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Air Pollution Mapping During Diwali in Karnataka

-Mohamed Safwan Raheem,
Intern At AGSRT

INTRODUCTION

Air Pollution alters the ideal clean air composition approved by air quality professionals. According to World Health Organization (WHO), 99% of the world population breathes air that exceeds the air quality index approved by WHO and contains high percentage of air pollutants, with low income and middle income nations suffering the most. 
              One major subject that affects climate change is air pollution. Many of the actors of air pollution also cause greenhouse gas emission. 
       Therefore, well-informed strategies and policies on air pollution provides a two-way mitigation for climate and health by lowering risks of diseases made available by air pollution as well as contributing to the short and long term mitigation of climate change.                          The advent of geospatial tools and technologies has provided a way of addressing air pollution that supports the many anti-air pollution strategies. My project outlines how to use ArcGIS Pro to map air pollution using data from the Central Pollution Control Board (CPCB).

Project Objective

The objective of this study is to analyze and visualize the impact of Diwali celebrations on air pollution levels across Karnataka using GIS and Remote Sensing techniques.

This analysis aims to:

  1. Assess Air Quality Variations: Compare pollution levels (SO₂, NO₂, PM₁₀, PM₂.₅) before, during, and after Diwali to quantify the short-term spike in air pollution.

  2. Identify Pollution Hotspots: Map and analyze the most affected regions in Karnataka using spatial interpolation techniques (IDW) in ArcGIS Pro.

  3. Understand Pollution Dispersion: Study the geographical spread of pollutants and how urban vs. rural areas experience different pollution intensities.

  4. Evaluate Health & Environmental Risks: Examine the potential health risks posed by increased pollutant concentrations and their impact on air quality.

  5. Support Policy & Mitigation Strategies: Provide data-driven insights to assist policymakers, environmental agencies, and urban planners in designing better air quality management policies and promoting sustainable alternatives.

Methodology

Step 1: Acquire and Prepare Data​

​

  • Pollution Data Collection:

    • Obtain 2023 Diwali pollution data for Karnataka from sources like the Central Pollution Control Board (CPCB) or OpenAQ​

    • Data should include pollutant levels (PM2.5, PM10, SO2, NO2) recorded before, during, and after Diwali. Ensure data includes coordinates (latitude/longitude) and timestamps for each location

    • To get the data of pollutant levels, we have to download the necessary data from CPCB. From the image shown below in (fig 1), we first have to collect all the values of  SO2, NO2, PM10, and PM2.5 from the Karnataka state and its cities.

    • After  noting down the values from (fig 1), we then have to download the Air Quality Index data for all the cities of Karnataka. Which we can get from CPCB. An example of Air Quality Index (AQI) is shown in (Fig 1.2). Similarly, we have to download  AQI for all the cities.

    • After downloading all the AQI, we have to create an Excel file which should contain the following data parameter;  State, City/ town, SO2, NO2, PM10, PM2.5,  Latitude, Longitude, Time Stamp Considered:  Pre-Diwali, During Diwali, and Post-Diwali. For Diwali, pre-Diwali, and post-Diwali; Calculate average from the AQI data of all the cities, for this I considered the days from November 13 to 20 as the Diwali holidays. The (Fig 1.3) shows the final result post average correction and download as csv file.

1.png

[Fig:1]

1.2.png

[Fig:1.2]

[Fig:1.3]

Step 2: Download Geospatial Data

​

  • Download Karnataka's administrative boundary (shapefile) and any other relevant spatial layers (e.g., city boundaries, road networks) from OpenStreetMap or Karnataka State Remote Sensing Applications Centre (KSRSAC).

  • For precise analysis, ensure all datasets are projected to the same coordinate system, preferably WGS 1984.

  • From the (fig 2.0), we download the Karnataka State Boundary and District Boundaries

3_5.png

[Fig:2.0]

Step 3: Load Data Into ArcGIS Pro

​

  • Start ArcGIS Pro and create a new project.

  • We then have to load our csv file and Karnataka State Boundary along with the district boundary.

  • Then we have to load the csv point data from the csv file which can be done by, right clicking on the csv data on the standalone table, then select display XY Data.

  • This is shown in fig 3.1 and fig 3.2

5.png

[Fig:3.1]

6.png

[Fig:3.2]

Step 4: Create Separate Document

​

  • Create separate csv documents only for SO2, NO2, PM10, and PM2.5, so that it can later be used to add new fields and do calculations for their own data. 

  • Below is an example for SO2 shown in the fig 4, similarly to this do for NO2, PM10, and PM2.5

7.png

[Fig:4]

Step 5: Field Calculations

​

  • After creating separate documents for SO₂, NO₂, PM₁₀, and PM₂.₅, Now add a new field called Converted Concentration with Double as its data type and Pollution level with text as its data type as shown in fig 5.1.

  • Similarly, do this for NO2, PM10, and PM2.5 . The next step is to compute meaningful pollution metrics. Use the Calculate Field     tool in ArcGIS Pro to convert pollutant concentrations into standardized units if necessary.

  • Additionally, categorize pollution levels (e.g., low, moderate, high) based on standard threshold.

  • This is shown in fig 5.2

8.png

[Fig:5.1]

10.png

[Fig:5.2]

Step 6: Interpolation using IDW (Inverse Distance Weighting) & Applying Symbology

​IDW interpolation will be used to estimate pollution levels in areas without direct measurements by assigning weights based on proximity to known data points. This technique helps visualize how pollutants disperse spatially during Diwali.

To make the pollution data more interpretable, apply appropriate symbology:

  • Use a color gradient (e.g., blue to red) to represent pollution intensity.

  • Set threshold values based on CPCB/WHO standards.

  • Ensure uniform styling across pollutant maps for consistency.

  • The following images fig 6.1, fig 6.2, fig 6.3, fig 6.4 are the results obtained throught IDW and Symbology,

11.png

[Fig:6.1]

[Fig:6.2]

[Fig:6.3]

[Fig:6.4]

Step 7: Mean Pollution Chart (Pre-Diwali, Diwali, and Post-Diwali)

​

  • ​Create a chart that shows the mean pollution levels for each pollutant (SO₂, NO₂, PM₁₀, and PM₂.₅) across three phases: pre-Diwali, during Diwali, and post-Diwali. A bar or line chart will effectively highlight how pollution fluctuates. The following fig 7.1 shows the chart.

  • This chart shows that during pre-Diwali cities like Bidar, Davenagere, Haveri, Mangalore, and Tumkuru shows the highest level of Air Pollution and it also shows that Mangalore has the highest amount of air pollution before Diwali.

  • From below chart we can conclude that Bagalkote has the least amount of pollution before Diwali, Medikeri has the least amount of air pollution during Diwali and Bagalkote, Medikeri, and Chamarajana has the least amount of air pollution before diwali.

  • Similarly, Bidar, Tumkuru, and Raichur has the most amount of air pollution after Diwali. Cities like Bijapur, Chamarajana, Chikkamangalore, Hassan, Medikeri, Mysore, and Shivamogga show consistent amount of air pollution level throughout the year

15.png

[Fig:7]

Step 8: Layout Creation for Visualization

​Design separate layouts for each pollutant map, including:

  • A clear title and legend.

  • North arrow and scale bar for spatial reference

  • Proper labeling of affected regions.

  • A summary of findings for each pollutant. 

  • From the layout contained in the fig 8.1, we can see the areas affected by Sulphur Dioxide(SO₂). We can see the areas affected in the following

    1. Good - Bijapur, Hubli, Davenagere, Shivamogga, Chikkamangalore, Tumkuru, Bangalore, Madikeri, Mandya, Mysore, Hubli and Chamarajanagar​

    2. Moderate - Haveri, Gulbarga, Bagalkote

    3. Unhealthy - Belgaum, Yadgir, BIdar, Mangalore, Hassan, 

    4. Very Unhealthy - Gadag, Koppal, Udupi, Ramanagara

    5. Hazardouus - Raichur, Kolar

  • From the layout contained in the fig 8.2, we can see the areas affected by Nitrogen Dioxide(NO₂). We can see the areas affected in the following 

    1. Good - Bidar, Gulbarga, Yadgir, Bagalkote, Gadag, Koppal, Davenagere, Madikeri.

    2. Moderate - Hubli, Shivamogga, Udupi, Chikkamangalore.

    3. Unhealthy - Hassan

    4. Very Unhealthy - Mandya, Kolar.

    5. Hazardous - Chamarajanagar, Ramananagar, Mysore, Tumkuru, Chikkabellapur, Bangalore, Mangalore, Haveri, Raichur, Bijapur, Belgaum

  • From the layout contained in the fig 8.3, we can see the areas affected by Particulate Matter(PM₁₀). We can see the areas affected in the following 

    1. Good - Bijapur, Bagalkote, Gadag, Koppal, Shivamogga, Udupi, Chikkamangalore, Madikeri, Mandya, Mysore, Chamararajanagar.

    2. Moderate - Ramanagara, Chikkabellapur, Yadgir, Gulbarga, Bidar, Haveri

    3. Unhealthy -Hassan,, Bangalore.

    4. Very Unhealthy - Mangalore, Davanagere, Belgaum

    5. Hazardous - Raichur, Kolar, Tumkuru, and Hubli

  • From the layout contained in the fig 8.4, we can see the areas affected by Fine Particulate Matter(PM₂.₅). We can see the areas affected in the following 

    1. Good - Bijapur, Bagalkote, Koppal, Gadag, Raichur, Shivammogga, Chikkamangalore, Madikeri, Mysore, Chamarajanagar.

    2. Moderate - Mandya, 

    3. Unhealthy -Hassan, Kolar, Ramanagar, Davanagere, Udupi, Hubli, Belgaum, and Bidar

    4. Very Unhealthy - Gulbarga, Haveri, Chikkabellapur.

    5. Hazardous - Bangalore, Tumkuru, Mangalore, and Yadgir.

[Fig:8.1]

[Fig:8.2]

[Fig:8.3]

[Fig:8.4]

Interpretation of Results: Key Findings from IDW Maps and Pollution Trends

​The IDW (Inverse Distance Weighting) interpolation method provides a spatial representation of pollution dispersion across Karnataka during Diwali. The key observations from the IDW maps for SO₂, NO₂, PM₁₀, and PM₂.₅ are as follows:

  1. ​Pollution Peaks During Diwali

    • The IDW maps reveal a significant spike in pollutant concentrations during Diwali, especially in urban areas such as Bengaluru, Mysuru, Hubli-Dharwad, and Belagavi, where firecracker usage and vehicular emissions are highest.​

    • The highest levels of PM₂.₅ and PM₁₀, which are the most harmful to human health, are concentrated in densely populated regions and areas with heavy traffic.

  2. Pre-Diwali vs. Post-Diwali Pollution Trends

    • Pre-Diwali: Pollution levels remain relatively stable, with background emissions from vehicles, industries, and biomass burning.

    • During Diwali: A sharp increase in PM₂.₅, PM₁₀, SO₂, and NO₂ is observed, indicating the immediate impact of firecracker emissions.

    • Post-Diwali: The levels of gaseous pollutants such as SO₂ and NO₂ begin to decrease within a day due to dispersion, while PM pollutants (PM₂.₅ and PM₁₀) linger in the air for longer, particularly under conditions of low wind speed and temperature inversion.

  3. Regional Variations in Pollution Distribution

    • Urban vs. Rural: Urban areas show higher pollution spikes, whereas rural regions, particularly forested zones in the Western Ghats, experience minimal impact.

    • Wind Influence: Areas downwind of major cities show secondary pollution hotspots, indicating pollutant transport due to atmospheric circulation.

  4. Health & Environmental Implications

    • High PM₂.₅ concentrations increase the risk of respiratory diseases, cardiovascular issues, and eye irritation, especially among children and the elderly.​

    • Increased NO₂ and SO₂ levels contribute to acid rain, soil degradation, and damage to vegetation over time.

CONCLUSION

  • The analysis highlights that Diwali celebrations significantly elevate air pollution levels, particularly for PM₂.₅ and PM₁₀, which have severe health consequences.

  • Urban centers are most affected, while rural areas experience relatively lower pollution spikes.

  • The pollution trend follows a sharp increase during Diwali and a gradual decline post-Diwali, but particulate matter lingers longer, posing prolonged health risks.

  • Geospatial analysis through IDW interpolation effectively visualizes pollution dispersion, making it a valuable tool for air quality management.

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