Statistical analysis of ambient air pollution in selected areas of Bengaluru city

Introduction

Air pollution is one of the major concerns in environment effecting public health in the world. Increasing urbanization, growth in industry, increasing vehicles and heavy use in consumption of energy have decreased the ambient air quality in almost all countries. It is also a major issue in India especially in metro cities. The accumulation of certain unwanted substances in air which effects man and the surrounding environment is called air pollution. Substances such as particulate matter (PM₂.₅ to PM₁₀) which are considered to be most harmful due to their ability to harm the respiratory system. These are formed due to smoke, dust, fumes etc. Apart from these gaseous pollutants such as nitrogen dioxide (NO₂), sulphur dioxide (SO₂) and carbon monoxide (CO) are also present which are formed due to burning of fuel such as through emissions from vehicles and emissions from industries. High concentrations of these pollutants can lead to diseases of respiratory system. Most of these substances are naturally present in air in low concentrations and are considered to be harmless (Rao, 1996). But heavy concentrations of these pollutants harm the quality of environment. The Central Pollution Control Board (CPCB) (2009) [1] has prescribed some limits to these pollutants by the National Ambient Air Quality Standards (NAAQS). If the concentrations exceed these limits then the air is harmful. Also, the World Health Organization (2024) [2] states that exposure to ambient air pollutants contributes to serious respiratory diseases, cardiovascular illness and death, premature mortality, cancers and asthma. Indian metropolitan cities such as Delhi, Mumbai, Chennai, Hyderabad, Kolkata etc are facing major challenges with high increase in air pollution over the past few years. Among them, Bengaluru known as "Silicon Valley of India", is witnessing a heavy growth in population, expansion in industry and a heavy increase in vehicles. Few studies have been made on the ambient air pollution in India and also in some areas of Bengaluru. Sangeetha MD, et al 2024 [3] have evaluated the average concentrations of particulate matter (PM₁₀, PM₂.₅), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO) in four distinct areas of Bengaluru in a year. Dr. Praseeda E, et al [6] have assessed the Air Quality in Bangalore Before, During & after Covid-19 Lockdown. Ganesh Babu Kn, et al 2025 [4] have determined Ambient Air Quality in Peenya Industrial Area of the Bangalore City, India. Shobitha Sunil, et al have analysed Air Quality in Bengaluru city. However limited work has been done on the trend of these pollutants in different areas within Bengaluru and their deviation from the prescribed standards and also the interrelationship among the air pollutants. The present study aims to bridge this gap by conducting a comprehensive statistical analysis of key air pollutants PM₁₀, PM₂.₅, SO₂, NO₂, and CO across selected locations in Bangalore city. The study tries to analyse the deviation from the standard limits, the trend analysis and correlation among the pollutants.

Objectives of the Study

1. To study the deviation of major air pollutant concentrations from National Ambient Air Quality Standards (NAAQS). 2. To examine the relationship between different air pollutants. 3. To analyze the temporal trends of air pollutant concentrations in selected areas of Bangalore city.

Materials and Methods (Methodology) 2.1 Study area and Data Collection

Our study selects the three air pollutant stations of Bangalore city namely which differ in their urban activity. Air quality in these areas is prone to be affected by the concentrations of particulate matter and gaseous pollutants. The analysis of air pollution levels around these heterogeneous areas may provide information regarding variability of these pollutants. It will also give an insight into the interrelations among the air pollutants and the sources of their emission.

The data for the study is collected from KARNATAKA STATE POLLUTION CONTROL BOARD Bengaluru for different pollutants like concentrations of particulate matter PM10, PM2.5, Sulfur dioxide SO₂, nitrogen dioxide NO₂ and carbon monoxide CO for the period for three months from 1 st August 2025 to 31 st October 2025.

Standards for Comparison

The observed pollutant concentrations were compared with the National Ambient Air Quality Standards (NAAQS) prescribed by the Central Pollution Control Board (CPCB) [1] .

Units of measurement for each pollutant  PM₁₀, PM₂.₅, SO₂, NO₂ → µg/m³  CO → mg/m³ An increased industrial activity, urbanization and an increased growth of vehicles in India have reduced the ambient air quality in almost all the urban and industrial regions. Also, there is scientific evidence which shows that exposure to air pollutants such as particulate matter, sulfur dioxide, nitrogen dioxide, and carbon monoxide adversely effects health. People face respiratory illness, cardiovascular illness, premature mortality and cancers. In order to protect public health and environment air quality standards have been set up.

The National Ambient Air Quality Standards (NAAQS) which was set up by the Central Pollution Control Board (CPCB) in 2009 [1] were prescribed to safeguard human health and ensure clear environment. These permissible concentration limits for major air pollutants were set up based on international guidelines and national air quality monitoring data.

Descriptive Statistics

Basic descriptive statistics such as mean, standard deviation, minimum, and maximum values and coefficient of variation were computed to understand the distribution and variability of air pollutant concentrations. Correlation Analysis was also done to study the relationship between air pollutants; Pearson's correlation coefficient was used. The analysis was done using SPSS.

Results and Discussion

This section presents and discusses the results of the statistical analysis of air pollutant concentrations across the selected locations in Bangalore city, in line with the stated objectives of the study. The analysis focuses on deviation from prescribed standards, trend analysis, average distribution of air pollutants across the three stations from August 2025 to October 2025, interrelationships among pollutants.

Trend Analysis

The average distribution of all the pollutants across the three stations from August 2025 to October 2025 was studied and analysed. A multiple bar diagram was prepared using excel. In City Railway Station, Majestic which is a major transportation area the concentration is a little lesser than at H.S.R Layout, Near Central Silkboard and it may be due to heavy vehicle emissions.

In contrast, the concentration of PM₁₀ is relatively less in NTTF, Peenya Industrial Area which shows that relatively there is less industrial emission. This shows that there may be regulated industrial emissions and relatively less vehicular activity during the study period. Also the concentration of PM₂.₅ is highest at NTTF, Peenya Industrial Area than at H.S.R Layout, Near Central Silkboard showing that the finer particals are more due to industrial emissions.

When compared with the CPCB, the concentration of PM₁₀ at H.S.R Layout, Near Central Silkboard and City Railway Station, Majestic are close to the permissible limit while it is very low in NTTF, Peenya Industrial Area. Also the concentration of PM₂.₅ at both the areas are within the permissible limits. When compared with the CPCB, the concentration of NO₂ and SO₂ at all the three stations were within the permissible limits. The CO concentration was highest at City Railway Station, Majestic followed by NTTF, Peenya Industrial Area and least concentration is at H.S.R Layout, Near Central Silkboard. This is may be due to heavy transportation and vehicular smoke in City Railway Station, Majestic.

Correlation Analysis

To study the relationship between air pollutants, Pearson's correlation coefficient was used. The analysis was done using SPSS. The correlation analysis at City Railway Station, Majestic different levels of association between the air pollutants which shows differences in their atmospheric behaviour which may be due to different emission sources.

There is a weak correlation between PM₁₀ and CO (r = 0.387) showing that they may share some common sources of emissions such as vehicular emissions and road dust. But PM₁₀ shows a weak positive correlation with SO₂ (r = 0.227) and a very low correlation with NO₂ (r = 0.025) which shows that the particulate matter is not much related with the gaseous pollutants. We observe a strong negative correlation between SO₂ and NO₂ (r = -0.790) showing that these pollutants may originate from different sources. The source of SO₂ is due to fuel combustion in vehicles and NO₂ is due to vehicular emissions. Also SO₂ shows a moderate positive correlation with CO (r = 0.480) indicating that combustion in vehicles are the sources for SO₂ and CO.

NO₂ shows a weak negative correlation with CO (r = -0.138) indicating that these pollutants may not vary together. Correlation analysis at City Railway Station, Majestic shows that the source of these pollutants are due to traffic-related sources. The correlation analysis at HSR Layout, near Central Silkboard, shows positive associations among particulate and gaseous pollutants.

There is a moderately strong positive correlation between PM₁₀ and PM₂.₅ (r = 0.603) which shows that the source of coarse and fine particulate matter is due to vehicular emissions and road dust resuspension. Also, PM₁₀ showed moderate positive correlations with NO₂ (r = 0.333) and CO (r = 0.378) showing that the common source is the vehicular emission. There is a low positive correlation between PM₁₀ and SO₂ (r = 0.287). PM₂.₅ shows moderate positive correlations with SO₂ (r = 0.304) and NO₂ (r = 0.276) which indicates that the fine particulates are due to combustion processes. Also a very weak correlation between PM₂.₅ and CO (r = 0.043) indicate that concentrations may not always vary with each other which may be due to emission timing. Among gaseous pollutants, SO₂ showed moderate positive correlations with NO₂ (r = 0.410) and CO (r = 0.469) showing common sources such as vehicle combustion and a low positive association between NO₂ and CO (r = 0.284). There is a weak correlation between PM₁₀ and PM₂.₅(r = 0.131) indicating that these pollutants may originate from different sources. PM₂.₅ showed weak positive correlations with SO₂ (r = 0.174) and NO₂ (r = 0.076), but a moderate association with CO (r = 0.354). SO₂ exhibited moderate positive correlations with NO₂ (r = 0.368) and CO (r = 0.419).

A strong positive correlation between NO₂ and CO (r = 0.766) was observed.

Conclusion

The present study indicates that there is a heterogeneity in pollutant concentrations, showing differences in land use patterns and weak correlation among the pollutants. It provides a brief analysis of ambient air quality across the three selected areas of Bangalore city, namely City Railway Station, Majestic (transport hub), HSR Layout, near Central Silkboard (traffic-residential zone), and NTTF, Peenya Industrial Area for the data obtained from the Karnataka State Pollution Control Board (KSPCB). The analysis focused on key air pollutants PM₁₀, PM₂.₅, SO₂, NO₂, and CO with emphasis on deviation from standards, trend analysis and interrelationships among pollutants. Such studies which are on larger scale and multicentric, policy measures such as control over traffic emission, monitoring of industrial emission, trend monitoring and improvement in the public health may be recommended.

References

1. Central Pollution Control Board (CPCB). National Ambient Air Quality Standards. New Delhi: Ministry of Environment and Forests, Government of India; 2009.
2. World Health Organization (WHO). WHO global air quality guidelines: particulate matter (PM₂.₅ and PM₁₀), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Geneva: WHO Press; 2021.
3. Sangeetha MD, et al. Evaluation of particulate matter and air pollutants level in the ambient air of Bengaluru city. Indian J Community Health. 2024;36(3):452-458.
4. Ganesh Babu KN, Vasanthy Muthunarayanan, Selvi KG. Determination of ambient air quality in Peenya Industrial Area of the Bangalore City, India. Int J Environ Sci. 2025;11(23s):5781-5788.
5. S DV. Assessing the long-run impact of vehicular movement on air quality: an ARDL approach. Stud Appl Econ. 2023;41(1).