Prediction of groundwater quality index to assess suitability for drinking purpose using averaged neural network and geospatial analysis.

Groundwater quality management is pivotal for ensuring public health and ecological resilience. However, the conventional water quality indices often face challenges related to parameter selection, geographic coverage, and scalability. The integration of machine learning and spatial analysis represents a promising methodological shift, allowing for high accuracy and adaptive management strategies. The Safe Groundwater Project in Unsupplied Areas (2017-2020) employed a comprehensive Groundwater Quality Index (GQI) to evaluate potable groundwater quality across South Korea, utilizing a large dataset comprising 28 water quality parameters and 3552 wells. This study revealed that over 50 % of the evaluated wells (Total 8326 wells) were inappropriate as sources of drinking water, indicating a pressing need for policy revision. The averaged neural network model achieved a high predictive accuracy of approximately 95 % for GQI grades, outperforming other classification models. The introduction of 2D spatial analysis in conjunction with machine learning algorithms notably increased the predictive accuracy for unevenly distributed groundwater samples. Moreover, this combined approach enabled the intuitive visualization of groundwater vulnerability across various regions, which can inform targeted interventions for effective resource allocation and management. This research represents a methodologically robust, interdisciplinary approach that holds significant implications for a framework for future groundwater quality management and vulnerability assessment.

Color Image Generation from Range and Reflection Data of LiDAR.

Recently, it has been reported that a camera-captured-like color image can be generated from the reflection data of 3D light detection and ranging (LiDAR). In this paper, we present that the color image can also be generated from the range data of LiDAR. We propose deep learning networks that generate color images by fusing reflection and range data from LiDAR point clouds. In the proposed networks, the two datasets are fused in three ways-early, mid, and last fusion techniques. The baseline network is the encoder-decoder structured fully convolution network (ED-FCN). The image generation performances were evaluated according to source types, including reflection data-only, range data-only, and fusion of the two datasets. The well-known KITTI evaluation data were used for training and verification. The simulation results showed that the proposed last fusion method yields improvements of 0.53 dB, 0.49 dB, and 0.02 in gray-scale peak signal-to-noise ratio (PSNR), color-scale PSNR, and structural similarity index measure (SSIM), respectively, over the conventional reflection-based ED-FCN. Besides, the last fusion method can be applied to real-time applications with an average processing time of 13.56 ms per frame. The methodology presented in this paper would be a powerful tool for generating data from two or more heterogeneous sources.

Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET.

In this paper, a modified encoder-decoder structured fully convolutional network (ED-FCN) is proposed to generate the camera-like color image from the light detection and ranging (LiDAR) reflection image. Previously, we showed the possibility to generate a color image from a heterogeneous source using the asymmetric ED-FCN. In addition, modified ED-FCNs, i.e., UNET and selected connection UNET (SC-UNET), have been successfully applied to the biomedical image segmentation and concealed-object detection for military purposes, respectively. In this paper, we apply the SC-UNET to generate a color image from a heterogeneous image. Various connections between encoder and decoder are analyzed. The LiDAR reflection image has only 5.28% valid values, i.e., its data are extremely sparse. The severe sparseness of the reflection image limits the generation performance when the UNET is applied directly to this heterogeneous image generation. In this paper, we present a methodology of network connection in SC-UNET that considers the sparseness of each level in the encoder network and the similarity between the same levels of encoder and decoder networks. The simulation results show that the proposed SC-UNET with the connection between encoder and decoder at two lowest levels yields improvements of 3.87 dB and 0.17 in peak signal-to-noise ratio and structural similarity, respectively, over the conventional asymmetric ED-FCN. The methodology presented in this paper would be a powerful tool for generating data from heterogeneous sources.

Efficacy of in situ well-based denitrification bio-barrier (WDB) remediating high nitrate flux in groundwater near a stock-raising complex.

This study assessed the feasibility of an in situ well-based denitrification bio-barrier (WDB) for managing groundwater contaminated with high-strength nitrate. To evaluate the efficacy of WDB using fumarate as a carbon source and/or electron donor, three sequential single-well push-pull tests (SWPPTs) were conducted at six test sites. The values of the isotope enrichment factor (ɛ) ranging from -6.5‰ to -22.6‰ and the detection and degradation of nitrite and nitrous oxide confirmed complete in situ denitrification of nitrate to nitrogen gas. The ratio of the first-order rate coefficient of fumarate to nitrate (k1,fum/k1,NO3) was obtained to estimate the amount and frequency of fumarate injection for the effective design of WDB. At three sites, the ratios ranged from 0.67 to 0.80, while the other two sites showed higher ratios of 2.97 and 2.20 than the theoretical values and significant amounts of sulfate reduction, theoretically equivalent to 6.5% of total fumarate consumption. Considering the theoretical mole ratio of fumarate to nitrate of 0.98, the amount and frequency of fumarate injection is site specific. During the operating WDB, the average annual nitrate mass degraded (95% CI) was 2.2 ± 1.0 kg N/yr/well. The amount of N reduced by one well of WDB is equivalent to treating 110 m3 of groundwater at 30 mg N/L to the level of 10 mg N/L for one year. WDB would be an effective remediation option for managing high nitrate flux in groundwater.

Traffic Light Recognition Based on Binary Semantic Segmentation Network.

A traffic light recognition system is a very important building block in an advanced driving assistance system and an autonomous vehicle system. In this paper, we propose a two-staged deep-learning-based traffic light recognition method that consists of a pixel-wise semantic segmentation technique and a novel fully convolutional network. For candidate detection, we employ a binary-semantic segmentation network that is suitable for detecting small objects such as traffic lights. Connected components labeling with an eight-connected neighborhood is applied to obtain bounding boxes of candidate regions, instead of the computationally demanding region proposal and regression processes of conventional methods. A fully convolutional network including a convolution layer with three filters of (1 × 1) at the beginning is designed and implemented for traffic light classification, as traffic lights have only a set number of colors. The simulation results show that the proposed traffic light recognition method outperforms the conventional two-staged object detection method in terms of recognition performance, and remarkably reduces the computational complexity and hardware requirements. This framework can be a useful network design guideline for the detection and recognition of small objects, including traffic lights.

Asymmetric Encoder-Decoder Structured FCN Based LiDAR to Color Image Generation.

In this paper, we propose a method of generating a color image from light detection and ranging (LiDAR) 3D reflection intensity. The proposed method is composed of two steps: projection of LiDAR 3D reflection intensity into 2D intensity, and color image generation from the projected intensity by using a fully convolutional network (FCN). The color image should be generated from a very sparse projected intensity image. For this reason, the FCN is designed to have an asymmetric network structure, i.e., the layer depth of the decoder in the FCN is deeper than that of the encoder. The well-known KITTI dataset for various scenarios is used for the proposed FCN training and performance evaluation. Performance of the asymmetric network structures are empirically analyzed for various depth combinations for the encoder and decoder. Through simulations, it is shown that the proposed method generates fairly good visual quality of images while maintaining almost the same color as the ground truth image. Moreover, the proposed FCN has much higher performance than conventional interpolation methods and generative adversarial network based Pix2Pix. One interesting result is that the proposed FCN produces shadow-free and daylight color images. This result is caused by the fact that the LiDAR sensor data is produced by the light reflection and is, therefore, not affected by sunlight and shadow.

Health risk assessment of uranium intake from private residential drinking groundwater facilities based on geological characteristics across the Republic of Korea.

Groundwater contributes to an average of 8 % of the total water source capacity in the Republic of Korea. Hence, private residential households in rural areas in Korea are still using groundwater for drinking without any regular water quality inspection. This can increase the risk of exposure to natural radionuclides like uranium through drinking groundwater. This study investigated the uranium level in drinking groundwater all over the country by analyzing 11,451 samples from private residential drinking groundwater facilities and compared the exposure amount and its associated carcinogenic and non-carcinogenic risk based on the geological characteristics of the aquifer. Results yield that although the average hazard quotient (HQ) and excess cancer risk (ECR) of exposure to natural uranium through drinking groundwater were respectively below 1 and 1 × 10-6 and do not indicate a potential health hazard, significantly high HQ and ECR up to respectively 70 and 4 × 10-4 in samples where the aquifer is the Jurassic granite observed. Accordingly, regular water quality investigation and onsite treatment methods are required to provide healthy drinking water in such areas.

Nitrate sources, timing, and pathways of a permeable volcanic aquifer system with mixed land use in Jeju Island, South Korea.

The occurrence of various N-related human activities increases the difficulty in distinguishing the major sources of NO3- contamination in groundwater, especially in areas with mixed land uses. In addition, the estimation of the timing and pathways of NO3- is necessary to better understand the processes of NO3- contamination in the subsurface aquifer system. This study applied environmental tracers, such as stable isotopes and age tracers (δ15N and δ18O of NO3-, δ11B, chlorofluorocarbons, and 3H), to elucidate the sources, timing, and pathways of NO3- contamination in the groundwaters of the Hanrim area, which has suffered from illegal disposal of livestock wastes since the 1980s, and also characterizes them based on mixed N-contaminant sources such as chemical fertilizers and sewage. The combined use of δ15N and δ11B overcame the limitation of using only NO3- isotopes for the identification of overlapping sources of N and successfully identified the major source of N as livestock wastes. The lumped parameter model (LPM) estimated the binary mixing of the young (age: 23-40 years, NO3-N: 2.55-15.10 mg/L) and old (age: >60 years, NO3-N: <3 mg/L) groundwaters, and explained their age mixing behaviors. The young groundwater was highly affected by livestock-derived N loading during 1987-1998, which coincides with the period of improper dumping of livestock wastes. Furthermore, the young groundwater with elevated NO3-N followed the historical NO3-N curves with younger ages (6 and 16 years) than those derived from the LPM, suggesting the possibility of faster inflows of livestock wastes through the permeable volcanic structures. This study demonstrated that a comprehensive understanding of NO3- contamination processes can be achieved using environmental tracer methods, which enables the efficient management of groundwater resources in areas with multiple N sources.

Characterization of seasonal behaviors of naturally occurring radioactive materials in a groundwater borehole using an in situ monitoring system.

Understanding the temporal behaviors of naturally occurring radioactive materials is important for safeguarding groundwater as a secure water resource for drinking, agriculture, and industry usage. This study reports the vertical profiles of 238U concentration and 222Rn activity and the management of in situ monitoring systems during intensive field sampling of a national groundwater-monitoring borehole for seven years (2015-2021). The aim was to capture the seasonal characteristics of the 238U concentrations and 222Rn activity. Both factors were low in the rainy season and high in the winter season, reflecting the dilution effect of rainfall recharge. The 238U and 222Rn behaviors were associated with water-rock interactions of calcite dissolution in fracture zones filled with carbonate minerals. Furthermore, multilayer perceptron models estimated the 238U concentration and 222Rn activity with reasonable regression and classification accuracy. Hydrometeorological indicators (temperature and groundwater-level fluctuations) were more important estimators of 238U concentration and 222Rn activity than geochemical process indicators. The regression accuracy performance was higher at deeper sampling depths, where seasonality in the 238U and 222Rn behaviors dominated. From the predicted distributions of 238U concentrations and 222Rn activities, we could estimate the ranges of 238U concentrations and 222Rn activities emerging from groundwater boreholes. High exposure threats from 238U and 222Rn during groundwater usage were found in the winter season. When the multilayer perceptron models use the entire in situ monitoring data at refined temporal resolution, we can quickly determine the naturally occurring radioactive materials and further develop the national groundwater-monitoring borehole equipped with the in-situ monitoring system, supplementing the occasionally obtained field-measurement data.

A supervised machine learning approach to discriminate the effect of carcass leachate on shallow groundwater quality around on-farm livestock mortality burial sites.

The evaluation of leachate leakage at livestock mortality burial sites is challenging, particularly when groundwater is previously contaminated by agro-livestock farming. Supervised machine learning was applied to discriminate the impacts of carcass leachate from pervasive groundwater contamination in the following order: data labeling, feature selection, synthetic data generation, and classification. Physicochemical data of 359 water samples were collected from burial pits (LC), monitoring wells near pits (MW), pre-existing shallow household wells (HW), and background wells with pervasive contamination (BG). A linear classification model was built using two representative groups (LC and BG) affected by different pollution sources as labeled data. A classifier was then applied to assess the impact of leachate leakage in MW and HW. As a result, leachate impacts were observed in 40% of MW samples, which indicates improper construction and management of some burial pits. Leachate impacts were also detected in six HW samples, up to 120 m downgradient, within one year. The quantitative decision-making tool to diagnose groundwater contamination with leachate leakage can contribute to ensuring timely responses to leakage. The proposed machine learning approach can also be used to improve the environmental impact assessment of water pollution by improper disposal of organic waste.

Evaluation of residual level and distribution characteristics of organochlorine pesticides in agricultural soils in South Korea.

In this study, we evaluated the residual levels and distribution characteristics of 10 organochlorine pesticides (OCPs) in the soil of agricultural lands (paddy field, vegetable field, and orchard, n = 150) nationwide. As a result of the investigation, 3-10 types of OCPs were detected in combination in 144 locations (96%) of the total 150 locations. Among those OCPs, dichlorodiphenyltrichloroethane (DDT) and endosulfan were detected in high amounts. The geometric mean concentrations of DDT and endosulfan were ∑DDT 0.31 ng g-1 (ND-2187.18 ng g-1) and ∑endosulfan 0.33 ng g-1 (ND-312.68 ng g-1), respectively. OCP concentrations were generally higher in orchards than in other agricultural fields due to a large amount of pesticides used. DDT in two orchards was detected at 2187.18 ng g-1 and 1061.78 ng g-1, exceeding the levels stated in the Canadian DDT Soil Quality Guidance (SQG, 700 ng g-1). The result of applying the isomer ratio to evaluate the recent or past use of DDT, endosulfan, and hexachlorocyclohexane (HCH), which had a high detection rate, showed that it could be attributable to historical usage. DDT and HCH showed statistically similar residual distribution patterns; however, endosulfan did not show a clear correlation with DDT and HCH due to the effect of its recent use. Thus, the results of evaluation of OCP residual levels and distribution characteristics can be used for the soil management policy of OCPs in agricultural lands in Korea.

Uranium Concentrations in Private Wells of Potable Groundwater, Korea.

Uranium (U) is one of the typical naturally occurring radioactive elements enriched in groundwater through geological mechanisms, thereby bringing about adverse effects on human health. For this reason, some countries and the World Health Organization (WHO) regulate U with drinking water standards and monitor its status in groundwater. In Korea, there have been continuous investigations to monitor and manage U in groundwater, but they have targeted only public groundwater wells. However, the features of private wells differ from public ones, particularly in regard to the well's depth and diameter, affecting the U distribution in private wells. This study was initiated to investigate U concentrations in private groundwater wells for potable use, and the significant factors controlling them were also elucidated through statistical methods. The results obtained from the analyses of 7036 groundwater samples from private wells showed that the highest, average, and median values of U concentrations were 1450, 0.4, and 4.0 µg/L, respectively, and 2.1% of the wells had U concentrations exceeding the Korean and WHO standard (30 µg/L). In addition, the U concentrations were highest in areas of the Jurassic granite, followed by Quaternary alluvium and Precambrian metamorphic rocks. A more detailed investigation of the relationship between U concentration and geology revealed that the Jurassic porphyritic granite, mainly composed of Daebo granite, showed the highest U contents, which indicated that U might originate from uraninite (UO2) and coffinite (USiO4). Consequently, significant caution should be exercised when using the groundwater in these geological areas for potable use. The results of this study might be applied to establish relevant management plans to protect human health from the detrimental effect of U in groundwater.

Performance Pressure as an Antecedent and Authentic Leadership as a Moderator of the Relationship Between Co-worker Undermining and Psychological Capital.

As a component of organizational aggression, co-worker undermining erodes the well-being of the victims and the sustainability of the organization. Drawing on conservation of resources theory, this study identified the negative impact of co-worker undermining on the victim's psychological capital, and empirically examined the influence of performance pressure as an antecedent and of authentic leadership as a moderator to suggest approaches to minimize this negative impact. A total of 485 subordinate employees from 10 organizations in South Korea completed a questionnaire survey. To prevent common method bias, the survey was designed to recruit participants from multiple organizations and was conducted in two waves. First, the results revealed that performance pressure had a positive relationship with the perception of co-worker's undermining. Second, this perception of co-worker undermining had a negative influence on the victim's psychological capital. Third, authentic leadership had the moderating effect of decreasing the negative relationship between co-worker undermining and psychological capital. Furthermore, authentic leadership moderated the mediating relationship between the performance pressure and psychological capital through co-worker's undermining. These findings suggest that the level of performance pressure should be managed in advance so as not to reach excessive levels and the psychological capital of victims should be preserved through authentic leadership to minimize the negative impact of co-worker undermining.

Characterizing land use effect on shallow groundwater contamination by using self-organizing map and buffer zone.

Nitrate-nitrogen (NO3-N) contamination in groundwater is a major problem of drinking and domestic waters in rural areas. This study revealed the influence of land use type on shallow alluvial groundwaters in a typical rural area in South Korea by applying a self-organizing map (SOM), principal component analysis (PCA), and hierarchical cluster analysis (HCA). The uncertainty of spatial information on land use was improved by using a buffer zone of the average influence radius of 32.65 m surrounding wells. Two major land-use types, forests (44.9%) and rice fields (28.8%), occupied a total of 73.7% of the rural area. The higher concentrations of NO3-N in public facilities and livestock areas were demonstrated to directly recharge groundwater pollutants. NO3-N contamination in rice paddies, which also contained chlorine (Cl) and sulfate (SO4), was assessed according to the nutrients and residual salt in the soil. In addition, different NO3-N concentrations for the same land use indicate various biochemical reactions and NO3-N recharge types into the groundwater system. The shallow groundwaters in the study area were classified into three clusters according to their chemical constituents and land-use properties, especially NO3-N concentration, including pH, Cl, and SO4, using a SOM, PCA, and HCA. Unlike existing studies, we applied a buffer zone based on the Cooper-Jacob equation to obtain an improved SOM model prediction accuracy approximately 10% greater than that using the original dataset.

Identification of Metal Contamination Sources and Evaluation of the Anthropogenic Effects in Soils near Traffic-Related Facilities.

Traffic-related facilities typically have much lower metal emissions than other sources; however, they can be numerous and widespread as well. Subdividing pollution sources is necessary to assess soil contamination characteristics and identify sources according to the contamination cause. Anthropogenic contamination by metals was quantitatively determined using contamination factor (Cf) and evaluated using multivariate analysis. More than half of the concentrations for Zn, Pb, and Cu in soils were higher than that in the natural background (NB). Cf of metals was, in decreasing order, Zn > Pb = Cu > Ni = As. Zn, Pb, and Cu were identified as anthropogenic contaminants in correlation analysis. Principal component analysis showed that the two main contamination causes were coarse particles from the maintenance or crushing activities of vehicles and nonexhaust/exhaust emissions. Clusters were classified according to those two anthropogenic and lithogenic causes and included Group I (Zn, Pb, and Cu in garages, auto repair shops, and auto salvage yards), Group II (Zn, Pb, and Cu in parking lots, driving schools, and roadsides), and Group III (As and Ni with high lithogenic properties). Anthropogenic input and sources of soil contamination by metals in traffic-related facilities were appropriately estimated through the combination of Cf and multivariate analysis.

Shift of nitrate sources in groundwater due to intensive livestock farming on Jeju Island, South Korea: With emphasis on legacy effects on water management.

Time lags between anthropogenic nitrogen inputs and their impacts to nitrate levels cause a misunderstanding for sources and subsequently misguide the groundwater management.We investigated the hydrochemical data of groundwater samples (n = 172 from 49 wells) with chlorofluorocarbons (CFCs)-based groundwater age dating and stable N (δ15N) and O isotopes (δ18O) of nitrate to assess the legacy effect of livestock farming to groundwater in an agricultural area where intensive livestock farming started in the 1970s and illegal dumping of manure wastewater in a lava cave was revealed in 2015. Approximately 90% of the groundwater samples had nitrate concentrations exceeding the natural threshold (5.5 mg/L NO3-) for nitrate contamination and 34% exceeded the World Health Organization's guideline for drinking water quality (44.3 mg/L), indicating severe nitrate contamination. The δ15NNO3 values (5.5 to 24.3‰) in groundwater exceeding the threshold of nitrate showed that livestock manure was a major nitrate source, while ammonium fertilizer also seemed influential given the δ15NNO3 values in the overlapping fields of N sources. Factor analysis of hydrochemical data also supported nitrate contamination by manure as well as by plant farming in the study area. Based on the spatial distribution of nitrate levels and δ15NNO3, livestock farming affected nitrate contamination by illegal manure dumping in the leakage cave. According to a Bayesian mixing model, the contribution of manure wastewater was 33.5 to 81.8% as of 2015-2018, with the rest from fertilizers. Meanwhile, the groundwater ages showed negative correlations with both nitrate levels (r = -0.90) and δ15NNO3 values (r = -0.74) on a log scale, consistent with the increasing N release from livestock farming since the 1960s. In particular, the median value of δ15NNO3 rapidly increased to 9.2‰ in groundwater recharged between the late 1970s and early 1990s when N production exponentially increased, implying a significant effect of livestock farming after the 1980s. Groundwater quality is expected to deteriorate over the next several decades based on the groundwater ages (> 23.5 years), the increased N production from livestock farming, and the legacy effect of N. Long-term groundwater management plans (> 25 years) are required to decrease N loads in the study area, because it takes time for management practices to take effect. The study results are a good reference for groundwater management in regions with a source shift to livestock farming under intensive livestock production systems. Moreover, the chronological study using historical N production, groundwater age data, and dual nitrate isotopes can be applied to other regions with multiple N sources and their shifting for identifying sources and estimating time lags.

Nationwide groundwater monitoring around infectious-disease-caused livestock mortality burials in Korea: Superimposed influence of animal leachate on pre-existing anthropogenic pollution.

A foot-and-mouth disease (FMD) outbreak during 2010 affected the entire country of South Korea and approximately 3.4 million swine and bovine mortalities were disposed of at approximately 4800 on-farm burial sites for a few months following the first outbreak. Furthermore, outbreaks of avian influenza (AI) have struck Korea consistently since 2014. Public concern regarding the deterioration of the surrounding environment has been raised aiming at the enormous infected animal carcass burials. On behalf of the Ministry of Environment (ME) of the Korean government, we conducted groundwater monitoring at approximately 3000 wells around the burial sites for 7 years from 2011 to 2017. The baseline groundwater already had enriched levels of nitrogen and chloride compounds masking the influence of leachate with the pre-existing anthropogenic contamination. Based on the trend analysis, most monitoring wells had no significant trends in NO3-N and Cl implying that an abrupt degradation in water quality was not expected across the country. Increasing proportions of total coliform detection in the groundwater of the majority of provinces will cause potential damage to human health around the carcass burial sites. Wells showing frequent upward and downward trends near the carcass burial pits were targeted for principal component analysis (PCA) and the results showed that NH4-N, TOC, Cl, and K could be indicators distinguishing the sole impacts of animal leachate on the groundwater. Analyses of the nationwide monitoring data indicated possibly delayed and prolonged impacts of the widespread burials of livestock mortalities on the groundwater environment. The finding provides realistic insight regarding how to manage the mass burial of livestock mortalities to protect groundwater resources.

A multi-center retrospective study of single-port versus multi-port video-assisted thoracoscopic lobectomy and anatomic segmentectomy.

BACKGROUND: To assess the feasibility and perioperative outcomes of single-port (SP) and multi-port (MP) approaches for video-assisted thoracoscopic surgery (VATS) lobectomy and anatomical segmentectomy. METHODS: Retrospective data from 458 patients who received VATS lobectomy or anatomical segmentectomy at Shanghai Chest Hospital, Korea University Guro Hospital, Affiliated Hospital of National Taiwan University, University of Hong Kong Queen Mary Hospital and Shenzhen Hospital were collected. Patients were divided into SP group and MP group according to the surgical approach. Perioperative factors such as operation time, blood loss during surgery, conversion rate, the number and stations of lymph nodes harvested, postoperative chest tube drainage time, postoperative hospitalization time, perioperative morbidity and mortality, and pain scores during the first 3 days after surgery were compared between the two groups. RESULTS: There were no differences in the number (P=0.278) and stations (P=0.564) of lymph nodes harvested, postoperative morbidity (P=0.414) or mortality(P=0.246), and pain score on the third day (P=0.630) after surgery between the two groups. The SP group had a longer operation time (P=0.042) and greater intraoperative blood loss (P<0.001), but the conversion rate was even higher in the MP group (P=0.018). Patients in the SP group had shorter chest tube removal time (P=0.012) and postoperative hospitalization time (P=0.005). Pain scores were lower on the first (P=0.014) and second (P=0.006) day after surgery in the SP group. CONCLUSIONS: SP VATS lobectomy and anatomical segmentectomy is technologically more demanding than MP VATS. It can be safe and feasible in the hands of experienced surgeons, with comparable preoperative outcomes to MP VATS, but less pain in the early postoperative period.

Removal efficiency and homologue patterns of dioxins in drinking water treatment.

Polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs) and coplanar polychlorinated biphenyls (Co-PCBs) analyses in raw and treated water throughout Japan were implemented to identify the concentration and homologue patterns of dioxins before and after the water treatment process. In 40 surface water and 5 ground water treatment plants, the removal efficiency of dioxins and the influence of extent chlorination on dioxins increase in drinking water were also studied. Raw water and treated water were sampled twice, summer and winter. The mean concentration in raw water and treated water of dioxins was 56.45 pg/L (0.15 pg WHO-TEQ/L) and 4.24 pg/L (0.019 pg WHO-TEQ/L), respectively. Location of water treatment plants not only significantly influenced the concentration level of dioxins but also resulted in different homologue patterns of dioxins. Levels of dioxins in ground water were much less than that of surface water in both raw and treated water. This study shows most dioxin congeners are well removed (87% removal efficiency) by water treatment. However, in some water treatment plants, the level of TeCDFs (pg WHO-TEQ/L) increased as a result of chlorination.