Pollution Data Analysis (AQI)
1. Introduction
Objective: Analyze Air Quality Index (AQI) data to identify seasonal trends and
provide insights into pollution patterns.
Purpose: Assist policymakers, researchers, and the public in understanding air
quality and its fluctuations over time.
2. Project Workflow
1. Problem Definition:
- Understand seasonal variations in
air quality across different regions.
- Key questions:
- Which seasons have the worst air
quality?
- How does air quality differ across
regions?
2. Data Collection:
- Source: Government AQI data, Kaggle
datasets, or APIs (e.g., OpenWeatherMap).
- Fields: Date, Time, Location, AQI,
Pollutant Levels (PM2.5, PM10, CO, etc.).
3. Data Preprocessing:
- Clean and format data for analysis.
4. Analysis:
- Detect seasonal trends in AQI data.
5. Visualization:
- Create graphs and interactive
dashboards.
6. Deployment:
- Build a dashboard for stakeholders
to explore data.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn,
Plotly
- Time Series Analysis: Statsmodels,
Scikit-learn
- Dashboard: Dash or Streamlit
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn plotly dash statsmodels
scikit-learn
```
Step 2: Load and Explore Dataset
Load the AQI dataset:
```
import pandas as pd
data = pd.read_csv("aqi_data.csv")
print(data.head())
```
Explore data for missing values and outliers:
```
print(data.describe())
print(data.isnull().sum())
```
Step 3: Preprocess Data
Handle missing or inconsistent data:
```
data.dropna(inplace=True)
data['Date'] = pd.to_datetime(data['Date'])
data['Month'] = data['Date'].dt.month
data['Season'] = data['Month'].apply(lambda x: 'Winter' if x in [12, 1, 2] else
'Spring' if x in [3, 4, 5] else
'Summer' if x in [6, 7, 8] else 'Autumn')
```
Aggregate AQI by season and location:
```
seasonal_data = data.groupby(['Season',
'Location'])['AQI'].mean().reset_index()
print(seasonal_data)
```
Step 4: Analyze Seasonal Trends
1. Visualize Seasonal Trends:
```
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
sns.barplot(data=seasonal_data, x='Season', y='AQI', hue='Location')
plt.title('Average AQI by Season and Location')
plt.xlabel('Season')
plt.ylabel('Average AQI')
plt.show()
```
2. Perform Time Series Analysis:
```
from statsmodels.tsa.seasonal import seasonal_decompose
decomposition = seasonal_decompose(data['AQI'], model='additive', period=12)
decomposition.plot()
plt.show()
```
Step 5: Build Dashboard
1. Create Interactive Visualizations:
```
import plotly.express as px
fig = px.bar(seasonal_data, x='Season', y='AQI', color='Location',
title='Seasonal AQI Trends')
fig.show()
```
2. Develop a Dash-based Dashboard:
```
from dash import Dash, dcc, html
app = Dash(__name__)
app.layout = html.Div([
dcc.Graph(figure=fig)
])
if __name__ == '__main__':
app.run_server(debug=True)
```
Step 6: Deployment
Host the dashboard on a platform like Heroku, AWS, or Google Cloud to share
insights with stakeholders.
5. Expected Outcomes
1. Seasonal trends in AQI visualized by region.
2. Identification of the most polluted seasons and locations.
3. Dashboard enabling stakeholders to explore air quality data interactively.
6. Additional Suggestions
- Include additional pollutants (e.g., PM2.5, PM10, CO) in the analysis.
- Correlate AQI data with external factors like temperature and industrial
activity.
- Integrate real-time AQI data from APIs for dynamic visualizations.