x̄ - > Comparative PM2.5 Analysis: Thailand & Nairobi

Comparative Spatiotemporal Analysis of PM2.5 Networks in Urban and Rural Asia-Africa: Insights from Northern Thailand and Nairobi

1. Introduction

Air quality is both local and global—borne on winds, forged by fires, machines, and the murmur of development. In Northern Thailand, seasonal burning and agricultural practices elevate PM2.5 levels, while Nairobi's urban sprawl and vehicular emissions choke the air invisibly, yet tangibly.

• PM2.5: Fine particulate matter smaller than 2.5 microns, a known health hazard.
• Thailand: Seasonal biomass burning; transboundary haze; rural-industrial overlap.
• Nairobi: Vehicular emissions; urban-industrial pollutants; informal settlements.

2. Methodology

A. Data Sources

• Thailand: Thai Pollution Control Department, MODIS fire data, sensors.
• Nairobi: OpenAQ, AirQo, PurpleAir, Sentinel-5P satellite.

B. Spatiotemporal Modeling

• STL Decomposition, moving averages, Fourier transforms.
• Spatial interpolation: Kriging, IDW, Gaussian processes.
• Network inference: Correlation, Granger causality.

Related Video: PM2.5 Network Analysis

C. Network-Based Analysis

• Nodes = Monitoring stations
• Edges = Correlation over time
• Metrics: Degree centrality, community detection, betweenness

3. Comparative Findings

Northern Thailand

• Seasonal PM2.5 spikes from agriculture
• March–April synchrony across stations
• Transboundary haze from Myanmar, Laos

Nairobi

• Persistent PM2.5 from traffic, industry
• Fewer seasonal shifts; more daily cycles
• Wind-driven pollution corridors

4. Applied Data Science (WQU Context)

• EDA: Hotspots, time trends
• ML Models: Random Forest, XGBoost, LSTM, Prophet
• Dashboards: Plotly Dash, Streamlit

Example Python Code

import plotly.graph_objs as go
import pandas as pd

# Sample time series data
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
thailand_pm25 = [30, 45, 80, 70, 50, 35]
nairobi_pm25 = [60, 58, 62, 65, 64, 63]

fig = go.Figure()
fig.add_trace(go.Scatter(x=months, y=thailand_pm25, mode='lines+markers', name='Thailand'))
fig.add_trace(go.Scatter(x=months, y=nairobi_pm25, mode='lines+markers', name='Nairobi'))

fig.update_layout(title='PM2.5 Levels: Thailand vs Nairobi',
                  xaxis_title='Month',
                  yaxis_title='PM2.5 (µg/m³)')
fig.show()

5. Implications and Policy Discussion

• Thailand: Manage agricultural fires, enforce ASEAN haze protocols.
• Nairobi: Transport reform, industrial zoning, sensor coverage.
• Lesson: Local data and public dashboards build accountability.

6. Conclusion

While the sources differ—fires in Chiang Mai’s highlands, fumes in Nairobi’s valleys—the breath of the people is affected all the same. A network-based view does not just quantify pollution—it gives it structure, context, and a story. And in that story, we find both caution and compass for the future.