Remote sensing-based mangrove blue carbon assessment in the Asia-Pacific: A systematic review.

Accurate measuring, mapping, and monitoring of mangrove forests support the sustainable management of mangrove blue carbon in the Asia-Pacific. Remote sensing coupled with modeling can efficiently and accurately estimate mangrove blue carbon stocks at larger spatiotemporal extents. This study aimed to identify trends in remote sensing/modeling employed in estimating mangrove blue carbon, attributes/variations in mangrove carbon sequestration estimated using remote sensing, and to compile research gaps and opportunities, followed by providing recommendations for future research. Using a systematic literature review approach, we reviewed 105 remote sensing-based peer-reviewed articles (1990 - June 2023). Despite their high mangrove extent, there was a paucity of studies from Myanmar, Bangladesh, and Papua New Guinea. The most frequently used sensor was Sentinel-2 MSI, accounting for 14.5 % of overall usage, followed by Landsat 8 OLI (11.5 %), ALOS-2 PALSAR-2 (7.3 %), ALOS PALSAR (7.2 %), Landsat 7 ETM+ (6.1 %), Sentinel-1 (6.7 %), Landsat 5 TM (5.5 %), SRTM DEM (5.5 %), and UAV-LiDAR (4.8 %). Although parametric methods like linear regression remain the most widely used, machine learning regression models such as Random Forest (RF) and eXtreme Gradient Boost (XGB) have become popular in recent years and have shown good accuracy. Among a variety of attributes estimated, below-ground mangrove blue carbon and the valuation of carbon stock were less studied. The variation in carbon sequestration potential as a result of location, species, and forest type was widely studied. To improve the accuracy of blue carbon measurements, standardized/coordinated and innovative methodologies accompanied by credible information and actionable data should be carried out. Technical monitoring (every 2-5 years) enhanced by remote sensing can provide accurate and precise data for sustainable mangrove management while opening ventures for voluntary carbon markets to benefit the environment and local livelihood in developing countries in the Asia-Pacific region.

Environmental assessment of oyster beds in the northern Arabian Gulf Coast of the United Arab Emirates.

The United Arab of Emirates (UAE) hosts valuable coastal and marine biodiversity, and oysters are one of the habitants of its marine ecosystem. Oysters play an essential role in the nearshore coasts where they work as an active filter. They filter nutrients, phytoplankton, sediments, heavy metals, and toxins out of the water, which improves the water quality. This is the first study that characterizes oyster bed habitats in the UAE by analyzing water quality parameters, polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs)and heavy metals in water, sediments and oyster samples collected from five locations along the coasts of Sharjah, Ajman, and Umm al Quwain. Oyster bed areas supported a diverse assemblage of benthic life including oysters, scallops, pen shells, hard corals, and macroalgae. Mobile species in these habitats included groupers, emperors, snappers, sea snakes, among others. The phytoplankton assemblages were dominated by diatoms, dinoflagellates, and small cryptophytes. Harmful diatom Pseudonitzschia was found in all locations. No detectable concentrations of PAHs and OCPs were reported in this study, and water quality parameters were within the acceptable levels for the region. On the other hand, water quality index was reported marginal, mostly due to the presence of higher than acceptable concentrations of chromium and mercury in all sites studied. Bioconcentration factors concluded that oysters were able to bioconcentrate metals such as arsenic, cadmium, chromium, and zinc, when compared to water. No detectable concentrations of lead and mercury were reported in oysters, suggesting higher depuration rates for those metals.

A Light-Weight Cropland Mapping Model Using Satellite Imagery.

Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine-learning-based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions, namely Iran, Mozambique, Sri-Lanka, and Sudan, where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction, and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 and 0.98 across the four test regions at hand.

Mapping of heavy metal contamination associated with microplastics marine debris - A case study: Dubai, UAE.

Microplastics (MPs) are ubiquitously detected in the environment. The adverse impact of microplastics on marine life is well documented. Previous research has shown that MPs can adsorb heavy metals, however, this fact has not been investigated along the coast of Dubai, UAE. Elemental composition of MPs debris was determined using X-ray fluorescence spectroscopic (XRF) analysis. The analyzed MPs were extracted from 80 sediment samples collected from the wrack lines of 16 Dubai, UAE beaches. A total of 480 MPs pieces extracted from the samples were analyzed in order to detect heavy metals. The polymer composition was previously confirmed by FTIR spectroscopy, showing that polyethylene (PE) and polypropylene (PP) were the most abundant MPs. 14 heavy metals were identified in the samples: Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Pb, Ce, Pr, Nd, Pd, and Co with different concentrations. Five of them (Cr, Ni, Cu, Zn, and Pb) are priority pollutants according to EPA. Their average concentrations in oxide form were Cr2O3 (2.96 %), NiO (0.32 %), CuO (0.45 %), ZnO (0.56 %), and PbO (1.49 %).

Continuous Evaluation of Shear Wave Velocity from Bender Elements during Monotonic Triaxial Loading.

Few researchers have attempted to experimentally evaluate the low-strain shear wave velocity (Vs) of specimens undergoing large strain deformations. They report that the Vs is practically unaffected by the strains, and the reasons behind this behavior are not fully understood. This study presents the continuous measurement of low-strain Vs with bender elements (BE) during monotonic shearing of two sand specimens in a triaxial device. The results are analyzed using a micro-mechanical model based on contact theory. The results of this study confirm that the Vs values from BE measurements are unaffected by an increase in axial strains that are induced by a separate mechanism. The micro-mechanical model predictions of Vs agree well with the results of this study and with the results of previous studies. They show that the mean effective stress and increase in inter-particle stiffness controls the low-strain stiffness despite a global increase in strains during monotonic loading.

Ammonium nitrate explosion at the main port in Beirut (Lebanon) and air pollution: an analysis of the spatiotemporal distribution of nitrogen dioxide.

An explosion of the ammonium nitrate (AN) stored at Beirut Port devastated the city on Tuesday 4 August 2020. Such an explosion produces pollutants such as nitrogen oxides (NO x ). The most common NO x is nitrogen dioxide (NO2), which is present in the atmosphere due to natural and anthropogenic processes. The presence of NO2 is used as indicator of air pollution. However, the specific contribution of NO2 to air quality is uncertain due to the presence of other constituents, especially particulate matter (PM10). Research has shown that extended exposure to NO2 may result in serious health effects. This study investigated the impact of the explosion on NO2 levels in the atmosphere above Beirut and the surrounding area. NO2 data from the Sentinel-5P program were used to map the levels of NO2. Furthermore, ground-monitoring data were used to assess the levels of PM10 and ozone (O3) due to the evident association between these constituents and NO2. Results showed that NO2 levels were higher than before the blast. However, 7 days after the explosion, NO2 levels had returned to normal, while the levels of PM10 and O3 remained normal following the explosion. However, a slight increase in the daily average atmospheric pressure was noticed after the explosion, which was attributed to the decomposition of ammonium nitrate.

Seismic risk quantification and GIS-based seismic risk maps for Dubai-UAE_Dataset.

The data provided in this article quantifies the potential seismic losses in multi-story buildings located in Dubai, UAE. Besides, it developed GIS-based seismic risk maps, which form essential inputs towards the seismic resilience of buildings. The GIS data described herein come from different sources. The base map including the major roads, key land use classes, and administrative boundaries are from the ArcMap maps library. The Dubai districts were digitized in ArcMap from a rectified Worldview imagery of Dubai. The seismic risk analysis data are represented as attributes of the centroids of Dubai districts, which is the main data layer. The GIS-based seismic risk maps are raster GIS layers, which are created in ArcGIS using the Inverse Distance Weighted (IDW) interpolation method. The base map template is from the ArcMap maps library.

Evaluation of microplastics in beach sediments along the coast of Dubai, UAE.

Microplastic contamination in beach sediments along coast of Dubai is un-documented. In this study, microplastic contamination in beach sediments collected from the wrack lines of 16 beaches in Dubai was evaluated. Five samples were collected from each beach along a 100 m stretch using a 0.5 m by 0.5 m, quadrant. The number, color, and shape of microplastics were documented. The polymer types of large fibers and strings were identified through FT-IR analysis. 480 microplastics from each of the 16 beaches were selected to detect heavy metals using XRF analysis. The results showed that the average weight of microplastic is 0.33 mg per gram of dry sediment (or 953 mg·m-2) and the number of microplastic is 59.71 items per kg of dry sediment (or 165 items·m-2). Blue and fibrous microplastics were dominant. Polyethylene strings and fibers were abundantly found. 13 heavy metals were identified of which five are priority pollutants.