In this study, we attempted to calculate the extinction parameters of PM_2.5 using images from a commercial camera. The photo pixels provided information on the characteristics of the objects (i.e., the reflectivity, transmittance, or extinction efficiency) and ambient brightness. Using the RGB values of pixels, we calculated the extinction coefficient and efficiency applied to the mass concentration of PM_2.5. The calculated extinction coefficient of PM_2.5 determined from the camera images had a higher correlation with the PM_2.5 mass concentration (R² = 0.7) than with the visibility data, despite the limited mass range. Finally, we identified that the method of calculating extinction parameters using the effective wavelength of RGB images could be applied to studies of changes in the atmosphere and aerosol characteristics. The mass extinction efficiency of PM_2.5, derived from images, and the mass concentration of PM_2.5 was (10.8 ± 6.9) m² g⁻¹, which was higher than the values obtained in Northeast Asia by previous studies. We also confirmed that the dry extinction efficiency of PM_2.5, applied with a DRH of 40%, was reduced to (6.9 ± 5.0) m² g⁻¹. The extinction efficiencies of PM_2.5, calculated in this study, were higher than those reported in previous other studies. We inferred that high extinction efficiency is related to changes in size or the composition of aerosols; therefore, an additional long-term study must be conducted.

Fine particulate matter (PM) release is regulated by environmental policies in most countries. This study investigated long–term trends in the mass extinction efficiency (Q_e) of aerosols in Northeast Asia. For this purpose, the Q_e was calculated using visibility, PM_2.5 recorded between 2015 and 2020, and PM₁₀ recorded between 2001 and 2020 at eight Korean sites. The Q_e of PM₁₀ (Q_e,10) showed an increasing trend with 0.06~0.22 (m²/g)/yr in seven cities except for Jeju. The Q_e of PM_2.5 (Q_e,2.5) also showed an increasing trend with 0.28–2.47 (m²/g)/yr in all cities. In this study, PM₁₀ and PM_2.5, were divided into low, moderate, and high concentrations, and the Q_e value change by year was examined. Q_e,10 showed a tendency to decrease at low concentrations (19–21 μg/m³). However, at moderate (69–71 μg/m³) and high concentrations (139–141 μg/m³), Q_e,10 increased in most regions. Q_e,2.5 showed an increasing trend at low concentration (9–11 μg/m³), moderate concentration (29–31 μg/m³), and high concentration (69–71 μg/m³), except for Suwon and Pohang, where data were insufficient for analysis. Both Q_e,10 and Q_e,2.5 showed an increasing trend. The increase in Q_e indicated that the visibility-impairing effect of PM can increase even if the same concentration of PM is present. The visibility-impairing effects of PM vary based on the composition, size and other characteristics of the particles in the atmosphere at a given point in time and not simply the quantity of particles. This means that reducing the quantity of particles does not reliably produce a proportionate improvement in visibility. Air quality policies must take the variable nature of PM particles and their effect on visibility into account so that more consistent improvements in air quality can be achieved.

To identify the long-term trend of particle size variation, we analyzed aerosol optical depth (AOD, τ) separated as dust (${\mathsf{\tau }}_{\mathrm{D}}$) and coarse-(${\mathsf{\tau }}_{\mathrm{PC}}$) and fine-pollution particles (${\mathsf{\tau }}_{\mathrm{PF}}$) depending on emission sources and size. Ångström exponent values are also identified separately as total and fine-mode particles (${\mathsf{\alpha }}_{\mathrm{T}}$ and ${\mathsf{\alpha }}_{\mathrm{PF}}$). We checked these trends in various ways; (1) first-order linear regression analysis of the annual average values, (2) percent variation using the slope of linear regression method, and (3) a reliability analysis using the Mann–Kendall (MK) test. We selected 17 AERONET sun/sky radiometer sites classified into six regions, i.e., Europe, North Africa, the Middle East, India, Southeast Asia, and Northeast Asia. Although there were regional differences, τ decreased in Europe and Asian regions and increased in the Middle East, India, and North Africa. Values of ${\mathsf{\tau }}_{\mathrm{PC}}$ and ${\mathsf{\tau }}_{\mathrm{PF}}$, show that aerosol loading caused by non-dust aerosols decreased in Europe and Asia and increased in India. In particular, ${\mathsf{\tau }}_{\mathrm{PF}}$ considerably decreased in Europe and Northeast Asia (95% confidential levels in MK-test), and ${\mathsf{\tau }}_{\mathrm{PC}}$ decreased in Northeast Asia (Z-values for Seoul and Osaka are −2.955 and −2.306, respectively, statistically significant if |z| ≥ 1.96). The decrease in ${\mathsf{\tau }}_{\mathrm{PC}}$ seems to be because of the reduction of primary and anthropogenic emissions from regulation by air quality policies. The meaningful result in this paper is that the particle size became smaller, as seen by values of ${\mathsf{\alpha }}_{\mathrm{T}}$ that decreased by −3.30 to −30.47% in Europe, North Africa, and the Middle East because ${\mathsf{\alpha }}_{\mathrm{T}}$ provides information on the particle size. Particle size on average became smaller over India and Asian regions considered in our study due to the decrease in coarse particles. In particular, an increase of ${\mathsf{\alpha }}_{\mathrm{PF}}$ in most areas shows the probability that the average particle size of fine-mode aerosols became smaller in recent years. We presumed the cause of the increase in ${\mathsf{\alpha }}_{\mathrm{T}}$ is because relatively large-sized fine-mode particles were eliminated due to air quality policies.

Rapid industrialization and the influx of human resources have led to the establishment of industrial complexes near urban areas, exposing residents to various air pollutants. This has led to a decline in air quality, impacting neighboring residential areas adversely, which highlights the urgent need to monitor air pollution in these areas. Recent advancements in technology, such as Solar Occultation Flux (SOF) and Sky Differential Optical Absorption Spectroscopy (SkyDOAS) used as remote sensing techniques and mobile extraction Fourier Transform Infrared Spectrometry (MeFTIR) used as an in situ technique, now offer enhanced precision in estimating the pollutant emission flux and identifying primary sources. In a comprehensive study conducted in 2020 in the Sinpyeong Jangrim Industrial Complex in Busan City, South Korea, a mobile laboratory equipped with SOF, SkyDOAS, and MeFTIR technologies was employed to approximate the emission flux of total alkanes, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), formaldehyde (HCHO), and methane (CH₄). Using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) diffusion model, pollutant dispersion to residential areas was simulated. The highest average daily emission flux was observed for total alkanes, with values of 69.9 ± 71.6 kg/h and 84.1 ± 85.8 kg/h in zones S₁ and S₂ of the Sinpyeong Jangrim Industrial Complex, respectively. This is primarily due to the prevalence of metal manufacturing and mechanical equipment industries in the area. The HYSPLIT diffusion model confirmed elevated pollution levels in residential areas located southeast of the industrial complex, underscoring the influence of the dominant northwesterly wind direction and wind speed on pollutant dispersion. This highlights the urgent need for targeted interventions to address and mitigate air pollution in downwind residential areas. The total annual emission fluxes were estimated at 399,984 kg/yr and 398,944 kg/yr for zones S₁ and S₂, respectively. A comparison with the Pollutant Release and Transfer Registers (PRTRs) survey system revealed that the total annual emission fluxes in this study were approximately 24.3 and 4.9 times higher than those reported by PRTRs. This indicates a significant underestimation of the impact of small businesses on local air quality, which was not accounted for in the PRTR survey system.

This study explores potential of LiDAR technology to rapidly detect aerosolized biological terror agents in the atmosphere. It assesses the application by simulating extinction coefficients and the Ångström exponent at various wavelengths (266, 1064, 1571, and 2000 nm), focusing on differentiating bioaerosols from typical atmospheric particles. The simulation analysis evaluates changes in aerosol distributions and related extinction coefficient and Ångström exponent shifts under clean, normal, and bad atmospheric conditions. The findings indicate that the 1064 nm wavelength effectively detects bioaerosol presence, with a combination of 1064 nm and 1571 nm providing optimal Ångström exponent use for particle size differentiation. This dual-wavelength approach is highlighted as a practical method for bioaerosol detection, showcasing a significant sensitivity to variations in particle quantity and size, which are critical in biological threat scenarios. In conclusion, the study offers guidance for selecting LiDAR wavelengths for biological agent detection systems. While providing a theoretical framework for practical applications, it also underlines the need for further experimental work to confirm findings and fine-tune technology for real-world monitoring and threat management. This research contributes to the development of effective monitoring strategies against the backdrop of biological terror threats. Graphical Abstract