Aging Aircraft and Emissions: Machine Learning Predictions in Takeoff and Landing Operations - An Article

Aug 3, 2026

The aviation industry drives global connectivity but its environmental burden keeps rising with growing air travel. This research uses a decade-long dataset to examine how aircraft age relates to key aviation emissions—hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx)—during landing and take-off (LTO) operations. Emissions inventories are built from fleet data at Queen Alia International Airport, correlations with age are analyzed, and predictive models for aircraft age are trained from emission features using advanced machine learning, achieving an MSE of about 3.0931.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Introduction

Environmental concerns of aircraft emissions

NOx formation and health impacts

NOx chemistry and secondary pollutants

Hydrocarbons (HC) as an aviation-related pollutant

| | Nature Environment and Pollution Technology
An International Quarterly Scientific Journal | | | p-ISSN: 0972-6268 (Print copies up to 2016)
e-ISSN: 2395-3454 | Vol. 24 | No. 3 | | Article ID
D1743 | | 2025 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Original Research Paper | | | [https://doi.org/10.46488/NEPT.2025.v24i03.D1743](https://doi.org/10.46488/NEPT.2025.v24i03.D1743) | | | | | | Open Access Journal | | | Aging Aircraft and Emissions: Machine Learning Predictions inTakeoff and Landing Operations Hala Alrawashdeh1, Laila A. Al-Khatib2† and Bassam Abed3 1Communication and Navigation Unit, Civil Aviation Regulatory Commission (CARC), Jordan 2Environmental Engineering Department, Faculty of Engineering, Al-Hussein Bin Talal University, Ma’an, Jordan 3Quality and Management System Unit, Civil Aviation Regulatory Commission (CARC), Jordan †Corresponding author: Laila A. Al-Khatib, [laila@ahu.edu.jo & lailaalkhatib2003@gmail.com](laila@ahu.edu.jo & lailaalkhatib2003@gmail.com) Abbreviation: Nat. Env. & Poll. Technol. [Website: www.neptjournal.com](Website: www.neptjournal.com) Received: 22-11-2024 Revised: 07-01-2025 Accepted: 16-01-2025 Key Words: Aircraft emissions Aging aircrafts Machine learning prediction Air pollutants Citation for the Paper: Alrawashdeh, H. , Al-Khatib, L.A. andAbed, B. , 2025. Aging aircraft and emissions: Machine learning predictions in takeoff and landing operations. Nature Environment and Pollution Technology, 24(3), D1743 . [https://](https://)[ ](https://)[doi.org/10.46488/NEPT.2025.v24i03.D1743](doi.org/10.46488/NEPT.2025.v24i03.D1743) Note: Since 2025, the journal has adopted the use of Article IDs in citations instead of traditional consecutive page numbers. Each article is now given individual page ranges starting from page 1. Copyright: © 2025 by the authors Licensee: Technoscience Publications This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/l](creativecommons.org/l)icenses/by/4 .0/) . ABSTRACT The aviation industry plays a crucial role in global connectivity and transportation. However, its environmental footprint continues to grow alongside the expanding popularity of aviation. By analyzing a decade-long dataset, the novelty of this research lies in delving into the relationship between aircraft age and major aviation emissions, such as hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx), during landing and take-off (LTO) operation using advanced machine learning algorithms. The analysis of this research comprises three horizons. Firstly, an inventory of aircraft emissions was constructed by analyzing aircraft fleet data at Queen Alia International Airport (QAIA) in Jordan. Secondly, the correlation between these emissions and aircraft age was rigorously examined. Finally, predictive models for aircraft age were developed based on pollutant emission features using advanced machine learning algorithms. The findings of the study revealed a discernible impact of aircraft age on emissions, underscoring the importance of considering the aging factor in assessing the environmental implications of aviation. The machine learning models exhibited a capacity to forecast pollutant emissions with a notable degree of accuracy, with a Mean Squared Error (MSE) of about 3.0931. This offers valuable perspectives that can enhance comprehension of aviation’s environmental footprint. INTRODUCTION The aviation industry plays a pivotal role in global connectivity and transportation, yet its environmental footprint is a subject of increasing concern (Quadros et al. 2020). As aviation continues to burgeon, it becomes imperative to comprehensively grasp the implications of aircraft emissions on environmental issues, such as global warming, and the well-being

FAQs

What emissions and operational phase does the study focus on?

The study focuses on hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx) during landing and take-off (LTO) operations.

How is the emissions inventory constructed in this research?

An inventory of aircraft emissions is constructed by analyzing aircraft fleet data at Queen Alia International Airport (QAIA) in Jordan.

What modeling approach is used to predict aircraft age from emissions features?

Advanced machine learning algorithms develop predictive models for aircraft age based on pollutant emission features.

Aug 3, 2026

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