7,600 research outputs found

    Deep learning approaches to predict sea surface height above geoid in Pekalongan

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    Rising sea surface height is one of the world's vital issues in marine ecosystems because it greatly affects the ecosystems as well as the socio-economic life of the surrounding environment. Pekalongan is one area in Indonesia facing the effects of this phenomenon. This problem deserves to be explored further with complex approaches. One of them is a neural network to perform forecasting more accurately. In neural networks, the time series approach can be used with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). By adding the bidirectional method to each of these two approaches, we will find the best method to use to perform the analysis. The best results were obtained by forecasting for 960 days using Vanilla BiGRU. The results can be interpreted from multiple perspectives. The forecasting results showed a fluctuating pattern as in previous periods, so it can be said that the pattern is still quite normal, which indicates that the terminal can continue to operate normally. However, the forecasting results from this study are expected to be a reference for information for the government to prevent future dangers

    A prioritization protocol for coastal wetland restoration on Molokaʻi, Hawaiʻi

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    Hawaiian coastal wetlands provide important habitat for federally endangered waterbirds and socio-cultural resources for Native Hawaiians. Currently, Hawaiian coastal wetlands are degraded by development, sedimentation, and invasive species and, thus, require restoration. Little is known about their original structure and function due to the large-scale alteration of the lowland landscape since European contact. Here, we used 1) rapid field assessments of hydrology, vegetation, soils, and birds, 2) a comprehensive analysis of endangered bird habitat value, 3) site spatial characteristics, 4) sea-level rise projections for 2050 and 2100 and wetland migration potential, and 5) preferences of the Native Hawaiian community in a GIS site suitability analysis to prioritize restoration of coastal wetlands on the island of Molokaʻi. The site suitability analysis is the first, to our knowledge, to incorporate community preferences, habitat criteria for endangered waterbirds, and sea-level rise into prioritizing wetland sites for restoration. The rapid assessments showed that groundwater is a ubiquitous water source for coastal wetlands. A groundwater-fed, freshwater herbaceous peatland or “coastal fen” not previously described in Hawaiʻi was found adjacent to the coastline at a site being used to grow taro, a staple crop for Native Hawaiians. In traditional ecological knowledge, such a groundwater-fed, agro-ecological system is referred to as a loʻipūnāwai (spring pond). Overall, 39 plant species were found at the 12 sites; 26 of these were wetland species and 11 were native. Soil texture in the wetlands ranged from loamy sands to silt and silty clays and the mean % organic carbon content was 10.93% ± 12.24 (sd). In total, 79 federally endangered waterbirds, 13 Hawaiian coots (‘alae keʻokeʻo; Fulica alai) and 66 Hawaiian stilts (aeʻo; Himantopus mexicanus knudseni), were counted during the rapid field assessments. The site suitability analysis consistently ranked three sites the highest, Kaupapaloʻi o Kaʻamola, Kakahaiʻa National Wildlife Refuge, and ʻŌhiʻapilo Pond, under three different weighting approaches. Site prioritization represents both an actionable plan for coastal wetland restoration and an alternative protocol for restoration decision-making in places such as Hawaiʻi where no pristine “reference” sites exist for comparison

    Time-based Geospatial Analysis of Night-Time Light Data and Citizen Movement Restriction During Covid-19 Period

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    Pembatasan kegiatan masyarakat atau di beberapa daerah disebut juga dengan lockdown sudah banyak dijalankan oleh beberapa negara demi menekan angka penyebaran Covid-19. Dalam penelitian ini, menggunakan foto satelit di malam hari, atau biasa disebut dengan Night Time Light (NTL) Data. Setelah itu diambil sample titik koordinat sebanyak 381 tempat umum di Jakarta dan diambil datanya menggunakan dataset VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1 selama kurun waktu Q1 2019 sampai dengan Q2 2022. Dari hasil foto satelit ini kemudian dikonversikan ke dalam bentuk numerik, dikorelasikan dengan timeline pembatasan kegiatan masyarakat di Indonesia dan juga data mobility untuk wilayah Jakarta. Hasilnya adalah ditemukan penurunan intensitas cahaya saat memasuki masa pembatasan kegiatann masyarakat sebanyak 1% - 16% di berbagai sektor. Penurunan intensitas ini tidak berkorelasi dengan kuat dengan data mobility untuk beberapa sektor yang menunjukkan perubahan penurunan aktivitas hingga 60%.Restrictions on community activities or in some areas also called lockdowns have been carried out by many countries in order to reduce the spread of Covid-19. Various methods are used to monitor the implementation of these restrictions, this study uses a new approach by using satellite imagery at night commonly called Night-Time Light (NTL) Data. This research using a sample of 381 coordinate points was taken in public places in Jakarta. The data was collected using the VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1 dataset from Q1 2019 to Q2 2022. The results of these satellite photos were then converted into numerical form and correlated with the timeline for the restriction of community activities in the Jakarta area. The result is there is a slightly decreasing in light intensity when entering a period of Covid-19 at the beginning of 2020 until 2022 with a percentage of around 1% - 16% in various sectors. This decreasing light intensity has a slight correlation with mobility data for several sectors. The mobility data show a huge difference at the beginning of Covid-19, which shows a decrease in the activity of up to 60%

    Irish Ocean Climate and Ecosystem Status Report

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    Summary report for Irish Ocean Climate & Ecosystem Status Report also published here. This Irish Ocean Climate & Ecosystem Status Summary for Policymakers brings together the latest evidence of ocean change in Irish waters. The report is intended to summarise the current trends in atmospheric patterns, ocean warming, sea level rise, ocean acidification, plankton and fish distributions and abundance, and seabird population trends. The report represents a collaboration between marine researchers within the Marine Institute and others based in Ireland’s higher education institutes and public bodies. It includes authors from Met Éireann, Maynooth University, the University of Galway, the Atlantic Technological University, National Parks and Wildlife, Birdwatch Ireland, Trinity College Dublin, University College Dublin, Inland Fisheries Ireland, The National Water Forum, the Environmental Protection Agency, and the Dundalk Institute of Technology.This report is intended to summarise the current trends in Ireland’s ocean climate. Use has been made of archived marine data held by a range of organisations to elucidate some of the key trends observed in phenomena such as atmospheric changes, ocean warming, sea level rise, acidification, plankton and fish distributions and abundance, and seabirds. The report aims to summarise the key findings and recommendations in each of these areas as a guide to climate adaptation policy and for the public. It builds on the previous Ocean Climate & Ecosystem Status Report published in 2010. The report examines the recently published literature in each of the topic areas and combines this in many cases with analysis of new data sets including long-term time series to identify trends in essential ocean variables in Irish waters. In some cases, model projections of the likely future state of the atmosphere and ocean are presented under different climate emission scenarios.Marine Institut

    Optical Remote Sensing of Oil Spills by using Machine Learning Methods in the Persian Gulf: A Multi-Class Approach

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    Marine oil spills are harmful for the environment and costly for society. Coastal areas are particularly vulnerable since they provide habitats for organisms, animals and marine ecosystems. This thesis studied machine learning methods to classify thick oil in a multi-class case, using remotely sensed multi-spectral data in the Persian Gulf. The study area covers a large area between United Arab Emirates (UAE) and Iran. The dataset is extracted from 10 Sentinel-2 tiles on six spectral bands between 492 nm to 2202 nm. These images were annotated for four classes, namely thick oil, thin oil, ocean water and turbid water by using the Bonn Agreement to analyse true color composite images. A variety of machine learning methods were trained and evaluated using this dataset. Then a robustness evaluation was done by using selected machine learning methods on an independent dataset. Initially multiple machine learning methods were included; three decision trees, six K-Nearest Neighbor (KNN) models, two Artificial Neural Network (ANN) models, two Naive bayes models, and two discriminant models. Two KNN models and two ANN models were then picked for further evaluation. The results show that the fine KNN approach with two nearest neighbors had the best performance based on the computed statistical measures. However, the robustness evaluation showed that the tri-layered NN performed better. This thesis has shown that supervised machine learning with a multi-class approach can be used for oil spill monitoring using multi-spectral remote sensing data in the Persian Gulf

    Marine biogenic emissions of benzene and toluene and their contribution to secondary organic aerosols over the polar oceans

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    Reactive trace gas emissions from the polar oceans are poorly characterized, even though their effects on atmospheric chemistry and aerosol formation are crucial for assessing current and preindustrial aerosol forcing on climate. Here, we present seawater and atmospheric measurements of benzene and toluene, two gases typically associated with pollution, in the remote Southern Ocean and the Arctic marginal ice zone. Their distribution suggests a marine biogenic source. Calculated emission fluxes were 0.023 ± 0.030 (benzene) and 0.039 ± 0.036 (toluene) and 0.023 ± 0.028 (benzene) and 0.034 ± 0.041 (toluene) μmol m−2 day−1 for the Southern Ocean and the Arctic, respectively. Including these average emissions in a chemistry-climate model increased secondary organic aerosol mass concentrations only by 0.1% over the Arctic but by 7.7% over the Southern Ocean, with transient episodes of up to 77.3%. Climate models should consider the hitherto overlooked emissions of benzene and toluene from the polar oceans

    Vulnerability of the Nigerian coast and communities to climate change induced coastal erosion

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    Improving coastal resilience to climate change hazards requires understanding past shoreline changes. As the coastal population grows, evaluation and monitoring of shoreline changes are essential for planning and development. Population growth increases exposure to sea level rise and coastal hazards. Nigeria, where the study is situated, is among the top fifteen countries in the world for coastal population exposure to sea level rise. This study provided a novel lens in establishing a link between social factors and the intensifying coastal erosion along the Akwa Ibom State study coast. The mixed-method approach used in the study to assess the vulnerability of the Nigerian coast and communities to climate change-induced coastal erosion proved to be essential in gathering a wide range of data (physical, socio economic, participatory GIS maps and social learning) that contributed to a more robust and holistic assessment of coastal erosion, which is a complex issue due to the interplay between the human and natural environments. Remotely sensed data was used to examine the susceptibility and coastal evolution of Akwa Ibom State over 36 years (1984 -2020). Longer-term (1984- 2020) and short-term (2015-2020) shoreline change analyses were used to understand coastal erosion and accretion. From 1984-2020, the total average linear regression rate (LRR) was - 2.7+0.18m/yr and from 2015-2020, it was -3.94 +1.28m/yr, demonstrating an erosional trend along the study coast. Although the rate of erosion varies along the study coast, the linear regression rates (LRR) results show a predominant trend of erosion in both the short and longer term. According to the 2022 Intergovernmental Panel on Climate Change report, loss of land, loss of assets, community disruption and livelihood, loss of environmental resources, ecosystem, loss of life, or adverse health impact are all potential risks along the African coast due to climate change – this study shows that these risks are already occurring today. To quantify the anticipated future coastal erosion risk by 2040 along the study coast, the findings in this study show an overall average LRR of -2.73+ 0.99 m/yr which anticipates that coastal erosion will still be prevalent along the coast by 2040. And, given the current global climate change situation, should be expected to be much higher than the current forecasting. This study re-conceptualised the European Environmental Agency Driver-Pressure StateImpact-Response (DPSIR) model to show Hazard-Driver-Pressure-State-Impact ResponseObservation causal linkages to coastal erosion hazards. The results showed how human activities and environmental interactions have evolved through time, causing coastal erosion. Removal of vegetation cover/backstop for residential and agricultural purposes, indicate that human activities significantly contribute to the study area's susceptibility, rapid shoreline changes, and vulnerability to coastal erosion, in addition to oceanic and climate change drivers such as sea level rise and storminess. Risk perception of coastal erosion in the study area was analysed using the rhizoanalytic method proposed by Deleueze. The method demonstrates how connections and movements can be related and how data can be used to show multiplicity, mark and unmark ideas, rupture pre-conceptions and make new connections. This study shows that coastal erosion awareness is insufficient to build a long-term management plan and sustain coastal resilience. The Hino's conceptual model which provides in-depth understanding on planned retreat was used to illustrate migratory and planned retreat for the study coast where relocation has already occurred due to coastal erosion. The result fell within the Self-Reliance quadrant, indicating that people left the risk zone without government backing or retreat plans. Other coastal residents who have not relocated fell within the Hunkered Down quadrant, showing that they are willing to stay in the risk zone and cope with the threat unless the government/environmental agencies relocate them. This study shows that coastal resilience requires adaptive capacity and government support. However, multilevel governance has inhibited government-community dialogue and involvement, increasing coastal erosion vulnerability. The coastal vulnerability index to coastal erosion was calculated using the Analytical Hierarchy Process weightings. It revealed that 67.55% of the study coast falls within the high-very high vulnerability class while 32.45% is within the very low-low vulnerability class. This study developed and combined a risk perception index to coastal erosion (RPIerosion) and participatory GIS (PGIS) mapping into a novel coastal vulnerability index called the integrated coastal erosion vulnerability index (ICEVI). The case study evaluation in Akata, showed an improvement in the overall vulnerability assessment to reflect the real-world scenario, which was consistent with field data. This study demonstrated not only the presence and challenges of coastal erosion in the research area but also the relevance of involvement between the local stakeholders, government and environmental agencies. Thus, showing the potential for the perspectives of the inhabitants of these regions to inform the understanding of the resilience capacity of the people impacted, and importantly to inform future co-design and/or selection of effective adaptation methods, to better support coastal climate change resilience in these communities. Overall, the study provides a useful contribution to coastal erosion vulnerability assessments in data-scarce regions more broadly, where the mixed-methods approach used here can be applied elsewhere

    Ciguatoxins

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    Ciguatoxins (CTXs), which are responsible for Ciguatera fish poisoning (CFP), are liposoluble toxins produced by microalgae of the genera Gambierdiscus and Fukuyoa. This book presents 18 scientific papers that offer new information and scientific evidence on: (i) CTX occurrence in aquatic environments, with an emphasis on edible aquatic organisms; (ii) analysis methods for the determination of CTXs; (iii) advances in research on CTX-producing organisms; (iv) environmental factors involved in the presence of CTXs; and (v) the assessment of public health risks related to the presence of CTXs, as well as risk management and mitigation strategies

    Weather or not? The role of international sanctions and climate on food prices in Iran

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    IntroductionThe scarcity of resources have affected food production, which has challenged the ability of Iran to provide adequate food for the population. Iterative and mounting sanctions on Iran by the international community have seriously eroded Iran's access to agricultural technology and resources to support a growing population. Limited moisture availability also affects Iran's agricultural production. The aim of this study was to analyze the influence of inflation, international sanctions, weather disturbances, and domestic crop production on the price of rice, wheat and lentils from 2010 to 2021 in Iran.MethodData were obtained from the statistical yearbooks of the Ministry of Agriculture in Iran, Statistical Center of Iran, and the Central Bank of Iran. We analyzed econometric measures of food prices, including CPI, food inflation, subsidy reform plan and sanctions to estimate economic relationships. After deflating the food prices through CPI and detrending the time series to resolve the non-linear issue, we used monthly Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data to analyze the influence of weather disturbances on food prices.Results and discussionThe price of goods not only provides an important indicator of the balance between agricultural production and market demand, but also has strong impacts on food affordability and food security. This novel study used a combination of economic and climate factors to analyze the food prices in Iran. Our statistical modeling framework found that the monthly precipitation on domestic food prices, and ultimately food access, in the country is much less important than the international sanctions, lowering Iran's productive capability and negatively impacting its food security
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