State of the Art and Future Perspectives of Atmospheric Chemical Sensing Using Unmanned Aerial Vehicles: A Bibliometric Analysis

Abstract

In recent years, unmanned aerial vehicles (UAVs) have been increasingly used to monitor and assess air quality. The interest in the application of UAVs in monitoring air pollutants and greenhouse gases is evidenced by the recent emergence of sensors with the most diverse specifications designed for UAVs or even UAVs designed with integrated sensors. The objective of this study was to conduct a comprehensive review based on bibliometrics to identify dynamics and possible trends in scientific production on UAV-based sensors to monitor air quality. A bibliometric analysis was carried out in the VOSViewer software (version 1.6.17) from the Scopus and Web of Science reference databases in the period between 2012 and 2022. The main countries, journals, scientific organizations, researchers and co-citation networks with greater relevance for the study area were highlighted. The literature, in general, has grown rapidly and has attracted enormous attention in the last 5 years, as indicated by the increase in articles after 2017. It was possible to notice the rapid development of sensors, resulting in smaller and lighter devices, with greater sensitivity and capacity for remote work. Overall, this analysis summarizes the evolution of UAV-based sensors and their applications, providing valuable information to researchers and developers of UAV-based sensors to monitor air pollutants.

Keywords: air pollutants; drones; greenhouse gases; particulate matter; sensors.

Figure 1

Temporal distribution of research publications concerning UAV-based sensors for atmospheric chemical sensing from 2012 to 2022.

Figure 2

Total citations by country from 2012 to 2022 in the Scopus previewer and Web of Science databases.

Figure 3

Scientific mapping of the co-citation of journals.

Figure 4

Map of network among author’s keywords. Lines indicate co-occurrences between terms.

Figure 5

Map based on the co-occurrence of the authors’ keywords and evolution from 2002 to 2022. The colour scale represents the year of keyword predominance.