The present study aims to document the contamination levels and ecological risks of heavy metals in the sediments of Kavaratti lagoon, India. A total of 15 sediment samples were collected for the analysis of Al, Pb, Cd, Cu, Cr, Mn, Ni and Zn. The decreasing trend of heavy metals was observed in the lagoon sediment as Pb > Zn > Al > Mn > Ni > Cr > Cd > Cu. The Geo-accumulation index (Igeo) results indicate that Cu, Cr, Mn, Ni and Zn were uncontaminated, while Cd was strong to extremely contaminated and Al and Pb were moderately contaminated. The enrichment factors (EF) of Cd and Pb range from moderate to extremely high (EF > 1) indicating that they have anthropogenic origin on Kavaratti Island. The Contamination factor (Cf) indicated that Cd, Pb and Al belong to a high risk of contamination (Cf > 6). The pollution load index (PLI) value near one suggested that a moderate level of pollution occurs in the study area. The modified degree of contamination (mCd) shows that Al, Cd and Pb have an ultra- higher degree of contamination (mCd ≤ 32). The potential ecological risk (RI) index confirmed that Pb and Cd have considerable to the serious thread of ecological risk (RI > 600). Additionally, multivariate statistical analysis and pollution indexes showed that the Kavaratti lagoon is moderate to considerably polluted by heavy metals. Diesel-based power generation, activities related to shipping, untreated sewage, fishing and tourism activities are the main anthropogenic sources of heavy metal pollution on Kavaratti Island.

Baseline data on concentration, fractionation, pollution level and ecological risk index for seventeen beach sediments from Santa Rosalia mining region of Baja California Sur, Mexico were assessed. Higher concentrations of Rare Earth Elements (REEs) (mean. 341.49 μg/g) indicated that it is higher than most of the mining regions around the world. Normalization pattern showed enrichment of Eu (>4) and calculated geochemical indices revealed that light and middle REEs are moderately polluted with most of the sampling points located closer to the river discharge. Potential Ecological Risk Index (PERI) showed that Eu (20.2), Tb (20.88), and Lu (28.57) pose moderate ecological risk to the soil at selected stations (10, 11, 15 and 16) with a risk index value ranging from 245 to 359. Pearson's correlation matrix suggested that all REEs are highly correlated (r² 0.95) with each other having similar geochemical characteristics and indicating identical source due to continuous mining activity.

The worldwide spread of the SARS-CoV-2 caused an unprecedented lockdown measures in most countries with consequences on the world society, economy, and sanitary systems. This situation provided an opportunity to identify the effects of human confinement on natural environments, like touristic sandy beaches, which are stressed due to anthropogenic pressures. Based on previous articles about heavy metals sources and levels in these ecosystems, this paper discusses the dynamic of these pollutants and a regulatory scenario associated with COVID-19 sanitation policies. The main findings suggest that 39% of the studies were on Asian sandy beaches, 16% from Europe, while America and Africa with 23% each. Also Co, Cd, Cu, Cr, Zn, Pb, Ni, Fe and Mn were the most frequently analyzed metals in sediments and in several cases their concentrations exceed international guidelines assessment. Finally, even though beaches are under several metals inputs, tourism plays a key role in these ecosystems quality. After analyzing the potential indirect effect of COVID-19 measures on metals dynamics, we propose some key recommendations and management strategies to mitigate heavy metal pollution on sandy tourist beaches. These proposals are useful for decision-makers and stakeholders to improve sandy beach management, mainly those beaches not addressed from a management perspective; and their implementation should be adapted according to the regulations and legislation of each country.

Oil exploration and drilling activities are known to have catastrophic effects on marine environments and ecosystems, yet full understanding of these effects is hindered by sparse observations in many areas of the world. We present annual and bimonthly records of coral Ba/Ca from Weizhou Island in Beibu Gulf in the northern South China Sea (SCS) – the largest oilfield in the SCS. On interannual timescales, a significant increase in Ba/Ca ratios is associated with high barite consumption used for oil exploration and drilling activities. On seasonal timescales, a strong winter monsoon signal is seen in the skeletal Ba/Ca records. Our data provide a long-term and continuous on-site record for oil exploration and drilling activities that can be used for offshore oil management. Furthermore, our approach offers a means to study the influence of oil-related activities on the marine environment when no oil exploration and drilling records are available.

Organic pollutants are ubiquitous in estuarine areas, nonetheless, the transport mechanisms of herbicides in such areas are limited. Atrazine and acetochlor were analyzed in suspended particle matter (SPM), surface sediment, and surface water from the Yellow River estuary and the surrounding rivers and sea. Among these rivers, the Yellow River contributes the most herbicide flux to the sea annually. The herbicide concentrations in water and sediment decreased from the estuarine areas to the deep sea. The fugacity fraction values of atrazine exceeded 0.5 in the Yellow River estuary, which supported that the herbicides in sediment desorbed at the estuarine areas. The herbicide in the SPM showed high concentration in the outer sea and increased as a power function with decreasing SPM content. The increasing partition capacity indicated that the herbicides tended to sink into sediment, increasing the ecological risk posed by herbicides. The ecological risk of acetochlor deserves continuous attention.

The East China Sea (ECS) is seriously impacted by harmful algal blooms (HABs). Therefore, early assessments of HAB risk in this area are extremely important. Using long-term historical HAB observation data and satellite-derived sea surface temperatures (SSTs), we found that the annual number of HAB events was positively correlated with the mean March SST and negatively correlated with the SST change rate from March to July in nearshore waters (< 50 m). A simple method of HAB risk assessment was therefore proposed based on either March SST (threshold: 13 °C) or SST change rate (threshold: 3.6 °C/month). Validation against a k-means classification scheme indicated that the overall accuracy based on the March SST threshold was 85%, with a kappa coefficient of 0.69. The SST-based method facilitates the assessment of HAB risk in the ECS 1–2 months in advance, thus helping to reduce the damage caused by HABs.

Coral reefs across the southern Arabian Gulf have declined in the past two decades, with extensive loss of formerly Acropora table corals, which are now functionally extinct in nearshore reefs. This study documents the coral community at Sir Bu Nair (SBN), an offshore island buffered by less extreme environmental conditions, which contains the last remaining large stands of Acropora in the southern Gulf. We found that Acropora is a major reef-building coral throughout SBN. Mean coral cover was 27% (range: 6%–49%) across all sites and depths, of which more than half was comprised by Acropora. This varied around the island, with the highest densities to the south and southwest in shallow waters. Our study provides essential information for the management and conservation of these highly valuable and vulnerable corals.

Here we present the first assessment of microplastic (1–5 mm) abundance in drift line sediments from nineteen sandy beaches at the Paranaguá Estuarine Complex, a subtropical estuarine system from South Brazil. This estuarine system harbors Brazil's second-largest grain port, the Guaraqueçaba Environmental Protection Area, and a RAMSAR site. Sediment samples were washed through a 5 and 1 mm mesh sieve and then visually inspected. We found a total of 398 microplastic particles, of which the majority were foams (63.7%), hard plastic fragments (13.8%), paint fragments (12.8%), and pellets (7.2%). Almost all sampled beaches, including those located within the Guaraqueçaba Environmental Protection Area, were contaminated by microplastics. The most likely microplastic sources for the Paranaguá Estuarine Complex beaches are urban and port activities. However, small communities and marine sources may also contribute to microplastic presence.

Microplastics (MPs) have become a dominant constituent of several oceanic islands. This study focuses on the occurrence and distribution of MPs present in the beach sediments of Saint Mary's Island (SMI), a geological heritage site located in the south-eastern part of the Arabian Sea. The average (standard deviation) abundance of MPs on this island was 97.18 (80.49) particles/kg. Attenuated total reflectance—Fourier transform infrared (ATR-FTIR) spectroscopy revealed that MPs are composed of high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyamide (PA). Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS) highlighted the presence of Cr, As, Pb, and Cd (harmful pollutants) on MP surfaces. The MPs in the SMI are largely contributed by the nearby fishing harbour, touristic beaches and estuaries. The results of this study, act as a starting point for continuous environmental monitoring in this unique region of the world.

With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life.