A GIS-based approach to determining optimal location for decentralized inner city smart filters: Toward net zero cities.

Climate change has already begun to take visible effect globally in recent years. Given the climate change paradox and urbanization trends, cities' success would not only depend on smartness and sustainability, but also resilience to all forthcoming economic, environmental, or behavioral changes. Numerous technologies have surfaced and proved effective in CO2 removal from the local environment. However, the optimal placement of these smart filters is a complex task and require logical and strategic decision-making. Determining the optimal location is one of the key factors for establishing a network of smart air filters. This study used a GIS-based suitability analysis for identifying optimal locations for smart filters based on pollution hotspots (population and spatial proximity to industry, commercial centers, roads, high-traffic areas, and intersections). The spatial analysis involves the determination and preparation of input layers, ranking layers, assigning weights to each criterion, and generation of a suitability map. The sites with a higher suitability score (7 or above) are optimum sites for air filters. The sites are spatially distributed over different regions. The findings revealed that GIS-based suitability analysis can be an effective technique for placing smart filters within an urban environment. These findings can help decision-makers to prioritize the location considering environmental constraints. The proposed solution aims to pave the way for fostering resilient, smart, and sustainable cities through a community sensing platform targeting hotspots within spatial variations.

The suitability mapping of an urban spatial structure for earthquake disaster response using a gradient rain optimization algorithm (GROA).

The urban spatial structure has a prominent role in the earthquake response process which should primarily be assessed in the areas that are most vulnerable to earthquake hazards. Search and rescue teams need to map and identify the appropriateness of urban infrastructures for disaster reaction after a quake to enable ease of movement and quick assistance to the casualties. The key objective of this study is to compute the appropriateness of a municipal spatial structure for crisis reaction after a destructive earthquake, with an emphasis on finding the most critical areas (those that are prone to emergency response disruption). The main contribution involves improving a geographic information system (GIS)-based earthquake-triggered hybrid framework for suitability analysis using a fuzzy analytical hierarchical process (FAHP) and gradient rain optimization algorithm (GROA). The modifying of a rain optimization algorithm (ROA) to a GROA based on gradient descent is carried out to avoid local optima, which results in optimizing the identification process of the key locations for emergency response. The planned approach has been executed in Tehran, the capital of Iran. The implementation consequences reveal the supreme crucial areas for emergency response in the study area with a demonstration of the efficiency of the GROA compared to the basic ROA. Both indicate that these sites are located in the west and southwest, while the junction degree and width of the roads are the most significant factors affecting a city's suitability for emergency response. In addition, the GROA is less sensitive to local optima and more economical than the ROA. Moreover, several rescue experts and urban planners expressed their high satisfaction (95 %) with the five-level suitability map for prioritizing the deployments of troops along with the critical area maps for preventing heavy casualties produced by the GROA.

Land suitability assessment for wheat-barley cultivation in a semi-arid region of Eastern Anatolia in Turkey.

The efficient use and sustainability of agricultural lands depend heavily on the characteristics of soil resources in a given area, as different soil properties can significantly impact crop growth and yield. Therefore, land suitability studies play a crucial role in determining the appropriate crops for a given area and ensuring sustainable agricultural practices. This study, conducted in Tusba District-Van, Turkey, represents a significant advancement in land suitability studies for wheat-barley cultivation. Using geographic information systems, the analytical hierarchical process method, and the standard scoring function, lands were determined based on the examined criteria for the suitability of wheat-barley cultivation. One of this study's main findings is identifying critical factors that influence the suitability of land for wheat-barley cultivation. These factors include slope, organic matter content, available water capacity, soil depth, cation exchange capacity, pH level, and clay content. It is important to note that slope is the most influential factor, followed by organic matter content and available water capacity. A Soil Quality Index map was produced, and the suitability of wheat-barley production in the studied area was demonstrated. More than 28% of the study area was very suitable for wheat-barley production (S2), and more than was 39% moderately suitable (S3). A positive regression (R2 = 0.67) was found between soil quality index values and crop yield. The relationship between soil quality index values and crop yield is above acceptable limits. Land suitability assessment can minimize labor and cost losses in the planning and implementation of sustainable ecological and economic agriculture. Furthermore, land suitability classes play an active role in the selection of the product pattern of the area by presenting a spatial decision support system.

Rainwater harvesting for agriculture development using multi-influence factor and fuzzy overlay techniques.

Rainwater harvesting (RWH) is an essential technique for enhancing agricultural development, particularly in regions facing water scarcity or unreliable rainfall patterns. Water shortage, however, is one of the key causes of low crop production especially in mountainous regions like the Khyber Pakhtunkhwa province where most rainwater is lost by runoff. Therefore, rainwater harvesting could be a suitable to make better use of runoff and increase crop production. The study focuses on selecting suitable rainwater harvesting sites in District Karak to enhance agriculture by utilizing multi-influence factor (MIF) and fuzzy overlay techniques. We considered seven factors, i.e., land use land cover (LULC), slope, geology, soil, rainfall, lineament, drainage density, to create a ranking system to understand its application in site selection analysis. The results were combined into one overlay process to produce a rainwater harvesting suitability map. The weighted overlay analysis of the MIF model results reveals that 167.96 km2 area has a very high potential for rainwater harvesting, 874.17 km2 has a high potential, 1182.92 km2 has a moderate and 354.50 km2 has a poor potential for rainwater harvesting. The fuzzy overlay analysis revealed that 257.53 km2 has a very high potential for rainwater harvesting, 896.56 km2 area is classified as high, 1018.30 km2 moderate, and 407.7 km2 has poor potential for rainwater harvesting. The findings of this research work will help the policymakers and decision-makers construct various rainwater harvesting structures in the study area to overcome the water shortage problems.

A site suitability analysis for castor (Ricinus communis L.) production during Brazil's second harvest incorporating disease prediction.

Castor (Ricinus communis L.) is an important industrial crop with a wide range of industrial and pharmaceutical applications. Brazil is among the largest castor-producing countries. Between 2004 and 2010, castor cultivation was stimulated with an emphasis towards biodiesel production. However, this was not enough to leverage the production of castor in Brazil, mainly due to the lack of structured trade and the competition with other cheaper raw materials for the production of biodiesel. Despite this failure, the species presents itself as an excellent alternative for crop rotation in the second crop among soybean, corn, beans, and cotton cultivation areas as the oil is highly valuable for other products. Moreover, it has drawn the attention of producers and researchers in Brazil for this potential rotation as it is considered a plant tolerant of water-deficiency and is highly susceptible to gray mold, a disease favored by high humidity in the final stages of the crop. For instance, its cultivation in the second crop in Cerrado regions, where rains occur in the early stages of the crop and cease when the plants reach the final stage of production, has been successful and shows great promise. The current study aimed to evaluate the suitability of environments throughout Brazil to grow castor, incorporating variables associated with the incidence of gray mold and confirm these findings based on existing castor trial data obtained from the literature. The site suitability analysis determined that 74.99 million hectares - 8.8% of Brazilian territory - are highly suitable for castor production during second harvest, mostly located in the Northeastern and Midwestern regions. These results are surprising since Brazil currently has around 7.8% (∼66.81 million hectares) of its territory occupied with agriculture (grains, fruits, vegetables, and perennial crops). The findings of this study provide a method to perform site suitability for crops using data associated with agronomic and disease characteristics, as is the case with gray mold that often results in significant losses in castor production. Also, this analysis provides evidence for the great potential of Brazil to increase castor production and meet the world demand for its oil through utilization of second-crop cultivation.

Site suitability analysis for locating construction and demolition waste transfer station: an Indian case study.

Construction and Demolition Waste Management (CDWM) includes collecting, transporting, processing, and disposing construction and demolition (C&D) waste, where collection and transportation of bulky and voluminous C&D waste contribute significantly to economic and environmental impacts. Transfer station (TS) being a link between various waste management (WM) facilities plays a significant role in collection and transportation of waste. Thus, locating TS at suitable site can help in reducing the overall impacts. Employment of Geographic Information System (GIS) analysis tools in CDWM is a powerful strategy for site suitability study. A case study in Coimbatore, India, is presented in this study using GIS-based multi-criteria analysis for locating C&D waste TS. The criteria for site suitability analysis are chosen based on literature review, regulations, and experts' opinions. Weights of the chosen criteria are estimated using analytic hierarchy process (AHP), and the final suitability map is created by weighted overlay analysis (WOA) in GIS environment. Results provide first-hand information for local decision makers to locate C&D waste transfer station in the chosen study region and report that 12% of the entire area is "highly suitable" for transfer station location.

Standardizing the factors used in wind farm site suitability models: A review.

As global wind energy capacity continues to expand, the need to site commercial wind farms in productive, affordable, and technically feasible locations has become increasingly important. The use of wind farm site suitability models to identify these locations has grown consequently, thus increasing interest in standardizing certain aspects of these models' development. This systematic review of wind farm site suitability studies seeks to identify similarities and differences in the selection and representation of their enlisted siting factors. The review focuses on how subjective modeling decisions, such as vocabulary choices and dataset selection, occur in the literature, based on five identified themes: 1) Deciding Upon Siting Factors, which explains how a study's geographical context, selected modeling approach, and modeler decisions can influence siting factor selection; 2) Classifying Data and Siting Factor Terminology, which addresses the extent and the advantages of consistent siting factor vocabulary; 3) Implementing Siting Factors as Constraints or as Evaluation Criteria, which covers the importance of consistent implementation and of specifying logic when enlisting siting factors to assess potential wind farm sites; 4) Utilizing Primary and Secondary Data, which details how a study's reliance on external or self-collected datasets influences siting factor representation; and 5) Data Source and Accessibility, which highlights the inconsistent provision of citations and dataset sources, and the availability of datasets for siting factors to the broader scientific community. Standardizing the selection and representation of siting factors would benefit comparisons between wind farm site suitability studies and communication of model outputs to a wider audience.

Current and Potential Future Global Distribution of the Raisin Moth Cadra figulilella (Lepidoptera: Pyralidae) under Two Different Climate Change Scenarios.

Global trade facilitates the introduction of invasive species that can cause irreversible damage to agriculture and the environment, as well as stored food products. The raisin moth (Cadra figulilella) is an invasive pest that poses a significant threat to fruits and dried foods. Climate change may exacerbate this threat by expanding moth's distribution to new areas. In this study, we used CLIMEX and MaxEnt niche modeling tools to assess the potential global distribution of the raisin moth under current and future climate change scenarios. Our models projected that the area of suitable distribution for the raisin moth could increase by up to 36.37% by the end of this century under high emission scenario. We also found that excessive precipitation decreased the probability of raisin moth establishment and that the optimum temperature range for the species during the wettest quarter of the year was 0-18 °C. These findings highlight the need for future research to utilize a combined modeling approach to predict the distribution of the raisin moth under current and future climate conditions more accurately. Our results could be used for environmental risk assessments, as well as to inform international trade decisions and negotiations on phytosanitary measures with regards to this invasive species.

Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass Supply Chain.

A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about 30% more distance covered for feedstock transportation.

GIS-based spatial suitability assessment for pacific oyster Crassostrea gigas reef restoration: A case study of Laizhou Bay, China.

The oyster reef restoration has raised concerns worldwide due to the advantage of alleviating eutrophication and other ecosystem services. The necessary prerequisite for oyster restoration is identifying suitable sites. A restoration suitability index model for C. gigas reefs was built by combining the Monte Carlo simulation, analytic hierarchy process, hydrodynamic modelling and geographic information systems and applied in Laizhou Bay (LZB), China. The model outputs and sensitivity analysis showed that the east coastal area, the southern part, and the western part of LZB were restoration sites with the maximum suitability, accounting for 15.3 % of LZB. The middle and northern parts of LZB were unsuitable sites restricted by water depth and flow velocity. Ship waterways and marine industries were the major factors causing degradation of the historically recorded oyster reefs. This model can support oyster restoration planning, and can be applied to other oyster species with adjusted indicator system.

GIS-based weighted overlay model for wind and solar farm locating in Sri Lanka.

With regard to wind and solar energy, research has shown a clear discrepancy between the amount of energy currently being used with the existing potential. The present research has attempted to develop a geographic information systems (GIS) based weighted overlay model to identify potential locations for the development of wind and solar farms in Sri Lanka, which are rapidly advancing renewable energy resources globally. Four major land-use types were considered for the model development as least productive land areas to establish wind and solar farms, namely, barren lands, sand areas, open forests, and scrublands. Nine and seven criteria were identified for the wind and solar farm siting respectively, and the relative weights for each criterion were obtained through focused group discussions. The weighted overlay model was used to locate suitable areas and higher the values generally indicate that a location is more suitable. The outcome of the weighted overlay model shows that for the wind farm locating, 141 km2 of highly suitable lands available accounts for 3% of the total land area of Sri Lanka. Furthermore, the model results indicated a total area of 99 km2 as highly suitable for solar power generation. Central, Sabaragamuwa, Uva, and Northern provinces were found to be the most suitable for wind farm locating and Eastern, Southern, and Northern provinces we identified as highly suitable for the solar farm locating. The model validation performed by overlaying the results with the existing solar and wind farm map of Sri Lanka indicated that the highly suitable, moderately suitable, and marginally suitable lands identified by the weighted overlay lied within 1 km radius of the existing wind and solar power plants. Thus, it can be concluded that GIS-based weighted overlay model can serve as a primary screening tool to shortlist the optimum land areas to locate future solar and wind farms in any selected geographical location.

Geo environmental green growth towards sustainable development in semi-arid regions using physicochemical and geospatial approaches.

The process of determining whether a specific portion of land is suitable for a specific purpose is known as land suitability analysis (LSA). In order to promote sustainable development in semi-arid regions, the objective of this study is to analyse, evaluate, and identify the land for green growth based on topography, climate, and soil characteristics. Twelve thematic maps are prepared by using remote sensing satellite data. The Landsat 8 OLI/TIRS is used for the preparation of the thematic maps like land use land cover (LULC), normalized difference vegetation index (NDVI), top soil grain size index (TGSI), and geomorphology (GM), and DEM data is used for the preparation slope, and drainage density (DD). The collateral data is used to prepare geology and soil thematic maps. From the field work, we have collected soil samples for the compulsory physicochemical parameters such as soil EC and soil N-P-K which were taken into consideration and prepared thematic maps. The analytical hierarchy process (AHP) was used to generate the LSA of the research region, by assigning the appropriate weights to each criterion and sub-criterion for the thematic maps. Geographic information systems (GIS) and the multicriteria decision-making (MCDM) approach were used in the study's methodology. The LSA of the study area has been categories in to four types, i.e., highly suitable, moderately suitable, marginally suitable, and not suitable. The results revealed that 421.31 sq.km (40.09%) is not suitable for agriculture green growth in the study region, whereas 89.58 sq.km (8.52%) is moderately suitable, 267.66 sq.km (25.47%) is marginally suitable, and 266.54 sq.km (25.36%) is highly suitable. Accuracy assessment has validated the LSA map's accuracy (AA). The AA of LSA is 84.22%, which demonstrates a strong connection with the actual data. The research's results could be helpful in locating productive agricultural areas in various parts of the world. The decision-making AHP tool paired with GIS provides a novel method.

Analytical hierarchy process tool in Google Earth Engine platform: a case study of a tropical landfill site suitability.

Kolkata being a metropolitan city in India has its main municipal solid waste dumpsite situated at Dhapa just adjacent to the East Kolkata Wetlands (Ramsar site). The current prevalent situation at Dhapa is open dumping leading to various contaminations and hazards putting forth the need to look for alternative sites where the landfiilling operation can be shifted to using scientific methods. A user interface (UI)-based analytical hierarchy process (AHP) tool has been developed within the Google Earth Engine (GEE) cloud platform to find out the alternative dumping sites using geospatial layers. AHP function is not available as a native algorithm or developed by any researcher in GEE. The tool has three major functionalities, of which the first one handles the UI elements. The AHP procedure is within another function, and the last function integrates the AHP coefficients to the layers generating the final suitability layer. Users can also upload comparison matrix as GEE asset in the form of CSV file which gets automatically integrated into the AHP to calculate the coefficients and consistency ratio to generate the spatial suitability layers. This approach showcases a generalized AHP function within the GEE environment, which has been done for the first time. The tool is designed in the cloud platform which is dynamic, robust and suitable for use in various AHP-based suitability analysis in environmental monitoring and assessment.

Marble waste site suitability assessment using the GIS-based AHP model.

Site suitability with regards to environmental protection, public concerns, and the legitimate prerequisite is a basic issue that has been tended to in this study. By and large, marble waste is being unloaded on accessible open spaces or released in water to the close by waterways in the territory, Mohmand marble zone (Shabqadar), Khyber Pakhtunkhwa, Pakistan. Suitability assessment for marble waste collection and disposal was carried out through the integrated approach of analytical hierarchy process (AHP) and geographic information system (GIS) to limit the ecological dangers, public, and government concerns related to marble waste. The available land use was ordered into three main land use classes followed by six sub-classes including water bodies and agriculture (environmental), settlement and social site (social), and marble units and roads (economic). These sub-classes in the investigation region were organized through pairwise correlation and weighted sum analysis, AHP procedure. The AHP results were interpreted through GIS tools of digitization, buffering, and overlay in ArcMap, ArcGIS. The integrated AHP and GIS outcomes were consolidated to get the optimum results of the study, marble waste collection, and disposal options. It was concluded that priority should be given to water bodies followed by agricultural land while protecting the available land use classes from marble waste hazards. The percent priority values calculated are 32.33%, 30.50%, 12.16%, 10.66%, 8.50%, and 6% for water bodies, agricultural land, settlements, marble processing units, roads, and cultural sites respectively. The sequence of priority of the land use values are waterbodies > agriculture > settlement > marble industries > road > cultural site. The proposed integrated model is helpful in site suitability for waste management by the authorities and decision-makers associated with waste management.

Waste to energy spatial suitability analysis using hybrid multi-criteria machine learning approach.

Municipal solid waste is typically managed in developing countries through various disposal methods, such as sanitary landfills or dumpsites. Alternatively, waste to energy (WTE) systems have been recently adopted to provide sustainable waste management and diversify the energy mix. The abundance of remotely sensed datasets and derivatives, along with the rapid development of artificial intelligence, can offer an effective solution for WTE site selection. In this study, an analytical hierarchy process (AHP)-based framework supported by multiple machine learning algorithms (gradient boosted tree (GBT), decision tree (DT), and support vector machines (SVMs)) was established to explore the optimum location for WTE facilities. Various social, legal, environmental, economic, morphological, and land cover parameters were considered under 11 thematic geospatial raster layers. The proposed framework was applied to the 1.5-million-capita city of Sharjah, United Arab Emirates. A novel approach was developed to incorporate Gaussian dispersion modeling for the expected air pollution emissions from a WTE facility. The results showed that the accuracy performance sequence of the algorithms was 94.6, 93.9, and 91.8% for GBT, DT, and SVM, respectively. It was found that the distance from existing landfills had the most critical impact on the optimum location of the WTE facility, followed by the distance from coastline and elevation. The AHP consistency check revealed an acceptable overall criteria consistency index and the ratio of 0.0344 and 0.019, respectively. The results showed that 16.6% of Sharjah was considered extremely highly suitable areas. This research supports decision-makers in developing local guidelines for siting WTE facilities and determining the most suitable locations for such projects.

Suitability Analysis of Acoustic Refugia for Endangered Killer Whales (Orcinus orca) Using the GIS-based Logic Scoring of Preference Method.

An emerging priority in marine noise pollution research is identifying marine "acoustic refugia" where noise levels are relatively low and good-quality habitat is available to acoustically sensitive species. The endangered Southern Resident population of killer whales (Orcinus orca) that inhabits the transboundary Salish Sea in Canada and the USA are affected by noise pollution. Geographic Information Systems (GIS) and spatial multicriteria evaluation (MCE) methods have been used to operationalize suitability analysis in ecology and conservation for site selection problems. However, commonly used methods lack the ability to represent complex logical relationships between input criteria. Therefore, the objective of this study is to apply a more advanced MCE method, known as Logic Scoring of Preference (LSP), to identify acoustic refugia for killer whales in the Salish Sea. This GIS-based LSP-MCE approach considers multiple input criteria by combining input data representing killer whale habitat requirements with noise pollution and other factors to identify suitable acoustic refugia. The results indicate the locations of suitable acoustic refugia and how they are affected by noise pollution from marine vessels in three scenarios developed to represent different levels of vessel traffic. Identifying acoustic refugia can contribute to efforts to reduce the effect of marine noise pollution on killer whale populations by highlighting high-priority areas in which to implement policies such as traffic-limiting measures or marine protected areas. Moreover, the proposed LSP-MCE procedure combines criteria in a stepwise manner that can support environmental management decision-making processes and can be applied to other marine suitability analysis contexts.

Assessing the Impacts of Relative Wealth and Geospatial Factors on Water Access in Rural Nepal: A Community Case Study.

As one of the poorest nations, citizens of Nepal lack access to safe, affordable, and sufficient drinking water. While many nationwide studies have been performed at a country or regional level in Nepal to determine regions of the highest vulnerability, this study uniquely recognizes the economic heterogeneity within a single rural village and assesses the impact of household socioeconomic status on water access at the intracommunity level. Household surveys in a rural village setting provided the information for a locally-informed relative wealth index. A spatial analysis determined suitable locations for future installation of improved water sources to prioritize water access for the community's most vulnerable households. Three sites were shown to be optimal for future water source construction. This study provides a blueprint to assess water inequalities within a single village and incorporate forward-thinking development approaches to water access.

[Research on planting area and industrial development strategy of Tibetan medicine].

Appropriate producing areas can guarantee the quality of Tibetan medicine, but research on the suitable ecological factors and suitable producing areas of Tibetan medicinal plants is scarce. This paper used the Geographic Information System for Global Medicinal Plants(GMPGIS) to analyze the ecological suitability of Tibetan medicinal plants nationwide. For the first time, the range of ecological factors and main soil types of Tibetan medicinal plants were extracted, such as the average annual temperature-19.4-24.2 ℃, annual average precipitation 17-4 088 mm, annual average sunshine 124.2-171.6 W·m~(-2). The main soil types were black calcareous soil, thin layer soil, chestnut soil and so on. Based on 337 sampling points, the largest ecological similarity area of Tibetan medicine across the country was obtained through ecological similarity analysis. In addition to Tibet and Qinghai provinces and Tibetan Autonomous Prefectures in Sichuan, Gansu, and Yunnan provinces, Jiuquan city and Linxia county in Gansu province, Panzhihua and Ya'an in Sichuan province, and Xinjiang, Inner Mongolia, and Shanxi provinces also had larger suitable cultivation areas. In addition, by analyzing the current situation of Tibetan medicine industry, the research pointed out that there were some problems such as unreasonable development and utilization of resources, lack of standards and norms, weak basic research and imperfect industrial system, and made corresponding countermeasures for sustainable development of resources, formulation of standards and specifications, promotion of medicine through science and technology, expansion of domestic and foreign markets, etc. This study provided the basis for guiding the rational layout of production bases, introduction and breeding of plant Tibetan medicine nationwide, laying the foundation for the scientific and standardized production of high-quality Tibetan medicine, clarifying the development direction of Tibetan medicine industry, and providing ideas for the development strategy of Tibetan medicine and other national medicine industry.

Applying a combined geospatial and farm scale model to identify suitable locations for mussel farming.

Mussel farming has increasingly come into focus as a potential mitigation measure for fish farms and eutrophication, in addition to being a food source. This study presents a GIS-based suitability analysis combined with a farm scale model to identify appropriate mussel farming sites. The sites are investigated in terms of potential mussel harvest, nutrient removal, and effects on water transparency. The model is applied to the south-western Baltic Sea. The identified suitable area is about 5-8% of the case study extent. The model shows that elevated chlorophyll levels stimulate mussel growth and that upon mussel harvest, nutrients can be removed. A single mussel farm cannot compensate for all nutrients emitted by a fish farm, but it can increase water transparency up to at least 200 m from the farm. Potential nutrient removal and water transparency increases are essential criteria for site selection in eutrophic seas, such as the Baltic Sea.

The UK's suitability for Aedes albopictus in current and future climates.

The Asian tiger mosquito Aedes albopictus is able to transmit various pathogens to humans and animals and it has already caused minor outbreaks of dengue and chikungunya in southern Europe. Alarmingly, it is spreading northwards and its eggs have been found in the UK in 2016 and 2017. Climate-driven models can help to analyse whether this originally subtropical species could become established in northern Europe. But so far, these models have not considered the impact of the diurnal temperature range (DTR) experienced by mosquitoes in the field. Here, we describe a dynamical model for the life cycle of Ae. albopictus, taking into account the DTR, rainfall, photoperiod and human population density. We develop a new metric for habitat suitability and drive our model with different climate data sets to analyse the UK's suitability for this species. For now, most of the UK seems to be rather unsuitable, except for some densely populated and high importation risk areas in southeast England. But this picture changes in the next 50 years: future scenarios suggest that Ae. albopictus could become established over almost all of England and Wales, indicating the need for continued mosquito surveillance.