Engineeering & IT Projects and Resources: Pollution Data Analysis (AQI)

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.

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