In-depth analysis of ambient air pollution changes due to the COVID-19 pandemic in the Asian Monsoon region.

The COVID-19 pandemic has given a chance for researchers and policymakers all over the world to study the impact of lockdowns on air quality in each country. This review aims to investigate the impact of the restriction of activities during the lockdowns in the Asian Monsoon region on the main criteria air pollutants. The various types of lockdowns implemented in each country were based on the severity of the COVID-19 pandemic. The concentrations of major air pollutants, especially particulate matter (PM) and nitrogen dioxide (NO2), reduced significantly in all countries, especially in South Asia (India and Bangladesh), during periods of full lockdown. There were also indications of a significant reduction of sulfur dioxide (SO2) and carbon monoxide (CO). At the same time, there were indications of increasing trends in surface ozone (O3), presumably due to nonlinear chemistry associated with the reduction of oxides of nitrogens (NOX). The reduction in the concentration of air pollutants can also be seen in satellite images. The results of aerosol optical depth (AOD) values followed the PM concentrations in many cities. A significant reduction of NO2 was recorded by satellite images in almost all cities in the Asian Monsoon region. The major reductions in air pollutants were associated with reductions in mobility. Pakistan, Bangladesh, Myanmar, Vietnam, and Taiwan had comparatively positive gross domestic product growth indices in comparison to other Asian Monsoon nations during the COVID-19 pandemic. A positive outcome suggests that the economy of these nations, particularly in terms of industrial activity, persisted during the COVID-19 pandemic. Overall, the lockdowns implemented during COVID-19 suggest that air quality in the Asian Monsoon region can be improved by the reduction of emissions, especially those due to mobility as an indicator of traffic in major cities.

Mapping nationwide concentrations of sulfate and nitrate in ambient PM2.5 in South Korea using machine learning with ground observation data.

Particulate matter (PM) is a major air pollutant in Northeast Asia, with frequent high PM episodes. To investigate the nationwide spatial distribution maps of PM2.5 and secondary inorganic aerosols in South Korea, prediction models for mapping SO42- and NO3- concentrations in PM2.5 were developed using machine learning with ground-based observation data. Specifically, the random forest algorithm was used in this study to predict the SO42- and NO3- concentrations at 548 air quality monitoring stations located within the representative radii of eight intensive air quality monitoring stations. The average concentrations of PM2.5, SO42-, and NO3- across the entire nation were 17.2 ± 2.8, 3.0 ± 0.6, and 3.4 ± 1.2 µg/m3, respectively. The spatial distributions of SO42- and NO3- concentrations in 2021 revealed elevated concentrations in both the western and central regions of South Korea. This result suggests that SO42- concentrations were primarily influenced by industrial activities rather than vehicle emissions, whereas NO3- concentrations were more associated with vehicle emissions. During a high PM2.5 event (November 19-21, 2021), the concentration of SO42- was primarily influenced by SOX emissions from China, while the concentration of NO3- was affected by NOX emissions from both China and Korea. The methodology developed in this study can be used to explore the chemical characteristics of PM2.5 with high spatiotemporal resolution. It can also provide valuable insights for the nationwide mitigation of secondary PM2.5 pollution.

Mapping the spatial distribution of primary and secondary PM2.5 in a multi-industrial city by combining monitoring and modeling results.

Industrial cities are strongly influenced by primary emissions of PM2.5 from local industries. In addition, gaseous precursors, such as sulfur oxides (SOX), nitrogen oxides (NOX), and volatile organic compounds (VOCs), emitted from industrial sources, undergo conversion into secondary inorganic and organic aerosols (SIAs and SOAs). In this study, the spatial distributions of primary and secondary PM2.5 in Ulsan, the largest industrial city in South Korea, were visualized. PM2.5 components (ions, carbons, and metals) and PM2.5 precursors (SO2, NO2, NH3, and VOCs) were measured to estimate the concentrations of secondary inorganic ions (SO42-, NO3-, and NH4+) and secondary organic aerosol formation potential (SOAFP). The spatial distributions of SIAs and SOAs were then plotted by combining atmospheric dispersion modeling, receptor modeling, and monitoring data. Spatial distribution maps of primary and secondary PM2.5 provide fundamental insights for formulating management policies in different districts of Ulsan. For instance, among the five districts in Ulsan, Nam-gu exhibited the highest levels of primary PM2.5 and secondary nitrate. Consequently, controlling both PM2.5 and NO2 emissions becomes essential in this district. The methodology developed in this study successfully identified areas with dominant contributions from both primary emissions and secondary formation. This approach can be further applied to prioritize control measures during periods of elevated PM levels in other industrial cities.

Roles of ambient temperature and relative humidity on the relationship between fine particulate matter and gaseous pollutants in the largest industrial city of Ulsan, South Korea.

Although meteorological conditions play a significant role in air pollution, research on their effects on the relationship between air pollutants is limited. In this study, trends of six criteria air pollutants were investigated from 15 air quality monitoring stations (AQMSs) in Ulsan, a multi-industrial city in South Korea, during 2015-2019. Unlike CO and O3, SO2, NO2, PM10, and PM2.5 showed statistically significant decreasing trends over the period. The companion relationship between PM2.5 and gaseous pollutants was evaluated by their correlations [R (PM2.5-GPs)]. R (PM2.5-NO2) was relatively high at almost all AQMSs, whereas high R (PM2.5-SO2) was observed near the petrochemical industrial complex, suggesting a great influence of local emissions (vehicles and industries). R (PM2.5-CO) and the standardized regression coefficients of CO obtained from the multiple linear regression model were the highest, indicating that combustion processes may significantly contribute to PM2.5. The effect of temperature (T) was more apparent on R (PM2.5-GPs) than that of relative humidity, with significant values under T > 15 °C. Moreover, R (PM2.5-O3) was positive at the T range of 12-18 °C, suggesting that reducing GPs emitted by industrial facilities during May-June may control PM2.5 and O3 in Ulsan. The methodology demonstrated in this study can be further used for a better understanding of the influences of environmental factors on the secondary PM2.5 formation from gaseous precursors and the R (PM2.5-O3).

Factors affecting recent PM2.5 concentrations in China and South Korea from 2016 to 2020.

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 (PM2.5) 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 PM2.5 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, PM2.5 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 PM2.5 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 PM2.5 concentrations.

Effects of emission trading scheme (ETS) on change rate of carbon emission.

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.

The Korea-United States Air Quality (KORUS-AQ) field study.

The Korea-United States Air Quality (KORUS-AQ) field study was conducted during May-June 2016. The effort was jointly sponsored by the National Institute of Environmental Research of South Korea and the National Aeronautics and Space Administration of the United States. KORUS-AQ offered an unprecedented, multi-perspective view of air quality conditions in South Korea by employing observations from three aircraft, an extensive ground-based network, and three ships along with an array of air quality forecast models. Information gathered during the study is contributing to an improved understanding of the factors controlling air quality in South Korea. The study also provided a valuable test bed for future air quality-observing strategies involving geostationary satellite instruments being launched by both countries to examine air quality throughout the day over Asia and North America. This article presents details on the KORUS-AQ observational assets, study execution, data products, and air quality conditions observed during the study. High-level findings from companion papers in this special issue are also summarized and discussed in relation to the factors controlling fine particle and ozone pollution, current emissions and source apportionment, and expectations for the role of satellite observations in the future. Resulting policy recommendations and advice regarding plans going forward are summarized. These results provide an important update to early feedback previously provided in a Rapid Science Synthesis Report produced for South Korean policy makers in 2017 and form the basis for the Final Science Synthesis Report delivered in 2020.

Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia.

In East Asia, air quality has been recognized as an important public health problem. In particular, the surface concentrations of air pollutants are closely related to human life. This study aims to develop models for estimating high spatial resolution surface concentrations of NO2 and O3 from TROPOspheric Monitoring Instrument (TROPOMI) data in East Asia. The machine learning was adopted by fusion of various satellite-based variables, numerical model-based meteorological variables, and land-use variables. Four machine learning approaches-Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boost (XGB), and Light Gradient Boosting Machine (LGBM)-were evaluated and compared with Multiple Linear Regression (MLR) as a base statistical method. This study also modeled the NO2 and O3 concentrations over the ocean surface (i.e., land model for scheme 1 and ocean model for scheme 2). The estimated surface concentrations were validated through three cross-validation approaches (i.e., random, temporal, and spatial). The results showed that the NO2 model produced R2 of 0.63-0.70 and normalized root-mean-square-error (nRMSE) of 38.3-42.2% and the O3 model resulted in R2 of 0.65-0.78 and nRMSE of 19.6-24.7% for scheme 1. The indirect validation based on the stations near the coastline for scheme 2 showed slight decrease (~0.3-2.4%) in nRMSE when compared to scheme 1. The contributions of input variables to the models were analyzed based on SHapely Additive exPlanations (SHAP) values. The NO2 vertical column density among the TROPOMI-derived variables showed the largest contribution in both the NO2 and O3 models.

Effects of emission trading schemes on corporate carbon productivity and implications for firm-level responses.

Since the South Korean government enacted the Emission Trading Scheme (ETS), companies have been striving to simultaneously improve productivity and reduce carbon emissions, which represent conflicting goals. We used firm-level emissions and corporate variables to investigate how ETS enactment has affected carbon productivity, which is a firm-level revenue created per unit of carbon emission. Results showed that firm-level carbon productivity increased significantly under the ETS, and such a trend was more evident for high-emission industries. We also found that companies with high carbon productivity were (1) profitable, (2) innovative, and (3) managed by CEOs with experience in environmental fields. These findings suggest that to achieve the conflicting goals of increasing corporate profits while reducing emissions, firms have to invest in green technologies, and such decisions are supported by green leadership. Our findings also have implications for corporate leadership; data highlight the importance of managing human resources and deploying investment policies to respond to ETS.

Light-absorption enhancement of black carbon in the Asian outflow inferred from airborne SP2 and in-situ measurements during KORUS-AQ.

We investigated the changes in the size distribution, coating thickness, and mass absorption cross-section (MAC) of black carbon (BC) with aging and estimated the light absorption enhancement (Eabs) in the Asian outflow from airborne in-situ measurements during 2016 KORUS-AQ campaign. The BC number concentration decreased, but mass mean diameter increased with increasing altitude in the West Coast (WC) and Seoul Metropolitan Area (SMA), reflecting the contrast between freshly emitted BC-containing particles at the surface and more aged aerosol associated with aggregation during vertical mixing and transport. Contradistinctively, BC number and mass size distributions were relatively invariant with altitude over the Yellow Sea (YS) because sufficiently aged BC from eastern China were horizontally transported to all altitudes over the YS, and there are no significant sources at the surface. The averaged inferred MAC of refractory BC in three regions reflecting differences in their size distributions increased to 9.8 ± 1.0 m2 g-1 (YS), 9.3 ± 0.9 m2 g-1 (WC), and 8.2 ± 0.9 m2 g-1 (SMA) as BC coating thickness increased from 20 nm to 120 nm. The absorption coefficient of BC calculated from the coating thickness and MAC were highly correlated with the filter-based absorption measurements with the slope of 1.16 and R2 of 0.96 at 550 nm, revealing that the thickly coated BC had a large MAC and absorption coefficient. The Eabs due to the inferred coatings was estimated as 1.0-1.6, which was about 30% lower than those from climate models and laboratory experiments, suggesting that the increase in the BC absorption by the coatings in the Asian outflow is not as large as calculated in the previous studies. Organics contributed to the largest Eabs accounting for 69% (YS), 61% (WC), and 64% (SMA). This implies that organics are largely responsible for the lensing effect of BC rather than sulfates in the Asian outflow.

Interannual Variation of the East Asia Jet Stream and Its Impact on the Horizontal Distribution of Aerosol in Boreal Spring.

Interannual variation of the aerosol optical depth (AOD) in East Asia has been investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) data and Modern Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data for 2000-2018. The data analysis focuses on boreal spring when Siberian biomass burning is at its seasonal maximum. The results indicate that the significant increase in organic and black carbon is primarily caused by emissions from biomass burning in East Asia, which leads to significant interannual variations in aerosol loading and pan-Pacific transport. The anomalous large-scale climate variability associated with the East Asia Jet Stream (EAJS) provides favorable conditions for increasing the AOD of organic and black carbon in Northeast Asia and may represent an underlying physical mechanism. When the EAJS shows greater weakening than normal, abnormal high-pressure anomalies are maintained in East Asia, which tend to drive warm advection over Northeast Asia. This warm advection expedites the melting of the Eurasian snow cover, which helps increase surface dryness in late spring and provides favorable conditions for biomass burning. The EAJS index can be predictable with statistical significance up to lead 1 month by the dynamical ensemble seasonal forecasts, suggesting a possible implementation of the empirical AOD forecasts using climate forecast models.

Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models.

Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground-level particulate matter (PM10 and PM2.5) concentrations. Since AOD products, however, are only provided under high-quality conditions, missing values usually exist in areas such as clouds, cloud shadows, and bright surfaces. In this study, spatially continuous AOD and subsequent PM10 and PM2.5 concentrations were estimated over East Asia using satellite- and model-based data and auxiliary data in a Random Forest (RF) approach. Data collected from the Geostationary Ocean Color Imager (GOCI; 8 times per day) in 2016 were used to develop AOD and PM models. Three schemes (i.e. G1, A1, and A2) were proposed for AOD modeling according to target AOD data (GOCI AOD and AERONET AOD) and the existence of satellite-derived AOD. The A2 scheme showed the best performance (validation R2 of 0.74 and prediction R2 of 0.73 when GOCI AOD did not exist) and the resultant AOD was used to estimate spatially continuous PM concentrations. The PM models with location information produced successful estimation results with R2 of 0.88 and 0.90, and rRMSE of 26.9 and 27.2% for PM10 and PM2.5, respectively. The spatial distribution maps of PM well captured the seasonal and spatial characteristics of PM reported in the literature, which implies the proposed approaches can be adopted for an operational estimation of spatially continuous AOD and PMs under all sky conditions.

Recent increase of surface particulate matter concentrations in the Seoul Metropolitan Area, Korea.

Recent changes of surface particulate matter (PM) concentration in the Seoul Metropolitan Area (SMA), South Korea, are puzzling. The long-term trend of surface PM concentration in the SMA declined in the 2000s, but since 2012 its concentrations have tended to incline, which is coincident with frequent severe hazes in South Korea. This increase puts the Korean government's emission reduction efforts in jeopardy. This study reports that interannual variation of surface PM concentration in South Korea is closely linked with the interannual variations of wind speed. A 12-year (2004-2015) regional air quality simulation was conducted over East Asia (27-km) and over South Korea (9-km) to assess the impact of meteorology under constant anthropogenic emissions. Simulated PM concentrations show a strong negative correlation (i.e. R = -0.86) with regional wind speed, implying that reduced regional ventilation is likely associated with more stagnant conditions that cause severe pollutant episodes in South Korea. We conclude that the current PM concentration trend in South Korea is a combination of long-term decline by emission control efforts and short-term fluctuation of regional wind speed interannual variability. When the meteorology-driven variations are removed, PM concentrations in South Korea have declined continuously even after 2012.

Evaluating the predictability of PM10 grades in Seoul, Korea using a neural network model based on synoptic patterns.

As of November 2014, the Korean Ministry of Environment (KME) has been forecasting the concentration of particulate matter with diameters ≤ 10 µm (PM10) classified into four grades: low (PM10 ≤ 30 µg m-3), moderate (30 < PM10 ≤ 80 µg m-3), high (80 < PM10 ≤ 150 µg m-3), and very high (PM10 > 150 µg m-3). The KME operational center generates PM10 forecasts using statistical and chemistry-transport models, but the overall performance and the hit rate for the four PM10 grades has not previously been evaluated. To provide a statistical reference for the current air quality forecasting system, we have developed a neural network model based on the synoptic patterns of several meteorological fields such as geopotential height, air temperature, relative humidity, and wind. Hindcast of the four PM10 grades in Seoul, Korea was performed for the cold seasons (October-March) of 2001-2014 when the high and very high PM10 grades are frequently observed. Because synoptic patterns of the meteorological fields are distinctive for each PM10 grade, these fields were adopted and quantified as predictors in the form of cosine similarities to train the neural network model. Using these predictors in conjunction with the PM10 concentration in Seoul from the day before prediction as an additional predictor, an overall hit rate of 69% was achieved; the hit rates for the low, moderate, high, and very high PM10 grades were 33%, 83%, 45%, and 33%, respectively. Our findings also suggest that the synoptic patterns of meteorological variables are reliable predictors for the identification of the favorable conditions for each PM10 grade, as well as for the transboundary transport of PM10 from China. This evaluation of PM10 predictability can be reliably used as a statistical reference and further, complement to the current air quality forecasting system.

Human-model hybrid Korean air quality forecasting system.

UNLABELLED: The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6µ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. IMPLICATIONS: The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.

Effect of anthropogenic sulphate aerosol in China on the drought in the western-to-central US.

In recent decades, droughts have occurred in the western-to-central United States (US), significantly affecting food production, water supplies, ecosystem health, and the propagation of vector-borne diseases. Previous studies have suggested natural sea surface temperature (SST) forcing in the Pacific as the main driver of precipitation deficits in the US. Here, we show that the aerosol forcing in China, which has been known to alter the regional hydrological cycle in East Asia, may also contribute to reducing the precipitation in the western-to-central US through atmospheric teleconnections across the Pacific. Our model experiments show some indications that both the SST forcing and the increase in regional sulphate forcing in China play a similar role in modulating the western-to-central US precipitation, especially its long-term variation. This result indicates that regional air quality regulations in China have important implications for hydrological cycles in East Asia, as well as in the US.

The efficacy of transforaminal epidural steroid injection by the conventional technique in far-lateral herniation of lumbar disc.

BACKGROUND: Owing to the anatomical difference between the far lateral herniation of the lumbar disc (FHLD) and the intraspinal herniation of lumbar disc (iHLD), the outcome of transforaminal epidural steroid injections (TFESI) in patients with FHLD seems to be different from that in patients with iHLD. However, few studies have evaluated the efficacy of TFESI in FHLD. OBJECTIVE: To evaluate and compare the efficacy of TFESI in FHLD and iHLD patients. STUDY DESIGN: A retrospective design. METHODS: There were 15 and 70 patients in the FHLD and iHLD groups, respectively. Patients received a fluoroscopically guided TFESI. Failure rates of TFESI were recorded, and questionnaires, including a visual analog scale (VAS) for leg pain and Oswestry disability index (ODI) were administered before the initial injection, at 2 weeks, 6 weeks, and 12 weeks after the injections. RESULTS: There was no failure for TFESI in the iHLD group, while 9 patients had to undergo alternative blocks in the FHLD group due to lancinating leg pain when the needle was advanced for TFESI. In the iHLD group, there was a statistically significant improvement in the VAS and ODI score 12 weeks after injection. Considering only successful cases of the FHLD group, significant improvement in the VAS and ODI score was also demonstrated in the FHLD group 12 weeks after injection. Moreover, there was no statistically significant difference of the VAS and ODI between the both groups. LIMITATIONS: A relatively small numbers of cases were included in the FHLD group. CONCLUSION: The current study suggests that an alternative needle placement technique for TFESI appears to be necessary for FHLD patients.

Sulfur deposition simulations over China, Japan, and Korea: a model intercomparison study for abating sulfur emission.

In response to increasing trends in sulfur deposition in Northeast Asia, three countries in the region (China, Japan, and Korea) agreed to devise abatement strategies. The concepts of critical loads and source-receptor (S-R) relationships provide guidance for formulating such strategies. Based on the Long-range Transboundary Air Pollutants in Northeast Asia (LTP) project, this study analyzes sulfur deposition data in order to optimize acidic loads over the three countries. The three groups involved in this study carried out a full year (2002) of sulfur deposition modeling over the geographic region spanning the three countries, using three air quality models: MM5-CMAQ, MM5-RAQM, and RAMS-CADM, employed by Chinese, Japanese, and Korean modeling groups, respectively. Each model employed its own meteorological numerical model and model parameters. Only the emission rates for SO(2) and NO(x) obtained from the LTP project were the common parameter used in the three models. Three models revealed some bias from dry to wet deposition, particularly the latter because of the bias in annual precipitation. This finding points to the need for further sensitivity tests of the wet removal rates in association with underlying cloud-precipitation physics and parameterizations. Despite this bias, the annual total (dry plus wet) sulfur deposition predicted by the models were surprisingly very similar. The ensemble average annual total deposition was 7,203.6 ± 370 kt S with a minimal mean fractional error (MFE) of 8.95 ± 5.24 % and a pattern correlation (PC) of 0.89-0.93 between the models. This exercise revealed that despite rather poor error scores in comparison with observations, these consistent total deposition values across the three models, based on LTP group's input data assumptions, suggest a plausible S-R relationship that can be applied to the next task of designing cost-effective emission abatement strategies.

A New Anterior Approach for Fluoroscopy-guided Suprascapular Nerve Block - A Preliminary Report -.

BACKGROUND: The aim of the study was to investigate the feasibility of fluoroscopy-guided anterior approach for suprascapular nerve block (SSNB). METHODS: Twenty patients with chronic shoulder pain were included in the study. All of the nerve blocks were performed with patients in a supine position. Fluoroscopy was tilted medially to obtain the best view of the scapular notch (medial angle) and caudally to put the base of coracoid process and scapular spine on same line (caudal angle). SSNB was performed by introducing a 100-mm, 21-gauge needle to the scapular notch with tunnel view technique. Following negative aspiration, 1.0 ml of contrast was injected to confirm the scapular notch, and 1 % mepivacaine 2 ml was slowly injected. The success of SSNB was assessed by numerical rating scale (NRS) before and after the block. RESULTS: The average NRS was decreased from 4.8 ± 0.6 to 0.6 ± 0.5 after the procedure (P < 0.05). The best view of the scapular notch was obtained in a medial angle of 15.1 ± 2.2 (11-19°) and a caudal angle of 15.4 ± 1.7° (12-18°). The average distance from the skin to the scapular notch was 5.8 ± 0.6 cm. None of the complications such as pneumothorax, intravascular injection, and hematoma formation was found except one case of partial brachial plexus block. CONCLUSIONS: SSNB by fluoroscopy-guided anterior approach is a feasible technique. The advantage of using a fluoroscopy resulted in an effective block with a small dose of local anesthetics by an accurate placement of a tip of needle in the scapular notch while avoiding pneumothorax.

Effects of human amniotic membrane grafts combined with marrow mesenchymal stem cells on healing of full-thickness skin defects in rabbits.

We have investigated the wound-healing effects of mesenchymal stem cells (MSCs) in combination with human amniotic membrane (HAM) when grafted into full-thickness skin defects of rabbits. Five defects in each of four groups were respectively treated with HAM loaded with autologous MSCs (group A), HAM loaded with allologous MSCs (group B), HAM with injected autologous MSCs (group C), and HAM with injected allologous MSCs (group D). The size of the wounds was calculated for each group at 7, 12, and 15 days after grafting. The wounds were subsequently harvested at 25 days after grafting. Sections stained with hematoxylin and eosin were used to determine the quality of wound healing, as based on the characteristics and amount of granulated tissue in the epidermal and dermal layers. Groups A and B showed the most pronounced effect on wound closure, with statistically significant improvement in wound healing being seen on post-operative days 7, 12, and 15. Although a slight trend toward improved wound healing was seen in group A compared with group B, no statistically significant difference was found at any time point between the two groups. Histological examination of healed wounds from groups A and B showed a thin epidermis with mature differentiation and collagen bundle deposition plus recovered skin appendages in the dermal layer. In contrast, groups C and D showed thickened epidermis with immature epithelial cells and increased fibroblast proliferation with only partially recovered skin appendages in the dermal layer. Thus, the graft of HAM loaded with MSCs played an effective role during the healing of skin defects in rabbits, with no significant difference being observed in wound healing between autologous and allologous MSC transplantation.