Spatiotemporal Analysis of Global Particulate Matter and Air Quality Index Patterns: A Five-Year Comprehensive Study during 2020-24

DOI

10.2991/978-94-6239-616-6_91How to use a DOI?

Keywords

Air Quality Index (AQI); Descriptive Statistics; Environmental Monitoring; Environmental Policy; Global Health; Particulate Matter (PM); PM2.5; Pollution Hotspots; Seasonal Variation; Spatio-Temporal Analysis; Time-Series Analysis

Abstract

The Air Quality Index (AQI) and Particulate Matter (PM) are prominent measures of urban air pollution worldwide, which affect the climate and pose public health risks. This paper aims to conduct an extensive Spatiotemporal Analysis of comprehensive air quality from 2020 to 2024 across 147 countries. A specific robust methodological framework was implemented to handle monthly data, and statistical techniques for generating comprehensive outputs, including mean, median, standard deviation, and percentile distributions (25th, 50th, and 75th), were included minimally, which are prominent measures for indicating urban air pollution. The complex patterns in the data have been visualized using advanced techniques, including compact heatmaps, time-series plots, and comparative box plots. The main objective of this paper is to apply Grid-based aggregation and Kernel Density Estimation (KDE) to identify the temporal patterns and hotspots. Subsequently, a marked decline in particulate pollution was observed over the five years. The observations show a median of AQI and PM2.5 of 42 and 12.50, respectively. The top 12 countries were chosen to focus on and identify hotspots and the most polluted countries. Bangladesh, Pakistan, India, and Tajikistan are the most polluted countries, with AQI averages of 143.25, 115.25, 113.13, and 106.07, respectively, and PM2.5 averages of 80.88, 61.28, 53.3, 47.5, and 41.63, respectively. The United Arab Emirates (UAE) and China are moderate. The analysis of real-time data helps policymakers and environmental agencies clarify intervention actions to monitor air quality and support livelihoods comprehensively.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Katikala Jyothi
AU  - M. Senthil
AU  - Nidamanuri Srinu
AU  - D. Bujji Babu
AU  - R. Manasa
AU  - Rajasekhar Manda
PY  - 2026
DA  - 2026/03/31
TI  - Spatiotemporal Analysis of Global Particulate Matter and Air Quality Index Patterns: A Five-Year Comprehensive Study during 2020-24
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1236
EP  - 1250
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_91
DO  - 10.2991/978-94-6239-616-6_91
ID  - Jyothi2026
ER  -