This paper investigates the effects of Emission Trading Scheme (ETS) adoption on the country-level reduction rate of carbon emission. We first used Environmental Kuznets Curve (EKC) tests to group countries into three categories: inverse U-shaped and gamma-shaped EKC for decoupled countries, and a positive linear EKC for non-decoupled countries. We then examined the effectiveness of ETS adoption. We found ETS was effective for both post-industrial and pre-industrial economies. Compared to countries that have not adopted ETS, the carbon emission reduction (increment) rate of decoupled (non-decoupled) countries that have adopted ETS is faster (slower). Furthermore, ETS adoption significantly reduces overall carbon emissions per capita compared to other global events, such as oil crises. The results imply that a market-based mechanism is an effective strategy for achieving sustainable development, thus, providing insights for policymakers and governments to design effective carbon neutrality policies to achieve sustainable development.

Satellite aerosol optical depth (AOD) data assimilation (DA) using numerical air quality forecast models has shown a limited improvement due to large uncertainties in the AOD observation operator. This study employed a machine learning (ML) algorithm to estimate the ground‐level particulate matter (PM) from the Geostationary Ocean Color Imager (GOCI) AOD through the random forest with high accuracy. Analysis fields were subsequently produced by applying PM estimations to the Weather Research and Forecasting‐Chemistry/three‐dimensional variational DA system. Initialization of the model with the new analysis remarkably reduced the analysis error and increased the forecast skill. The PM₁₀ prediction showed significant benefits for up to 24 forecast hours, whereas PM_2.5 prediction was improved for up to six forecast hours. Considering a broad spatial coverage by satellites, the synergistic use of DA and ML can maximize the effectiveness of satellite DA for air quality forecasts at the ground.

The spatiotemporal inhomogeneity of the total column NO₂ amounts (TCN) in the Seoul Metropolitan Area (SMA), Korea, was quantitatively assessed through year-round (October 2019–May 2021) TROPOMI and ground-based Pandora measurements. The average TCN over the SMA was comparable to that of major Chinese megacities, being consistently high (> 0.8 DU; Dobson Unit) during the daytime (10–17 local standard time). The autocorrelation scores of the Pandora-measured TCNs demonstrated high temporal variability attributed to the spatial inhomogeneity of NO₂ emissions within the SMA and near-surface advection. Accordingly, the adequate temporal collocation range for Pandora measurements for the intercomparison with the satellite sensors was considered to be ± 5 min to avoid significant uncertainty from the temporal variability (RMSE < 0.1 DU, R² > 0.96). TROPOMI showed better agreement with conventionally collocated Pandora measurements (0.73 < R² < 0.76, 26–29% negative bias) than the other two satellite sensors (OMI and OMPS) attributed to its highest spatial resolution. The application of the wind-based collocation revealed that the TROPOMI showed a greater negative bias on the upwind side, which was less affected by anthropogenic emissions from the urban area, than the downwind side, and the increasing distance of the TROPOMI pixel from Pandora was the most critical factor deteriorating the intercomparison scores. The FRESCO-S TROPOMI cloud algorithm update to FRESCO-wide yielded a general increase in TROPOMI TCN, especially in the partially cloudy pixels, leaving only 11% (downwind) and 29% (upwind) negative bias from coincident Pandora measurements. Furthermore, the wind-based collocation method revealed the spatial distribution pattern of NO_X (NO + NO₂) emissions in the SMA, with significant emission sources in the northeastern and southeastern sides of the ground-based Pandora site in Seoul.

This study used observational data and a chemical transport model to investigate the contributions of several factors to the recent change in air quality in China and South Korea from 2016 to 2020. We focused on observational data analysis, which could reflect the annual trend of emission reduction and adjust existing emission amounts to apply it into a chemical transport model. The observation data showed that the particulate matter (PM_2.5) concentrations during winter 2020 decreased by -23.4 % (-14.68 μg/m³) and - 19.5 % (-5.73 μg/m³) in China and South Korea respectively, compared with that during winter 2016. Meteorological changes, the existing national plan for a long-term emission reduction target, and unexpected events (i.e., Coronavirus disease 2019 (COVID-19) in China and South Korea and the newly introduced special winter countermeasures in South Korea from 2020) are considered major factors that may affect the recent change in air quality. The impact of different meteorological conditions on PM_2.5 concentrations was assessed by conducting model simulations by fixing the emission amounts; the results indicated changes of +7.6 % (+4.77 μg/m³) and + 9.7 % (+2.87 μg/m³) in China and South Korea, respectively, during winter 2020 compared to that during winter 2016. Due to the existing and pre-defined long-term emission control policies implemented in both countries, PM_2.5 concentration significantly decreased from winter 2016-2020 in China (-26.0 %; -16.32 μg/m³) and South Korea (-9.1 %; -2.69 μg/m³). The unexpected COVID-19 outbreak caused the PM_2.5 concentrations in China to decrease during winter 2020 by another -5.0 % (-3.13 μg/m³). In South Korea, the winter season special reduction policy, which was introduced and implemented in winter 2020, and the COVID-19 pandemic may have contributed to -19.5 % (-5.92 μg/m³) decrease in PM_2.5 concentrations.

In this study, the spatio-temporal variability of aerosol optical depth (AOD), total column ozone (TCO), and total column NO₂ (TCN) was identified over East Asia using long-term datasets from ground-based and satellite observations. Based on the statistical results, optimized spatio-temporal ranges for the validation study were determined with respect to the target materials. To determine both spatial and temporal ranges for the validation study, we confirmed that the observed datasets can be statistically considered as the same quantity within the ranges. Based on the thresholds of R²>0.95 (temporal) and R>0.95 (spatial), the basic ranges for spatial and temporal scales for AOD validation was within 30 km and 30 min, respectively. Furthermore, the spatial scales for AOD validation showed seasonal variation, which expanded the range to 40 km in summer and autumn. Because of the seasonal change of latitudinal gradient of the TCO, the seasonal variation of the north-south range is a considerable point. For the TCO validation, the north-south range is varied from 0.87° in spring to 1.05° in summer. The spatio-temporal range for TCN validation was 20 min (temporal) and 20–50 km (spatial). However, the nearest value of satellite data was used in the validation because the spatio-temporal variation of TCN is large in summer and autumn. Estimation of the spatio-temporal variability for respective pollutants may contribute to improving the validation of satellite products.