Air Quality Index – A Comparative Study for Assessing the Status of Air Quality

 

Shivangi Nigam1*, B.P.S. Rao1, N. Kumar1, V. A. Mhaisalkar2

1CSIR-National Environmental Engineering Research Institute APC Division, CSIR-National Environmental Engineering Research Institute, Nehru Marg, Nagpur-440020

2Head Environmental Engineering Department, VNIT, Nagpur

*Corresponding Author Email: s_nigam@neeri.res.in

 

ABSTRACT:

Air quality Index is a tool for identify the present scenario of air quality. Six different methods of estimating Air quality Index (AQI) based on four pollutants synergistic effect viz., PM10, PM2.5, SO2 and NO2 were used to compare the prevailing ambient air quality in the study region. The average concentration of PM10, PM2.5, SO2 and NO2 are in 82.59, 61.61, 27.19 and 3.92 µg/m3 in was observed in May June respectively. Similarly the levels in June-July 2014 were observed as 57.96, 43.27, 14.24 and 2.54 µg/m3 respectively while the concentration in July-August 2014 were found as 39.37, 32.89, 10.44 and 2.92µg/m3 respectively, in August-September 2014 were 30.08, 32.53, 12.18 and 2.90 µg/m3 respectively and the levels in Sept-Oct 2014 were found as PM10, PM2.5, SO2 and NO2 are in 93.66, 94.04, 23.39 and 6.85 µg/m3 respectively. Seasonal and daily AQI calculation revealed that air quality status in the study region under various classes ranging from good, moderate, satisfactory and unacceptable class for different AQI calculation.

 

KEYWORDS: Air Quality Index (AQI), Oak Ridge National Air Quality Index (ORAQI), Break Point Concentration, SPSS-Factor Analysis, Nagpur.

 

 

1. INTRODUCTION:

Air Pollution is a complex mixture of gases, particles, aerosols, water vapour which has originated due to human development and other natural/anthropogenic activities. Its close relation to human development, complex structure containing infinite proportions of particles and gaseous matrix makes it more challenging towards its management. Air pollution management is lie at the interface of science and public policy. These decisions involve a number of stakeholders with competing agendas and vested interests in the ultimate decision.

It is then appropriate to adopt formal methods for consensus building to ensure transparent and repeatable decisions. In this paper, different method for estimating the Air Quality Index is evaluated as a tool for assessing the impact of air pollution with a case study. Air Quality Index (AQI) is such an indicator tool which is widely used worldwide and in India since last 2-3 decades. Essentially it is used for assessing the air pollution hot spots in the region for delineating management and concrete actions. The earlier version was mostly based on exceedance to the compliances (health based) set for a country’s ambient air for a time period.

 

Various AQI used in the country as well world over include synergistic effect estimation based on mean of the ratios of pollutant over guideline levels for a certain time period. These can further be classified as AQIs using various mean values viz., geometric, arithmetic mean, weighted average, logarithmic mean and break point concentration. Air Quality Index is the simplest and widely used measure of measure of overall air pollution of a region. More recently the breakpoint concentration method of measurement of AQI was proposed by CPCB which is for individual pollutants AQI estimation followed by max of these as synergistic level of AQI which may be used for decision making. This was also adopted by China and is USEPA concept of break point concentration level which they have adopted since last decade for their development. The pollutant with the highest AQI value determines the overall AQI for that hour. The four pollutants measured for the AQI are good indicators of daily air quality, but are not the only air pollutants which may cause health effects, such as air toxics pollutants. Additionally, the AQI does not account for temperature or pollen levels, which may increase sensitivity to air pollutants.

 

2. MATERIALS AND METHODS:

The real time Continuous air pollution monitoring is undertaken at residential site NEERI, Nagpur by Environment S. A. CAAMQS analyzer during May to October 2014 with reference to PM10, PM2.5, SO2 and NO2.

 

2.1 Study Area:

Nagpur (21◦15’N, 79◦08’E) is the Capital of Maharashtra in the winter season. The district stretches to almost 9897 sq km. Nagpur is surrounded by plateau rising northward to the Satpura Range, Kanhan and Pench rivers are the two important rivers of the district. It is situated 274.5m to 652.7m above sea level and 28% of the town is covered by forest. The city has a typical seasonal monsoon weather which is normally dry. Annual Average relative humidity (RH) is 60%. Annual average temperature ranges from 33.2 to 17.1◦C with average annual rain fall 112 mm.

 

2.2 METHODOLOGY:

To understand the temporal variation and episodic rise of the air pollution in the study region, real time air quality monitoring was carried out at residential site NEERI, Nagpur (Figure 1). In the present study ambient air quality was measured by Environment S.A CAAMS Analyzer (Continuous Ambient Air Monitoring Station) for fine particulate matter PM10, PM2.5, SO2 and NO2. The fine particulate monitor of CAAMS works on principle of Beta Attenuation Method for measuring and analysis of the concentration of PM10 and PM2.5. Every hour, a small C14 (Carbon -14 or Krypton 85) element emits a constant source of high-energy electrons (known as beta rays) through a spot of clean filter tape. UV fluorescence method is used for SO2 monitoring. The UV fluorescence method is based on the fluorescence emission of light by SO2 molecules excited by UV radiation. Chemiluminescence Analyzer is used for measurement of oxides of nitrogen in air (NO2). The calibration is undertaken by traceable standard reference gas method.

 

2.2.1 Air Quality Index (AQI):

Now a day, it is important to the society to look for Awareness of daily levels of air pollution.AQI is a tool which is used to report the overall air quality status and trends based on a specific standard. In India we are using CPCB Standard for calculating air quality index or environment pollution index. This index gives an idea about the environmental status as air quality. And also tells the general public to understand how clean or pollute air is breathe daily.


 

Figure.1. Sampling Site at NEERI, Nagpur (Residential Area)

 

Overall this index can be used to give meaningful evaluation of air pollution to the common man. It also helps to identify the air pollution control policies or control equipment can reduce level of dominating pollutant. AQI is representing the cumulative effect of all the pollutant to show overall air quality status in better way. The AQI of specific pollutant is derived mainly from the physical measurement of pollutant like PM10, PM2.5, NO2 and SO2 etc. In the present study, six different methods were used to calculate ambient air quality index.

 

Method I:

Air quality Index (AQI) is calculated based on the arithmetic mean of the ratio of concentration of pollutants to the standard value of that pollutant such as PM10, PM2.5, NO2 and SO2. The average is then multiplied by 100 to get the AQI index.  AQI was then compared with rating scale (Kaushik et al., 2006). For individual pollutant AQI was calculated by the following formula

 

Where

AQI = Air Quality Index

C= the observed value of the air quality parameters pollutant (PM10, PM2.5, NO2 and SO2)

Cs= CPCB standard for residential Area (CPCB, 2009)

 

Method II:

In this procedure AQI is calculated by taking the geometric mean of the ratio of concentration of pollutants to the standard value of that pollutant such as PM10, PM2.5, NO2 and SO2. AQI was then compared with rating scale. (Ravikumar et. al., 2014)

 

Method III:

Oak Ridge National Air Quality Index (ORNAQI) is used for the relative ranking of an overall air quality status. Over all AQI was estimated by the following mathematical equation developed by the Oak Ridge National Laboratory (ORNL), USA is given below.

 

Air quality Index then measured and compared with relative ORAQI values (Bhuyan et al. 2010).

 

Method IV:

Air Quality Index was done for combining qualitative measures with qualitative concept of the environment. The individual air quality index here is calculated as follow:

Where

AQI = Air Quality Index

W= Weighted of Pollutant

C= the observed value of the air quality parameters pollutant (PM10, PM2.5, NO2 and SO2)

Cs= CPCB standard for residential Area (CPCB, 2009)

 

Method V:

Air Quality Index was done based on dose response relationships of pollutants to obtain break point concentration.(USEPA, 2006,CPCB 2014)  The individual air quality index for a given pollutant concentration (Cs) as based on linear segmented principle is calculated as 

 

Where

 

 

 

Finally;

AQI=Max (Ip) (where p=1, 2, 3…n; denotes n pollutants)

 

3. RESULT:

Data obtained from monitoring of ambient air at Residential site is used to calculate the air quality index (air pollution index) for critical parameter. Different AQI were estimated for various months and varying results were observed ranging from good to unacceptable for the same set of data. This may be due to eclipsing effect of the values used in the formulas. The statistical theory behind these AQI makes it more prone to variations viz. the use of means from simple arithmetic to logarithmic and weighted averages to use of breakpoint concentration as basis of estimation. As reported in USEPA, CPCB, 2014, the breakpoint concentration based AQI is more robust and can be used for decision making. Accordingly, the AQI values are calculated based on Break Point concentration for 24 hourly averages for PM10, PM2.5, SO2 and NO2 concentrations and are categorized as satisfactory to moderate during the study period at the residential site.

 

 

Figure 2. Variation in air quality index in the study area during May-June (2014)

 

Figure 3. Variation in air quality index in the study area during June-July (2014)

 

Figure 4. Variation in air quality index in the study area during July-August (2014)

 

The diurnal variation of different AQI has been shown in figure 2, figure 3, figure 4, figure 5 and figure 6 for the month of May-June, June-July, July-August, August-September 2014 respectively  .AQI values calculated for PM10 is found in the Satisfactory, PM2.5 in Poor and NO2 and SO2 in Good category during May-June 2014. While they were found as PM10: Satisfactory, PM2.5: Moderate and NO2 and SO2 in Good category during June-July 2014. The AQI values calculated for PM10 is found in Good category, PM2.5 in the Moderate, NO2 and SO2 is coming in the range of Good in July-August 2014. AQI values calculated for PM10 is coming in the Good, PM2.5 is coming in the Satisfactory, NO2 and SO2 is coming in the range of Good in August-September. AQI values calculated for PM10 is coming in the Satisfactory, PM2.5 is coming in the Poor, NO2 and SO2 is coming in the range of Good in September-October 2014.

 

The overall Air Quality Index was found to fall under the category of satisfactory to moderately polluted area (figure.7).

 

Figure 5. Variation in air quality index in the study area during August-September (2014)

 

Figure 6. Variation in air quality index in the study area during September- October (2014)

 

Figure 7. Classification of air quality index in the study area during study time

Table 1: Classification of AQI used for Comparative Study

AQI

(CPCB,1994)

AQI (Malaysia,1999)

AQI

(Wt. Avg.)

AQI (ORAQI)

AQI

(USEPA, 2006)

AQI

(CPCB, 2014)

(AQI<100)

Good

(AQI<10)

Very Clean

(0≥AQI≤0.5) Acceptable

(0≥AQI≤25) Clean

(up to 50) Good

(0-50) Good

(AQI>100)

Harmful

(10≥AQI<25) Clean

(0.51≥AQI≤1) Unacceptable

(26≥AQI≤50)

Light Air Pollution

(51-100) Moderate

(51-100) Satisfactory

 

(25≥AQI<50)

Fairly Clean

(1.01≥AQI≤2)

Alert

(51≥AQI≤75)

Moderately Polluted

(101-150) Unhealthy for sensitive Groups

(101-200) Moderately polluted

 

(50≥AQI<75)

Moderately Polluted

(AQI≥2.01)

Significant harmful

(76≥AQI≤100)

Heavy Air Pollution

(151-200) Unhealthy

(201-300)

Poor

 

(75≥AQI<100)

Polluted

 

(AQI>100)

Severe Air Polluted

(201-300)

Very Unhealthy

(301-400)

Very Poor

 

(100≥AQI<125)

Highly Polluted

 

 

(301-500) Hazardous

(401-500)

Severe

 

(AQI≥125)

severely Polluted

 

 

 

 

 

 

Figure 8. Percentage occurrences of four pollutants in the study area during study

 

In order to study the frequency and occurrence of individual pollutant in diurnal variation study factor analysis method through SPSS 13.0 software is used (Anikender et al. 2011). The frequency of occurrence of different pollutant has been shown in figure 8. It has been observed that occurrence of particulate matter is more as compared to other pollutant in all the seasons from May to October 2014. PM10 is found more polluting parameter as compared to PM2.5, SO2 and NO2 with variance of average as 62%.

 

Figure 9. CPCB 2014 Individual pollutant classification in the study area during study

Based on break point concentration, AQI of individual pollutant has been shown in figure. 9. It has been observed that Particulate matter (PM2.5) is satisfactory, SO2 and NO2 is Good from May- October 2014. While PM10 is to be appear in moderate to poor from May- October 2014. This PM10 is factor which is the major pollutant for causing the overall air quality reduction. Source of PM10 may be thermal power plant, small-medium scale industry and vehicle etc. PM10 may cause lot of respiratory problem to human health (Ekpenyong et. al. 2012).

 

Table 2: Correlation coefficient of four Pollutant using SPSS during May-July 2014

 

May-June(2014)

June-July(2014)

 

PM10

PM2.5

NO2

SO2

PM10

PM2.5

NO2

SO2

PM10

1

0.946

0.275

0.227

1

0.748

0.502

0.137

PM2.5

0.946

1

0.365

0.197

0.748

1

0.417

0.099

NO2

0.275

0.365

1

0.037

0.502

0.417

1

0.347

SO2

0.227

0.197

0.037

1

0.137

0.099

0.347

1

 

Table 3: Correlation coefficient of four Pollutant using SPSS during July –September 2014

 

July-Aug(2014)

Aug-Sept(2014)

 

PM10

PM2.5

NO2

SO2

PM10

PM2.5

NO2

SO2

PM10

1

0.609

0.728

0.244

1

0.912

0.808

0.568

PM2.5

0.609

1

0.570

0.004

0.912

1

0.826

0.618

NO2

0.728

0.570

1

0.110

0.808

0.826

1

0.720

SO2

0.244

0.004

0.110

1

0.568

0.618

0.720

1

 

Table 4: Correlation coefficient of four Pollutant using SPSS during September-October 2014

 

PM10

PM2.5

NO2

SO2

PM10

1

0.964

0.461

0.452

PM2.5

0.964

1

0.339

0.281

NO2

0.461

0.339

1

0.181

SO2

0.452

0.281

0.181

1

 

 

Correlation matrix has been made using Pearson correlation coefficient with two tailed significant test. Correlation coefficient Matrix has been shown in table 2, table 3 and table 4.  It has been observed that correlation coefficient is very strong between PM10 to PM2.5 as compared to other pollutant during May-October 2014 except July –August 2014.PM10 and PM2.5 may be due to power plant /industrial emissions but further particulate characterization study would strength the source identification. During the July –August 2014, strong correlation has been found between Particulate matter (PM10) and NO2. This may be due to excessive rain and less photochemical reaction between pollutants (Analitiset al. 2006).

 

4. CONCLUSION:

Air quality Index can give clear view about ambient air and critical pollutant mainly responsible for the quality of air. The AQIs were calculated according to CPCB break point concentration. The AQI study reveals that particulate matter (mainly PM10) was mainly responsible for maximum times in the residential site NEERI, Nagpur. These also have identified that PM10 as the dominant pollutant in the index value (pipalatkar et. al. 2012). Particulate Matter is causing serious worldwide public health problem for residents because of their synergetic action. We have to look for appropriate pollution control and management plans like plantation and green belt etc for the betterment of the civic life. The use of this tool in decision making for development but may involve risk as it does not clearly address the temporal AAQ variation due to meteorology, land use, ecosystem geology of the region and its impact, population exposure (poor) who cannot afford air conditioning comfort, chemical conversion and synergistic effect particle/ gas combination leading to smoke acid rain and other climate change phenomena, health impact of raised AAQ due to agglomeration of finer particle-gas and their synergistic combination on health of exposed/poor under privilege population which may defeat the purpose of inclusive development(Anderson et. al. 2005, Pope et al. 2006).

 

5. ACKNOWLEDGEMENTS:

This study was carried out as part of the project funded by Council of Scientific Industrial Research. The authors are grateful to Director NEERI, Nagpur for according to permission to publish this paper.

 

 

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