Exploratory Data Analytics of Air Pollutant Data for Air Quality Management Application

Authors

  • Omoniyi Samuel Aiyenuro
  • Oluwatomisin Ajayi

DOI:

https://doi.org/10.47604/ijts.2790

Keywords:

Air Quality Management, Topic Modelling, Exploratory Data Analysis

Abstract

Purpose: With escalating concerns over the detrimental impacts of air pollution on public health and the environment, this study embarks on a comprehensive exploration of air quality dynamics, focusing on Ogun state metropolis of Nigeria. The primary objective is to contribute to the burgeoning field of air quality management by harnessing the power of data analytics, exploratory data analysis, and advanced python programming libraries. The study seeks to address critical questions regarding the current state of air quality, major sources of pollution, government interventions, and individual contributions to better air quality.

Methodology: The research employs Exploratory Data Analysis to unveil intrinsic patterns and trends within historical air quality datasets extracted from IoT devices located across Ogun state. This initial phase aims to discern key statistical parameters, including mean concentrations, variations, and correlations among various pollutants. Subsequently, the study employs Topic Modeling to extract latent themes and sentiments from qualitative data, specifically focusing on public opinions gathered through digital surveys. Resulting topics provide insights into public perceptions regarding air quality, pollution sources, and the efficacy of governmental interventions.

Findings: Experimental result is revealing of the diversity of the dataset. The EDA returned a mean Particulate Matter concentration of approximately 12.65 µg/m³, while the mean Nitric Oxide concentration is approximately 13.56 µg/m³. Notable correlations include a strong positive correlation between PM2.5 and PM10 (0.69), indicating a substantial association between fine and coarse particulate matter. Additionally, there is a noteworthy positive correlation between NO and Nox (0.97), suggesting a high degree of correlation between nitric oxide and nitrogen oxide levels. However, negative correlations are observed, such as the substantial negative correlation between RH (relative humidity) and PM2.5 (-0.46), implying an inverse relationship between humidity and fine particulate matter levels. The study develops an innovative air quality management application.

Unique Contribution to Theory, Practice and Policy: The application aims to empower both the public and policymakers with actionable insights, fostering informed decision-making and collaborative efforts towards improving air quality. It is highly recommended that industry experts should continue to imbibe ethical standards in data acquisition and deployment practices. 

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Published

2024-07-22

How to Cite

Omoniyi, S., & Ajayi, O. (2024). Exploratory Data Analytics of Air Pollutant Data for Air Quality Management Application. International Journal of Technology and Systems, 9(2), 82–107. https://doi.org/10.47604/ijts.2790

Issue

Vol. 9 No. 2 (2024)

Section

Articles

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Copyright (c) 2024 Oluwatomisin Ajayi

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