Predicting Air Pollution Levels in India Using Multivariate Regression Analysis

Abstract

Air pollution remains one of India's most pressing environmental challenges, significantly impacting public health, the economy, and climate change. This project aims to predict air pollution levels in India by developing a multivariate regression model using various predictors such as location, season, weather data, temperature, and other relevant environmental and socio-economic factors. By leveraging data mining techniques and analyzing existing literature, the study seeks to identify key determinants of air pollution and provide insights that could inform policy decisions and mitigation strategies.

Introduction

Air pollution in India has reached alarming levels, with several cities consistently ranking among the most polluted globally. The adverse effects of air pollution extend beyond environmental degradation, affecting human health, economic productivity, and contributing to climate change. According to the World Health Organization, air pollution is a leading cause of premature deaths worldwide, with India accounting for a significant share.

Understanding the factors contributing to air pollution is crucial for developing effective mitigation strategies. This project focuses on predicting air pollution levels using a multivariate regression approach, considering variables such as geographic location, seasonal variations, temperature, and other environmental and socio-economic factors. The study aims to contribute to the body of knowledge by providing a predictive model that can aid policymakers and stakeholders in addressing air pollution challenges in India.

Data Collection and Initial Analysis

Data Sources

• Air Quality Data: National Air Quality Monitoring Programme (NAMP) by the Central Pollution Control Board (CPCB) provides data on key pollutants like PM2.5, PM10, NOx, SOx, etc.
• Meteorological Data: India Meteorological Department (IMD) offers data on temperature, humidity, wind speed, and precipitation.
• Geographical Data: GIS data for locations to analyze spatial variations.
• Socio-economic Data: Census data for population density, industrial activity, and vehicle density.

Initial Findings

Preliminary analysis indicates that air pollution levels exhibit significant spatial and temporal variations:

• Seasonal Trends: Higher pollution levels during winter months due to temperature inversions and biomass burning.
• Geographical Variations: Northern India, particularly the Indo-Gangetic Plain, experiences higher pollution levels compared to other regions.
• Correlation with Meteorological Factors: Temperature and wind speed show an inverse relationship with pollutant concentrations.

Proposed Methodology for Prediction

Regression Model Selection

• Multivariate Linear Regression: To model the relationship between air pollution levels and continuous predictors.
• Support Vector Regression (SVR): For handling non-linear relationships in the data.
• Random Forest Regression: To capture complex interactions between variables and improve prediction accuracy.

Variables Considered

• Independent Variables (Regressors):

  • Location: Latitude, longitude, and elevation.
  • Season: Categorical variable representing different seasons.
  • Temperature: Daily average temperature.
  • Humidity: Atmospheric moisture content.
  • Wind Speed: Affects dispersion of pollutants.
  • Industrial Activity: Proximity to industrial zones.
  • Traffic Density: Number of vehicles in the area.
  • Population Density: Human activities contributing to emissions.

• Dependent Variable:

  • Air Pollution Level: Concentration of pollutants like PM2.5 and PM10.

Data Preprocessing

• Handling Missing Values: Imputation techniques or exclusion based on data availability.
• Normalization: Scaling variables to ensure uniformity.
• Encoding Categorical Variables: Converting seasons and other categorical data into numerical format using one-hot encoding.

Model Training and Validation

• Training Set: 70% of the data for training the model.
• Validation Set: 15% for tuning hyperparameters.
• Test Set: 15% for evaluating model performance.

Evaluation Metrics

• Mean Squared Error (MSE): Measures the average squared difference between observed and predicted values.
• R-squared (R²): Indicates the proportion of variance explained by the model.
• Mean Absolute Error (MAE): Provides the average magnitude of errors in predictions.

Expected Outcomes

• Identification of Key Predictors: Understanding which variables significantly impact air pollution levels.
• Predictive Model: A reliable model that can forecast air pollution levels based on the given regressors.
• Policy Implications: Insights that can help in formulating targeted interventions to reduce pollution.

Conclusion

Predicting air pollution levels using multivariate regression analysis is a critical step toward mitigating its adverse effects in India. By integrating data from various sources and employing robust statistical models, this project aims to provide valuable insights into the determinants of air pollution. The findings could serve as a foundation for policymakers, helping to implement strategies that address the root causes and improve air quality for millions of residents.

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