Detailed Inventory Research Articles

Optimization of Car Use Time for Different Maintenance and Repair Scenarios Based on Life Cycle Assessment

New propulsion technologies and the development of electromobility increase the environmental efficiency of the transport sector. However, the current structure of car fleets with a large number of old cars with worse environmental parameters undermines the benefits of technological progress in the automotive industry. Older cars may require a more extensive service, involving a thorough overhaul, repair or even replacement of many parts. The result of using additional resources is a deterioration of environmental performance. This study considers various maintenance and repair scenarios and focuses on the use phase using the example of the popular Ford Focus passenger car model. The life cycle optimization model was used for environmental assessment, which determines the optimal duration of the car use phase, accounting for detailed service information and the type and environmental efficiency class of the different fuel versions. This model can be an effective tool for car fleet management and car replacement policy. Detailed life cycle inventories of 40 Ford Focus II diesel and petrol cars that represent all maintenance and repairs are the inputs to the life cycle optimization model. The inventory data were provided by the Ford Service Center in Szczecin (Poland), which regularly serviced the cars with a mileage of up to 200,000 km. Inventory at other stages of the cars’ life cycles was modeled using Ecoinvent data. The results show that by considering these vehicle servicing aspects on the basis of more detailed data, impact assessments are better represented compared to other studies. Depending on the replacement policy, the contribution of vehicle maintenance and repair can account for almost half of the cumulative life cycle burdens.

Reliability assessment of open-source multiscale landslide susceptibility maps and effects of their fusion

ABSTRACT Several landslide susceptibility (LS) maps at various scales of analysis have been performed with specific zoning purposes and techniques. Supervised machine learning algorithms (ML) have become one of the most diffused techniques for landslide prediction, whose reliability is firmly based on the quality of input data. Site-specific landslide inventories are often more accurate and complete than national or worldwide databases. For these reasons, detailed landslide inventory and predisposing variables must be collected to derive reliable LS products. However, high-quality data are often rare, and risk managers must consider lower-resolution available products with no more than informative purposes. In this work, we compared different ML models to select the most accurate for large-scale LS assessment within the Municipality of Rome. The ExtraTreesClassifier outperformed the others reaching an average F1-score of 0.896. Thereafter, we addressed the reliability of open-source LS maps at different scales of analysis (global to regional) by means of statistical and spatial analysis. The obtained results shed light on the difference in hazard zoning depending on the scale and mapping unit. An approach for low-resolution LS data fusion was attempted, assessing the importance of the adopted criteria, which increased the ability to detect occurred landslides while maintaining precision.

Life Cycle Assessment of Composites Additive Manufacturing Using Recycled Materials

Additive manufacturing (AM) of composite materials is promising to create customizable products with enhanced properties, utilizing materials like carbon fibers (CFs). To increase their circularity, composite recycling has been proposed to re-introduce the recovered components in AM. A careful evaluation of recycling is necessary, considering the sustainability and functionality (i.e., mechanical properties) of the recovered components. Thus, Life Cycle Assessment (LCA) is applied to estimate the environmental impacts of AM via Fused Filament Fabrication (FFF), using virgin or recycled CFs via solvolysis at a laboratory scale. This study aims to provide a detailed Life Cycle Inventory (LCI) of FFF and evaluate the sustainability of using recycled CFs in AM. For both virgin CF manufacturing and CF recycling, electricity consumption was the main contributor to environmental impacts. CF recovery via solvolysis resulted in lower impacts across most impact categories compared to AM with virgin CFs. Different scenarios were examined to account for the mechanical properties of recycled CFs. AM with 75% recycled CFs, compared to 100% virgin CFs undergoing landfilling, resulted in over 22% reduction in climate change potential, even after a 50% loss of recycled CF functionality. Overall, this study offers insights into the LCI of FFF and shows that CF recycling from composites is worth pursuing.

An integrated perspective on the spatial–temporal characteristics of China’s manufacturing carbon emissions at the regional and industry levels

Given that manufacturing industry is the second-largest source of carbon emissions in China, effective carbon emission reduction plans require a comprehensive examination of the spatial–temporal characteristics (STC) of manufacturing carbon emissions (MCE) from both regional and industrial perspectives. However, most studies on the STC of MCE focus solely on regional perspectives, overlooking the importance of integrating both regional and industrial perspectives to fully understand the STC of China’s MCE. Moreover, there is limited research on the spatial disparities in China’s MCE. Building on a detailed carbon emissions inventory of 27 manufacturing industries across 30 provinces in China from 2001 to 2020, this study employed descriptive statistics, natural breakpoint classification, spatial–temporal transition, and the Dagum Gini ratio with its cluster decomposition method to analyze the STC of MCE from regional and industrial perspectives. The assessment of STC of MCE includes an examination of both spatial–temporal changes and disparities. Results show that the MCE of the three regions in China follow the order of east > central > west, with most of the super-heavy-duty MCE and heavy-duty MCE provinces located in the eastern and central regions. China’s three-tier manufacturing industries exhibit a pattern of “low-end high-end industries with small carbon emissions, while mid-end industries have large carbon emissions”. The regional differences show that the gap in the high-end MCE among the three regions is gradually narrowing, while the gap between mid-end MCE in the east and those in the central and western regions is widening. The overall disparities of MCE exhibited a continuous widening trend. In terms of sources, inter-regional disparities had the highest contribution rate, followed by intra-regional disparities, and super-dense disparities had the least contribution. Based on the research findings, this paper proposes targeted policy suggestions for carbon emission reduction in the manufacturing industry.

Barriers to and Facilitators of Participation in Health and Social Care Governance: Categories and Cross-Cutting Themes from a Survey of SHAPES Project Partners.

Good governance-aligned with human rights and rights-based care, participation, inclusion, and person-centredness-of digital care systems is integral to their ability to meet their objectives. To gain insight into existing governance structures and processes and participation experiences across Europe and lay foundations for the SHAPES Project's network governance (a healthy and active ageing Innovation Action consortium), our objectives included: 1) expand the list of known stakeholders, 2) explore how the range of stakeholders participate in health and social care governance, 3) develop an inventory of barriers and facilitators. Using an empirical, survey method, we consulted SHAPES Project partner organisations, with respondents invited to suggest specific participation barriers and facilitators. 16 organisations responded. Numerous additional stakeholders were identified. Circa 150 unique barriers and facilitators were reported, rationalised into 20 superordinate categories. Six cross-cutting themes were assembled: dimensionality and flux; power; opportunity and environments; interest, motivation, and choice; valuing governance participation, and duality. This work allows consideration of a wide range of stakeholders for the SHAPES collaborative governance model and future research, and for system design with the benefit of a detailed inventory of barriers and facilitators, and thematic contextualisation. Participation is modifiable and we suggest intervention targets and mechanisms.

Global site-specific health impacts of fossil energy, steel mills, oil refineries and cement plants

Climate change and particulate matter air pollution present major threats to human well-being by causing impacts on human health. Both are connected to key air pollutants such as carbon dioxide (CO_text {2}), primary fine particulate matter (PM_text {2.5}), sulfur dioxide (SO_text {2}), nitrogen oxides (NO_text {x}) and ammonia (NH_text {3}), which are primarily emitted from energy-intensive industrial sectors. We present the first study to consistently link a broad range of emission measurements for these substances with site-specific technical data, emission models, and atmospheric fate and effect models to quantify health impacts caused by nearly all global fossil power plants, steel mills, oil refineries and cement plants. The resulting health impact patterns differ substantially from far less detailed earlier studies due to the high resolution of included data, highlighting in particular the key role of emission abatement at individual coal-consuming industrial sites in densely populated areas of Asia (Northern and North-Eastern India, Java in Indonesia, Eastern China), Western Europe (Germany, Belgium, Netherlands) as well as in the US. Of greatest health concern are the high SO_text {2} emissions in India, which stand out due to missing flue gas treatment and cause a particularly high share of local health impacts despite a limited number of emission sites. At the same time, the massive infrastructure and export capacity build-up in China in recent years is taking a substantial toll on regional and global health and requires more stringent regulation than in the rest of the world due to unfavorable environmental conditions and high population densities. The current phase-out of highly emitting industries in Europe is found not to have started with sites having the greatest health impacts. Our detailed site-specific emission and impact inventory is able to highlight more effective alternatives and to track future progress.

Tree species richness and phytomass carbon stock along the urban-rural gradient in trees outside forests of Kathmandu valley, Nepal

ABSTRACT Trees outside forests (TOF) are woody plants found on land which is less than 0.5 ha. These are important for biodiversity conservation and carbon storage. This study was conducted in Kathmandu Valley, central Nepal to determine tree species diversity and phytomass carbon stock patterns along the urban to rural spatial gradient of TOF. A detailed inventory was conducted across a total of 209 randomly selected points generated by MS Excel using circular plots of 20 m radius each. A total of 6,210 trees from 150 species belonging to 111 genera and 57 families were recorded under three strata (urban, suburban and rural). Out of 150 species, 53.33% were native and 46.67% were exotic. The average tree density was found to be higher in the suburban stratum (248.44 ha−1) compared to the urban (232.58 ha−1) and the rural (224.88 ha−1) strata. Species richness was found to be higher in the rural stratum (14.09 ha−1) compared to the urban (9.85 ha−1) and the suburban (9.70 ha−1) strata. But average species richness was found to be highest in the urban stratum (55.95 ha−1) followed by the suburban (46.31 ha−1) and the rural (45.74 ha−1) strata, respectively. Similarly, the average phytomass carbon stock recorded was highest in the urban stratum (39.41 t ha−1) compared to the rural (29.19 t ha−1) and the suburban (26.06 t ha−1) strata. The average phytomass carbon stock varied significantly between the native and the exotic species only in the urban stratum but not in the suburban and the rural strata. This study provides baseline data useful for green urban planning and development as well as carbon sink management.

The characteristics and driving factors of household CO2 and non-CO2 emissions in China

Approximately 60% of global greenhouse gas (GHG) emissions are attributable to household consumption. This presents a great opportunity to reduce them. However, most research has focused only on CO2 emissions and paid limited attention to non-CO2 emissions which account for a quarter of household emissions. This study investigates key characteristics and driving factors associated with Chinese household emissions, which include CO2, CH4, N2O, and F-gases. Our work also combines detailed household survey data, GHG inventory, and input-output tables. The empirical results suggest that household GHG emissions per capita in 2015 were 2.74 tCO2eq, of which non-CO2 gases accounted for 27%, mainly from food (48%) and dwelling-related consumption (18%). Urban households emit 4.02 tCO2eq per capita—nearly twice that of rural households—and have a higher rate of non-CO2 emissions. Consumption expenditure is the primary driver of emissions differences between rural and urban households, while consumption structure only plays a minor role. The reduction in CO2 emissions resulting from urban homes switching from coal to natural gas has been offset by an increase in CH4 emissions from gas production and supply. These findings highlight the significance of non-CO2 emissions in policy-making, as well as the important potential of adjusting households' consumption structure.

Comprehensive landslide susceptibility map of Central Asia

Abstract. Central Asia is an area characterized by complex tectonics and active deformation; the related seismic activity controls the earthquake hazard level that, due to the occurrence of secondary and tertiary effects, also has direct implications for the hazard related to mass movements such as landslides, which are responsible for an extensive number of casualties every year. Climatically, this region is characterized by strong rainfall gradient contrasts due to the diversity of climate and vegetation zones. The region is drained by large, partly snow- and glacier-fed rivers that cross or terminate in arid forelands; therefore, it is also affected by a significant river flood hazard, mainly in spring and summer seasons. The challenge posed by the combination of different hazards can only be tackled by considering a multi-hazard approach harmonized among the different countries, in agreement with the requirements of the Sendai Framework for Disaster Risk Reduction. This work was carried out within the framework of the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project as part of a multi-hazard approach and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. To this aim the most detailed landslide inventories, covering both national and transboundary territories, were implemented in a random forest model, together with several independent variables. The proposed approach represents an innovation in terms of resolution (from 30 to 70 m) and extension of the analyzed area with respect to previous regional landslide susceptibility and hazard zonation models applied in Central Asia. The final aim was to provide a useful tool for land use planning and risk reduction strategies for landslide scientists, practitioners, and administrators.

Anthropic Vulnerability of a Hydrothermal Mineral Deposit and Related Mining Heritage, a Case Study from a Medieval Gold-Silver Mining Area, Telkibánya, Hungary

The movable geoheritage elements (minerals, fossils, rocks) are important part of geodiversity. Their anthropic vulnerability includes collection by geo-amateurs and professionals which has become a very popular activity in the last decades. The in situ protection of such geoheritage elements preserves their authenticity and integrity, but needs detailed inventory and assessment providing recommendation for geoconservation. The aim of this study is to investigate a medieval mining site of the Carpathians (Telkibánya, Hungary) where recent anthropic vulnerability related to mineral collecting. Earlier works emphasized the scientific, aesthetic, and geo-educational potential of the mineral association and mining heritage of the study area. Our field survey gives a review of medieval mining works and the current state of the surviving infrastructure identifies the major areas of mineral collecting disturbances and the integrity of the main and additional geological elements. Based on the degradation risk assessment, further conservation management initiatives are proposed by open collecting outcrops (exposure sites) and declared protection with controlled collecting (finite sites).

The interdependent networked community resilience modeling environment (IN-CORE)

In 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a fourteen university-based consortium of almost 100 collaborators, including faculty, students, post-doctoral scholars, and NIST researchers. This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE - the Interdisciplinary Networked Community Resilience Modeling Environment (IN-CORE). IN-CORE enables communities, consultants, and researchers to set up complex interdependent models of an entire community consisting of people, businesses, social institutions, buildings, transportation networks, water networks, and electric power networks and to predict their performance and recovery to hazard scenario events, including uncertainty propagation through the chained models. The modeling environment includes a detailed building inventory, hazard scenario models, building and infrastructure damage (fragility) and recovery functions, social science data-driven household and business models, and computable general equilibrium (CGE) models of local economies. An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population, economics, physical services, and social services. An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM) and support community resilience informed decision-making. Each testbed within IN-CORE has been developed by a team of engineers, social scientists, urban planners, and economists. Community models, begin with a community description, i.e., people, businesses, buildings, infrastructure, and progresses to the damage and loss of functions caused by a hazard scenario, i.e., a flood, tornado, hurricane, or earthquake. This process is accomplished through chaining of modular algorithms, as described. The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models, which have been the least studied area of community resilience but arguably one of the most important. Communities can then test the effect of mitigation and/or policies and compare the effects of “what if” scenarios on physical, social, and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.

Social insect transcriptomics and the molecular basis of caste diversity.

Studies of gene expression provide fundamentally important information on the molecular mechanisms underlying variation in phenotype. Recent technological advances have allowed for the robust study of gene expression through analysis of whole transcriptomes. Here, we review current advances in social insect transcriptomics and discuss their implications in understanding phenotypic diversity. Recent transcriptomic studies provide detailed inventories of the genes involved in producing distinct phenotypes in social species. These investigations have identified key genes and networks involved in producing distinct social insect castes. Nevertheless, questions concerning the evolution of gene expression patterns remain. We suggest a path forward for studying gene expression in future studies of biological systems.

Strategies to reduce power consumption in pico-satellites

To assess and rehabilitate degraded forests are global challenges. Due to the various causes and severity of degradation, they typically show a very diverse structure, which makes acquiring a detailed inventory, as a first step toward rehabilitation measures, difficult and costly. Area-based inventories based on satellite imagery are a well-established methodology to assess and classify the forest cover, but the information obtained is often not detailed enough to fulfil the needs of site-adapted rehabilitation in degraded forests with a highly diverse structure. Furthermore, due to the great variability of the forest structure, a great number of ground sample plots are necessary to establish statistically sound predictions of structural parameters. We propose to test a real mission in Azores Island, based on the typical pico-satellites architectures, after provide some strategies to create a new architecture design and predict power consumption, based on the sentences of low-power, very small scale, low-cost and very integrated system.

Landslides Triggered by the 2016 Heavy Rainfall Event in Sanming, Fujian Province: Distribution Pattern Analysis and Spatio-Temporal Susceptibility Assessment

Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous area. From 5 to 10 May 2016, Sanming City in Fujian Province, China, experienced a heavy rainfall event that caused massive landslides, leading to significant loss of life and property. Using high-resolution satellite imagery, we created a detailed inventory of landslides triggered by this event, which totaled 2665 across an area of 3700 km2. The majority of landslides were small-scale, shallow and elongated, with a dominant distribution in Xiaqu town. We analyzed the correlations between the landslide abundance and topographic, geological and hydro-meteorological factors. Our results indicated that the landslide abundance index is related to the gradient of the hillslope, distance from a river and total rainfall. The landslide area density, i.e., LAD increases with the increase in these influencing factors and is described by an exponential or linear relationship. Among all lithological types, Sinian mica schist and quartz schist (Sn-s) were found to be the most prone to landslides, with over 35% of landslides occurring in just 10% of the area. Overall, the lithology and rainfall characteristics primarily control the abundance of landslides, followed by topography. To gain a better understanding of the triggering conditions for shallow landslides, we conducted a physically based spatio-temporal susceptibility assessment in the landslide abundance area. Our numerical simulations, using the MAT.TRIGRS tool, show that it can accurately reproduce the temporal evolution of the instability process of landslides triggered by this event. Although rainfall before 8 May may have contributed to decreased slope stability in the study area, the short duration of heavy rainfall on 8 May is believed to be the primary triggering factor for the occurrence of massive landslides.

Global declines of offshore gas flaring inadequate to meet the 2030 goal

Monitoring of gas flaring (GF)—the burning of natural gas associated with oil extraction—over most oil- and gas-producing areas is challenging due to high costs or difficult field investigations. As GF contributes to both global warming and air pollution, an up-to-date picture (locations, emissions and trends) of global offshore GF can help countries’ energy decarbonization efforts substantially. Although high-resolution satellite sensors regularly capture high-temperature signals from GF, retrieving spatially explicit information and estimating GF volumes from petabyte images remain challenging. Here we developed a monitoring framework and compiled a 20 m resolution inventory of offshore GF sites by analysing ~8.53 million Sentinel-2 images. A robust model (R2 > 0.99) was established to estimate offshore GF volumes from Sentinel-2 metrics. Our findings reveal that Sentinel-2 can pinpoint global offshore GFs to support scientifically sound decision-making; a vital few (~20%) sites are responsible for >80% of offshore GF volumes, calling for more targeted regulations; and the offshore GF volumes have declined by 26.4% from 2016 to 2021. We conclude that the Zero Routine Flaring Initiative by the Global Gas Flaring Reduction Partnership committing governments and oil companies to end routine flaring by no later than 2030 could be 5 years behind schedule. Monitoring of gas flaring (GF) can be expensive and practically difficult, but better information about global offshore GF is needed to inform decarbonization policies. This study presents a monitoring framework, a detailed inventory of offshore GF sites and estimates of GF volumes globally.

Life Cycle Assessment of an Industrial Laundry: a Case Study in the Italian Context

The high volumes of wastewater from industrial laundry with known toxicological concerns represent a relevant source of pollution for water bodies. Moreover, the unavailability of a detailed and specific Life Cycle Inventory (LCI) referring to the use of detergent within the laundry system could undermine the overall quality of the environmental assessment. This is related to the use of a substitutional product or proxy dataset for specific processes like the use of detergents. Laundry services are also known as highly energy consuming sites. This paper thus aims to make a Life Cycle Inventory (LCI) and Assessment (LCA) for an industrial laundry to provide the environmental profile for an Italian case study. The primary data input to finalize the LCI came from data collected directly from an Italian industrial laundry, integrated with literature, data provided from supporting databases (i.e. Ecoinvent 3.8), and data specifically obtained from the technical datasheets of detergents. The industrial laundry system considers the product’s overall supply chain: extraction and manufacturing of raw materials, including the detergent, transportation and logistics, the industrial process associated with the laundry activity, wastewater treatment, recirculation packaging, and final disposal stages. The calculated environmental profiles refer to the functional unit of 1 kg of linen washed by a standard washing cycle. The system boundaries of this study include the production stages of the process. The analysed activities are the transportation for the delivery and collection of linen, the purchase of raw materials, and the sanitization and washing processes. SimaPro 9.2 software and the ReCiPe 2016 H method are used for the LCA study. The baseline scenario has been compared with an alternative scenario introducing renewable energy technology (i.e. solar PV panel). The result shows a total impact of 12.77 mPt. The most impacting activities are the washing phase (4.62 mPt), the ironing phase (4.29 mPt), and the drying phase (1.56 mPt). The greatest impact in the washing phase is caused by the use of detergents and washing products. It is observed that most of the impacts fall into the categories of ‘Global Warming, Human Health’, ‘Fine Particulate Formation’, ‘Carcinogenic Human Toxicity’, ‘Non- Carcinogenic Human Toxicity’, ‘Fossil Resource Scarcity’. The midpoint category with the highest impact is ‘Fine Particulate Formation’ with a value of 5.18 mPt. The alternative scenario introducing renewable energy technology (i.e. solar PV panel) reduces the impact by 19.7 %. Sensitivity analyses have been performed to evaluate the LCA model’s uncertainty, with specific reference to the washing agents, the transportation of raw materials, and the energy consumption.

Using machine learning to identify urban forest crown bounding boxes (CBB): Exploring a new method to develop urban forest policy

Collecting and managing individual tree data is a critical activity for green sustainability strategies. Local governments are able to easily collect detailed public tree inventories, however data on trees located on private land are much more challenging and costly to collect. This means that new regulations to limit the removal of trees on private land go untested prior to their implementation, or fail to pass regulatory review processes. Without knowledge of the location of trees or the range of their different sizes, Local Government Authorities (LGAs) are unable to predict where a new policy to prohibit the removal of trees of a certain size is likely to have the greatest effect, where enforcement should be concentrated, or to convince government, the development sector and local communities of the need for action to preserve trees.The aim of this study was to explore the potential of a supervised machine learning algorithm as a cost-efficient method to understand tree sizes and locations on private land and to discuss how this information could be used for sustainable urban greening. We conclude by discussing some of the affordances of this approach to better target native vegetation protection and protect large trees; and report on the precision and recall of the detection of the urban forest.

Kitâbu’t-Tahrîş ve Kitâbu’l-Makâlât’ın Tahkikinde Esas Alınan El Yazmaları Üzerine

With this study, it is aimed to clarify some ambiguities about the origin of the manuscripts that the edition of Kitāb al-Tahrīsh and Kitāb al-Maqālāt, which we published recently, rest on. Those editions which we expect to open new horizons in terms of the history of Islamic theology and hadith criticism, have reached today via Yemeni libraries, like other Muʿtazila cultural heritage items. Due to the political instability caused by the long-term civil wars in the country, a detailed inventory of private or official libraries in Yemen, which has rich collections of manuscripts on Islamic culture, has not been made until now. Although some of these manuscripts were collected in the Sanaa Library (Maktabat al-Awqāf), most of them are still preserved in mosques’ libraries or private collections under inappropriate conditions. Since there are no reliable records or indexes of these manuscripts, it is not possible to give reliable information about the origin of each item especially the status of those found in private collections or family libraries as called Buyūt alʿilm. Today, most of the Yemeni manuscripts found in the libraries of Europe, America and Islamic countries have been purchased by manuscript collectors, merchants, foreign researchers, or other illegal ways. The ones that have been published so far are either based on these manuscripts or similarly on copies from Yemen. This ambiguity about the origin of the manuscripts can sometimes lead to unjust accusations that target the editors of them, as if they used illegal copies on purpose. In this article, we tried to clarify the ambiguities and controversial points related to those manuscripts which our edition is based on by giving information about the obtaining manuscripts and edition processes.

Iranian wetland inventory map at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform

Detailed wetland inventories and information about the spatial arrangement and the extent of wetland types across the Earth's surface are crucially important for resource assessment and sustainable management. In addition, it is crucial to update these inventories due to the highly dynamic characteristics of the wetlands. Remote sensing technologies capturing high-resolution and multi-temporal views of landscapes are incredibly beneficial in wetland mapping compared to traditional methods. Taking advantage of the Google Earth Engine's computational power and multi-source earth observation data from Sentinel-1 multi-spectral sensor and Sentinel-2 radar, we generated a 10m nationwide wetlands inventory map for Iran. The whole country is mapped using an object-based image processing framework, containing SNIC superpixel segmentation and a Random Forest classifier that was performed for four different ecological zones of Iran separately. Reference data was provided by different sources and through both field and office-based methods. Almost 70% of this data was used for the training stage and the other 30% for evaluation. The whole map overall accuracy was 96.39% and the producer's accuracy for wetland classes ranged from nearly 65 to 99%. It is estimated that 22,384 km2 of Iran are covered with water bodies and wetland classes, and emergent and shrub-dominated are the most common wetland classes in Iran. Considering the water crisis that has been started in Iran, the resulting ever-demanding map of Iranian wetland sites offers remarkable information about wetland boundaries and spatial distribution of wetland species, and therefore it is helpful for both governmental and commercial sectors.

Technology for adaptation: a case study of developing a detailed inventory of drinking water supply technologies along the salinity-affected coastal region of Bangladesh

Abstract The south-western coastal zone of Bangladesh is suffering from an acute crisis of freshwater due to salinity intrusion. First, the extent of the problem and its causes in detail were investigated. Climate change along with a few other anthropogenic impacts are the main causes. Exploring technologies for adaptation to climate change is emphasized today to overcome the problem of climate change impact. The coastal community was found to be already adopting technological measures as an adaptation means. This study developed a detailed inventory of all the available indigenous water supply technology options along the region and categorized them. An analysis of the suitability of the technologies was done, focusing on factors like the state of the technology, convenience in operation, quantity and quality of the supplied water, as well as financial viability or management practice. Both qualitative and quantitative approaches to the study were adopted to collect and analyze the data through extensive field visits, laboratory testing, and secondary data analysis. It is found that in most cases, solutions are on an ad hoc basis, having a lifetime of less than 5 years. In some places, people are gradually moving towards community-based and long-term hi-tech solutions.