A Review on Air Quality Indexing System

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Asian Journal of Atmospheric Environment Vol. 9-2, pp. 101-113, June 2015 Ozone Concentration in the Morning in InlandISSN(Online) Kanto Region 2287-1160 101 doi: http://dx.doi.org/10.5572/ajae.2015.9.2.101 ISSN(Print) 1976-6912

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: [email protected]

Globally, many cities continuously assess air quality Abstract using monitoring networks designed to measure and record air pollutant concentrations at several points Air quality index (AQI) or air pollution index (API) is deemed to represent exposure of the population to commonly used to report the level of severity of air these pollutants. Current research indicates that guide pollution to public. A number of methods were dev eloped in the past by various researchers/environ lines of recommended pollution values cannot be regar mental agencies for determination of AQI or API but ded as threshold values below which a zero adverse there is no universally accepted method exists, which response may be expected. Therefore, the simplistic is appropriate for all situations. Different method comparison of observed values against guidelines may uses different aggregation function in calculating mislead unless suitably quantified. In recent years, air AQI or API and also considers different types and quality information are provided by governments to the numbers of pollutants. The intended uses of AQI or public comes in a number of forms like annual reports, API are to identify the poor air quality zones and environment reviews, and site or subject specific anal public reporting for severity of exposure of poor air yses/report. These are generally having available or quality. Most of the AQI or API indices can be broad access to limited audiences and also require time, inter ly classify as single pollutant index or multi-pollut est and necessary background to digest its contents. ant index with different aggregation method. Every Presently, governments throughout the world have also indexing method has its own characteristic strengths started to use real-time access to sophisticated database and weaknesses that affect its suitability for particu management programs to provide their citizens with lar applications. This paper attempt to present a access to site-specific air quality index/air pollution review of all the major air quality indices developed index and its probable health consequences. Thus, a worldwide. more sophisticated tool has been developed to commu nicate the health risk of ambient concentrations using Key words: Air pollution, Air quality index, Health, air pollution index (API) or air quality index (AQI). Environmental factors, Literature review 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). 1. Introduction The problem of air pollution and its corresponding ad verse health impacts have been aggravated due to in Air pollution is global environmental problem that creasing industrial and other developmental activities. influences mostly health of urban population. Over the The monitoring concentrations of pre-determined air past few decades, epidemiological studies have dem pollutants in the residential/commercial/industrial areas onstrated adverse health effects due to higher ambient are used for the calculation of an air quality index (AQI) levels of air pollution. Studies have indicated that rep or air pollution index (API). The monitoring data are eated exposures to ambient air pollutants over a pro aggregated and converted into a single index with a longed period of time increases the risk of being sus variety of methods. This means that indexing systems ceptible to air borne diseases such as cardiovascular and air pollution descriptors often differ from one coun disease, respiratory disease, and lung cancer (WHO, try/region to another. The indicators of air quality give 2009). Air pollution has been consistently linked to the public an opportunity to track the state of their substantial burdens of ill-health in developed and devel local, regional and national air quality status without oping countries (Gorai et al., 2014; Bruce et al., 2000; the need for an understanding of the details of the mon Smith et al., 2000; WHO, 1999; Schwartz, 1994). itoring data upon which they are based. Since the sen 102 Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015 sitivity of the people to expose of air pollution chang mental agencies/researchers for different country/re es with changing in geographical location, quality of gions. Though, there is a widespread use of air pollu life etc., an universal technique to measure the air qua tion (quality) index systems but currently no interna lity index is not very much helpful. tionally accepted methodology for constructing such a system (Stieb et al., 2005; Maynard and Coster, 1999) 1. 1 ‌Design Criteria for an Ideal Air Quality are available. In this paper, an attempt has been done Index to demonstrate the critical review on different AQI The basic objective of any air quality index is to systems. transform the measured concentrations of individual In 1976, the U.S. EPA established a Pollutant Stan air pollutant into a single numerical index using suit dards Index (PSI) which rated air quality. They sug able aggregation mechanism. Ideally, every index gested the formula for aggregating pollutants to deter should reflect both the measured and publicly per mine PSI. The index ranged from 0-500, with 100 ceived quality of the ambient air for the time period it equal to the National Ambient Air Quality Standards covers. As a result, air quality indices attempt to stan (NAAQS). The PSI is calculated for every pollutant dardize and synthesize air pollution information and with a NAAQS, but the only level reported for a given permit comparisons to be readily undertaken, and to time and location is for the pollutant most exceeding satisfy public demands for accurate, easy to interpret its standard. The daily PSI is determined by the high data. In design of air quality indices, the following cri est value of one of the five main air pollutants: partic teria should be used: ulate material (PM10), ozone (O3), sulfur dioxide (SO2), 1. be readily understandable by the public; carbon monoxide (CO), and nitrogen dioxide (NO2). 2. ‌include the major criteria pollutants and their syn The PSI does not indicate exposure to many other pol ergisms; lutants, some of which may be dangerous for people 3. ‌be expandable for other pollutants and averaging with respiratory problems (Qian et al., 2004, Radojevic times; and Hassan, 1999). The PSI was revised, renamed to 4. ‌be related to National Ambient Air Quality stan the Air Quality Index (AQI), and subsequently imple dards used in individual provinces; mented in 1999 by the U.S. EPA. 5. ‌avoid “eclipsing” (eclipsing occurs when an air pollution index does not indicate poor air quality 2. 1 AQI System of U.S. EPA despite the fact that concentrations of one or more U.S. EPA’s AQI is defined with respect to the five air pollutants may have reached unacceptably main common pollutants: carbon monoxide (CO), high values); nitrogen dioxide (NO2), ozone (O3), particulate mat 6. ‌avoid “ambiguity” (ambiguity occurs when an air ter (PM10 and PM2.5) and sulphur dioxide (SO2). The pollution index gives falls alarm despite the fact individual pollutant index as in the eqn. (1) is calculat that concentrations of all the pollutants are within ed first by using the following linear interpolation the permissible limit except one); equation, pollutant concentration data and reference 7. ‌be usable as an alert system; concentration. The breakpoint concentrations have 8. ‌be based on valid air quality data obtained from been defined by the EPA on the basis of National monitoring stations that are situated so as to rep Ambient Air Quality Standards (NAAQS) as shown in resent the general air quality in the community; Table 1, and on the results of epidemiological studies which refer to the effect of single pollutants on human health.

2. Review of Air Quality (IHI -ILO) Ip = ------(CP -BPLO)+ILO (1) Indices (AQI) BPHI -BPLO where The large databases often do not convey the air qual I =Index for pollutant P ity status to the scientific community, government P C =Rounded concentration of pollutant P officials, policy makers, and in particular to the gener P BP =Break point that is greater than or equal to C al public in a simple and straightforward manner. This HI P BP =Breakpoint that is less than or equal to C problem is addressed by determining the Air Quality LO P I =AQI value corresponding to BP Index (AQI) of a given area. AQI, which is also known HI HI I =AQI value corresponding to BP as Air Pollution Index (API) (Murena, 2004; Ott and LO LO Thom, 1976; Thom and Ott, 1976; Shenfeld, 1970) or The highest individual pollutant index, IP, represents Pollutant Standards Index (PSI) (U.S. EPA, 1994; Ott the Air Quality Index (AQI) of the location. and Hunt, 1976), was developed by various environ The above method does not have the flexibility to A Review on AQI System 103

Table 1. Breakpoint Concentration of air pollutants defined by U.S. EPA. Breakpoints AQI Category O3 (ppm) O3 (ppm) PM10 PM2.5 CO SO2 NO2 8-hour 8-hour1 (μg/m3) (μg/m3) (ppm) (ppm) (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 Unhealthy 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 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 Very 4 0.205-0.404 355-424 150.5-250.4 15.5-30.4 0.305-0.604 0.65-1.24 201-300 (0.155-0.404) 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. 2 NO2 has no short term NAAQS and can generate an AQI only above a value of 200. 3 8-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. ≥ incorporate any number of air pollutants. The method 2. 2 Common Air Quality Index (CAQI) also not considers the pollutant aggregation and spatial The CAQI was developed by the Citeair project in aggregation. It can be used for determining the short 2008, which was co-funded by the INTERREG IIIC term and long term air quality indices. and INTERREG IVC programs in Europe. To present Cheng et al. (2004) proposed a revised EPA air qual the air quality situation in European cities in a compa ity index (RAQI) by introducing an entropy function rable and understandable way, all detailed measure to include effect of the concentrations of the rest of ments are transformed into a single relative figure call pollutants other than the pollutant with maximum ed the Common Air Quality Index (CAQI). An impor AQI. The revised Air Quality Index (RAQI) can be tant feature of this index system is that it differentiates determined by eqn. (2) as given below: between traffic and city background conditions. The Common Air Quality Index (CAQI) is designed to pre ∑ n Avgdaily j=1 I j sent and compare air quality in near-real time on an RAQI=Max (I1, I2 …In) × ------∑ n hourly or daily basis. It has 5 levels, using a scale from Avgannual [Avgdaily j=1 I j ] 0 (very low) to >100 (very high) and the matching Avg {Entropy Max[I , I ,… I ]} colours range from light green to dark red. The CAQI × ------annual daily * 1 2 n (2) is computed according to the grid system (shown in Entropydaily * Max[I1, I2,… In] Table 2) by linear interpolation between the class bor The second term on RHS establishes the background ders. The final index is the highest value of the sub- arithmetic mean index in which the numerator is the indices for each component (pollutant); nevertheless, sum of the daily arithmetic averages of all sub-indexes the choice of the classes for the CAQI is inspired by (I1…In), and the denominator is the yearly average of the EC legislation. The CAQI do not take into account the sum of daily average for these pollutants. the adverse effects due to the coexistence of all the The third term in RHS represents the background pollutants. The existing air quality indices were used arithmetic mean entropy index in which the numerator by van den Elshout et al. in 2008 for air quality assess is the yearly average of the average daily entropy, and ment in European cities. They compared all the exist the denominator is the entropy function of the sub- ing method for identification of suitable alternative. index pollutants. The above method can be applied to make compara The RAQI method facilitates for aggregation of pol tive study of urban air quality in real time without lutants sub-indices and also health based study but facilitating or considering the spatial aggregation, pol failed to measure uncertainty and spatial aggregation. lutant aggregation, uncertainty measures and health 104 Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015

Table 2. Pollutants and calculation grid for the CAQI. Traffic City background

Index Mandatory pollutant Auxiliary pollutant Mandatory pollutant Auxiliary pollutant Grid class PM10 PM10 NO2 CO NO2 O3 CO SO2 1-hr 24-hrs 1-hr 24-hrs 0 0 0 0 0 0 0 0 0 0 0 Very low 25 50 25 12 5000 50 25 12 60 5000 50 26 51 26 13 5001 51 26 13 61 5001 51 Low 50 100 50 25 7500 100 50 25 120 7500 100 51 101 51 26 7501 101 51 26 121 7501 101 Medium 75 200 90 50 10000 200 90 50 180 10000 300 76 201 91 51 10001 201 91 51 181 10001 301 High 100 400 180 100 20000 400 180 100 240 20000 500 Very high >100 >400 >180 >100 >20000 >400 >180 >100 >240 >20000 >500 3 NO2, O3, SO2: hourly value/maximum hourly value in μg/m CO: 8 hours moving average maximum 8 hours moving average in μg/m3 3 PM10: hourly value/daily value in μg/m

effects. tors contribute to about 60% of the variation of AQI are considered and the rest can be neglected (Dunteman, 2. 3 Oak Ridge Air Quality Index (ORAQI) 1994; Johnston, 1978; Harman, 1968). Also, the first The Oak Ridge Air Quality Index (ORAQI) method factor will cause the highest variance of AQI. The sec was developed by Oak Ridge National Laboratory. ond will contribute less variance than first but more The Oak Ridge Air Quality Index is defined for any than the third factor and so on. These factors are also number of pollutants. Thom and Ott (1975) suggested called the principal components or eigen vector pairs. the use of Oak Ridge Air Quality Index (ORAQI). The The New Air Quality Index (NAQI) is given by the ORAQI is given by eqn. (3) equation as below in the eqn. (4) b n n Ci NAQI={∑ (P E ) / ∑ E } (4) ORAQI= a ∑ ---- (3) i=1 i i i=1 i ( Cs ) = where n 3, P1, P2, P3 are the three Principal Compo where, nents for which the cumulative variance is more than Ci =Monitored/Predicted concentration of pollutant 60%. E1, E2 and E3 are the initial eigen values (≥1) ‘i’ with respect to the percentage variance. = Cs National ambient air quality standard (NAAQS) The principal components were given by Lohani for pollutant ‘i’ (1984) in the eqn. (5) a & b=constant for specific number of pollutants a X n ji j The above method can be applied to assess the air Pi =∑j ------(5) =1 λ quality in urban area without facilitating or consider i ing the spatial aggregation, and uncertainty measures where λi is the Eigen value associated with Pi th but considers pollutant aggregation and health effects. xj is the concentration of i pollutant and can be determined as: 2. 4 New Air Quality Index (NAQI) n New Air Quality Index is based on Factor Analysis Xj =∑ aji Pi of the major pollutants. The index is proposed by 1 Bishoi et al. in 2009. The method is based on principal The method can be applied to assess the relative air component factors which causes the variation of AQI. quality without facilitating or considering the spatial The concentration of each pollutant or there deviation aggregation, health effects and uncertainty measures from the mean or their standardized values are expres but considers pollutant aggregation. sed as a linear combination of these factors. The fac A Review on AQI System 105

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 Generally there aren’t risks for people. People with asthma, chronic 101-125 Mediocre 4 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 Many people may feel light adverse symptoms, however reversible. 151-175 Unhealthy 6 Weak people may feel gravest symptoms. People may feel light adverse effects for health. There are more risks >175 Very Unhealthy 7 for olds, children and people with respiratory diseases.

2. 5 Pollution Index (PI) ant for protection of human health Pollution index (PI) was developed and applied by The Pollution index classification, quality indicator, Cannistraro, et al. in 2009 for reporting air quality sta and its health risks are represented in Table 3. tus in the city of Naples, Italy. The pollution index The method considers the pollutant aggregation and method is based on a simple indicator of the air quality health effects to determine the air quality index but not in an urban context that is useful for communicating to considers spatial aggregation and uncertainty issues. 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 2. 6 Air Quality Depreciation Index (AQDI) most critical pollutants. Additive effects of air pollut AQDI was used by Singh in 2006 to measure the ants have also been considered and the PI re-evaluated. depreciation in air quality using the value function It is expressed by a numerical index ranging from 1 to curves for individual pollutants. The method considers 7. A highest value of the index represents a highest the pollutant aggregation to determine the depreciation value of environmental pollution, and, of course a in air quality with respect to standard air quality. The highest health risk. This index of air quality has been air quality depreciation is measured in a scale between developed by means of a series of critical pollutants in 0 to -10. An index value of ‘0’ represents most desir the Italian urban contexts and correlated with pollution able air quality having no depreciation from the best level and health risk and level of satisfaction of the possible air quality with respect to the pollutants under people. PI can be calculated with the arithmetic aver consideration, while an index value of -10 represents ages of sub-indices of two most critical pollutants. maximum depreciation or worst air quality. The shift This is given by eqn. (6) as given below: ing of Index value from 0 towards -10 represents suc cessive depreciation in air quality from the most desir (I1 +I2) I= ------(6) able. The AQDI is given as follows in the eqn. (8) 2 n n AQdep ={∑i=1(AQi * CWi)}-{∑i=1 CWi} (8) The two sub-indices (I1 and I2) are calculated for the two most critical pollutants having the highest concen where, trations. The sub-indices of each pollutant can be det th AQi =Air Quality Index for the i parameter and ermined by eqn. (7) as given below: obtained from value function curve defined by Jain et

Vmax hx al. in 1977. In the value function curve 0 represent the Ix = ------* 100 (7) worst quality and 1 represent the best quality of air V rif due to pollutant under consideration. th th where, Ix is the sub-index of x pollutant CWi is composite weight for i parameter. th Vmax hx is the maximum 1 hour mean value of the x n is the total no of pollutants considered. pollutant in a day in all the monitoring stations of Composite weight (CWi) in eqn. (8) can be calculat the area. ed using the following eqn. (9). th Vrif is maximum 1 hour limit value of the x pollut 106 Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015

TWi nonlinear aggregation of sub-indices. They proposed an CW = ------10 (9) i ∑ n * air pollution index which is a quantitative tool through { i=1 TWi} which air pollution data can be reported uniformly. An where, ambiguity and eclipcity-free function was presented th TWi =total weight of the i parameter=AWi + for aggregating the air pollutants sub-indices. BPIWi +HWi The sub-indices are aggregated to yield air quality th AWi =aesthetic weight of i parameter index using eqn. (12) as given below: BPIW =bio-physical impact weight of ith parameter i 1 p th N -p HWi =health weight of i parameter = ∑ I ( i=1 si ) (12) TW is computed by assigning weights between 1 to i where, I=aggregate index; 5 to AW , BPIW , and HW by a team of assessors or i i i N=the number of sub-indices; experts. 1 is the least and 5 is the highest weight. th si =sub-index of i pollutant, and = 2. 7 Integral Air Pollution Index (IAPI) p an exponent. Bezuglaya et al. (1993) suggested integral air pollu After an extensive study, authors suggested that the tion index (IAPI) for Russian cities that is simply a value of p=0.4 in the aggregation function minimize sum of the pre-selected number of the highest individ the effect of ambiguity. ual pollutant indices calculated by normalising the pollution concentrations to maximum permissible con 2. 8 Aggregate Air Quality Index (AQI) centration (MPC). Russian health experts had estab An aggregate AQI was proposed by Kyrkilis et al. in lished maximum permissible concentrations (MPC) 2007. The index is based on the combined effects of for more than 400 pollutants. To understand the degree five criteria pollutants (CO, SO2, NO2, O3, and PM10) of air pollution, the measured value of concentration taking into account the European standards. This pollutants are compared with either short-term MPC method was used for air quality evaluation for each or mean value of long-term MPCs. monitoring station situated in whole area of Athens, The sub-indices of IAPI can be determined using Greece. The indexing system is based on the AQI sys eqn. (10) as shown below: tem developed by Swamee and Tyagi in 1999. An aggregate AQI can be determined by eqn. (13) shown Xi below: I1i = ------(10) MPCi ---1 = ∑n p p th I [ i=1(AQIi) ] (13) where, xi is the concentration of i pollutant, th Ii is the sub-index of i pollutant, where, I is the aggregate AQI, th ci is the degree of exponent, and AQIi =the sub-index for i pollutant, and MPCi is the maximum permissible concentration of p=a constant. ith pollutant According to eqn. (13), when p =∞ the index I is The degree of air pollution with any pollutant can be equal to the max AQI of a single pollutant, regardless expressed through comparison with the degree of air of the rest of the pollutants’ AQI value. This kind of pollution with sulphur dioxide using the degree expo calculation corresponds to the way that EPA calculates nent ci as shown in eqn. (11): the overall AQI; however, it underestimates the air pollution levels. When p is equal to 1, at the other xi Ci I2i = ------(11) extreme, the overall index (I) is equal to the sum of all ( MPCi ) AQI indices. The selection of the most proper value The reason behind sulphur dioxide was used as a for p is still an open support this statement. basis is that this pollutant is monitor in all cities. IAPI The sub-indices are expressed as functions of the can be determined by the arithmetic sum of all the ratio of pollutant concentration q to standard concen sub-indices corresponding to each air pollutants con tration qs, shown in eqn. (14): sidered for air quality assessment. q Swamee and Tyagi (1999) had criticised the addition AQIi =AQIs ---- (14) qs of sub-indices suggested by Bezuglaya et al. (1993), ( ) th concluding that the function is ambiguous in nature where, AQIi =Sub-index of i pollutant, and and can lead to an index in the hazardous category, AQIs=a scaling coefficient equal to 500 (Swamee which may be a false alarm. Hence, they suggested a and Tyagi, 1999). A Review on AQI System 107

th 2. 9 The Aggregate Risk Index (ARI) From the published RRi values for i pollutant, the Sciard et al. (2011) designed the aggregate risk index coefficients for the terms ai can be determined using for assessing the health impact due to air pollution in eqn. (16) in order to derive a numerical scale specific the South East of France. The ARI is a measure of the to each of the pollutants. mortality risk associated with simultaneous exposure aPM10 * (RRi -1) to the common air pollutants and provides a ready ai = ------(16) method of comparing the relative contribution of each (RRPM10 -1) pollutant to total risk. An arbitrary index scale (1-10), The breakpoint values between different levels con with a colour coding system, was used to facilitate risk sidered correspond to the air quality standards defined communication. The ARI is based on the exposure- by the WHO (2005). The air quality classes and the response relationship and Relative Risk (RR) of the relevant RR values are determined in terms of the PM10 well-established increased daily mortality, en-abling concentration (significant RRi values). The breakpoint an assessment of additive effects of short-term expo concentrations for the remaining pollutants are calcu sure to the major air pollutants. This study presents the lated proportionally to the individual levels of the rela modified formula of AQI based on Cairncross’s con tive risk. cept (Cairncross et al., 2007). The total attributable risk for simultaneous short- 2. 10 ‌AQI Based on PCA-Neural Network term exposure to several air pollutants is given by the Model eqn. (15): Kumar and Goyal (2013) designed forecasting sys tem for daily AQI using a coupled artificial neural net ARI=∑i (RRi -1)=∑ Indexi =∑ ai * ci (15) work (ANN) - Principal component analysis (PCA) To account for the reality of the multiple exposures model. The architecture of the system is shown in Fig. impacts of chemical agents, the final index is the sum 1. It was designed for forecasting AQI in one day advan of the normalised values of the individual RRi val ce using the previous day’s AQI and meteorological ues (Pyta, 2008; Cairncross et al., 2007). It thus pro variables. vides a ready method of comparing the relative contri There are two steps involved in determining the AQI. bution of each pollutant to total risk. The index is The first step is the formation of sub-indices for each defined to reflect the contribution of individual pollut pollutant and the second one is the aggregation of sub ants to total risk. Ci is the corresponding time-aver indices. The sub-indices were calculated using the aged concentrations (in mg/m3) and the coefficient same formula as that of U.S. EPA but the breakpoint “ai” is proportional to the incremental risk values (RRi concentration of each pollutant is based on the Indian -1). This index uses exposure-response relative risk NAAQS and epidemiological studies, which are indi functions and a particular set of RRi values for a given cating the risk of adverse health effects of specific pol health endpoint associated with increasing major air lutants. pollutants. These functions and values were published The AQI value was calculated for each individual by the WHO (2008, 2004, 2001), APHEA (Air Pollu pollutant (SO2, NO2, RSPM, and SPM) and highest tion and Health- a European Approach)-2, (2006) and among them was declared as the AQI of the day. The InVS (PSAS-9 project, French air pollution and health previous day’s AQI value was used as one of the input surveillance program, 2008, 2006, 2002) under a pro parameters in the PCA-ANN model for forecasting the cedure for health impact assessment. 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 (18) as shown below: Mandal et al. (2012) developed a method for predic 10 tion of AQI on the basis of fuzzy aggregation. The out AQHI= ----- ∑ …. 100(eβiXi -1) (18) c i=1 p put AQI value using fuzzy aggregation method was compared to that of the output from conventional me where, βi is the regression coefficient from same Pois th thod. It was demonstrated that computing with linguis son model linking the i air pollutant with mortality, xi tic terms using fuzzy inference system improves toler is the concentration of the ith pollutant, and c is the ance for impression data. scaling factor. The relationship between air pollutants and output parameter (FAQI) is mathematically expressed as 2. 13 ‌Air Pollution Indexing System in South given in the eqn. (17) Africa An air pollution index was developed in a dynamic = FAQI f (SPM, RPM, SO2, NOX) (17) air pollution prediction system (DAPPS) project, Gorai et al. (2014) developed a fuzzy pattern recog which was led by a consortium of four South African nition model for AQI determination. The method was partners, including the Cape Peninsula University of used for air quality assessment of Agra city. This meth Technology (Cairncross et al., 2007). The API system is based on the relative risk of the well-established od considered five air pollutants (PM10, CO, SO2, NO2, excess daily mortality associated with short-term expo and O3) for AQI determination. The method also con siders weights of the individual pollutants on the basis sure to common air pollutants, including PM10, PM2.5, of its degree of health impacts during aggregation. SO2, O3, NO2, and CO. A scale of 0 to 10 was used for Analytical hierarchical process (AHP) was used for the assessment of air quality. Incremental risk values determination of weights of various pollutants. The air for each pollutant are assumed to be constant, and a quality index is ranged from 1 to 6. The higher is the continuous exposure metrics, the exposures that corre value of AQI, higher is the health risk and vice versa. spond to the same relative risk are assigned the same Authors suggested that depending upon the risk level, sub-index value. The final API is the sum of the nor air quality mangers can take preventive measures for malized values of the individual indices for all pollut reducing the level of index. Though, the formula or ants is given by the eqn. (19) method used for determination AQI is relatively com API=∑ PSIi =∑ a·C (19) plex in comparison to that of the arithmetic aggrega tion method but this can be easily programmed for det where, C is the time averaged concentration of pollut ermination of AQI. ant, and a is a coefficient directly proportional to the incremental risk value associated with the pollutant. 2. 12 Air Quality Health Index The proposed API was applied to ambient concen A new Air Quality health index (AQHI) was devel tration data collected at monitoring stations in the City oped in Canada to understand the state of local air of Cape Town for testing. quality with respect of public health. AQHI is avail able for about ten communities in Canada, including 2. 14 Air Pollution Indexing System in China Vancouver and Victoria. China’s state pollution control board is responsible The AQHI is constructed as the sum of excess mor for measuring the air pollution in the cities. The air tality risk associated with NO2, ground-level O3, and pollution index (API) in China is based on the five PM2.5 at certain concentrations. It is calculated hourly atmospheric pollutants sulfur dioxide (SO2), nitrogen based on 3-hour rolling average pollutant concentra dioxide (NO2), suspended particulates (PM10), carbon tions, and is then adjusted to a scale of 1 to 10. The monoxide (CO), and ozone (O3) measured in the mon value of 10 corresponds to the highest observed wei itoring stations. Chinese API system follow the same ghted average in an initial data set covering a reference method for calculating the air quality index but the period from 1998 to 2000 (Stieb et al., 2008; Taylor, health definition corresponding to each AQI classes 2008). are different. The scientific basis for the formulation of AQHI is Hämekoski et al. (1998) developed a simple air based on the epidemiological research undertaken at quality index (AQI) for the Helsinki Metropolitan Area Health Canada. Relative risk (RR) values are estimat in order to inform the public about the air quality status ed, based on the local time series analyses of air pollu for better understanding. The pollutants consider in the tion and mortality (Stieb et al., 2008; Taylor, 2008). AQI system are CO (1 hr. and 8 hrs.), NO2 (1 hr. and Air Quality Health Index is determined by the eqn. 24 hrs.), SO2 (1 hr. and 24 hrs.), O3 (1 hr.) and PM10 A Review on AQI System 109

) taking and CO) 10 2 , NO 3 and PM 3 , O 2 , O 2 , SO 2.5 , NO 2 , PM 10 Purpose/Application (CO, SO (PM

The system is based on the relative risk of well - established excess daily mortality associated with short - term exposure to common air pollutants The AQI is based on acute health effects, but AQI is based on acute health effects, The on nature and materials are also long term effects taken into consideration. To assess air quality status in metropolitan cities To To define the depreciation in air quality with To respect to standard of five criteria Based on the combined effects pollutants To produce an objective result in the long - term To of air pollution effect Comparing urban air quality in real time. Described the application of concentration - response functions from epidemiological studies AQI. of air pollution to an The index aims at measuring the status of air on human pollution with respect to its effect health. into account European standards. Useful towards the informing of citizens and protection human health in an urban agglomeration. To define the state of air in relative terms To Use for continuous reporting of air quality status to public No No No No Yes Yes Yes Yes Yes Yes Yes based Health No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Pollutant aggregation ]} n } i ] ,…I n 2 , I CW

1 ,…I n 2 i=1 ∑ { } , I i 1 E - Max[I } ] j ) * n i=1 )

I i n ) n n j=1 C ∑ Max[I

daily · / p,c

P j ∑ 1) b CW * …I I

) ) a

2 s i * 1 p

, …I -- - n j=1 i E ] 2 j ------, I i p ∑ ---- daily daily X 1 ∑ i ) , I i β (P = (I 1 n p=1

i ∑ (AQ

n i=1

daily

a [Avg {Entropy ∑ n ( i=1 ∑ PSI C (AQI

∑ (10/c) Max { = 100 Max n i=1

= ∑ annual annual = = Index and aggregation function PIb = = ∑ dep = i=1…p [ S ------= ∑ × × C C RAQI

NAQI={ Based on U.S. EPA formula Based on U.S. EPA

Based on U.S. EPA method but with Based on U.S. EPA criteria different AQHI Avg AQI API ORAQI C Avg Entropy PI BP AQ I Avg Name PI ORAQI RAQI Aggregate AQI NAQI A simple A AQI AQDI CAQI AQHI AQI API (1998)

(1975)

(2007)

(2004)

2008 (2009) (2004)

2008, (2006)

Method . AQI methods with their merits, demerits and application (2008)

Author/Organization/ able 4. Cheng et al. Thomas and Ott U.S. EPA, AQI U.S. EPA, Kyrkilis et al. Murena et al. Steib et al., et al., Taylor Bishoi et al. Singh G. T (1976, 1999) Cairncross et al., 2007 Hӓmekoski et al. CITEAIR II Project, Europe 110 Asian Journal of Atmospheric Environment, Vol. 9(2), 101-113, 2015 , 2 , SO 2.5 , PM 10 (PM

Purpose/Application and CO) 2 , NO 3 The AQI of each pollutant has been calculated The individually and highest among them is declared AQI of the day. as the A new aggregation approach suggested for air A quality assessment Useful for communicating to citizens’ Useful for communicating to citizens’ information about the state of air quality a waste urban area. 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 An arbitrary index each pollutant to total risk. The index scale facilitates risk communication. values may extend beyond 0 for highly polluted areas. new fuzzy pattern recognition model suggested A This can cover the for air quality assessment. uncertainty factors. API allowed obtaining a complex The use of degree of urban air pollution. An ambiguity and eclipsity free function was developed. Based API value is calculated per hour. The final on six atmospheric pollutant O No No No No No Yes Yes Yes based Health No Yes Yes Yes Yes Yes Yes Yes Pollutant aggregation i c *

i a

∑ ) i 2 = c i )

i i i , NO 2 Index X MPC

------

( ∑ n i=1

= ∑ 1) = are the sub - indices of - ) 2 i 2i p h (I )

, p 1 * --

i ) (RR n i=1

i (SPM, RPM, SO

2 s

and I i,h I f 1 u ∑

i=1 ∑ N

+ = Index and aggregation function 6 = 1 h=1 ∑ = ∑ ( ------= i = = Based on U.S. EPA formula Based on U.S. EPA FAQI (I ARI two most critical pollutants having highest concentration formula Based on U.S.EPA

2 Where I I

H IAPI I

Name AQI FAQI ARI FAQI PI (Aggregate Risk Index) API IAPI I .

(2009)

(1993)

(2012)

(2013)

(2011) et al.

(2014)

et al. Method Continued Author/Organization/ able 4. Mandal et al. Gorai et al. Sciard et al. Kumar et al. Bezuglaya Swamee and Tyagi Air Pollution Indexing System in China (1999) T Cannistraro A Review on AQI System 111

(24 hrs.). The AQI is based on acute health effects, but contribute to public understanding by providing infor long term effects on nature and materials are also taken mation that is easily accessible and allows them the into consideration. Sub-indices are calculated hourly opportunity to modify their behaviour appropriately in for all pollutants and for a given hour the highest sub- response to changes in air quality. However, much index becomes the AQI. The development is partly progress is still to be made, based on the work conducted in United States and Cana mainly through more careful consideration of the da, for example by Ott and Hunt (1976) and Mignacca combined impact of multiple pollutants, consideration and others (1991). 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 3. Summary and Conclusions listed in Table 4. These are mainly demonstrated in this review work. Table 4 represents the chronological This brief review on air quality indices shows the evolution of various air quality indices that are descri wide interest or concern for poor air quality problem. bed in this paper including to other important statisti But at the same time it shows lack of a common strate cal issues like aggregation function, the availability of gy, which allow to compare the state of the air for cit uncertainty measures for the index, availability of ies that follow different directives. The major differ health descriptors, and their purpose or applicability. ences among the indices in the literature are found in The review of various methods reveals that most of the aggregation function, type and number of pollut the methods do not have the flexibility to accommo ants, number of index classes (and their associated date a new pollutant. This is because, either the meth colors) and related descriptive terms. It was observed ods is designed for specific number of pollutants or that the guidelines are sometimes consistently differ the aggregation function does not have the suitability ent from place to place, not only in indicating the pol to accommodate this. Table 4 also clearly indicates lutants to be monitored, but also in setting the thresh that many of the indexing method do not consider the old values. It is also true that WHO recommended synergistic effects of the pollutants, that is, pollutants (WHO, 2006) that during formulating policy targets, are not aggregated in index calculation. Furthermore, governments should consider their own local circum many indexing method are not based on health criteri stances carefully, that is the specificities of places on. Thus, further work is required on the statistical must be taken into account. The complexity of air pol structure and effects of the aggregation function on lution and its science has created problems to both the index, the nature of the scale (1-10, 1-100, 1-500) and public and policy makers. There are many pollutants the multi-pollutant problem to make it uniform. to consider with some being secondary products of atmospheric transformations. The science is often so sophisticated that it becomes hard for politicians and Acknowledgement the public to interpret. Thus, it is desirable that air pol lution information will continue to be represented in The support from the Department of Science and simple forms such as indices. In an attempt to meet the Technology, New Delhi for research grant no.SR/FTP/ public’s needs for information on air quality a variety ES-17/2012 is acknowledged. 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 References for pollutants to be added or subtracted as changes to their health impact are revealed. The Air Quality Index Bezuglaya, E.Y., Shchutskaya, A.B., Smirnova, I.V. (1993) (AQI) is a widely used concept to communicate with Air Pollution Index and Interpretation of Measure ments of Toxic Pollutant Concentrations. Atmospheric the public on air quality. A growing number of nation Environment 27, 773-779. al and local environment agencies use the AQI for Bishoi, B., Prakash, A., Jain, V.K. (2009) A comparative (near) real-time dissemination of air quality informa study of air quality index based on factor analysis and tion. Although the basic concepts are similar, the AQIs US-EPA methods for an urban environment. Aerosol show large differences in practical implementation. 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