A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method

Yong Tian,Minhao Yang,Weifang Huang,Bojia Ye

doi:10.1155/2019/1562537

Abstract

In order to reduce the air pollution impacts by aircraft operations around airports, a fast and accurate prediction of air quality related to aircraft operations is an essential prerequisite. This article proposes a new framework with a combination of the standard assessment procedure and machine learning methods for fast and accurate prediction of air quality in airports. Instead of taking some specific pollutant as concerned metric, we introduce the air quality index (AQI) for the first time to evaluate the air quality in airports. Then, following the standard assessment procedure proposed by International Civil Aviation Organization (ICAO), the airports AQIs in different scenarios are classified with consideration of the airport configuration, actual flight operations, aircraft performance, and related meteorological data. Taking the AQI classification results as sample data, several popular supervised learning methods are investigated for accurately predicting air quality in airports. The numerical tests implicate that the accuracy rate of prediction could reach more than 95% with only 0.022 sec; the proposed framework and the results could be used as the foundation for improving air quality impacts around airports.

Highlights

Air quality degradation in airports is of concern due to the intensive growth of air traffic over years and its associated environmental risk to human health [1]
Take the air quality index (AQI) level as metric, and use the airport air quality classification method introduced in Section 3.1; we calculated the AQI levels for ZSNJ which were used as the training set for the airport air quality prediction
We reorder the AQI for ZSNJ in 2017 in Figure 4, and it can be seen that 61 days stayed at AQI level 1, 196 days stayed at AQI

Summary

Introduction

Air quality degradation in airports is of concern due to the intensive growth of air traffic over years and its associated environmental risk to human health [1]. The statistical methods had been proposed by many researchers to quantify the air quality impacts of aircraft activities at the airport level [5,6,7,8,9,10]. Hsu et al [15, 16] used high-resolution monitoring and flight activity data to quantify contributions from LTO to ultrafine particulate matter concentrations. Diez et al [17] proposed a statistical approach for identifying air pollutant mixtures associated with aircraft departures at Los Angeles

Methods

Results

Conclusion