ArticlePDF Available

A Review on Air Quality Indexing System


Abstract and Figures

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.
Content may be subject to copyright.
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
Key words: Air pollution, Air quality index, Health,
Environmental factors, Literature review
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
Asian Journal of Atmospheric Environment
Vol. 9-2, pp. 101-113, June 2015
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:
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
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-
3. be expandable for other pollutants and averaging
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;
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
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
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
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, I2In) Avgdaily
j=1 j
Avgannual [Avgdaily
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.
AQI Category
O3 (ppm)
O3 (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
for sensitive
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
(0.155-0.404)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
(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.
Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015
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)
Monitored/Predicted concentration of pollutant
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
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 (
with respect to the percentage variance.
The principal components were given by Lohani
(1984) in the eqn. (5)
n aji Xj
where λi is the Eigen value associated with Pi
xj is the concentration of ith pollutant and can be
determined as:
aji Pi
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.
class Grid
Traffic City background
Mandatory pollutant Auxiliary pollutant Mandatory pollutant Auxiliary pollutant
1-hr 24-hrs 1-hr 24-hrs
Very low 0
Low 26
Medium 51
High 76
Very high
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
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:
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
* 100 (7)
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)
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.
Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015
* 10 (9)
n TWi
total weight of the ith parameter =AWi+
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:
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
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:
i=1 si
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
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):
where, AQIi=Sub-index of ith pollutant, and
a scaling coefficient equal to 500 (Swamee
and Tyagi, 1999).
A Review on AQI System
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):
i (RRi-1)=
* 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.
* (RRi-1)
(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
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
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-
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.
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,
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:
i=1….p 100(eβiXi-1) (18)
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
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)
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
Table 4. AQI methods with their merits, demerits and application.
Method Name Index and aggregation function Pollutant
aggregation Health
based Purpose/Application
Thomas and Ott (1975) ORAQI Ci b
Yes Yes To assess air quality status in metropolitan cities
(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)
j=1 Ij
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
No Yes The index aims at measuring the status of air
pollution with respect to its effect on human
Singh G. (2006) AQDI n n
AQdep =
(AQi * CWi)
i=1 i=1 Yes No To define the depreciation in air quality with
respect to standard
Kyrkilis et al. (2007) Aggregate
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=
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)
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={
i=1(Pi Ei)
i=1 Ei} Yes No To define the state of air in relative terms
Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015
Cannistraro et al. (2009) PI
Where I1 and I2 are the sub-indices of the
two most critical pollutants having highest
Yes Yes Useful for communicating to citizens’
information about the state of air quality of a
waste urban area.
Sciard et al. (2011) ARI
Risk Index) ARI =
i (RRi-1)=
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
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
ui,h * h
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
Yes No The use of API allowed obtaining a complex
degree of urban air pollution.
Swamee and Tyagi
(1999) I
1 p
i=1 si
Yes No An ambiguity and eclipsity free function was
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.
Method Name Index and aggregation function Pollutant
aggregation Health
based Purpose/Application
A Review on AQI System
(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).
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.
The support from the Department of Science and
Technology, New Delhi for research grant no.SR/FTP/
ES-17/2012 is acknowledged.
Bezuglaya, E.Y., Shchutskaya, A.B., Smirnova, I.V. (1993)
Air Pollution Index and Interpretation of Measure-
ments of Toxic Pollutant Concentrations. Atmospheric
Environment 27, 773-779.
Bishoi, B., Prakash, A., Jain, V.K. (2009) A comparative
study of air quality index based on factor analysis and
US-EPA methods for an urban environment. Aerosol
Air Quality Research 9(1), 1-17.
Bruce, N., Perez-Padilla, R., Albalak, R. (2000) Indoor
air pollution in developing countries: a major environ-
mental and public health challenge. Bulletin of World
Health Organisation 78(9), 1078-1092.
Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015
Cairncros, E.K., John, J., Zunckel, M. (2007) A Novel
Air Pollution Index Based on the Relative Risk of
Daily Mortality Associated with Short-term Exposure
to Common Air Pollutants. Atmospheric Environment
41, 8442-8454.
Cannistraro, G., Ponterio, L. (2009) Analysis of Air Qual-
ity in the Outdoor Environment of the City of Messina
by an Application of the Pollution Index Method.
International Journal of Civil and Environment Engin-
eering 1, 4.
Cheng, W.L., Kuo, Y.C., Lin, P.L, Chang, K.H., Chen,
Y.S., Lin, T.M., Huang, R. (2004) Revised air quality
index derived from an entropy function. Atmospheric
Environment 38, 383-391.
Dunteman, G.N. (1994) In Factor Analysis and Related
Techniques. Vol. 5, Lewis-Beck, M.S. (Ed.), Sage
Publications, London, 157.
Gorai, A.K., Kanchan, Upadhyay, A., Goyal, P. (2014)
Design of fuzzy synthetic evaluation model for air
quality assessment. Environment Systems and Deci-
sions 34, 456-469. doi 10.1007/s10669-014-9505-6.
Gorai, A.K., Tuluri, F., Tchounwou, P.B. (2014) A GIS
Based Approach for Assessing the Association between
Air Pollution and Asthma in New York State, USA.
International Journal Environmental Research and
Public Health 11(5), 4845-4869. doi:10.3390/ijerph
Harman, H.H. (1968). Modern Factor Analysis, 2nd Ed.,
Revised. University of Chicago Press, Chicago.
Hämekoski, K. (1998). The Use of a Simple Air Quality
Index in the Helsinki Area, Finland. Environment
Management 22(4), 517-520.
Jain, R.K, Urban, L.V., Stacey, G.S. (1977) Environmen-
tal Impact analysis. Van Nustrand Reinhold, New York,
Johnston, R.J. (1978) Multivariate Statistical Analysis in
Geography, Longman, New York.
Kumar, A., Goyal, P. (2013) Forecasting of Air Quality
Index in Delhi Using Neural Network Based on Prin-
cipal Component Analysis. Pure and Applied Geo-
physics 170, 711-722. doi: 10.1007/s00024-012-0583-
Kyrkilis, G., Chaloulakou, A., Kassomenos, P.A. (2007)
Development of an aggregate Air Quality Index for an
urban Mediterranean agglomeration: Relation to poten-
tial health effects. Environment International 33, 670-
Leeuw, de F., Mol, W. (2005) Air quality and air quality
indices: a world apart? European Topic Centre on Air
and Climate Change, Technical paper 2005/5. Available
online at:
TechnPaper_2005_5_AQ_Indices.pdf (Last Accessed
on 04th April, 2015).
Lohani, B.N. (1984). Environmental Quality Management,
South Asian Publishers, New Delhi.
Mandal, T., Gorai, A.K., Pathak, G. (2012) Development of
fuzzy air quality index using soft computing approach.
Environmental Monitoring and Assessment 184, 6187-
6196. doi: 10.1007/s10661-011-2412-0.
Maynard, R.L., Coster, S.M. (1999) Informing the Public
about Air Pollution. In Air Pollution and Health, eds.
S.T. Holgate, J.M. Samet, H.S. Koren, and R.L.
Maynard, pp. 1019-1033. San Diego, CA: Academic
Murena, F. (2004) Measuring Air Quality over Large
Urban Areas: Development and Application of an Air
Pollution Index at the Urban Area of Naples. Atmos-
pheric Environment 38, 6195-6202.
Ott, W.R., Hunt, W.F. Jr. (1976) A Quantitative Evaluation
of the Pollutant Standards Index. Journal of the Air
Pollution Control Association 26, 1050-1054.
Ott, W.R., Thom, G.C. (1976) A Critical Review of Air
Pollution Index Systems in the United States and
Canada. Journal of the Air Pollution Control Associa-
tion 26, 460-470.
Pyta, H. (2008) Classification of air quality based on fac-
tors of relative risk of mortality increase. Environment
Protection Engineering 34(4), 111-117.
Qian, Z., Chapman, R.S., Hu, W., Wei, F., Korn, L.R.,
Zhang, J. (2004) Using air pollution based community
cluster to explore air pollution health effects in chil-
dren. Environment International 30, 611-620.
Radojevic, M., Hassan, H. (1999) Air quality in Brunei
Darussalam During the 1998 Haze Episode. Atmos-
pheric Environment 33, 3651-3658.
Schwartz, J. (1994) Air pollution and hospital admissions
for the elderly in Birmingham, Alabama. American
Journal of Epidemiology 139(6), 589-598.
Shenfeld, L. (1970) Note on Ontario’s Air Pollution Index
and Alert System. Journal of the Air Pollution Control
Association 20, 612.
Sicard, P., Lesne, O., Alexandre, N., Mangin, A., Collomp,
R. (2011) Air quality trends and potential health effects
- Development of an aggregate risk index. Atmospheric
Environment 45, 1145-1153.
Singh, G. (2006). An index to measure depreciation in air
quality in some coal mining areas of Korba industrial
belt of Chhattisgarh, India. Environmental Monitoring
and Assessment 122, 309-317.
Smith, K.R., Samet, J.M., Romieu, I., Bruce, N. (2000)
Indoor air pollution in developing countries and acute
lower respiratory infections in children. Thorax 55(6),
Stieb, D.M., Burnett, R.T., Smith-Doiron, M., Brion, O.,
Shin, H.H., Economou, V. (2008) A New Multi-pollu-
tant, No-Threshold Air Quality Health Index Based on
Short-Term Associations Observed in Daily Time-
Series Analyses. Journal of Air & Waste Management
Association 58, 435-450.
Stieb, D.M., Doiron, M.S., Blagden, P., Burnett, R.T.
(2005) Estimating the public health burden attribut-
able to air pollution: an illustration using the develop-
ment of an alternative air quality index. Journal of
Toxicology and Environment Health A 68(13), 1275-
Swamee, P.K., Tyagi, A. (1999) Formation of an Air
A Review on AQI System
Pollution Index. Journal of Air & Waste Management
Association 49, 88-91.
Taylor, E. (2008) The Air Quality Health Index and its
Relation to Air Pollutants at Vancouver Airport. B.C.
Ministry of Environment.
Thom, G.C., Ott, W.R. (1976) A proposed uniform air
pollution index. Atmospheric Environment 10, 261-
U.S. Environmental Protection Agency (1976) Federal
Register 41: 174 - Tuesday September 7, 1976.
U.S. Environmental Protection Agency (1994) Measuring
air quality: the pollutant standards index. Environmen-
tal Protection Agency 451: K-94-001 Environmental
Protection Agency, Office of Air Quality Planning and
Standards, Research Triangle Park.
U.S. Environmental Protection Agency (1999) Guideline
for reporting of daily air quality - air quality index
(AQI). EPA-454/R-99-010. Office of Air Quality Plan-
ning and Standards, Research Triangle Park, NC
Van den Elshout, S., Leger, K., Nussio, F. (2008) Com-
paring urban air quality in Europe in real time: A
review of existing air quality indices and the proposal
of a common alternative. Environment International
34(5), 720-726.
WHO (1999) Monitoring ambient air quality for health
impact assessment. Copenhagen, World Health Organ-
ization Regional Office for Europe, WHO regional
publication, European Series, No. 85. Available online
0010/119674/E67902.pdf (Last accessed on 12th Sep-
tember, 2014).
WHO (2001) Health impact assessment of air pollution
in the WHO European region. WHO/Euro product n
WHO (2004) Meta-analysis of time-series studies and
panel studies of Particulate Matter (PM) and Ozone
(O3). WHO task group. WHO/EURO 04/5042688.
WHO (2005) Air quality guidelines for particulate matter,
ozone, nitrogen dioxide and sulfur dioxide: global
update 2005. Summary of risk assessment. Geneva,
World Health Organization.
WHO (2006) Preventing disease through healthy envi-
ronments-towards an estimate of the environmental
burden of disease. / Prüss-Üstün A, & Corvalán C.
ISBN 92 4 159382 2.
WHO (2008) Health Risks of Ozone from Long-range
Trans boundary Air Pollution, ISBN 978 92 890 42895
WHO/Euro product.
WHO (2009) Global health risks: Mortality and burden
of diseases attributable to selected major risks. Geneva,
World Health Organization. Available online at http://
GlobalHealthRisks_report_full.pdf (Last accessed on
12th September 2014).
(Received 27 November 2014, revised 5 April 2015,
accepted 6 May 2015)
... The measured indoor air quality data will be used to determine the pollution paramters across the different land uses. A pollutant's index is its concentration expressed as a percentage of the relevant air standard (Kanchan and Goyal, 2015). The index rating presented in Table 3.1 will be used to assess indoor pollution index across the different land uses. ...
... Pollution index (PI) was developed and applied by Cannistraro and Ponterio (2009) for reporting air quality status in a given area. 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 (Kanchan and Goyal, 2015). ...
... The results on PI of all the respective locations are shown in Table 4.6. The decision for ascertaining the PI according to Kanchan and Goyal (2015) is shown in Table 4.5. The PI values in Table 4.7 ranged from 0.06 to 19.5 which indicated the absence of pollution. ...
... Human activities as waste disposal, cement units, smelting, chemical industries etc., are the main causes of air pollution [4,5]. Air pollution directly affects human health and standard of living in light of the increase in industries that produce polluting materials and the aggravation of the resulting dangers, which has become an impediment to the normal life of many people [6,7]. Algeria has recorded many cases of concern, especially children, who suffer from health as a result of the waste that spreads in the atmosphere due to gaseous emissions from multiple sources. ...
Full-text available
In recent years, the world has been witnessing serious ecological imbalances due to the catastrophic situation and the damage caused to the environment. Human activities as waste disposal, cement units, smelting, chemical industries etc., are the main causes of pollution. Air pollution directly affects the human living standards, pollutants requires regular control in view of their direct impact on health, such as nitrogen oxide, sulfur dioxide, ozone, and particulate matter. Algeria adopts international standards to monitor the levels of pollution recorded in Algerian cities and compare them with global levels. Thus, the object of this study was the air quality index (AQI) in Annaba (Northeastern of Algeria). This study aimed to evaluate AQI in Annaba. In this context, quantitative estimates of polluted waste resulting from some industrial activities have been conducted in order to determine the degree of its danger and the extent of its contribution to the deterioration of the air quality. The monitoring of pollutants allowed to identify the benefits of comprehensive environmental assessment. The air quality index was determined using various pollutants parameters (dust, ozone, nitrogen dioxide and sulfur dioxide). A ten-point scale ranking of the overall air quality index of pollution accepted in Algeria allows making the differentiated assessment of negative impacts of existing industrial agglomerations on the environment. However, the analysis performed on samples DC1 and DC2 with SEM (TESCAN model VEGA II) and BSE detector (Backscattered Electrons) shows that the particles sizes are estimated to range from hundreds of microns to a few microns, a different morphology and irregular shape. Our results will enable policy makers to appropriate measures to be taken, and which are based mainly on sensitizing economic operators to environmental issues in order to adopt an environmentally friendly industrial system.
... By superimposing the risk information on the trends shown in graphs, moments of worse indoor air quality are easy to identify, even by non-experts (Leyva Pernia 2019; Schalm et al. 2019). For human health, several kinds of air quality indices (AQIs) exist to warn the public when outdoor air pollution is dangerously high (Tan et al. 2021;Plaia and Ruggieri 2011;Kanchan et al. 2015;Zhu and Li 2017;Leyva Pernia 2019). The AQIs could be merged with the environmental measurements and enhance the information in graphs. ...
Full-text available
The environmental conditions in a conservation-restoration studio for paintings induce an inherent risk to objects of art and to humans working on those objects. They are both subject to (sometimes dangerous) chemical substances and fluctuations in environmental conditions (e.g., temperature, relative humidity). In this paper, we report on a measuring campaign which lasted more than a year collecting data about the air quality within a painting studio of a higher education institute. An existing algorithm assessed the indoor air quality for heritage objects using international air quality standards. This contribution presents a new algorithm to assess indoor air quality for human health relying on thresholds imposed by legislation and recommended by reference institutes. This algorithm has been applied to the same measuring campaign. The assessments illustrate that the same environmental conditions have a different impact on canvas paintings, panel paintings, students, and staff. Air quality is thus a relative concept that depends on the object/subject that is considered in the analysis. Graphical abstract
... This conversion allows for the data to be expressed in a standard unit that can be utilized in statistical models and helps to establish a more meaningful relationship between PM2.5 concentrations and other variables.While the correlation between AQI and µg/m³ values not be 1, converting AQI to µg/m³ provides a more accurate representation of PM2.5 concentrations, enabling researchers to better understand its relationship with other variables in quantitative analyses. The AQI is given by eqn(1) [30] . 4 (1) where; ...
Full-text available
The outbreak of the COVID-19 pandemic has raised concerns about potential environmental factors that could influence the spread and severity of the SARS-CoV-2 virus. Atmospheric pollution, particularly particulate matter (PM), has been suggested as a contributing factor to viral infections and respiratory complications. This two-year observational study aimed to investigate the relation between air pollution and the spread of COVID-19, focusing on PM2.5. Unlike previous studies limited to specific cities or countries, inevitable to use temporal data. Our research analyzed data from various states across the United States, considering both spatial and temporal correlation. The analysis considered the number and geographic distribution of COVID-19 cases along with daily PM2.5 exposure levels, accounting for monthly average PM2.5 exposure, from March 2020 to December 2021. The observed conflicting results of the temporal and spatial correlation present challenges for researchers in understanding the true nature of the relationship between PM2.5 air pollution and COVID-19 cases. The correlation between Various factors, such as population density, PM2.5, temperature, and wind speed, and COVID-19 refers to an association or statistical relationship, not causation. Moreover, the intricate interplay of these variables makes it difficult to establish a clear cause-and-effect relationship.
... Further, as observed in the air pollution maps, change detection maps, and AQI maps shown in Figs. 4, 5, and 6; and AQI category status in Table 3, it is evident that areas, such as RK Puram, Paschim Vihar, Punjabi Bagh, Hauz Khas, Aero City, Connaught Place, Greater Kailash, and New Delhi, showed high levels of PM 10 , PM 2.5 in pre-lockdown phase and the pollution level got drastically decreased with the AQI category "Moderate" during Lockdown. The standard AQI values were taken into consideration for analyzing the AQI category status of pollutants in the study (Hu et al. 2021;Kanchan et al. 2015;Manjeet et al. 2022). Most of the affected regions are predominantly densely populated residential areas and commercial complexes. ...
COVID-19 had such a devastating effect on humanity that several governments worldwide were forced to establish regional and national level Lockdowns in an attempt to reduce the severity of the infection. The nationwide lockdown had been implemented on 24 March 2020 in India with the inevitable restrictions. Along with the effect on the population of more than 1.3 billion people, unprecedented variations in air pollution levels across the country have been witnessed. This Geographic Information System (GIS) approach aims to provide a detailed analysis of spatiotemporal variations in pollution levels prior to, during, and after the Lockdown at multiple locations in Delhi using pollution data from ground monitoring stations under the supervision of the Central Pollution Control Board (CPCB). The monthly contributions of toxic pollutants were determined using several statistical methods, among which the exponential averaging method demonstrated the most favorable outcomes. The interpolation techniques were used to estimate the spatial pollution extent. The study reveals that the residential areas and the commercial complexes had a significant reduction in the pollutant levels in the presence of the lockdown and again a sudden increase in pollution after the relaxation of the curbs. Furthermore, a site suitability analysis was utilized to tackle the growing pollutants level by choosing specific points wherein air quality purifiers can be installed.
... Each phase has its clear and precise significance and contribution in the next phase. AQA and ACR involve estimation of bio-tolerable gas threshold [18,19] such as hazardous gas magnitudes and pollutant ratios in atmospheric volume; geospatial AQA to orchestrate regional AQM [19][20][21][22], finally, design a model of regional air volume with effective and contributory variables to provide mitigation plan [23]. ...
Full-text available
Air quality and environmental fairness have always been an area of prime interest across the globe. The significance low-cost air quality sensing and practices spikes during the time of pan-demic and epidemics when the air becomes a threat to living beings especially human beings. The gradual innovation and enrichment in low-cost air quality sensing sensors, nodes or devices, and systems are exponentially increasing for the last three decades. This work reviews the major contributions in a) low-cost scalable air quality assessment; b) low-cost air quality sensors, sensing approaches and technologies; c) low-cost state-of-the-art gas sensors fabrication methods (MEMS and CMOS); d) low-cost gas sensors measurement configurations and assemblies; and e) low-cost air quality sensors calibration and testing systems. A systematic review of past work with a goal to assist end-users, public health facilities, state agencies, researchers, scientists and air quality protection agencies has been rendered in this work. Starting from sensors electrodes to IoT based mobile smart nodes; all have been introduced in this article.
Conference Paper
Full-text available
India is the second most populated country globally and requires massive urban infrastructure. As a result of this rapid growth, air quality in cities has deteriorated. A World Health Organization survey found that 147 males and 136 females per 100,000 persons in India are died by air pollution. In recent times, Delhi, the capital city of India, experienced the worst condition of air pollution. Therefore, different air pollutants were assessed for Delhi city using the Central Pollution Control Boards report in this study. The study indicated that a city's air quality has considerably beyond the safety limitations of the Central Pollution Control Board. From the study, it is clear that the various activities in the city are causing air pollution, but neighboring towns are equally responsible for it. Countries suffered enormous economic losses due to the COVID-19 shutdown, but air quality improved. Pollution levels fell by half during the shutdown. The Delhi government established an odd/even system and educated the people on the benefits of carpooling to curb air pollution. Recently, smog towers were installed to clear a larger volume of polluted air and supply fresh air to the surrounding community. The study recommends that reducing pollution is not just a government duty, but the general public still plays an important role.
Full-text available
In recent years, air pollution in Chennai city in India causes some health effects. This study examines the spatial-temporal characteristics of ambient air quality in five stations Adyar, Anna Nagar, Kilpauk, Nungambakkam and Thiyagaraya Nagar from 2017 to 2022. The surface level aerosol pollutants like particulate matters (PM2.5 & PM10) and gaseous pollutants Sulfur dioxide (SO2) & Nitrogen dioxide (NO2) were obtained from Tamilnadu Pollution Control Board (TPCB) for five years which includes pre-COVID, during and Post- COVID - period. The results showed that fine particulate matter (PM2.5) and coarse particulate matter (PM10), decreased by 19.49% and 31.91% respectively and gaseous pollutant SO2 and NO2 slightly increased by 7.84% and 1.2 % respectively during 2021 as compared with 2017.The particulate matter (PM2.5 & PM10) level exceeded the National Ambient Air Quality Standards (NAAQS) as well as the WHO recommended Air Quality Guidelines during 2017-2019(Pre-COVID) and low during 2020-2021(During COVID and Post-COVID). The average Air Quality Index (AQI), calculated from the date decreased from 120(2018-2019) to 93(2020-2021) in Chennai city. The AQI and PM2.5/PM10 showed the highest pollution level in winter and lower in summer. PM10 was the primary pollutant, followed by NO2, PM2.5 & SO2 with spatial and temporal variations. The proportion of pollutants PM2.5 and PM10 decreased but increased for SO2 and NO2. This study offers useful data and resources for further research on Chennai's air quality.
Long-term observations indicate that, the ambient air quality in Shanghai continues to improve, however the synergistic effects between the air pollutants PM2.5, O3 and NO2 are also increasing. The concentration of chemical components included in PM2.5 is higher in moderately polluted air containing multiple pollutants. This suggests that air pollution metrics based on multi-pollutant synergy are more descriptive of ambient air quality than single-pollutant air quality index (AQI) models that may ignore the effect of synergy between pollutants on ambient air quality forecasts. Therefore, this study proposes a new multi-pollutant air quality index model (NMAQI) based on four air pollutants (PM2.5, SO2, NO2 and O3) that emphasizes the relationship between PM2.5, NO2 and O3 in ambient air. The model successfully categorized observational data into classes of good, moderate, and polluted air quality ratings. Verification of the NMAQI model using the PM2.5 chemical composition spectrum shows that the NMAQI model can more accurately classify samples with high concentrations of chemical components (often misclassified by AQI) into high pollution levels. The model has an improved capacity to assess the degree of pollution in urban ambient air and to reduce the risk of public exposure to highly polluted atmospheric environments.
Full-text available
Studies on asthma have shown that air pollution can lead to increased asthma prevalence. The aim of this study is to examine the association between air pollution (fine particulate matter (PM2.5), sulfur dioxide (SO2) and ozone (O3)) and human health (asthma emergency department visit rate (AEVR) and asthma discharge rate (ADR)) among residents of New York, USA during the period 2005 to 2007. Annual rates of asthma were calculated from population estimates for 2005, 2006, and 2007 and number of asthma hospital discharge and emergency department visits. Population data for New York were taken from US Bureau of Census, and asthma data were obtained from New York State Department of Health, National Asthma Survey surveillance report. Data on the concentrations of PM2.5, SO2 and ground level ozone were obtained from various air quality monitoring stations distributed in different counties. Annual means of these concentrations were compared to annual variations in asthma prevalence by using Pearson correlation coefficient. We found different associations between the annual mean concentration of PM2.5, SO2 and surface ozone and the annual rates of asthma discharge and asthma emergency visit from 2005 to 2007. A positive correlation coefficient was observed between the annual mean concentration of PM2.5, and SO2 and the annual rates of asthma discharge and asthma emergency department visit from 2005 to 2007. However, the correlation coefficient between annual mean concentrations of ground ozone and the annual rates of asthma discharge and asthma emergency visit was found to be negative from 2005 to 2007. Our study suggests that the association between elevated concentrations of PM2.5 and SO2 and asthma prevalence among residents of New York State in USA is consistent enough to assume concretely a plausible and significant association.
In the German concept of social market economy, a functional competition order is coupled with the implementation of socio-political aims. Within this successful model, ecological aims have become increasingly significant for approximately the last decade, apart from socio-political targets. Enterprises are brought increasingly face to face with the demands of an ecological and social market economy. Protection of the environment is becoming one of the central tasks of management.
The study presents the modified formula of air quality index, based on Cairncross's concept of API index (DAPPS system for Cape Town, South Africa), enabling an assessment of additive effects of short-term exposure to the main air pollutants. The API index refers directly to health risk, since it is based on the factors of the total incremental daily mortality risk. The results of air quality classification using modified API were exemplified by the data originating from the monitoring station in Da̧browa Górnicza (urban background) for the year 2006.
This chapter provides suggestions on informing the public about air pollution. Providing the public with information about levels of air pollution should be an integral part of any air quality strategy. Such information should be accurate, accessible, and provided in a form that is easily understood. Air pollution information can be provided in a number of tiers of increasing complexity and detail. This should both enable the lay public to take on board relevant air pollution information, and at the same time give air pollution specialists access to increasingly disaggregated data sets, including raw data sets for those interested to use in their own data analysis. In addition, air pollution information can be provided in different formats and over different time frames ranging from real-time data via the electronic media to publications in the form of periodic or annual reports, or occasional publicity material such as leaflets. Such information should be up to date and should include forecasts for the following day or days so that individuals can take steps: to avoid the effects of pollution and to reduce activities that lead to raised levels of pollution. Many countries provide such advice.
BACKGROUND—A critical review was conducted of the quantitative literature linking indoor air pollution from household use of biomass fuels with acute respiratory infections in young children, which is focused on, but not confined to, acute lower respiratory infection and pneumonia in children under two years in less developed countries. Biomass in the form of wood, crop residues, and animal dung is used in more than two fifths of the world's households as the principal fuel. METHODS—Medline and other electronic databases were used, but it was also necessary to secure literature from colleagues in less developed countries where not all publications are yet internationally indexed. RESULTS—The studies of indoor air pollution from household biomass fuels are reasonably consistent and, as a group, show a strong significant increase in risk for exposed young children compared with those living in households using cleaner fuels or being otherwise less exposed. Not all studies were able to adjust for confounders, but most of those that did so found that strong and significant risks remained. CONCLUSIONS—It seems that the relative risks are likely to be significant for the exposures considered here. Since acute lower respiratory infection is the chief cause of death in children in less developed countries, and exacts a larger burden of disease than any other disease category for the world population, even small additional risks due to such a ubiquitous exposure as air pollution have important public health implications. In the case of indoor air pollution in households using biomass fuels, the risks also seem to be fairly strong, presumably because of the high daily concentrations of pollutants found in such settings and the large amount of time young children spend with their mothers doing household cooking. Given the large vulnerable populations at risk, there is an urgent need to conduct randomised trials to increase confidence in the cause-effect relationship, to quantify the risk more precisely, to determine the degree of reduction in exposure required to significantly improve health, and to establish the effectiveness of interventions.
Little attention was paid to growing air quality concerns until about a decade earlier in India. Indian Government started continuous monitoring of the urban air quality in Taj corridor area to protect the heritage monuments like Agra Fort, Fatehpur Sikri, the bird sanctuary at Bharatpur National Park and also the human health associatedwithairpollution.Theaimofthisstudywastoaddress air quality assessment using fuzzy synthetic evaluation model. The model was designed for four air pollutants (sulphur dioxide, nitrogen dioxide, suspended particulate matters and respirable suspended particulate matter). In the present paper, an approach is demonstrated for the determination of fuzzy air quality index by aggregating the four pollutants. The model also considers the weights of individual pollutants during aggregation. The weights of individual pollutants were determined using analytical hierarchical process. The model was applied for air quality assessment in four monitoring stations situated in Taj Trapezium Zone.