Geographic Information System Layers Research Articles

A Python toolkit for integrating geographic information system into regulatory dispersion models for refined pollution modeling

AERMOD is designated as U.S. Environmental Protection Agency (EPA)'s preferred air dispersion model for refined transportation project hot-spot analyses beginning in 2020. One of the key challenges in its modeling process is spatially encoding roadway geometry, especially when simulating highways with complex geometric designs. This research proposed an open-source Python package, GTA, which enables conversion of publicly available roadway Geographic Information System (GIS) layers into defined sources, and source-based emission rates from MOtor Vehicle Emissions Simulator (MOVES) output for AERMOD modeling. The research selected a suburban area in Atlanta, and conducted a comprehensive analysis in terms of annual PM2.5 concentration results and the speed of preparing AERMOD input files for highway network modeling both manually and using software developed based on the proposed methodology. The results prove that the proposed methodology significantly expedites the AERMOD input preparation process, and facilitates convenient testing of multiple modeling configurations for multi-scenario or sensitivity analysis.

QGIS‐based support tools to simplify the complex design challenges of subsurface drainage systems

AbstractWe developed a comprehensive set of tools to simplify the design and layout of subsurface drainage systems. These tools, which utilize publicly available LiDAR (light detection and ranging) datasets, may also be used for other applications. The tool streamline operations include harmonizing geographic information system (GIS) layers into a single State Plane or Universal Transverse Mercator Coordinate System, clipping and thinning LiDAR data within a defined boundary, generating drainage networks with contour lines and laylines for surface flow visualization, identifying depressions and creating gridlines with user‐specified angles and spacings. To showcase their use, an illustrative example using a diverse dataset from the University of Illinois’ South Farm is provided. These user‐friendly tools, optimized for compatibility with all versions of QGIS3 (quantum geographic information system 3), are freely accessible for download or installation from the official QGIS plugin repository. A user manual with step‐by‐step instructions is available online on the Illinois Drainage Guide website.

Satellite-based rainwater harvesting sites assessment for Dera Ghazi Khan, Punjab, Pakistan.

Water scarcity in arid regions poses significant livelihood challenges and necessitates proactive measures such as rainwater harvesting (RWH) systems. This study focuses on identifying RWH sites in Dera Ghazi Khan (DG Khan) district, which recently experienced severe water shortages. Given the difficulty of large-scale ground surveys, satellite remote sensing data and Geographic Information System (GIS) techniques were utilized. The Analytic Hierarchy Process (AHP) approach was employed for site selection, considering various criteria, including land use/land cover, precipitation, geological features, slope, and drainage. Landsat 8 OLI imagery, GPM satellite precipitation data, soil maps, and SRTM DEM were key inputs. Integrating these data layers in GIS facilitated the production of an RWH potential map for the region. The study identified 9 RWH check dams, 12 farm ponds, and 17 percolation tanks as suitable for mitigating water scarcity, particularly for irrigation and livestock consumption during dry periods. The research region was classified into four RWH zones based on suitability, with 9% deemed Very Good, 33% Good, 53% Poor, and 5% Very Poor for RWH projects. The generated suitability map is a valuable tool for hydrologists, decision-makers, and stakeholders in identifying RWH potential in arid regions, thereby ensuring water reliability, efficiency, and socio-economic considerations.

Assessment and Optimization of Rainwater Harvesting Potential for Urban Sustainability: A Case of Lahore

This study employs a multi-criteria decision analysis method utilizing Geographic Information System (GIS) to pinpoint appropriate locations for rainwater harvesting (RWH) structures, to fulfill water demand in Lahore, Pakistan. Data from satellite imagery, soil composition, climate, and digital elevation models (DEM) were integrated into GIS layers and amalgamated to establish a ranking system. This ranking system was then employed to identify suitable areas for rainwater harvesting. Thematic layers, encompassing variables such as rainfall, land use/land cover, soil type, slope, runoff depth, drainage density, and stream order, were combined into a comprehensive overlay, resulting in a map delineating RWH suitability. The outcomes revealed that based on Average Annual Rainfall, 1.44% of the study area is classified as 'very highly suitable,' while 5.67% is designated as 'highly suitable.' Conversely, 24.54% of the region is labeled as 'moderately suitable.' Additionally, 65.44% of the region, situated in the east and southeast, exhibits 'low suitability,' and 2.91% is deemed 'unsuitable.' The findings of this research are poised to foster the broader adoption of RWH in the Lahore region to meet water demands. Furthermore, the developed methodology holds adaptability for implementation in other regions or countries.

ANALYSIS OF SUITABLE DUMP SITES IN KAFANCHAN, JEMA’A LOCAL GOVERNMENT AREA KADUNA STATE, NIGERIA

The research focuses on assessing the suitability of dump sites for solid waste disposal in Kafanchan town, Kaduna state, utilizing Surveying and Geo informatics techniques. With the escalating environmental impact of waste due to population growth, urbanization, and industrialization, the study aims to identify appropriate locations for waste disposal. The methodology employs Geographic Information System (GIS) tools, involving the overlaying of datasets and criteria such as drainage, land use, and road networks. The process includes investigating solid waste disposal in Kafanchan, acquiring data through a Global Positioning System (GPS) receiver, converting criteria into GIS layers, and processing the collected information on a computer system using ArcGIS software. The spatial analysis reveals potential sites suitable for solid waste disposal based on connectivity, contiguity, and overlay methods. The results advocate for the effectiveness of GIS in promptly selecting suitable waste disposal sites. The conclusion emphasizes the recommendation to adopt the identified sites for solid waste disposal in the area. Overall, the study underscores the significance of GIS in addressing contemporary environmental challenges related to waste management and highlights the practical application of spatial tools for sustainable solutions.

Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.

Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq

Abstract One of the most significant issues facing local governments and municipalities worldwide is choosing an appropriate location for landfills. In order to suggest appropriate places for waste sites, this study proposes a strategy using a fuzzy logic model and spatial analytic tools. The proposed approach utilized five parameters extracted from Geographic Information System (GIS) layers in Hillah city in Iraq, which include distances from hydrology features, residential or urban areas, transportation, and historical locations. These parameters were used as standards to construct a geodatabase for generating suitability maps. These indicators are relevant factors in determining the suitability of a site for an urban landfill. It is important to consider the distance from residential or urban areas when selecting a landfill site. A significant distance helps reduce the potential negative impacts on nearby communities, such as odors, noise, traffic, and health concerns associated with waste disposal, and for the other indicators. The fuzzy logic model is more accurate than conventional methods such as the weighted overlay approach. Based on a fuzzy logic model, the total surface area of acceptable locations is 1.1 km². According to the fuzzy logic model, some frequent adequate places were discovered to be beyond the city borders, to the west and south of the research region. To minimize the negative impacts on the environment and its residents, this study demonstrated how the fuzzy logic model and GIS might be combined for waste management decision concerns connected to landfill site selection.

Modeling Flood Susceptible Areas Using Deep Learning Techniques with Random Subspace: A Case Study of the Mae Chan Basin in Thailand

Flooding is a recurring global issue that leads to substantial loss of life and property damage. A crucial tool in managing and mitigating the impact of flooding is using flood hazard maps, which help identify high-risk areas and enable effective planning and management. This study presents a study on developing a predictive model to identify flood-prone areas in the Mae Chan Basin of Thailand using machine learning techniques, precisely the random sub-space ensemble method combined with a deep neural network (RS-DNN) and Nadam optimizer. The model was trained using 11 geographic information system (GIS) layers, including rainfall, elevation, slope, distance from the river, soil group, NDVI, road density, curvature, land use, flow accumulation, geology, and flood inventory data. Feature selection was carried out using the Gain Ratio method. The model was validated using accuracy, precision, ROC, and AUC metrics. Using the Wilcoxon signed-rank test, the effectiveness was compared to other machine learning algorithms, including random tree and support vector machines. The results showed that the RS-DNN model achieved a higher classification accuracy of 97% in both the training and testing datasets, compared to random tree (93%) and SVM (82%). The model's performance was also validated by its high AUC value of (0.99), compared to a random tree (0.93) and SVM (0.82) at a significance level of 0.05. In conclusion, the RS-DNN model is a highly accurate tool for identifying flood-prone areas, aiding in effective flood management and planning.

Reducing non-revenue water in Luxor-Egypt using GIS

Unaccounted for Water, referring to all losses from the water system, illegal or otherwise, frequently exceeds 40% of all water produced. This represents a major strain on utility operations and budgets. A large amount of Unaccounted for Water indicates large losses in the revenue of the utilities. Deficiency in financial resources normally leads to a deficiency in the performance of the utility. This deficiency in performance may affect the operation and maintenance of treatment plants and network components. The main objective is to reduce Unaccounted for Water rates. The multi-stage systematic process involves measuring water flow, leak detection, installing and fixing meters, reducing illegal connections, and increasing billing and collections, and developing Geographic Information System layers and databases for all of them. Another objective is to connect Geographic Information System maps with Billing, hydraulic systems, hotline systems, etc. After choosing a pilot area, we review similar activities, conduct field visits, and hold meetings, prepare the timeline schedule, prepare, and implement base maps and the water network, analyze, and compare end-line results, and report the results. A high reduction in the Unaccounted for Water percentage is expected due to fixing the discovered leaks. The issue of Unaccounted for Water, encompassing losses from the system, whether legal or illegal. This places a significant burden on both utility operations and budgets. A substantial volume of Unaccounted for Water signifies considerable revenue losses for the utilities, and the financial constraints often translate into performance deficiencies within the utility. We formulated a methodology aimed at assisting utilities in lowering Unaccounted for Water rates. This structured, multi-stage process includes activities such as measuring water flow, implementing leak detection measures, installing, and repairing meters, minimizing illegal connections, and enhancing billing and collection.

A Data Processing Methodology to Analyze Construction and Demolition Dynamics in the European Metropolis of Lille, France

In the absence of industry data, organisms, and researchers leverage free and available data, specifically building and demolition permits. Geospatial processing is essential to integrate information from various files into a single GIS layer containing all relevant attributes for analysis. This article proposes a Geographic Information System (GIS) processing model aimed at monitoring construction and demolition dynamics in the European metropolis of Lille to quantify the urban production of mineral waste from buildings. Author methodology is based on that that the deposit potential can be analyzed using the observation of the spatiotemporal dynamics of building and demolition permits. The results demonstrate that combining construction and demolition (C&D) permits with other GIS layers allows us to produce data to quantify demolition surfaces per year in a given French area. The applicability of this methodology extends to all French regions, providing insights into the impact of crises on deconstruction activities and C&D waste generation. The study focuses on C&D French public data bases (French government and European Metropolis of Lille) attributed to the region (area) of the European Metropolis of Lille (MEL) between 2013 and 2022. Some data for 2022 were incomplete due to ongoing treatment, emphasizing the importance of understanding the dynamics of demolition rates or surfaces to identify data gaps or errors. Historical trajectories of C&D permits were quantified and analyzed, revealing over 21,000 permits granted from 2013 to 2022, categorized by site type (new construction, rehabilitations, prior declarations, and demolitions). Construction sites during this period covered approximately 3,345,948 m2, constituting 20% of the MEL’s building stock, while demolition sites amounted to 1,977,911 m2, equivalent to 5% of the total area of buildings in the metropolis. Employing GIS allowed for a spatial analysis, visualizing data by municipality, urban fabric, and year. The analysis highlighted territories with high and low potential for demolition and construction, as well as the most impacted urban fabrics and dynamic periods. The article discusses potential crisis impacts (e.g., COVID-19 or economic downturns) and the implications of incomplete data. Finally, the study demonstrates how these findings can be utilized to quantify C&D waste, leveraging GIS and the production rate calculation method (GRC).

An Integrated Approach to Leak Detection in Water Distribution Networks (WDNs) Using GIS and Remote Sensing

Leakages in the water distribution networks (WDNs) are real problems for utilities and other governmental agencies. Timely leak detection and location identification have been challenges. In this paper, an integrated approach to geospatial and infrared image processing was used for robust leak detection. The method combines drops in flow, pressure, and chlorine residuals to determine potential water leakage locations in the WDN using Geographic Information System (GIS) techniques. GIS layers were created from the hourly values of these three parameters for the city of Sharjah provided by the Sharjah Electricity, Water, and Gas Authority (SEWA). These layers are then analyzed for locations with dropped values of each of the parameters and are overlaid with each other. In the case where there were no overlaying locations between flow and pressure, further water quality analysis was avoided, assuming no potential leak. In the case where there are locations with drops in flow and pressure layers, these overlaying locations are then examined for drops in chlorine values. If overlaying locations are found, then these regions are considered potential leak locations. Once potential leak locations are identified, a specialized remote sensing technique can be used to pinpoint the leak location. This study also demonstrated the suitability of using an infrared camera for leak detection in a laboratory-based setup. This paper concludes that the following methodology can help water utility companies in the timely detection of leaks, saving money, time, and effort.

Hydric erosion rates in Raouz watershed, Morocco: RUSLE, GIS, and remote sensing

Water erosion significantly contributes to global soil degradation, inducing detrimental consequences. One of the most adverse impacts is the sedimentation buildup in reservoirs, which results in reduced water storage capacity and damage to the agro-pedological environment. In Morocco's western Rif region, the Raouz watershed faces soil losses annually that disrupt the basin's ecosystems and impede socio-economic progress. These erosional losses manifest in the Moulay El Hassan Ben Mehdi (MHBM) Dam's sedimentation, causing substantial financial repercussions of several million dirhams/year. To comprehensively assess and quantify this soil degradation phenomenon, this study evaluates soil losses employing the revised universal soil loss equations (RUSLE), strategically integrating geographic information system (GIS) layers that encapsulate pertinent environmental variables such as climate, topography, soil composition, and land use. By utilizing these empirical equations, the identification of predominant factors responsible for soil water erosion within the Raouz watershed is achieved. The outcomes of this study methodology demonstrate an average soil loss estimate of 16.53 t/ha/yr through RUSLE application. Furthermore, comparing soil loss values from this study using RUSLE to those derived from prior research using the Caesium-137 method reveals relatively minor disparities.

Remote sensing and geographic information system (GIS)-based high-resolution mapping of potential groundwater recharge zones on the hard rock terrains of the Cameroon volcanic line (CVL)

Groundwater is the major water reserve in the present context of global warming-related droughts that appear to be more intense in hard rock terrains. The use of Geographical Information Systems (GIS) and Remote Sensing (RS) technologies are increasingly beneficial to groundwater research, by allowing for low cost and larger-scale high-resolution mapping compared to conventional hydrogeological exploration methods. This study aimed at developing a high-resolution map of potential groundwater recharge (GWRpot) zones for the drought-stricken Banka hard rock terrain, straddling the Cameroon Volcanic Line (CVL). Shuttle Radar Tomography Mission (SRTM)-30m and Landsat 8 satellite images constituted the main data source that was ground-truthed through field mapping and used to produce various thematic GIS layers: geology slope, aspect, land use & land cover, drainage density and lineament density of spatial resolution 16m x 16m. The layers were each attributed a fixed score and weight to groundwater recharge, computed using Multi-Influencing Factor (MIF) and Analytical Hierarchy Process of Multi-Criteria Decision Analysis (AHP-MCDA) techniques. Lastly, a Weighted Overlay Analysis was done using the layers to produce the GWRpot zones for the study area. The resulting map shows that 60% of the study area, covering the south, west-northwest and the north-northeast portions of the map have moderate to very high recharge potentials. This result is particularly useful for groundwater targeting in the area and demonstrates the effectiveness of the method in hard rock terrains where traditional methods have been less efficient in properly delineating groundwater recharge zones.Article highlightsGroundwater is generally the safest and most reliable source of water in water scarce environments, and forms when surface water goes into the ground.More areas where water can enter the ground means more chances of getting enough groundwater. Conventional ways of knowing these areas are time consuming and costly, whereas RS and GIS-based methods are less costly and use lesser time.We used the RS, GIS, high-resolution field mapping and statistical methods of blending several factors to produce the potential groundwater recharge zones on a part of the hardrocks of the CVL, that shows 60% of the study area, covering the south, west-northwest and the north-northeast portions of the area have moderate to very high recharge potentials.

Evaluating the Eligibility of Abandoned Agricultural Land for the Development of Wind Energy in Lithuania

Land requirements of wind power (WP) are often seen as a constraint to future broad-scale deployment. The aim of the study is to evaluate the eligibility of abandoned agricultural land (AAL) areas, covered with woody plants, for the development of wind energy (WE) in Lithuania. Agricultural land abandonment (ALA) has numerous negative aspects and its use for WE must be a profitable choice for the landowner, as for the energy developers also. A newly developed methodological approach, a multi-criteria decision-making (MCDM) method known as TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution) was applied to select suitable areas for wind power plants (WPP). The authors have used various data sets, as follows: protected areas (reserves, parks) combined into one common geographic information system (GIS) layer, forest cadaster data, water (lakes, rivers) area layer; abandoned land area layer; buildings layer, taken from the Lithuania Georeferenced Data Base. The results were generated for the entire territory of Lithuania and separately for AAL using the algorithms of the open source QGIS program. The results showed that the central part of Lithuania is most suitable for the development of WE. However, ALA in this part is low, because of the high soil yield potential and suitable conditions for farming. According to the selection criteria, about 7% of AAL are suitable for WE, and more than 18% of AAL have an average suitability.

Identification of Suitable Sites Using GIS for Rainwater Harvesting Structures to Meet Irrigation Demand

This study uses a multi-criteria decision analysis approach based on geographic information system (GIS) to identify suitable sites for rainwater harvesting (RWH) structures (such as farm dam, check dam and contour bund) to meet irrigation demand in Greater Western Sydney region, New South Wales, Australia. Data on satellite image, soil, climate, and digital elevation model (DEM) were stored in GIS layers and merged to create a ranking system, which were then used to identify suitable RWH (rainwater harvesting) areas. The resulting thematic layers (such as rainfall, land use/land cover, soil type, slope, runoff depth, drainage density, stream order and distance from road) were combined into one overlay to produce map of RWH suitability. The results showed that 9% of the study region is ‘very highly suitable’ and 25% is ‘highly suitable’. On the other hand, 36% of the area, distributed in the north-west, west and south-west of the study region, is ‘moderately suitable’. While 21% of the region, distributed in east and south-east part of the region, has ‘low suitability’ and 9% is found as ‘unsuitable area’. The findings of this research will contribute towards wider adoption of RWH in Greater Western Sydney region to meet irrigation demand. The developed methodology can be adapted to any other region/country.

China's provincial wind power potential assessment and its potential contributions to the "dual carbon" targets.

Wind power development is one of the important measures to achieve China's committed dual carbon targets (carbon peak before 2030 and carbon neutrality before 2060). This study assessed the technical and economic potential of China's onshore and offshore wind power potential through Geographic Information System (GIS) layer overlay and raster calculations. Based on the assessment, provincial contributions of wind power under the 'dual carbon' targets are also estimated. The results show that (a) the technical potential of China's wind power is 25.57PWh/year, and the economic potential is 11.69PWh/year. Fifty-six percent of the potential is located in Inner Mongolia, Xinjiang, and Gansu provinces, while 75% of the offshore potential is distributed over the coastal provinces (Liaoning, Zhejiang, Fujian, Guangdong, and Shandong). (b) Ten provinces, including Anhui, Jiangxi, and Henan, have insufficient remaining economic potential. In comparison, five provinces, including Jiangxi, Henan, Hubei, Yunnan, and Ningxia, have advanced wind power development, with 2020 as the base year. (c) For carbon neutrality, China's potential contributions of wind power may need 1088-2620GW by 2060. Eight provinces can constitute 72% of the new potential contributions. China may need approximately 9.23 trillion CNY (2020 constant price) investment in wind power for the contributions.

Resource Selection of Domestic Sheep on Mountainous Summer Habitat in Utah, United States

Past research has documented livestock grazing patterns and dynamics across pastoral landscapes. We used spatial locations of domestic range sheep (Ovis aries) obtained from Global Positioning System collars to study habitat selection on summer mountain habitat North of Scofield Reservoir Utah, United States. Data were collected between the months of July to September 2020. We developed a resource selection function to determine the influence of slope, distance to water, aspect, ruggedness, elevation, and vegetation types on habitat selection by sheep while grazing on summer mountain habitat. We found sheep selected for sites closer to water, with more gentle terrain, higher in elevation, and north-facing slopes. Vegetation types were less predictive, ostensibly due to the lack of species composition information in available geographic information system layers and the possibility of sheep being herded to avoid areas of overuse. Although it is often assumed that sheep use slopes more than their heavier and larger cattle counterparts, overall they tended to avoid steep slopes compared with all other predictor variables. This information on selection for higher elevations and avoidance of steep slopes can be used to improve livestock management practices including flock management that increases sheep foraging patterns and energy efficiency.

Allocation of Regional Logistics Hubs and Assessing Their Contribution to Saudi Arabia’s Logistics Performance Index Ranking

In 2016, Saudi Arabia published its vision for the year 2030, which is based on the Kingdom’s geographical, financial, social and religious potentials. Developing the logistics sector and improving the Kingdom’s ranking in the World Bank’s Logistics Performance Index (LPI) to the twenty-fifth rank was one of the most targeted success factors. Unfortunately, Saudi Arabia’s rank in this index has declined over the past years, until it reached the fifty-fifth rank as per the last published LPI report. This research proposes a set of logistics hubs (LHs) which are located at key multi-logistics areas within the regional trading zones of the country. A spatial model was implemented on a macro level to integrate multi-logistical, infrastructural and natural geographic information system (GIS) layers, and highlight their intersections as initial feasible areas. Subsequently, an optimization model based on integer linear programming (ILP) was used to maximize the number of allocated LHs and minimize the overall distances between allocated LHs and international trading nodes considering multiple factors. More than 80 selected subject matter experts (SMEs) from 9 different countries have participated in World Bank’s driven surveys that assess the contribution of the LHs’ allocation proposal on Saudi Arabia’s LPI ranking. An improvement of about 10% in the LPI overall score with a 20 rank promotion has been estimated as per the SMEs responses. These results demonstrate that investing in logistics infrastructure and ranking in LPI are perfectly, positively and highly related.

Application of Spatial Risk Assessment Integrated With a Mobile App in Fighting Against the Introduction of African Swine Fever in Pig Farms in Thailand: Development Study.

BackgroundAfrican swine fever (ASF), a highly contagious disease affecting both domestic and wild pigs, has been having a serious impact on the swine industry worldwide. This important transboundary animal disease can be spread by animals and ticks via direct transmission and by contaminated feed and fomites via indirect transmission because of the high environmental resistance of the ASF virus. Thus, the prevention of the introduction of ASF to areas free of ASF is essential. After an outbreak was reported in China, intensive import policies and biosecurity measures were implemented to prevent the introduction of ASF to pig farms in Thailand.ObjectiveEnhancing prevention and control, this study aims to identify the potential areas for ASF introduction and transmission in Thailand, develop a tool for farm assessment of ASF risk introduction focusing on smallholders, and develop a spatial analysis tool that is easily used by local officers for disease prevention and control planning.MethodsWe applied a multi-criteria decision analysis approach with spatial and farm assessment and integrated the outputs with the necessary spatial layers to develop a spatial analysis on a web-based platform.ResultsThe map that referred to potential areas for ASF introduction and transmission was derived from 6 spatial risk factors; namely, the distance to the port, which had the highest relative importance, followed by the distance to the border, the number of pig farms using swill feeding, the density of small pig farms (<50 heads), the number of pigs moving in the area, and the distance to the slaughterhouse. The possible transmission areas were divided into 5 levels (very low, low, medium, high, and very high) at the subdistrict level, with 27 subdistricts in 10 provinces having very high suitability and 560 subdistricts in 34 provinces having high suitability. At the farm level, 17 biosecurity practices considered as useful and practical for smallholders were selected and developed on a mobile app platform. The outputs from the previous steps integrated with necessary geographic information system layers were added to a spatial analysis web-based platform.ConclusionsThe tools developed in this study have been complemented with other strategies to fight against the introduction of ASF to pig farms in the country. The areas showing high and very high risk for disease introduction and transmission were applied for spatial information planning, for example, intensive surveillance, strict animal movement, and public awareness. In addition, farms with low biosecurity were improved in these areas, and the risk assessment developed on a mobile app in this study helped enhance this matter. The spatial analysis on a web-based platform helped facilitate disease prevention planning for the authorities.

A multi-criteria CCUS screening evaluation of the Gulf of Mexico, USA

Continued research into reservoir characterization along with offshore carbon dioxide (CO2) transportation and infrastructure assets is needed to facilitate development of safe and successful carbon capture, utilization, and storage (CCUS) projects. This paper outlines a multi-criteria evaluation methodology that incorporates disparate sets of quantitative, spatially variable data into a decision-making framework for screening the Gulf of Mexico (GOM) outer continental shelf (OCS) for potentially viable CO2 storage and enhanced oil recovery (EOR) sites. Criteria categories include favorable geologic characteristics, logistics, and potential risks. Data compiled for 14 criteria from several publicly available geographic information system (GIS) layers was aggregated over 2559 spatially balanced points across the study area using the National Energy Technology Laboratory (NETL)-developed Cumulative Spatial Impact Layers™ (CSIL) GIS tool. Criteria are weighted by qualitative expert opinion relative to their perceived importance to given scenarios— the output of combined criteria values and weights enables regional CO2 storage suitability differentiation. The methodology considers both technical and non-technical factors impacting CCUS decision-making. The flexible methodology enables a systematic approach to regional ranking at high spatial resolution over a large study domain. Additionally, the framework enables high-grading of priority sites that warrant further characterization and follow-on analysis. Areas along the Louisiana coast and Mississippi River Delta consistently rank high for all scenarios largely a result of the favorable geology with the potential for stacked storage, as well as the density of existing pipelines and platforms, and proximity to several onshore CO2 sources. High-graded regions for the CO2 EOR-related scenarios are typically located further offshore towards the middle and edge of the OCS compared to higher priority regions for the geologic storage scenarios which fall closer to the Louisiana coastline.