Plastic pollution is one of the pressing issues in freshwater ecosystems that may further contribute to coastal pollution. The present study aimed to address the state of macroplastics pollution in the Surma River system, Bangladesh. Six sampling sites were allocated in the river starting from upstream to downstream, water parameters and fin fish assemblage were recorded, and plastic debris was collected from each site. Afterward, macroplastics were categorized and weighed to measure their abundance. Previous data on rainfall, water discharge, and depth were aggregated to study the trend of river depth changes. A survey was conducted to identify the possible sources of plastic pollution in the river and awareness of the pollution. The results showed that Kazir Bazar (Site 4) and Beter Bazar (Site 5), comparatively contained poor water quality, diverse macroplastics categories, and higher macroplastics abundance. The water pollution index (WPI) also ranked the above sites as extremely polluted. Similarly, biodiversity indices revealed lower diversity at Site 4 and Site 5. The river depth analysis revealed that there was no remarkable tendency to change the depth. To conclude, the Surma River system is being polluted due to inadvertent plastic dumping. Contemporary awareness is highly required, and proper policies should be implemented to minimize the detrimental effects of macroplastics.

The Natuna Sea is located at the northwestern part of Indonesia. Previous studies had showed that ENSO has a stronger impact on SST than chlorophyll-a. According to several studies, Indonesian oceans are heavily impacted by IOD. This study uses SST data with high-resolution satellite imagery (MODIS and Pathfinder) and rainfall and wind data from the Reanalysis Model (ERA-5) which is processed using a composite method and correlation grid. This research results, when La-Niña negative IOD SST will decrease 1°C and rainfall rises 7 mm/day while when El-Niño IOD positive SST will increase by 1°C while in rainfall will decrease by 3 mm/day. The variation of SST and rainfall is more influenced by ENSO than IOD.

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<p>In comparison with the number of tide gauges measuring in-situ sea-level change along the Northern Hermisphere coastlines, the Southern Hemisphere has a poor spatial distribution of stations. For example, along the South American Atlantic coastline, only 12 tide gauges are registered at the Permanent Service for Mean Sea-level (PSMSL), of which only two have been updated in the last three years. While satellite altimetry can be used to provide data in locations where there is no in-situ data, estimating coastal sea-level change using altimetry data is challenging due to the distortion of the satellite signal close to the land. Consequently, sea-level change along the South American Atlantic coastline is still poorly understood. Here, we fill this gap by using coastal altimetry products together with a new network of tide gauges deployed along the coast of Brazil (by the SIMCosta project). Via a sea-level budget analysis, we look at the regional drivers of sea-level change along the coast.</p> <p> </p> <p>Recently, a large effort has been put towards developing algorithms that improve the accuracy of standard radar altimetry in coastal regions. Here, we compare both a coastal altimetry product (XTRACT/ALES) and a standard altimetry product (from CMEMS) to the local tide gauges. Previous studies have shown that, for some regions, coastal sea level is driven by open ocean sea-level change ( e.g., Dangendorf et al, 2021). Following this approach, we use clusters of coherent sea-level variability (Camargo et al., 2022), extracted with a network detection algorithm (delta-Maps), that extend to the open ocean, as proxies of the drivers of sea-level change along the coast.  The northern part of the study region, covering the Amazon Plateau, has a good match between the coastal altimetry-observed sea-level change and the sum of the drivers. The sum of the drivers and coastal altimetry trends also match, considering the uncertainty bars, for the most southern part, covering the Patagonian Shelf. For the other regions, we find a large difference between the coastal altimetry-observed sea-level change and the sum of the drivers. Thus, it is possible that these regions cover large-scale features, which are not strongly correlated with coastal sea level.</p> <p> </p> <p><strong>References</strong></p> <p>Camargo, C. M. L., Riva, R. E. M., Hermans, T. H. J., Schütt, E. M., Marcos, M., Hernandez-Carrasco, I., and Slangen, A. B. A.: Regionalizing the Sea-level Budget With Machine Learning Techniques, <em>EGUsphere</em> [preprint, accepted], https://doi.org/10.5194/egusphere-2022-876, 2022.</p> <div> <p>Dangendorf, S., Frederikse, T., Chafik, L., Klinck, J. M., Ezer, T., & Hamlington, B. D.: Data-driven reconstruction reveals large-scale ocean circulation control on coastal sea level. <em>Nature Climate Change,</em> <em>11</em>, 514-520. https://doi.org/10.1038/s41558-021-01046-1, 2021.</p> </div>

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