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Introduction: The construction of coastal industrial zones has significantly impacted the marine environment, with the extent of these effects varying based on the type of industrial activity. This study compares the environmental impact of two prevalent types of industrial zones along South Korea's coast: ironworks and shipyards. Methods: We assessed heavy metal pollution near these industrial zones using the Pollution Load Index (PLI) and Nemerow Pollution Index (Pn). To evaluate the impact of heavy metals and organic matter on macrobenthic communities, we employed redundancy analysis (RDA) and Spearman correlation analysis. Additionally, we used the AZTI's Marine Biotic Index (AMBI), Multivariate AMBI (M-AMBI), and Benthic Pollution Index (BPI) to gauge the ecological quality of the affected marine environments. Results and discussion: Our findings indicated that the biodiversity and abundance of species near shipyards were significantly lower than those near ironworks. Results of PLI and Pn revealed that the ecological risk posed by heavy metals from shipyard activities was significantly higher than from ironworks. The AZTI's Marine Biotic Index (AMBI), Multivariate AMBI (M-AMBI), and Benthic Pollution Index (BPI) demonstrated that the ecological quality of Asan Bay (ironworks) is better than that of Dangdong Bay (shipyard). RDA analysis identified Lumbrineris longifolia, Ancistrosyllis hanaokai, and Theora fragilis as biological indicators for assessing heavy metal and organic matter pollution. Spearman correlation analysis indicated that BPI and species richness index are effective indicators for evaluating such pollution. Overall, the negative impact of shipyards on the marine environment was significantly more significant than that of ironworks. Our study provides valuable insights for the South Korean government in managing coastal industrial zones and formulating relevant policies. [ABSTRACT FROM AUTHOR]

Bay ecosystems with unique economic and ecological value are more vulnerable to heavy metal pollution than other marine ecosystems. In South Korea, rapid economic development has exacerbated heavy metal pollution in bay environments. This study analyzed concentrations of seven heavy metals and the structure of macrobenthic community in Asan Bay. Five heavy metal indices (PLI, Pn, RI, TRI, and MERMQ) were used to assess ecological risk. Additionally, spearman correlation, biota--environment matching (BIO-ENV), and redundancy analysis (RDA) were utilized to determine the influence of heavy metals on macrobenthic communities. Although average concentrations of seven heavy metals were below sediment quality guidelines (SQGs), the average concentration of Cd was close to the threshold effects level (TEL). The nemerow pollution index (Pn) and the potential ecological risk index (RI) indicated that Cd was the primary heavy metal contributing to ecological risk. Spearman correlation, BIO-ENV, and RDA indicated that heavy metals were the main environmental factors influencing macrobenthic communities in Asan Bay. However, five heavy metal indices (PLI, Pn, RI, TRI, and MERMQ) did not show a response to the macrobenthic communities. Overall, low concentrations of heavy metals have a certain negative impact on macrobenthic communities in Asan Bay. This study can serve as an important reference for marine environmental protection and policy-making in Asan Bay. [ABSTRACT FROM AUTHOR]

Since the first receiver independent exchange format (RINEX) version was released in 1989, it has gone through several versions, making the existing software, such as TEQC, incompatible with certain later versions. This study proposes a new Python package named PyRINEX, which is developed to batch process the most generally used versions of RINEX files, namely 2.0 and 3.0. The proposed package can be used to manage and edit numerous RINEX files as well as perform a data quality check function. PyRINEX can be easily imported into any Python IDE similar to any other open-source Python package, it also makes secondary development easy for users. [ABSTRACT FROM AUTHOR]

Accurate bathymetry estimation is made possible by combining depth data with free-air gravity anomalies on the sea surface recovered from the geoidal heights that are equivalent to the mean sea surface derived from satellite radar altimetry. The residual gravity anomalies that represent the short-wavelength effect are required to accurately estimate bathymetry by combining satellite altimetry-derived free-air gravity anomalies and shipborne data including depth and gravity anomalies. In this study, the optimized ensemble model of machine learning techniques was applied to the residual gravity anomalies to estimate bathymetry by the gravity--geologic method (GGM) from various geospatial information including shipborne depth, shipborne gravity anomalies, and satellite altimetry-derived free-air gravity anomalies, in the Ulleung Basin in the East Sea. From the results, the GGM bathymetry predicted using the optimized ensemble model of machine learning was improved by 32.3 m over the GGM bathymetry estimated using the original depth and gravity anomalies. The method presented in this study is for estimating deep-water bathymetry using machine learning, and it has been proven to have superior performance compared with conventional methods. [ABSTRACT FROM AUTHOR]

In this study, gridded gravity data (GGD) were generated by using the correlation between gravity and the height extracted from a high-resolution digital elevation model (DEM) in an area where the national control points where gravity was measured are not densely distributed. Comparison of the measured gravity and the gravity generated by using GGD, which is produced by the linear regression analysis of the height extracted from DEM at the national control points, revealed that the accuracy of GGD is about 33 mGal. The difference is very small at about 4 and 5 mm, when comparing the orthometric correction for each section and the cumulative orthometric correction calculated using the gravity interpolated from GGD with the results calculated from the measured gravity, respectively. Thus, it is considered that the orthometric correction can be effectively calculated by using the gravity interpolated from GGD at the points where there are no measured gravity data. If the gravity distribution measured at national control points becomes dense, it is expected to further improve the accuracy of the gravity interpolated from the GGD, which is generated by using the linear correlation between the gravity and the height extracted from high-resolution DEM. [ABSTRACT FROM AUTHOR]

Bathymetric mapping is traditionally implemented using shipborne single-beam, multi-beam, and side-scan sonar sensors. Procuring bathymetric data near coastlines using shipborne sensors is difficult, however, this type of data is important for maritime safety, marine territory management, climate change monitoring, and disaster preparedness. In recent years, the bathymetric light detection and ranging (LiDAR) technique has been tried to get seamless geospatial data from land to submarine topography. This paper evaluated the accuracy of bathymetry generated near coastlines from satellite altimetry-derived gravity anomalies and multi-beam bathymetry using a tuning density contrast of 5000 kg/m3 determined by the gravity-geologic method. Comparing with the predicted bathymetry of using only multi-beam depth data, 78% root mean square error from both multi-beam and airborne bathymetric LiDAR was improved in shallow waters of nearshore coastlines of the western Korea. As a result, the satellite-derived bathymetry estimated from the multi-beam and the airborne bathymetric LiDAR was enhanced to the accuracy of about 0.2 m. [ABSTRACT FROM AUTHOR]

The gravity-geologic method (GGM) was used to enhance the bathymetry of the East Sea (Sea of Japan) with satellite altimetry-derived free-air gravity anomalies and shipborne depth measurements. By comparison with the bathymetry model of Smith and Sandwell's (SAS) approach (1994), GGM was found to have an advantage with short wavelength (≤12 km) components, while SAS better predicts longer wavelength (≥25 km) components, despite its dependency on density contrast. To mitigate this limitation, a tuning density contrast of 10.25 g/cm between seawater and the seafloor was primarily estimated by the downward continuation method and then validated by the check points method with GGM. Similarly, SAS is limited by the ' A' value in low-pass part of the Wiener filter, which defines the effective range of the wavelength components on bathymetry. As a final result, we present an enhanced GGM bathymetry model by integrating all available data. [ABSTRACT FROM AUTHOR]