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Air Quality Analysis in India: Unveiling Pollution Hotspots and Health Impacts (Python Data Analysis)
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Project Title: Air Quality Analysis in India: Unveiling Pollution Hotspots and Health Impacts (Python Data Analysis)
(Optimized for Keywords: Air Quality Analysis, India, Pollution, Data Analysis, Python, Health Impacts)
1. Data Sources: India's Air Pollution Data Hubs
- Central Pollution Control Board (CPCB) - India (Official Source): https://cpcb.nic.in/ - Your primary resource for India's air quality data. Explore the National Air Quality Monitoring Programme (NAMP) for pollutant concentrations like PM2.5, PM10, SO2, NOx, and Ozone. Look for downloadable datasets; availability may vary.
- Open Government Data (OGD) Platform India (Data Repository): https://data.gov.in/ - Search for "air quality," "pollution," and "CPCB" to find relevant datasets. Consider this a supplementary data source.
- State Pollution Control Boards (SPCBs) (Regional Data): Check individual SPCBs for state-specific air pollution data.
Data Collection – Challenges & Solutions for India's Data:
- Accessibility: Air quality data in India can be scattered. Be prepared to combine data from multiple sources.
- Data Quality: Expect missing values and inconsistencies. Data cleaning is critical.
2. Project Goals: India Air Quality Insights
- Analyze air quality trends across Indian cities.
- Identify major air pollutants in India.
- Explore seasonal air pollution patterns affecting Indian cities.
- Investigate links between air pollution and health indicators (requires external health data).
- Visualize India's air quality data effectively.
- Potentially identify sources of air pollution based on data analysis.
3. Python Libraries: Tools for Air Quality Analysis
- Pandas: For data manipulation and cleaning air quality data.
- NumPy: For numerical operations essential to air pollution analysis.
- Matplotlib: For creating basic data visualizations of air quality.
- Seaborn: Enhanced data visualization to highlight air pollution trends.
- datetime: Handling dates and times for time series analysis of air quality.
- SciPy: For statistical analysis to understand air pollution correlations.
4. Code Implementation (Python): Air Quality Analysis Workflow
Implementation.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
from scipy.stats import pearsonr
# 1. Data Loading and Cleaning - Essential for reliable results
def load_and_clean_data(air_quality_file):
"""Loads and cleans Indian air quality data."""
air_quality_data = pd.read_csv(air_quality_file)
# Data Cleaning (Examples)
air_quality_data['date'] = pd.to_datetime(air_quality_data['date'])
air_quality_data.dropna(inplace=True) # Handle missing values
air_quality_data = air_quality_data[air_quality_data['pm25'] > 0] # Remove invalid PM2.5
# Handle outliers appropriately (IQR, z-score)
return air_quality_data
# 2. Exploratory Data Analysis (EDA) - Discovering patterns
def perform_eda(data):
"""Performs EDA and generates visualizations for Indian air quality data."""
# Average PM2.5 levels by City - Identifying Hotspots
city_pm25 = data.groupby('city')['pm25'].mean().sort_values(ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x=city_pm25.index, y=city_pm25.values)
plt.title('Average PM2.5 Levels by City in India')
plt.xlabel('City')
plt.ylabel('Average PM2.5 (µg/m³)')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
# Time Series Plot of PM2.5 for Delhi - Analyzing Trends
delhi_data = data[data['city'] == 'Delhi'].sort_values('date')
plt.figure(figsize=(12, 6))
plt.plot(delhi_data['date'], delhi_data['pm25'])
plt.title('PM2.5 Levels in Delhi over Time')
plt.xlabel('Date')
plt.ylabel('PM2.5 (µg/m³)')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
# Seasonal Trends - Understanding the impact of weather
data['month'] = data['date'].dt.month
monthly_pm25 = data.groupby('month')['pm25'].mean()
plt.figure(figsize=(10, 6))
sns.lineplot(x=monthly_pm25.index, y=monthly_pm25.values)
plt.title('Seasonal Trends in PM2.5 Levels in India')
plt.xlabel('Month')
plt.ylabel('Average PM2.5 (µg/m³)')
plt.show()
# Correlation Analysis - Identifying pollutant relationships
correlation, p_value = pearsonr(data['pm25'], data['so2']) # PM2.5 vs. SO2
print(f"Correlation between PM2.5 and SO2: {correlation:.2f} (p-value: {p_value:.3f})")
# 3. Main Execution
if __name__ == "__main__":
# Replace with your actual file path
air_quality_file = 'indian_air_quality.csv' # Your India air quality dataset
air_quality_data = load_and_clean_data(air_quality_file)
perform_eda(air_quality_data)
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5. Sample Data File (indian_air_quality.csv) - India Air Quality Example
date,city,pm25,pm10,so2,nox
2023-01-01,Delhi,250,400,30,80
2023-01-01,Mumbai,80,150,15,40
2023-01-01,Kolkata,180,300,25,60
2023-01-02,Delhi,270,420,32,85
2023-01-02,Mumbai,85,160,16,42
2023-01-02,Kolkata,190,310,27,65
-
6. Expected Insights: India-Specific Findings
- Cities with the highest PM2.5 and other pollutant concentrations.
- Air pollution trends over time in Indian cities.
- Seasonal variations in air pollution (e.g., winter smog).
- Correlations between different air pollutants in India.
7. Further Enhancements: Deep Dive into India's Air Quality
- Health Impact Assessment: (If possible) Link air pollution to health outcomes in India.
- Geospatial Analysis: Map air pollution levels using location data for monitoring stations.
- Statistical Modeling: Predict air pollution based on weather, traffic, etc.
- Source Apportionment: Identify the main sources of air pollution.
- Policy Evaluation: Assess the impact of air pollution control policies in India.
Key Considerations for Analyzing India's Air Quality Data:
- Data Sources: Use reliable sources like CPCB, OGD, and SPCBs.
- Data Cleaning: Handle missing data and inconsistencies carefully.
- Units: Ensure consistent units for all pollutants.
- Indian Air Quality Standards: Compare pollution levels to NAAQS.
- Context: Consider factors like weather, geography, and local sources.
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