI).
The World Health Organization (WHO) estimates that
25% of all deaths in the developing world can be direct-
ly attributed to environmental factors (WHO, 2006).
The problem of air pollution and its corresponding ad -
verse health impacts have been aggravated due to in -
creasing industrial and other developmental activities.
The monitoring concentrations of pre-determined air
pollutants in the residential/commercial/industrial areas
are used for the calculation of an air quality index (AQI)
or air pollution index (API). The monitoring data are
aggregated and converted into a single index with a
variety of methods. This means that indexing systems
and air pollution descriptors often differ from one coun-
try/region to another. The indicators of air quality give
the public an opportunity to track the state of their
local, regional and national air quality status without
the need for an understanding of the details of the mon-
itoring data upon which they are based. Since the sen-
Ozone Concentration in the Morning in Inland Kanto Region
101
Asian Journal of Atmospheric Environment
Vol. 9-2, pp. 101-113, June 2015
doi: http://dx.doi.org/10.5572/ajae.2015.9.2.101
ISSN(Online) 2287-1160
ISSN(Print) 1976-6912
A Review on Air Quality Indexing System
Kanchan, Amit Kumar Gorai1),* and Pramila Goyal2)
Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi-835215, India
1)Department of Mining Engineering, National Institute of Technology, Rourkela, Odisha-769008, India
2)Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi, Delhi-110016, India
*Corresponding author. Tel: +91-661-2462938, E-mail: amit_gorai@yahoo.co.uk

102
Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015
sitivity of the people to expose of air pollution chang-
es with changing in geographical location, quality of
life etc., an universal technique to measure the air qua-
lity index is not very much helpful.
1. 1 Design Criteria for an Ideal Air Quality
Index
The basic objective of any air quality index is to
transform the measured concentrations of individual
air pollutant into a single numerical index using suit-
able aggregation mechanism. Ideally, every index
should reflect both the measured and publicly per-
ceived quality of the ambient air for the time period it
covers. As a result, air quality indices attempt to stan-
dardize and synthesize air pollution information and
permit comparisons to be readily undertaken, and to
satisfy public demands for accurate, easy to interpret
data. In design of air quality indices, the following cri-
teria should be used:
1. be readily understandable by the public;
2. include the major criteria pollutants and their syn-
ergisms;
3. be expandable for other pollutants and averaging
times;
4. be related to National Ambient Air Quality stan-
dards used in individual provinces;
5. avoid “eclipsing” (eclipsing occurs when an air
pollution index does not indicate poor air quality
despite the fact that concentrations of one or more
air pollutants may have reached unacceptably
high values);
6. avoid “ambiguity” (ambiguity occurs when an air
pollution index gives falls alarm despite the fact
that concentrations of all the pollutants are within
the permissible limit except one);
7. be usable as an alert system;
8. be based on valid air quality data obtained from
monitoring stations that are situated so as to rep-
resent the general air quality in the community;
2. REVIEW OF AIR QUALITY
INDICES (AQI)
The large databases often do not convey the air qual-
ity status to the scientific community, government
officials, policy makers, and in particular to the gener-
al public in a simple and straightforward manner. This
problem is addressed by determining the Air Quality
Index (AQI) of a given area. AQI, which is also known
as Air Pollution Index (API) (Murena, 2004; Ott and
Thom, 1976; Thom and Ott, 1976; Shenfeld, 1970) or
Pollutant Standards Index (PSI) (U.S. EPA, 1994; Ott
and Hunt, 1976), was developed by various environ-
mental agencies/researchers for different country/re -
gions. Though, there is a widespread use of air pollu-
tion (quality) index systems but currently no interna-
tionally accepted methodology for constructing such a
system (Stieb et al., 2005; Maynard and Coster, 1999)
are available. In this paper, an attempt has been done
to demonstrate the critical review on different AQI
systems.
In 1976, the U.S. EPA established a Pollutant Stan-
dards Index (PSI) which rated air quality. They sug-
gested the formula for aggregating pollutants to deter-
mine PSI. The index ranged from 0-500, with 100
equal to the National Ambient Air Quality Standards
(NAAQS). The PSI is calculated for every pollutant
with a NAAQS, but the only level reported for a given
time and location is for the pollutant most exceeding
its standard. The daily PSI is determined by the high-
est value of one of the five main air pollutants: partic-
ulate material (PM10), ozone (O3), sulfur dioxide (SO2),
carbon monoxide (CO), and nitrogen dioxide (NO2).
The PSI does not indicate exposure to many other pol-
lutants, some of which may be dangerous for people
with respiratory problems (Qian et al., 2004, Radojevic
and Hassan, 1999). The PSI was revised, renamed to
the Air Quality Index (AQI), and subsequently imple-
mented in 1999 by the U.S. EPA.
2. 1 AQI System of U.S. EPA
U.S. EPA’s AQI is defined with respect to the five
main common pollutants: carbon monoxide (CO),
nitrogen dioxide (NO2), ozone (O3), particulate mat-
ter (PM10 and PM2.5) and sulphur dioxide (SO2). The
individual pollutant index as in the eqn. (1) is calculat-
ed first by using the following linear interpolation
equation, pollutant concentration data and reference
concentration. The breakpoint concentrations have
been defined by the EPA on the basis of National
Ambient Air Quality Standards (NAAQS) as shown in
Table 1, and on the results of epidemiological studies
which refer to the effect of single pollutants on human
health.
(IHI -ILO)
Ip=
-------------------
(CP-BPLO)+ILO (1)
BPHI -BPLO
where
IP=Index for pollutant P
CP=Rounded concentration of pollutant P
BPHI =Break point that is greater than or equal to CP
BPLO =Breakpoint that is less than or equal to CP
IHI =AQI value corresponding to BPHI
ILO =AQI value corresponding to BPLO
The highest individual pollutant index, IP, represents
the Air Quality Index (AQI) of the location.
The above method does not have the flexibility to

A Review on AQI System
103
incorporate any number of air pollutants. The method
also not considers the pollutant aggregation and spatial
aggregation. It can be used for determining the short
term and long term air quality indices.
Cheng et al. (2004) proposed a revised EPA air qual-
ity index (RAQI) by introducing an entropy function
to include effect of the concentrations of the rest of
pollutants other than the pollutant with maximum
AQI. The revised Air Quality Index (RAQI) can be
determined by eqn. (2) as given below:
RAQI =Max (I1, I2 …In) Avgdaily
∑
n
I
j=1 j
×
---------------------------------
Avgannual [Avgdaily
∑
n
I ]
j=1 j
Avgannual{Entropydaily * Max[I1, I2,… In]}
×
----------------------------------------------------------
Entropydaily * Max[I1, I2,… In] (2)
The second term on RHS establishes the background
arithmetic mean index in which the numerator is the
sum of the daily arithmetic averages of all sub-indexes
(I1…In), and the denominator is the yearly average of
the sum of daily average for these pollutants.
The third term in RHS represents the background
arithmetic mean entropy index in which the numerator
is the yearly average of the average daily entropy, and
the denominator is the entropy function of the sub-
index pollutants.
The RAQI method facilitates for aggregation of pol-
lutants sub-indices and also health based study but
failed to measure uncertainty and spatial aggregation.
2. 2 Common Air Quality Index (CAQI)
The CAQI was developed by the Citeair project in
2008, which was co-funded by the INTERREG IIIC
and INTERREG IVC programs in Europe. To present
the air quality situation in European cities in a compa-
rable and understandable way, all detailed measure-
ments are transformed into a single relative figure call-
ed the Common Air Quality Index (CAQI). An impor-
tant feature of this index system is that it differentiates
between traffic and city background conditions. The
Common Air Quality Index (CAQI) is designed to pre-
sent and compare air quality in near-real time on an
hourly or daily basis. It has 5 levels, using a scale from
0 (very low) to
>
100 (very high) and the matching
colours range from light green to dark red. The CAQI
is computed according to the grid system (shown in
Table 2) by linear interpolation between the class bor-
ders. The final index is the highest value of the sub-
indices for each component (pollutant); nevertheless,
the choice of the classes for the CAQI is inspired by
the EC legislation. The CAQI do not take into account
the adverse effects due to the coexistence of all the
pollutants. The existing air quality indices were used
by van den Elshout et al. in 2008 for air quality assess-
ment in European cities. They compared all the exist-
ing method for identification of suitable alternative.
The above method can be applied to make compara-
tive study of urban air quality in real time without
facilitating or considering the spatial aggregation, pol-
lutant aggregation, uncertainty measures and health
Table 1. Breakpoint Concentration of air pollutants defined by U.S. EPA.
Breakpoints
AQI Category
O3 (ppm)
8-hour
O3 (ppm)
8-hour1PM10
(μg/m3)
PM2.5
(μg/m3)
CO
(ppm)
SO2
(ppm)
NO2
(ppm)
0-0.064 - 0-54 0-15.4 0-4.4 0-0.034 (2) 0-50 Good
0.065-0.084 - 55-154 15.5-40.4 4.5-9.4 0.035-0.144 (2) 51-100 Moderate
0.085-0.104 0.125-0.164 155-254 40.5-65.4 9.5-12.4 0.145-0.224 (2) 101-150
Unhealthy
for sensitive
groups
0.105-0.124 0.165-204 255-354 65.5-150.4 12.5-15.4 0.225-0.304 (2) 151-200 Unhealthy
0.125-0.374
(0.155-0.404)40.205-0.404 355-424 150.5-250.4 15.5-30.4 0.305-0.604 0.65-1.24 201-300 Very
unhealthy
(3) 0.405-504 425-504 250.5-350.4 30.5-40.4 0.605-0.804 1.25-1.64 301-400 Hazardous
(3) 0.505-0.604 505-604 350.5-500.4 40.5-50.4 0.805-1.004 1.65-2.04 401-500 Hazardous
1Areas are required to report the AQI based on 8 hour ozone values. However, there are areas where an AQI based on 1-hour ozone values would
be more protective. In these cases the index for both the 8-hour and the 1-hour ozone values may be calculated and the maximum AQI reported.
2NO2 has no short term NAAQS and can generate an AQI only above a value of 200.
38-hour O3 values do not define higher AQI values (≥301). AQI values of 301 or higher are calculated with 1-hour O3 concentration.
4The numbers in parentheses are associated 1 hour values to be used in this overlapping category only.

104
Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015
effects.
2. 3 Oak Ridge Air Quality Index (ORAQI)
The Oak Ridge Air Quality Index (ORAQI) method
was developed by Oak Ridge National Laboratory.
The Oak Ridge Air Quality Index is defined for any
number of pollutants. Thom and Ott (1975) suggested
the use of Oak Ridge Air Quality Index (ORAQI). The
ORAQI is given by eqn. (3)
Ci
b
ORAQI =
(
a
∑
----
)
(3)
Cs
where,
Ci=
Monitored/Predicted concentration of pollutant
‘i’
Cs=
National ambient air quality standard (NAAQS)
for pollutant ‘i’
a & b=constant for specific number of pollutants
The above method can be applied to assess the air
quality in urban area without facilitating or consider-
ing the spatial aggregation, and uncertainty measures
but considers pollutant aggregation and health effects.
2. 4 New Air Quality Index (NAQI)
New Air Quality Index is based on Factor Analysis
of the major pollutants. The index is proposed by
Bishoi et al. in 2009. The method is based on principal
component factors which causes the variation of AQI.
The concentration of each pollutant or there deviation
from the mean or their standardized values are expres-
sed as a linear combination of these factors. The fac-
tors contribute to about 60% of the variation of AQI
are considered and the rest can be neglected (Dunteman,
1994; Johnston, 1978; Harman, 1968). Also, the first
factor will cause the highest variance of AQI. The sec-
ond will contribute less variance than first but more
than the third factor and so on. These factors are also
called the principal components or eigen vector pairs.
The New Air Quality Index (NAQI) is given by the
equation as below in the eqn. (4)
n n
NAQI ={
∑
i=1(Pi Ei) /
∑
i=1 Ei} (4)
where n=3, P1, P2, P3 are the three Principal Compo-
nents for which the cumulative variance is more than
60%. E1, E2 and E3 are the initial eigen values (
≥
1)
with respect to the percentage variance.
The principal components were given by Lohani
(1984) in the eqn. (5)
n aji Xj
Pi=
∑
j=1
--------
(5)
λi
where λi is the Eigen value associated with Pi
xj is the concentration of ith pollutant and can be
determined as:
n
Xj=
∑
aji Pi
1
The method can be applied to assess the relative air
quality without facilitating or considering the spatial
aggregation, health effects and uncertainty measures
but considers pollutant aggregation.
Table 2. Pollutants and calculation grid for the CAQI.
Index
class Grid
Traffic City background
Mandatory pollutant Auxiliary pollutant Mandatory pollutant Auxiliary pollutant
NO2
PM10 CO NO2
PM10 O3CO SO2
1-hr 24-hrs 1-hr 24-hrs
Very low 0
25
0
50
0
25
0
12
0
5000
0
50
0
25
0
12
0
60
0
5000
0
50
Low 26
50
51
100
26
50
13
25
5001
7500
51
100
26
50
13
25
61
120
5001
7500
51
100
Medium 51
75
101
200
51
90
26
50
7501
10000
101
200
51
90
26
50
121
180
7501
10000
101
300
High 76
100
201
400
91
180
51
100
10001
20000
201
400
91
180
51
100
181
240
10001
20000
301
500
Very high
>
100
>
400
>
180
>
100
>
20000
>
400
>
180
>
100
>
240
>
20000
>
500
NO2, O3, SO2: hourly value/maximum hourly value in μg/m3
CO: 8 hours moving average maximum 8 hours moving average in μg/m3
PM10: hourly value/daily value in μg/m3

A Review on AQI System
105
2. 5 Pollution Index (PI)
Pollution index (PI) was developed and applied by
Cannistraro, et al. in 2009 for reporting air quality sta-
tus in the city of Naples, Italy. The pollution index
method is based on a simple indicator of the air quality
in an urban context that is useful for communicating to
citizens’ information about the state of air quality of a
waste urban area. The calculation of the PI is based on
the weighted mean value of the sub-indexes of the
most critical pollutants. Additive effects of air pollut-
ants have also been considered and the PI re-evaluated.
It is expressed by a numerical index ranging from 1 to
7. A highest value of the index represents a highest
value of environmental pollution, and, of course a
highest health risk. This index of air quality has been
developed by means of a series of critical pollutants in
the Italian urban contexts and correlated with pollution
level and health risk and level of satisfaction of the
people. PI can be calculated with the arithmetic aver-
ages of sub-indices of two most critical pollutants.
This is given by eqn. (6) as given below:
(I1+I2)
I=
------------
(6)
2
The two sub-indices (I1 and I2) are calculated for the
two most critical pollutants having the highest concen-
trations. The sub-indices of each pollutant can be det-
ermined by eqn. (7) as given below:
Vmax hx
Ix=
-----------
* 100 (7)
Vrif
where, Ix is the sub-index of xth pollutant
Vmax hx is the maximum 1 hour mean value of the xth
pollutant in a day in all the monitoring stations of
the area.
Vrif is maximum 1 hour limit value of the xth pollut-
ant for protection of human health
The Pollution index classification, quality indicator,
and its health risks are represented in Table 3.
The method considers the pollutant aggregation and
health effects to determine the air quality index but not
considers spatial aggregation and uncertainty issues.
2. 6 Air Quality Depreciation Index (AQDI)
AQDI was used by Singh in 2006 to measure the
depreciation in air quality using the value function
curves for individual pollutants. The method considers
the pollutant aggregation to determine the depreciation
in air quality with respect to standard air quality. The
air quality depreciation is measured in a scale between
0 to -10. An index value of ‘0’ represents most desir-
able air quality having no depreciation from the best
possible air quality with respect to the pollutants under
consideration, while an index value of -10 represents
maximum depreciation or worst air quality. The shift-
ing of Index value from 0 towards -10 represents suc-
cessive depreciation in air quality from the most desir-
able. The AQDI is given as follows in the eqn. (8)
n n
AQdep ={
∑
i=1(AQi * CWi)}-{
∑
i=1 CWi} (8)
where,
AQi=Air Quality Index for the ith parameter and
obtained from value function curve defined by Jain et
al. in 1977. In the value function curve 0 represent the
worst quality and 1 represent the best quality of air
due to pollutant under consideration.
CWi is composite weight for ith parameter.
n is the total no of pollutants considered.
Composite weight (CWi) in eqn. (8) can be calculat-
ed using the following eqn. (9).
Table 3. Values, Index and Health Risks for PI.
Numeric value Quality indicator Numeric index Health risks
0-50 Optimum 1 No risks for people
51-75 Good 2 No risks for people
76-100 Moderate 3 No risks for people
101-125 Mediocre 4
Generally there aren’t risks for people. People with asthma, chronic
bronchitis, croniche o cardiopathy may feel light respiratory symptoms
only during an intense physical activity
126-150 Not Much Healthy 5 There risks for people with heart diseases, olds and children
151-175 Unhealthy 6 Many people may feel light adverse symptoms, however reversible.
Weak people may feel gravest symptoms.
>
175 Very Unhealthy 7 People may feel light adverse effects for health. There are more risks
for olds, children and people with respiratory diseases.

106
Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015
TWi
CWi=
{
-------------
}
* 10 (9)
∑
n TWi
i=1
where,
TWi=
total weight of the ith parameter =AWi+
BPIWi+HWi
AWi=aesthetic weight of ith parameter
BPIWi=bio-physical impact weight of ith parameter
HWi=health weight of ith parameter
TWi is computed by assigning weights between 1 to
5 to AWi, BPIWi, and HWi by a team of assessors or
experts. 1 is the least and 5 is the highest weight.
2. 7 Integral Air Pollution Index (IAPI)
Bezuglaya et al. (1993) suggested integral air pollu-
tion index (IAPI) for Russian cities that is simply a
sum of the pre-selected number of the highest individ-
ual pollutant indices calculated by normalising the
pollution concentrations to maximum permissible con-
centration (MPC). Russian health experts had estab-
lished maximum permissible concentrations (MPC)
for more than 400 pollutants. To understand the degree
of air pollution, the measured value of concentration
pollutants are compared with either short-term MPC
or mean value of long-term MPCs.
The sub-indices of IAPI can be determined using
eqn. (10) as shown below:
Xi
I1i=
---------
(10)
MPCi
where, xi is the concentration of ith pollutant,
Ii is the sub-index of ith pollutant,
ci is the degree of exponent, and
MPCi is the maximum permissible concentration of
ith pollutant
The degree of air pollution with any pollutant can be
expressed through comparison with the degree of air
pollution with sulphur dioxide using the degree expo-
nent ci as shown in eqn. (11):
xi Ci
I2i=
(
---------
)
(11)
MPCi
The reason behind sulphur dioxide was used as a
basis is that this pollutant is monitor in all cities. IAPI
can be determined by the arithmetic sum of all the
sub-indices corresponding to each air pollutants con-
sidered for air quality assessment.
Swamee and Tyagi (1999) had criticised the addition
of sub-indices suggested by Bezuglaya et al. (1993),
concluding that the function is ambiguous in nature
and can lead to an index in the hazardous category,
which may be a false alarm. Hence, they suggested a
nonlinear aggregation of sub-indices. They proposed an
air pollution index which is a quantitative tool through
which air pollution data can be reported uniformly. An
ambiguity and eclipcity-free function was presented
for aggregating the air pollutants sub-indices.
The sub-indices are aggregated to yield air quality
index using eqn. (12) as given below:
N
-
1
-
p
I=
(
∑
i=1 si
p
)
(12)
where, I=aggregate index;
N=the number of sub-indices;
si=sub-index of ith pollutant, and
p=an exponent.
After an extensive study, authors suggested that the
value of p=0.4 in the aggregation function minimize
the effect of ambiguity.
2. 8 Aggregate Air Quality Index (AQI)
An aggregate AQI was proposed by Kyrkilis et al. in
2007. The index is based on the combined effects of
five criteria pollutants (CO, SO2, NO2, O3, and PM10)
taking into account the European standards. This
method was used for air quality evaluation for each
monitoring station situated in whole area of Athens,
Greece. The indexing system is based on the AQI sys-
tem developed by Swamee and Tyagi in 1999. An
aggregate AQI can be determined by eqn. (13) shown
below:
1
n
---
I=[
∑
i=1(AQIi) p] p (13)
where, I is the aggregate AQI,
AQIi=the sub-index for ith pollutant, and
p=a constant.
According to eqn. (13), when p=∞ the index I is
equal to the max AQI of a single pollutant, regardless
of the rest of the pollutants’ AQI value. This kind of
calculation corresponds to the way that EPA calculates
the overall AQI; however, it underestimates the air
pollution levels. When p is equal to 1, at the other
extreme, the overall index (I) is equal to the sum of all
AQI indices. The selection of the most proper value
for p is still an open support this statement.
The sub-indices are expressed as functions of the
ratio of pollutant concentration q to standard concen-
tration qs, shown in eqn. (14):
q
AQIi=AQIs
(
----
)
(14)
qs
where, AQIi=Sub-index of ith pollutant, and
AQIs=
a scaling coefficient equal to 500 (Swamee
and Tyagi, 1999).

A Review on AQI System
107
2. 9 The Aggregate Risk Index (ARI)
Sciard et al. (2011) designed the aggregate risk index
for assessing the health impact due to air pollution in
the South East of France. The ARI is a measure of the
mortality risk associated with simultaneous exposure
to the common air pollutants and provides a ready
method of comparing the relative contribution of each
pollutant to total risk. An arbitrary index scale (1-10),
with a colour coding system, was used to facilitate risk
communication. The ARI is based on the exposure-
response relationship and Relative Risk (RR) of the
well-established increased daily mortality, en-abling
an assessment of additive effects of short-term expo-
sure to the major air pollutants. This study presents the
modified formula of AQI based on Cairncross’s con-
cept (Cairncross et al., 2007).
The total attributable risk for simultaneous short-
term exposure to several air pollutants is given by the
eqn. (15):
ARI =
∑
i (RRi-1)=
∑
Indexi=
∑
ai
* ci (15)
To account for the reality of the multiple exposures
impacts of chemical agents, the final index is the sum
of the normalised values of the individual RRi val-
ues (Pyta, 2008; Cairncross et al., 2007). It thus pro-
vides a ready method of comparing the relative contri-
bution of each pollutant to total risk. The index is
defined to reflect the contribution of individual pollut-
ants to total risk. Ci is the corresponding time-aver-
aged concentrations (in mg/m3) and the coefficient
“ai” is proportional to the incremental risk values (RRi
-1). This index uses exposure-response relative risk
functions and a particular set of RRi values for a given
health endpoint associated with increasing major air
pollutants. These functions and values were published
by the WHO (2008, 2004, 2001), APHEA (Air Pollu-
tion and Health- a European Approach)-2, (2006) and
InVS (PSAS-9 project, French air pollution and health
surveillance program, 2008, 2006, 2002) under a pro-
cedure for health impact assessment.
From the published RRi values for ith pollutant, the
coefficients for the terms ai can be determined using
eqn. (16) in order to derive a numerical scale specific
to each of the pollutants.
aPM10
* (RRi-1)
ai=
------------------------
(16)
(RRPM10 -1)
The breakpoint values between different levels con-
sidered correspond to the air quality standards defined
by the WHO (2005). The air quality classes and the
relevant RR values are determined in terms of the PM10
concentration (significant RRi values). The breakpoint
concentrations for the remaining pollutants are calcu-
lated proportionally to the individual levels of the rela-
tive risk.
2. 10 AQI Based on PCA-Neural Network
Model
Kumar and Goyal (2013) designed forecasting sys-
tem for daily AQI using a coupled artificial neural net-
work (ANN) - Principal component analysis (PCA)
model. The architecture of the system is shown in Fig.
1. It was designed for forecasting AQI in one day advan-
ce using the previous day’s AQI and meteorological
variables.
There are two steps involved in determining the AQI.
The first step is the formation of sub-indices for each
pollutant and the second one is the aggregation of sub
indices. The sub-indices were calculated using the
same formula as that of U.S. EPA but the breakpoint
concentration of each pollutant is based on the Indian
NAAQS and epidemiological studies, which are indi-
cating the risk of adverse health effects of specific pol-
lutants.
The AQI value was calculated for each individual
pollutant (SO2, NO2, RSPM, and SPM) and highest
among them was declared as the AQI of the day. The
previous day’s AQI value was used as one of the input
parameters in the PCA-ANN model for forecasting the
AQI value of subsequent day.
Fig. 1. Architecture of PCA-neural network model for the forecasting of AQI.

108
Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015
2. 11 Fuzzy Air Quality Index
Mandal et al. (2012) developed a method for predic-
tion of AQI on the basis of fuzzy aggregation. The out-
put AQI value using fuzzy aggregation method was
compared to that of the output from conventional me -
thod. It was demonstrated that computing with linguis-
tic terms using fuzzy inference system improves toler-
ance for impression data.
The relationship between air pollutants and output
parameter (FAQI) is mathematically expressed as
given in the eqn. (17)
FAQI =f (SPM, RPM, SO2, NOX) (17)
Gorai et al. (2014) developed a fuzzy pattern recog-
nition model for AQI determination. The method was
used for air quality assessment of Agra city. This meth-
od considered five air pollutants (PM10, CO, SO2, NO2,
and O3) for AQI determination. The method also con-
siders weights of the individual pollutants on the basis
of its degree of health impacts during aggregation.
Analytical hierarchical process (AHP) was used for
determination of weights of various pollutants. The air
quality index is ranged from 1 to 6. The higher is the
value of AQI, higher is the health risk and vice versa.
Authors suggested that depending upon the risk level,
air quality mangers can take preventive measures for
reducing the level of index. Though, the formula or
method used for determination AQI is relatively com-
plex in comparison to that of the arithmetic aggrega-
tion method but this can be easily programmed for det-
ermination of AQI.
2. 12 Air Quality Health Index
A new Air Quality health index (AQHI) was devel-
oped in Canada to understand the state of local air
quality with respect of public health. AQHI is avail-
able for about ten communities in Canada, including
Vancouver and Victoria.
The AQHI is constructed as the sum of excess mor-
tality risk associated with NO2, ground-level O3, and
PM2.5 at certain concentrations. It is calculated hourly
based on 3-hour rolling average pollutant concentra-
tions, and is then adjusted to a scale of 1 to 10. The
value of 10 corresponds to the highest observed wei-
ghted average in an initial data set covering a reference
period from 1998 to 2000 (Stieb et al., 2008; Taylor,
2008).
The scientific basis for the formulation of AQHI is
based on the epidemiological research undertaken at
Health Canada. Relative risk (RR) values are estimat-
ed, based on the local time series analyses of air pollu-
tion and mortality (Stieb et al., 2008; Taylor, 2008).
Air Quality Health Index is determined by the eqn.
(18) as shown below:
10
AQHI =
-----
∑
i=1….p 100(eβiXi-1) (18)
c
where, βi is the regression coefficient from same Pois-
son model linking the ith air pollutant with mortality, xi
is the concentration of the ith pollutant, and c is the
scaling factor.
2. 13 Air Pollution Indexing System in South
Africa
An air pollution index was developed in a dynamic
air pollution prediction system (DAPPS) project,
which was led by a consortium of four South African
partners, including the Cape Peninsula University of
Technology (Cairncross et al., 2007). The API system
is based on the relative risk of the well-established
excess daily mortality associated with short-term expo-
sure to common air pollutants, including PM10, PM2.5,
SO2, O3, NO2, and CO. A scale of 0 to 10 was used for
the assessment of air quality. Incremental risk values
for each pollutant are assumed to be constant, and a
continuous exposure metrics, the exposures that corre-
spond to the same relative risk are assigned the same
sub-index value. The final API is the sum of the nor-
malized values of the individual indices for all pollut-
ants is given by the eqn. (19)
API =
∑
PSIi=
∑
a·C (19)
where, C is the time averaged concentration of pollut-
ant, and a is a coefficient directly proportional to the
incremental risk value associated with the pollutant.
The proposed API was applied to ambient concen-
tration data collected at monitoring stations in the City
of Cape Town for testing.
2. 14 Air Pollution Indexing System in China
China’s state pollution control board is responsible
for measuring the air pollution in the cities. The air
pollution index (API) in China is based on the five
atmospheric pollutants sulfur dioxide (SO2), nitrogen
dioxide (NO2), suspended particulates (PM10), carbon
monoxide (CO), and ozone (O3) measured in the mon-
itoring stations. Chinese API system follow the same
method for calculating the air quality index but the
health definition corresponding to each AQI classes
are different.
Hämekoski et al. (1998) developed a simple air
quality index (AQI) for the Helsinki Metropolitan Area
in order to inform the public about the air quality status
for better understanding. The pollutants consider in the
AQI system are CO (1 hr. and 8 hrs.), NO2 (1 hr. and
24 hrs.), SO2 (1 hr. and 24 hrs.), O3 (1 hr.) and PM10

A Review on AQI System
109
Table 4. AQI methods with their merits, demerits and application.
Author/Organization/
Method Name Index and aggregation function Pollutant
aggregation Health
based Purpose/Application
Thomas and Ott (1975) ORAQI Ci b
ORAQI=
(
a
∑
----
)
Cs
Yes Yes To assess air quality status in metropolitan cities
U.S. EPA, AQI
(1976, 1999) AQI AQI=Max (I1, I2, …In) No Yes Use for continuous reporting of air quality status
to public
Hӓmekoski et al. (1998) A simple
AQI Based on U.S. EPA formula Yes Yes The AQI is based on acute health effects, but
long term effects on nature and materials are also
taken into consideration.
Cheng et al. (2004) RAQI
RAQI=Max (I1, I2 …In)
Avgdaily
∑
n
j=1 Ij
×
-----------------------------------
Avgannual[Avgdaily
∑
n
j=1 Ij]
Avgannual{Entropydaily * Max[I1, I2,…In]}
×
--------------------------------------------------------------
Entropydaily * Max[I1, I2,…In]
Yes Yes To produce an objective result in the long-term
effect of air pollution
Murena et al. (2004) PI CP
PIS=PIbC
∑
n
p=1
---------
BPp,c
No Yes The index aims at measuring the status of air
pollution with respect to its effect on human
health.
Singh G. (2006) AQDI n n
AQdep =
{
∑
(AQi * CWi)
}
-
{
∑
CWi
}
i=1 i=1 Yes No To define the depreciation in air quality with
respect to standard
Kyrkilis et al. (2007) Aggregate
AQI
--
1
I=[
∑
n
i=1(AQIi) p]pYe s No
Based on the combined effects of five criteria
pollutants (CO, SO2, NO2, O3 and PM10) taking
into account European standards. Useful towards
the informing of the citizens and protection of
human health in an urban agglomeration.
Cairncross et al., 2007 API API=
∑
PSIi=
∑
a·CYes Yes
The system is based on the relative risk of the
well-established excess daily mortality associated
with short-term exposure to common air
pollutants (PM10, PM2.5, SO2, O3, NO2 and CO)
CITEAIR II Project,
Europe (2008) CAQI Based on U.S. EPA method but with
different criteria No No Comparing urban air quality in real time.
Steib et al., 2008,
Taylor et al., 2008 AQHI AQHI =(10/c)
∑
i=1…p100 (eβiXj-1) Yes Ye s Described the application of concentration of -
response functions from epidemiological studies
of air pollution to an AQI.
Bishoi et al. (2009) NAQI NAQI={
∑
n
i=1(Pi Ei)
/
∑
n
i=1 Ei} Yes No To define the state of air in relative terms

110
Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015
Cannistraro et al. (2009) PI
(I1+I2)
I=
------------
,
2
Where I1 and I2 are the sub-indices of the
two most critical pollutants having highest
concentration
Yes Yes Useful for communicating to citizens’
information about the state of air quality of a
waste urban area.
Sciard et al. (2011) ARI
(Aggregate
Risk Index) ARI =
∑
i (RRi-1)=
∑
Indexi=
∑
ai * ciYes Yes
The index measure of the mortality/morbidity
risk associated with simultaneous exposure to the
common air pollutants and provides a ready
method of comparing the relative contribution of
each pollutant to total risk. An arbitrary index
scale facilitates risk communication. The index
values may extend beyond 0 for highly polluted
areas.
Kumar et al. (2013) AQI Based on U.S. EPA formula No Yes The AQI of each pollutant has been calculated
individually and highest among them is declared
as the AQI of the day.
Mandal et al. (2012) FAQI FAQI=f (SPM, RPM, SO2, NO2)Ye s No A new aggregation approach suggested for air
quality assessment
Gorai et al. (2014) FAQI 6
Hi=
∑
ui,h * h
h=1
Yes No A new fuzzy pattern recognition model suggested
for air quality assessment. This can cover the
uncertainty factors.
Bezuglaya et al. (1993) IAPI Xi ci
IAPI=
∑
n
i=1(I2i)=
∑
n
i=1
(
---------
)
MPCi
Yes No The use of API allowed obtaining a complex
degree of urban air pollution.
Swamee and Tyagi
(1999) I
--
1 p
I=
(
∑
N
i=1 si
p
)
Yes No An ambiguity and eclipsity free function was
developed.
Air Pollution Indexing
System in China API Based on U.S.EPA formula Yes No The final API value is calculated per hour. Based
on six atmospheric pollutant (PM10, PM2.5, SO2,
O3, NO2 and CO)
Table 4. Continued.
Author/Organization/
Method Name Index and aggregation function Pollutant
aggregation Health
based Purpose/Application

A Review on AQI System
111
(24 hrs.). The AQI is based on acute health eff ects, but
long term effects on nature and materials are also taken
into consideration. Sub-indices are calculated hourly
for all pollutants and for a given hour the highest sub-
index becomes the AQI. The development is partly
based on the work conducted in United States and Cana-
da, for example by Ott and Hunt (1976) and Mignacca
and others (1991).
3. SUMMARY AND CONCLUSIONS
This brief review on air quality indices shows the
wide interest or concern for poor air quality problem.
But at the same time it shows lack of a common strate-
gy, which allow to compare the state of the air for cit-
ies that follow different directives. The major differ-
ences among the indices in the literature are found in
the aggregation function, type and number of pollut-
ants, number of index classes (and their associated
colors) and related descriptive terms. It was observed
that the guidelines are sometimes consistently differ-
ent from place to place, not only in indicating the pol-
lutants to be monitored, but also in setting the thresh-
old values. It is also true that WHO recommended
(WHO, 2006) that during formulating policy targets,
governments should consider their own local circum-
stances carefully, that is the specificities of places
must be taken into account. The complexity of air pol-
lution and its science has created problems to both the
public and policy makers. There are many pollutants
to consider with some being secondary products of
atmospheric transformations. The science is often so
sophisticated that it becomes hard for politicians and
the public to interpret. Thus, it is desirable that air pol-
lution information will continue to be represented in
simple forms such as indices. In an attempt to meet the
public’s needs for information on air quality a variety
of indexes have been developed and they continue to
evolve. In terms of their ongoing development, an AQI
also needs to be useful for forecasting, and the method
of calculation needs to be sufficiently flexible to allow
for pollutants to be added or subtracted as changes to
their health impact are revealed. The Air Quality Index
(AQI) is a widely used concept to communicate with
the public on air quality. A growing number of nation-
al and local environment agencies use the AQI for
(near) real-time dissemination of air quality informa-
tion. Although the basic concepts are similar, the AQIs
show large differences in practical implementation. It
is also observed that when applying the AQIs on a
common set of air quality data, large differences in
index value and the determining pollutant are found
(de Leeuw and Mol 2005).Current AQIs potentially
contribute to public understanding by providing infor-
mation that is easily accessible and allows them the
opportunity to modify their behaviour appropriately in
response to changes in air quality. However, much
progress is still to be made,
mainly through more careful consideration of the
combined impact of multiple pollutants, consideration
of low level exposure, and with more timely transfer
of usable information to the public. The summary of
various AQI systems and its aggregation methods are
listed in Table 4. These are mainly demonstrated in
this review work. Table 4 represents the chronological
evolution of various air quality indices that are descri-
bed in this paper including to other important statisti-
cal issues like aggregation function, the availability of
uncertainty measures for the index, availability of
health descriptors, and their purpose or applicability.
The review of various methods reveals that most of
the methods do not have the flexibility to accommo-
date a new pollutant. This is because, either the meth-
ods is designed for specific number of pollutants or
the aggregation function does not have the suitability
to accommodate this. Table 4 also clearly indicates
that many of the indexing method do not consider the
synergistic effects of the pollutants, that is, pollutants
are not aggregated in index calculation. Furthermore,
many indexing method are not based on health criteri-
on. Thus, further work is required on the statistical
structure and effects of the aggregation function on
index, the nature of the scale (1-10, 1-100, 1-500) and
the multi-pollutant problem to make it uniform.
ACKNOWLEDGEMENT
The support from the Department of Science and
Technology, New Delhi for research grant no.SR/FTP/
ES-17/2012 is acknowledged.
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