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Citation: Kim, K.-H. Advances in
Gaseous and Particulate Air
Pollutants Measurement. Appl. Sci.
2023, 13, 7438. https://doi.org/
10.3390/app13137438
Received: 7 June 2023
Accepted: 17 June 2023
Published: 23 June 2023
Copyright: © 2023 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Attribution (CC BY) license (https://
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4.0/).
applied 
sciences
Editorial
Advances in Gaseous and Particulate Air
Pollutants Measurement
Kyung-Hwan Kim
Center for Sustainable Environment Research, Climate and Environmental Research Institute,
Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; khkim@kist.re.kr
1. Introduction
In recent years, notable advancements have been achieved in the science of aerosol
and precursor gas measurements as well as the techniques used to apply them. These
advances have led to a more thorough understanding of how aerosols and gases behave as
primary and secondary air pollutants. Additionally, these improved monitoring techniques
have enabled the detection and estimation of air pollutant behavior with higher accuracy,
particularly in terms of spatial and temporal variations. Overall, these developments
have substantially contributed to our knowledge of air pollution and its effects on the
environment and human health.
2. Advances in Gaseous and Particulate Air Pollutants Measurement
This Special Issue covers a wide range of important topics related to monitoring tech-
niques and methodologies in air quality research. The included papers address important
areas such as the understanding of aerosol properties using multidimensional polarization
scattering optical measurements, the estimation of emission factors for vehicle-emitted air
pollutants in real-world conditions, the role of photochemical reactions in the formation of
secondary products, the enhancement in low-cost sensors for air quality monitoring, the
development of a multi-item air quality monitoring system, insights into airborne particle
characteristics, the potential of convolutional neural networks for improving sensor perfor-
mance, and the long-term trends in air pollutant concentrations and aerosol composition in
northeast Asia.
In the first paper, the authors advance our understanding of aerosol properties through
multidimensional polarization scattering optical measurements, with applications in at-
mospheric science, environmental monitoring, and pollution control. This study provides
valuable insights into aerosol behavior and its various impacts [1]. The second paper
introduces a novel method for estimating the emission factors of air pollutants emitted by
vehicles in real-world conditions. This approach enables the cost-effective and short-term
assessments of emission factors, facilitating the evaluation and implementation of traffic-
related environmental policies [2]. The third paper highlights the role of photochemical
reactions in the formation of secondary products, specifically addressing PM2.5 pollution in
agricultural regions. The findings contribute to an in-depth understanding of the underly-
ing processes and can inform strategies for mitigating pollution and safeguarding human
and environmental health [3]. In the fourth paper, the focus is improving the effectiveness
of low-cost sensors for air quality monitoring by advancing calibration methods. This study
supports air quality control efforts and promotes more reliable and accurate monitoring
and management practices [4]. The fifth paper presents the development of a multi-item air
quality monitoring system that enables the real-time monitoring of various parameters. The
system includes a compact measuring device and a user-friendly smartphone application,
providing convenient access to air quality information [5]. Insights into the characteristics
of airborne particles are provided in the sixth paper, with an emphasis on the influence of
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traffic and potential transboundary effects on air quality. This study highlights the impor-
tance of continuous monitoring and further research to enhance air quality in the studied
area [6]. The seventh paper describes the potential of convolutional neural networks in
enhancing the performance of low-cost sensors for air quality monitoring. The researchers
also investigated the impact of the COVID-19 lockdown on air pollutant concentrations,
suggesting advancements in modeling and calibration techniques to improve the accuracy
of air quality monitoring under different conditions [7]. The eighth paper focuses on
analyzing the long-term trends in air pollutant concentrations and aerosol composition in
northeast Asia. The study sheds light on the effects of emission changes in the region and
provides valuable insights into the dynamics of air quality over an extended period [8].
Overall, this Special Issue encompasses a broad range of topics that contribute to the
advancement of air quality research and provides valuable scientific findings for addressing
environmental challenges and improving public health.
Conflicts of Interest: The author declares no conflict of interest.
References
1. Liao, R.; Guo, W.; Zeng, N.; Guo, J.; He, Y.; Di, H.; Hua, D.; Ma, H. Polarization Measurements and Evaluation Based on
Multidimensional Polarization Indices Applied in Analyzing Atmospheric Particulates. Appl. Sci. 2021, 11, 5992. [CrossRef]
2. Lee, S.-B.; Kim, K.H.; Park, B.-E.; Bae, G.-N. A Fast Method for Estimating the Emission Factors of Air Pollutants from In-Use
Vehicles Fleet. Appl. Sci. 2021, 11, 7206. [CrossRef]
3. Song, M.; Kim, M.; Oh, S.-H.; Park, C.; Kim, M.; Kim, M.; Lee, H.; Choe, S.; Bae, M.-S. Influences of Organic Volatile Compounds
on the Secondary Organic Carbon of Fine Particulate Matter in the Fruit Tree Area. Appl. Sci. 2021, 11, 8193. [CrossRef]
4. Horender, S.; Tancev, G.; Auderset, K.; Vasilatou, K. Traceable PM2.5 and PM10 Calibration of Low-Cost Sensors with Ambient-like
Aerosols Generated in the Laboratory. Appl. Sci. 2021, 11, 9014. [CrossRef]
5. Park, B.; Kim, S.; Park, S.; Kim, M.; Kim, T.Y.; Park, H. Development of Multi-Item Air Quality Monitoring System Based on
Real-Time Data. Appl. Sci. 2021, 11, 9747. [CrossRef]
6. Amin, M.; Handika, R.A.; Putri, R.M.; Phairuang, W.; Hata, M.; Tekasakul, P.; Furuuchi, M. Size-Segregated Particulate Mass and
Carbonaceous Components in Roadside and Riverside Environments. Appl. Sci. 2021, 11, 10214. [CrossRef]
7. Vajs, I.; Drajic, D.; Cica, Z. COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model
for the Correction of Low-Cost Sensors’ Measurements. Appl. Sci. 2021, 11, 10563. [CrossRef]
8. Kim, N.-K.; Kim, Y.-P.; Shin, H.-J.; Lee, J.-Y. Long-Term Trend of the Levels of Ambient Air Pollutants of a Megacity and a
Background Area in Korea. Appl. Sci. 2022, 12, 4039. [CrossRef]
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	Introduction 
	Advances in Gaseous and Particulate Air Pollutants Measurement 
	References