Thursday, July 31, 2025

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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.

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.

kapitals-pi & SEN