Hygrothermal Properties and Performance of Bio-Based Insulation Materials Locally Sourced in Sweden

In recent years, there has been a paradigm shift in the building sector towards more sustainable, resource efficient, and renewable materials. Bio-based insulation derived from renewable resources, such as plant or animal fibres, is one promising group of such materials. Compared to mineral wool and polystyrene-based insulation materials, these bio-based insulation materials generally have a slightly higher thermal conductivity, and they are significantly more hygroscopic, two factors that need to be considered when using these bio-based insulation materials. This study assesses the hygrothermal properties of three bio-based insulation materials: eelgrass, grass, and wood fibre. All three have the potential to be locally sourced in Sweden. Mineral wool (stone wool) was used as a reference material. Hygrothermal material properties were measured with dynamic vapour sorption (DVS), transient plane source (TPS), and sorption calorimetry. Moisture buffering of the insulation materials was assessed, and their thermal insulation capacity was tested on a building component level in a hot box that exposed the materials to a steady-state climate, simulating in-use conditions in, e.g., an external wall. The tested bio-based insulation materials have significantly different sorption properties to stone wool and have higher thermal conductivity than what the manufacturers declared. The hot-box experiments showed that the insulating capacity of the bio-based insulators cannot be reliably calculated from the measured thermal conductivity alone. The results of this study could be used as input data for numerical simulations and analyses of the thermal and hygroscopic behaviour of these bio-based insulation materials.

Sediment carbon storage differs in native and non-native Caribbean seagrass beds

Non-native species are expanding globally and can alter ecosystem functions, including food web dynamics, community structure and carbon storage. Seagrass are foundation species that contribute a variety of ecosystem services in near-shore coastal ecosystems, including a significant sink of carbon. In the Caribbean, the rapidly expanding non-native Halophila stipulacea has unknown impacts on carbon storage. To investigate the impacts on carbon storage, we quantified organic carbon (Corg) content in sediment and seagrass tissues from monotypic H. stipulacea beds, mixed native seagrass beds dominated by Thalassia testudinum and Syringodium filiforme, and unvegetated substrate in St. John, USVI. We found native seagrass-vegetated sediment contained 1.3 times more Corg than sediment covered by H. stipulacea, and 1.6 times more Corg than unvegetated areas on average. Whereas, H. stipulacea-dominated substrate stored 1.2 times more Corg than unvegetated substrate. Likewise, native species contained 2.2 times more aboveground biomass and 6.0 times more belowground biomass than H. stipulacea. Since seagrasses are critical sources of carbon sequestration, our results suggest that invading H. stipulacea is associated with lower carbon stocks which has potential implications for conservation activities and climate change mitigation.

Loss of POC and DOC on seagrass sediments by hydrodynamics

Coastal development and climate change are sparking growing concern about the vulnerability of the organic carbon (OC) stocks in marine sediments to remineralization, especially in high threaten coastal ecosystems like seagrass meadows. Uncertainties still exist regarding the role played by hydrodynamics, seagrass canopies and sediment properties in OC resuspension and remineralization. A set of laboratory experiments were conducted to assess, for the first time, the mechanisms by which the particulate and dissolved organic carbon (POC and DOC) may be released and remineralized under hydrodynamic conditions (i.e., unidirectional and oscillatory flows) in two eelgrass densities and sediments properties (i.e., grain size and OC content). After a gradually increase in hydrodynamic forces, our results demonstrated that the presence of eelgrass reduced sediment erosion and OC loss in high-density canopies, while low-density canopies promote OC resuspension (on average, 1.8-fold higher than high-density canopies). We also demonstrated that unidirectional and oscillatory flows released similar DOC from surface sediments (on average, 15.5 ± 1.4 and 18.4 ± 1.8 g m-2, respectively), whereas oscillatory flow released significantly more POC than unidirectional flows (from 10.8 ± 1.1 to 32.1 ± 5.6 g m-2 for unidirectional and oscillatory flows, respectively). POC and DOC released was strongly influenced by both seagrass meadow structure (i.e., lower eelgrass density and shoot area) and sediment properties (i.e., lower mud and higher sediment water content). We found that, although >74 % of OC in upper sediments was remineralized within 30 days, a relatively high amount of OC in high-density canopies is recalcitrant, highlighting its potential for the formation of blue carbon deposits. This study highlights the vulnerability of OC deposits in seagrass sediments to resuspension if the meadow is degraded and/or the climate change yield stronger storms, which could potentially weaken the seagrass meadows' service as blue carbon ecosystem in the future.

Mapping seagrass habitats of potential suitability using a hybrid machine learning model

Seagrass meadows provide essential ecosystem services globally in the context of climate change. However, seagrass is being degraded at an accelerated rate globally due to ocean warming, ocean acidification, aquaculture, and human activities. The need for more information on seagrasses’ spatial distribution and health status is a serious impediment to their conservation and management. Therefore, we propose a new hybrid machine learning model (RF-SWOA) that integrates the sinusoidal chaos map whale optimization algorithm (SWOA) with a random forest (RF) model to accurately model the suitable habitat of potential seagrasses. This study combines in situ sampling data with multivariate remote sensing data to train and validate hybrid machine learning models. It shows that RF-SWOA can predict potential seagrass habitat suitability more accurately and efficiently than RF. It also shows that the two most important factors affecting the potential seagrass habitat suitability on Hainan Island in China are distance to land (38.2%) and depth to sea (25.9%). This paper not only demonstrates the effectiveness of a hybrid machine learning model but also provides a more accurate machine learning model approach for predicting the potential suitability distribution of seagrasses. This research can help identify seagrass suitability distribution areas and thus develop conservation strategies to restore healthy seagrass ecosystems.

Sedimentary organic carbon and nitrogen stocks of intertidal seagrass meadows in a dynamic and impacted wetland: Effects of coastal infrastructure constructions and meadow establishment time

Seagrass meadows, through their large capacity to sequester and store organic carbon in their sediments, contribute to mitigate climatic change. However, these ecosystems have experienced large losses and degradation worldwide due to anthropogenic and natural impacts and they are among the most threatened ecosystems on Earth. When a meadow is impacted, the vegetation is partial- or completely lost, and the sediment is exposed to the atmosphere or water column, resulting in the erosion and remineralisation of the carbon stored. This paper addresses the effects of the construction of coastal infrastructures on sediment properties, organic carbon, and total nitrogen stocks of intertidal seagrass meadows, as well as the size of such stocks in relation to meadow establishing time (recently and old established meadows). Three intertidal seagrass meadows impacted by coastal constructions (with 0% seagrass cover at present) and three adjacent non-impacted old-established meadows (with 100% seagrass cover at present) were studied along with an area of bare sediment and two recent-established seagrass meadows. We observed that the non-impacted areas presented 3-fold higher percentage of mud and 1.5 times higher sedimentary organic carbon stock than impacted areas. Although the impacted area was relatively small (0.05-0.07 ha), coastal infrastructures caused a significant reduction of the sedimentary carbon stock, between 1.1 and 2.2 Mg OC, and a total loss of the carbon sequestration capacity of the impacted meadow. We also found that the organic carbon stock and total nitrogen stock of the recent-established meadow were 30% lower than those of the old-established ones, indicating that OC and TN accumulation within the meadows is a continuous process, which has important consequences for conservation and restoration actions. These results contribute to understanding the spatial variability of blue carbon and nitrogen stocks in coastal systems highly impacted by urban development.

The planetary role of seagrass conservation

Seagrasses are remarkable plants that have adapted to live in a marine environment. They form extensive meadows found globally that bioengineer their local environments and preserve the coastal seascape. With the increasing realization of the planetary emergency that we face, there is growing interest in using seagrasses as a nature-based solution for greenhouse gas mitigation. However, seagrass sensitivity to stressors is acute, and in many places, the risk of loss and degradation persists. If the ecological state of seagrasses remains compromised, then their ability to contribute to nature-based solutions for the climate emergency and biodiversity crisis remains in doubt. We examine the major ecological role that seagrasses play and how rethinking their conservation is critical to understanding their part in fighting our planetary emergency.

Novel approach to large-scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden

According to the EU Habitats directive, the Water Framework Directive, and the Marine Strategy Framework Directive, member states are required to map, monitor, and evaluate changes in quality and areal distribution of different marine habitats and biotopes to protect the marine environment more effectively. Submerged aquatic vegetation (SAV) is a key indicator of the ecological status of coastal ecosystems and is therefore widely used in reporting related to these directives. Environmental monitoring of the areal distribution of SAV is lacking in Sweden due to the challenges of large-scale monitoring using traditional small-scale methods. To address this gap, in 2020, we embarked on a project to combine Copernicus Sentinel-2 satellite imagery, novel machine learning (ML) techniques, and advanced data processing in a cloud-based web application that enables users to create up-to-date SAV classifications. At the same time, the approach was used to derive the first high-resolution SAV map for the entire coastline of Sweden, where an area of 1550 km² was mapped as SAV. Quantitative evaluation of the accuracy of the classification using independent field data from three different regions along the Swedish coast demonstrated relative high accuracy within shallower areas, particularly where water transparency was high (average total accuracy per region 0.60-0.77). However, the classification missed large proportions of vegetation growing in deeper water (on average 31%-50%) and performed poorly in areas with fragmented or mixed vegetation and poor water quality, challenges that should be addressed in the development of the mapping methods towards integration into monitoring frameworks such as the EU directives. In this article, we present the results of the first satellite-derived SAV classification for the entire Swedish coast and show the implementation of a cloud-based SAV mapping application (prototype) developed within the frame of the project. Integr Environ Assess Manag 2022;18:909-920. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).