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Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric column density of NO2 (TCDNO2) from the Geostationary Environment Monitoring Spectrometer (GEMS) during 2021–2023. Initially, we validate GEMS TCDNO2 against Pandora observations and compare its accuracy with measurements from the TROPOspheric Monitoring Instrument (TROPOMI). GEMS displays acceptable accuracy in TCDNO2 measurements, with a correlation coefficient (R) of 0.68, an index of agreement (IOA) of 0.79, and a mean absolute bias (MAB) of 5.73321 × 1015 molecules/cm2, though it is not highly accurate. The evaluation showcases moderate to high accuracy of GEMS TCDNO2 across all Pandora stations, with R values spanning from 0.46 to 0.80. Comparing TCDNO2 from GEMS and TROPOMI at TROPOMI overpass time shows satisfactory performance of GEMS TCDNO2 measurements, achieving R, IOA, and MAB values of 0.71, 0.78, and 6.82182 × 1015 molecules/cm2, respectively. However, these figures are overshadowed by TROPOMI’s superior accuracy, which reports R, IOA, and MAB values of 0.81, 0.89, and 3.26769 × 1015 molecules/cm2, respectively. While GEMS overestimates TCDNO2 by 52 % at TROPOMI overpass time, TROPOMI underestimates it by 9 %. The deep learning bias corrected GEMS TCDNO2 (GEMS-DL) demonstrates a marked enhancement in the accuracy of original GEMS TCDNO2 measurements. The GEMS-DL product improves R from 0.68 to 0.88, IOA from 0.79 to 0.93, MAB from 5.73321 × 1015 to 2.67659 × 1015 molecules/cm2, and reduces MAB percentage (MABP) from 64 % to 30 %. This represents a significant reduction in bias, exceeding 50 %. Although the original GEMS product overestimates TCDNO2 by 28 %, the GEMS-DL product remarkably minimizes this error, underestimating TCDNO2 by a mere 1 %. Spatial cross-validation across Pandora stations shows a significant reduction in MABP, from a range of 45 %-105.6 % in original GEMS data to 24 %-59 % in GEMS-DL.

Uncertainty in ammonia (NH3) emissions causes the inaccuracy of fine particulate matter simulations, which is associated with human health. To address this uncertainty, in this work, we employ the iterative finite difference mass balance (iFDMB) technique to revise NH3 emissions over East Asia using the Cross-track Infrared Sounder (CRIS) satellite for July, August, and September 2019. Compared to the emissions, the revised NH3 emissions show an increase in China, particularly in the North China Plain (NCP) region, corresponding to agricultural land use in July, August, and September and a decrease in South Korea in September. The enhancement in NH3 emissions resulted in a remarkable increase in concentrations of NH3 by 5 ppb. in July and September, there is an increase in ammonium (NH4+) and nitrate (NO3-) concentrations by 5 μg/m3, particularly in the NCP region, while in August, both NH4+ and NO3- concentrations exhibit a decrease. For sulfate (SO42-), in August and September, the concentrations decreased over most regions of China and Taiwan, as a result of the production of ammonium sulfate; increased concentrations of SO42-, however, were simulated over South Korea, Japan, and the southern region of Chengdu, caused by higher relative humidity (RH). In contrast, during the month of July, our simulations showed an increase in SO42- concentrations over most regions of China. To gain a more comprehensive understanding, we defined a sulfur conversion ratio (SCR=(Spost-Sprior)/(SO42-post-SO42-prior)), which explains how changes in sulfur in the gas phase affect changes in sulfate concentrations. A subsequent sensitivity analysis performed in this study indicated the same relationship between changes in ammonia and its effect on inorganic fine particulate matter (PM2.5). This study highlights the challenge of controlling and managing inorganic PM2.5 and indicates that reducing the emissions of air pollutants do not necessarily lead to a reduction in their concentrations.

Abstract Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.

Abstract To improve the representation of convective mixing of atmospheric pollutants in the presence of clouds, we developed a convection module based on Kain and Fritsch (KF) method and implemented it in the Community Multiscale Air Quality model. The KF‐convection method is a mass‐flux‐based model that accounts for updraft flux, downdraft flux, entrainment, detrainment, and the subsidence effect. The method is consistent with the convection parametrization of the meteorology model. We apply the KF‐convection model to an idealized case and to a reference setup prepared for East Asia during the KORUS‐AQ campaign period to investigate its impact on carbon monoxide (CO) concentration at various atmospheric altitudes. We investigate the impact of KF‐convection on the horizontal distribution of CO concentrations by comparing it to aircraft measurements and the MOPITT CO column. We further discuss two types of impacts of KF‐convection: the direct impact caused by vertical movement of CO concentrations by updraft or downdraft and the indirect impact caused by transport of lifted CO concentrations to another region. May 12 saw a high indirect impact originating from the Shanghai region at higher altitudes and a high direct impact of updraft fluxes at 1 km altitude. However, May 26 revealed an immense updraft increasing higher altitude concentrations (up to 40 ppbv) and diverse indirect impacts over the region of the study (±50 ppbv). The overall comparison shows a strong connection between differences in the amount of concentration caused by the direct impact at each altitude with the presence of an updraft at that altitude. The developed model can be employed in large domains (i.e., East Asia, Europe, North America, and Northern Hemisphere) with sub‐grid scale cloud modeling to include the impacts of convection.

To fulfil the strong need for monitoring seasonal difference of velocity over the Greenland ice sheet (GrIS), we developed an approach based on the fusion of multiple temporal and multi sensor remote sensing observations. We used spaceborne synthetic aperture radar (SAR) and optical data over the Russell glacier in southwestern Greenland. Firstly, offset tracking and InSAR time series analyses were employed for deriving the glacier’s velocity in planimetric and line of sight (LOS) directions. Next, a three-dimensional (3D) decomposition was applied for estimating the 3D velocity vectors of the glacier. Once the reliability of the results was validated, a numerical ice sheet model (ISM) was further applied to derive the modelled basal friction in different seasons. We concluded that the overall data integration using multiple open-accessed satellite image employed in this study demonstrated a decent method to analyze seasonal velocity difference of the Russell glacier. Based on the proposed monitoring strategy, it is of great potential to further investigate other polar and inland glaciers with various remote sensed data.

The Gobi Desert in southern Mongolia has been identified as the strongest dust storm hot spot threatening public health and socio-economic activities in East Asian countries. Despite its significance, the complete mapping of the aeolian surface erosion in southern Mongolia remains unresolved because of extensive region of interest cannot be interpreted easily by conventional approaches. Therefore, in this study, we built a mapping scheme to define on going aeolian erosion and applied it over the southern Gobi Desert. The remote sensing approach applied here was based on an interferometric synthetic-aperture radar (InSAR) time series technique. A number of Sentinel-1 InSAR pairs that generate phase coherences for a certain period were synthesized via the means of principle component analysis to extract the topographic persistence indicative of surface erosion rates. Validation analyses performed through inter-comparisons of phase coherence signals over landmark areas and residuals between global digital elevation models confirmed the reliability of outputs. The results revealed geological lineaments in southern Mongolia confining sandy deposits and the sediment transportation pathways. Apparently, such bounded eolian deposits and transportation mechanisms within geological structures have significantly contributed to dust generation in the Gobi Desert over southern Mongolia. In addition, this study demonstrated that the newly developed InSAR time series technique has great potential for identifying intensified land erosion and dust sources.

Diesel emissions from freight transportation activities are a key threat to public health. This study examined the air quality and public health impacts of projected freight-related emissions in 2050 over the continental United States. Three emission scenarios were considered: (1) a projected business-as-usual socioeconomic growth with freight fleet turnover and stringent emission control (CTR); (2) the application of a carbon pricing climate policy (PO); and (3) further technology improvements to eliminate high-emitting conditions in the truck fleet (NS). The PO and NS cases are superimposed on the CTR case. Using a WRF-SMOKE-CMAQ-BenMAP modeling framework, we quantified the impacts of diesel fine particulate matter (PM2.5) emissions change on air quality, health, and economic benefits. In the CTR case, we simulate a widespread reduction of PM2.5 concentrations, between 0.5 and 1.5 μg m−3, comparing to a base year of 2011. This translates into health benefits of 3600 (95% CI: 2400–4800) prevented premature deaths, corresponding to $38 (95% CI: $3.5–$100) billion. Compared to CTR case, the PO case can obtain ~9% more health benefits nationally, however, climate policy also affects the health outcomes regionally due to transition of demand from truck to rail; regions with fewer trucks could gain in health benefits, while regions with added rail freight may potentially experience a loss in health benefits due to air quality degradation. The NS case provides substantial additional benefits (~20%). These results support that a combination of continuous adoption of stringent emission standards and strong improvements in vehicle technology are necessary, as well as rewarding, to meet the sustainable freight and community health goals. States and metropolitan areas with high population density and usually high freight demand and emissions can take more immediate actions, such as accelerating vehicle technology improvements and removing high-emitting trucks, to improve air quality and health benefits. Keywords: Freight transportation, Diesel emissions, Air quality, Public health, Particulate matter

Entrepreneurial firms in their early stages cannot finance their own capital sufficiently; hence, external investment is crucial for their survival. Angels, venture capital, and governmental support have played key roles in entrepreneurial financing. Accelerators, a new type of early-stage investor, have been rapidly growing in recent years, and have attracted strong attention. They provide financial support to entrepreneurial firms in their early stages along with mentorship, education, and networking services. However, the advantages of accelerators over existing funding avenues have yet to be proven. Therefore, this study analyzes the behavior and performance of accelerators, angels, and venture capital. We used 30,523 investment data regarding accelerators, angels, and venture capital from the CrunchBase database. By conducting multiple regression and survival analyses, we found that the performance of accelerators differs from that of venture capital, but is similar to that of angels. While accelerators’ investees perform well post funding, venture capitals’ investees perform well in terms of survivability. This study empirically verifies accelerator-related qualitative research. Additionally, we believe our results will contribute to future accelerator-related research and policy.

The unprecedentedly strong 2016 Gyeongju and 2017 Pohang earthquakes on the Korean Peninsula aroused public concern regarding seismic hazards previously considered improbable. In this study, we investigated the effects of recent seismic activity close to the epicenters of both earthquakes in the heavy industrial complex of Ulsan. This was performed using Sentinel-1 InSAR time series data combined with on-site GPS observations and background GIS data. The interpretations revealed ongoing topographic deformation of a fault line and surrounding geological units of up to 15 mm/year. Postseismic migrations through the fault line, coupled with the two earthquakes, were not significant enough to pose an immediate threat to the industrial facilities or the residential area. However, according to InSAR time series analyses and geophysical modelling, strain from the independent migration trend of a fault line and eventual/temporal topographic changes caused by potential seismic friction could threaten precisely aligned industrial facilities, especially chemical pipelines. Therefore, we conducted probabilistic seismic hazard and stress change analyses over surrounding areas of industrial facilities employing modelled fault parameters based on InSAR observations. These demonstrate the potential of precise geodetic survey techniques for constant monitoring and risk assessment of heavy industrial complexes against seismic hazards by ongoing fault activities.

Mt. Baekdu is a volcano near the North Korea-Chinese border that experienced a few destructive eruptions over the course of its history, including the well-known 1702 A.D eruption. However, signals of unrest, including seismic activity, gas emission and intense geothermal activity, have been occurring with increasing frequency over the last few years. Due to its close vicinity to a densely populated area and the high magnitude of historical volcanic eruptions, its potential for destructive volcanic activity has drawn wide public attention. However, direct field surveying in the area is limited due to logistic challenges. In order to compensate for the limited coverage of ground observations, comprehensive measurements using remote sensing techniques are required. Among these techniques, Differential Interferometric SAR (DInSAR) analysis is the most effective method for monitoring surface deformation and is employed in this study. Through advanced atmospheric error correction and time series analysis, the accuracy of the detected displacements was improved. As a result, clear uplift up to 20 mm/year was identified around Mt. Baekdu and was further used to estimate the possible deformation source, which is considered as a consequence of magma and fault interaction. Since the method for tracing deformation was proved feasible, continuous DInSAR monitoring employing upcoming SAR missions and advanced error regulation algorithms will be of great value in monitoring comprehensive surface deformation over Mt. Baekdu and in general world-wide active volcanoes.

The article presents the discussion on PAR estimates indicate that a variety of physiological and ecological conditions can control the onset of spring blooms. Topics include satellite observations have started to report optimal light intensities for phytoplankton blooming in various physiological and ecological environments; and investigating the optimal light intensities required for phytoplankton spring bloom development in the East Sea (ES).