Natural and Engineered Guide RNA-Directed Transposition with CRISPR-Associated Tn7-Like Transposons.

CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated nuclease) defense systems have been naturally coopted for guide RNA-directed transposition on multiple occasions. In all cases, cooption occurred with diverse elements related to the bacterial transposon Tn7. Tn7 tightly controls transposition; the transposase is activated only when special targets are recognized by dedicated target-site selection proteins. Tn7 and the Tn7-like elements that coopted CRISPR-Cas systems evolved complementary targeting pathways: one that recognizes a highly conserved site in the chromosome and a second pathway that targets mobile plasmids capable of cell-to-cell transfer. Tn7 and Tn7-like elements deliver a single integration into the site they recognize and also control the orientation of the integration event, providing future potential for use as programmable gene-integration tools. Early work has shown that guide RNA-directed transposition systems can be adapted to diverse hosts, even within microbial communities, suggesting great potential for engineering these systems as powerful gene-editing tools.

Jpx RNA regulates CTCF anchor site selection and formation of chromosome loops.

Chromosome loops shift dynamically during development, homeostasis, and disease. CCCTC-binding factor (CTCF) is known to anchor loops and construct 3D genomes, but how anchor sites are selected is not yet understood. Here, we unveil Jpx RNA as a determinant of anchor selectivity. Jpx RNA targets thousands of genomic sites, preferentially binding promoters of active genes. Depleting Jpx RNA causes ectopic CTCF binding, massive shifts in chromosome looping, and downregulation of >700 Jpx target genes. Without Jpx, thousands of lost loops are replaced by de novo loops anchored by ectopic CTCF sites. Although Jpx controls CTCF binding on a genome-wide basis, it acts selectively at the subset of developmentally sensitive CTCF sites. Specifically, Jpx targets low-affinity CTCF motifs and displaces CTCF protein through competitive inhibition. We conclude that Jpx acts as a CTCF release factor and shapes the 3D genome by regulating anchor site usage.

Assembly of the Tn7 targeting complex by a regulated stepwise process.

The Tn7 family of transposons is notable for its highly regulated integration mechanisms, including programmable RNA-guided transposition. The targeting pathways rely on dedicated target selection proteins from the TniQ family and the AAA+ adaptor TnsC to recruit and activate the transposase at specific target sites. Here, we report the cryoelectron microscopy (cryo-EM) structures of TnsC bound to the TniQ domain of TnsD from prototypical Tn7 and unveil key regulatory steps stemming from unique behaviors of ATP- versus ADP-bound TnsC. We show that TnsD recruits ADP-bound dimers of TnsC and acts as an exchange factor to release one protomer with exchange to ATP. This loading process explains how TnsC assembles a heptameric ring unidirectionally from the target site. This unique loading process results in functionally distinct TnsC protomers within the ring, providing a checkpoint for target immunity and explaining how insertions at programmed sites precisely occur in a specific orientation across Tn7 elements.

Transcription factor IID parks and drives preinitiation complexes at sharp or broad promoters.

Core promoters are sites where transcriptional regulatory inputs of a gene are integrated to direct the assembly of the preinitiation complex (PIC) and RNA polymerase II (Pol II) transcription output. Until now, core promoter functions have been investigated by distinct methods, including Pol II transcription initiation site mappings and structural characterization of PICs on distinct promoters. Here, we bring together these previously unconnected observations and hypothesize how, on metazoan TATA promoters, the precisely structured building up of transcription factor (TF) IID-based PICs results in sharp transcription start site (TSS) selection; or, in contrast, how the less strictly controlled positioning of the TATA-less promoter DNA relative to TFIID-core PIC components results in alternative broad TSS selections by Pol II.

Mouse nuclear RNAi-defective 2 promotes splicing of weak 5' splice sites.

Removal of introns during pre-mRNA splicing, which is central to gene expression, initiates by base pairing of U1 snRNA with a 5' splice site (5'SS). In mammals, many introns contain weak 5'SSs that are not efficiently recognized by the canonical U1 snRNP, suggesting alternative mechanisms exist. Here, we develop a cross-linking immunoprecipitation coupled to a high-throughput sequencing method, BCLIP-seq, to identify NRDE2 (nuclear RNAi-defective 2), and CCDC174 (coiled-coil domain-containing 174) as novel RNA-binding proteins in mouse ES cells that associate with U1 snRNA and 5'SSs. Both proteins bind directly to U1 snRNA independently of canonical U1 snRNP-specific proteins, and they are required for the selection and effective processing of weak 5'SSs. Our results reveal that mammalian cells use noncanonical splicing factors bound directly to U1 snRNA to effectively select suboptimal 5'SS sequences in hundreds of genes, promoting proper splice site choice, and accurate pre-mRNA splicing.

Anticipating anthropogenic threats in acquiring new protected areas.

Biodiversity continues to decline despite protected area expansion and global conservation commitments. Biodiversity losses occur in existing protected areas, yet common methods used to select protected areas ignore postimplementation threats that reduce effectiveness. We developed a conservation planning framework that considers the ongoing anthropogenic threats within protected areas when selecting sites and the value of planning for costly threat-mitigating activities (i.e., enforcement) at the time of siting decisions. We applied the framework to a set of landscapes that contained the range of possible correlations between species richness and threat. Accounting for threats and implementing enforcement activities increased benefits from protected areas without increasing budgets. Threat information was valuable in conserving more species per spending level even without enforcement, especially on landscapes with randomly distributed threats. Benefits from including threat information and enforcement were greatest when human threats peaked in areas of high species richness and were lowest where human threats were negatively associated with species richness. Because acquiring information on threats and using threat-mitigating activities are costly, our findings can guide decision-makers regarding the settings in which to pursue these planning steps.

Anticipación de las amenazas antropogénicas durante la adquisición de áreas protegidas nuevas Resumen La biodiversidad sigue declinando a pesar de la expansión de áreas protegidas y los compromisos mundiales con la conservación. La pérdida de la biodiversidad ocurre en las áreas protegidas existentes, y todavía los métodos comunes usados para seleccionar las áreas protegidas ignoran las amenazas posteriores a la implementación, las cuales reducen la efectividad. Desarrollamos un marco de planeación de la conservación que considera las amenazas antropogénicas actuales dentro de las áreas protegidas durante la selección de sitios y el valor de la planeación de actividades mitigantes costosas, como la aplicación, al momento de decidir. Aplicamos nuestro marco a un conjunto de paisajes que comprende el rango de correlaciones posibles entre las amenazas y la riqueza de especies. Si consideramos las amenazas y la implementación de actividades de aplicación, los beneficios de las áreas protegidas incrementan sin incrementar el presupuesto. La información sobre las amenazas fue importante para la conservación de especies por nivel de gasto incluso sin la aplicación, especialmente en paisajes con amenazas distribuidas de forma azarosa. Los beneficios de incluir la información sobre las amenazas y la aplicación fueron mayores cuando las amenazas humanas llegaron a su tope en áreas con gran riqueza de especies y alcanzaron su punto más bajo cuando las amenazas humanas estaban asociadas negativamente con la riqueza de especies. Ya que es costoso adquirir información sobre las amenazas y mitigar las amenazas con actividades, nuestros descubrimientos pueden informar a los tomadores de decisiones con respecto al entorno para seguir los pasos de la planeación.

FDA experiences with a centralized statistical monitoring tool.

The U.S. Food and Drug Administration (FDA) has broadly supported quality by design initiatives for clinical trials - including monitoring and data validation - by releasing two related guidance documents (FDA 2013 and 2019). Centralized statistical monitoring (CSM) can be a component of a quality by design process. In this article, we describe our experience with a CSM platform as part of a Cooperative Research and Development Agreement between CluePoints and FDA. This agreement's approach to CSM is based on many statistical tests performed on all relevant subject-level data submitted to identify outlying sites. An overall data inconsistency score is calculated to assess the inconsistency of data from one site compared to data from all sites. Sites are ranked by the data inconsistency score (-log10p,where p is an aggregated p-value). Results from a deidentified trial demonstrate the typical data anomaly findings through Statistical Monitoring Applied to Research Trials analyses. Sensitivity analyses were performed after excluding laboratory data and questionnaire data. Graphics from deidentified subject-level trial data illustrate abnormal data patterns. The analyses were performed by site, country/region, and patient separately. Key risk indicator analyses were conducted for the selected endpoints. Potential data anomalies and their possible causes are discussed. This data-driven approach can be effective and efficient in selecting sites that exhibit data anomalies and provides insights to statistical reviewers for conducting sensitivity analyses, subgroup analyses, and site by treatment effect explorations. Messy data, data failing to conform to standards, and other disruptions (e.g. the COVID-19 pandemic) can pose challenges.

A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective.

Properly selecting landfill sites for waste disposal is crucial for mitigating environmental and public health risks. Geographic Information Systems (GISs) and Artificial Intelligence (AI) techniques have emerged as valuable tools for identifying suitable landfill locations. This study presents a systematic mapping study (SMS) that investigates the usage of GIS and AI in landfill site selection. We searched six databases (IEEE Xplore, ACM Digital Library, Science Direct, Emerald Insight, Taylor & Francis Online and Web of Science) using predefined keywords related to landfills, GIS and AI. From 858 initially retrieved articles, we selected 48 relevant articles for in-depth analysis. Our research aimed to answer various questions, such as publication trends, the geographic distribution of case studies, criteria for assessing landfill suitability, tools and techniques employed, preliminary site screening methods, decision-making processes, limitations and future research directions. We used bubble charts, bar charts and tables to visualize the results. The findings of our study highlight the growing interest in using GIS and AI for landfill site selection and emphasize the importance of incorporating multi-criteria decision-making techniques. Furthermore, the results reveal the need for developing more advanced AI models, addressing the limitations of current approaches and exploring novel visualization techniques for enhancing landfill site selection processes. This study provides valuable insights for researchers and practitioners in waste management, environmental science and geoinformatics. It sets the groundwork for future research on improving GIS- and AI-based landfill site selection methodologies.

Breeding site selection and co-existing patterns of tropical mosquitoes.

We investigated the physicochemical properties and the biotic interactions of breeding sites of tropical mosquito species. Field sampling was done in 12 study areas in Sri Lanka covering areas with secondary natural forests and human settlements. A total of 226 breeding sites were investigated to determine the biotic interactions and physiochemical properties of breeding water (pH, Conductivity, Dissolved Oxygen, Total Dissolved Solids and Temperature). A total of 80.5% of breeding sites from both habitats were positive for mosquito larvae of seven genera and 24 species. Orthopodomyia flavithorax (297) and Aedes albopictus (295) were dominated in tree holes of Alstonia macrophylla, Vateria copallifera and Artocarpus nobilis. Diversity indices showed that the diversity of mosquitoes is high in wet zone habitats of Sri Lanka compared to dry and intermediate zone habitats. Aedes albopictus coexisted with 11 different mosquito species while it avoided larvae of Culex fuscanus, Cx. uniformis and Tripteroides affinis. Strong positive associations were reported between Ae. albopictus and Ar. subalbatus while larvae of Or. flavithorax mosquitoes were not co-occurred with the larvae of Ae. vittatus, Ae. aegypti, Cx. sitiens, Ar. subalbatus, Anopheles spp and Tr. affinis. The findings identified the breeding adaptability and tolerance to a wide range of physiochemical properties of tropical mosquito communities.

A Route Planning Method for UAV Swarm Inspection of Roads Fusing Distributed Droneport Site Selection.

Current methods that use Unmanned Aerial Vehicle (UAV) swarms to inspect roads still have many limitations in practical applications, such as the lack of or difficulty in the route planning, the unbalanced utilization rate of the UAV swarm and the difficulty of the site selection for the distributed droneports. To solve the limitations, firstly, we construct the inspection map and remove the redundant information irrelevant to the road inspection. Secondly, we formulate both the route planning problem and the droneport site selection problem in a unified multi-objective optimization model. Thirdly, we redesign the encoding strategy, the updating rules and the decoding strategy of the particle swarm optimization method to effectively solve both the route planning problem and the droneport site selection problem. Finally, we introduce the comprehensive evaluation indicators to verify the effectiveness of the route planning and the droneport site selection. The experimental results show that (1) with the proposed method, the overlapped part of the optimized inspection routes is less than 7% of the total mileage, and the balanced utilization rate of the UAVs is above 75%; (2) the reuse rate of the distributed droneports is significantly improved after optimization; and (3) the proposed method outperforms the ant colony optimization (ACO) method in all evaluation indicators. To this end, the proposed method can effectively plan the inspection routes, balance the utilization of the UAVs and select the sites for the distributed droneports, which has great significance for a fully autonomous UAV swarm inspection system for road inspection.

Ensemble habitat suitability modeling for predicting optimal sites for eelgrass (Zostera marina) in the tidal lagoon ecosystem: Implications for restoration and conservation.

Seagrass systems are in decline, mainly due to anthropogenic pressures and ongoing climate change. Implementing seagrass protection and restoration measures requires accurate assessment of suitable habitats. Commonly, such assessments have been performed using single-algorithm habitat suitability models, nearly always based on low environmental resolution information and short-term species data series. Here we address eelgrass (Zoostera marina) meadows' large-scale decline (>80%) in Shandong province (Yellow Sea, China) by developing an ensemble habitat model (EHM) to inform eelgrass conservation and restoration strategies in the Swan Lake (SL). For this, we applied a weighted EHM derived from ten single-algorithm models including profile, regression, classification, and machine learning methods to generate a high-resolution habitat suitability map. The EHM was constructed based on the predictive performances of each model, by combining a series of present-absent eelgrass datasets from recent years coupled with oceanographic and sediment data. The model was cross-validated with independent historical datasets, and a final habitat suitability map for conservation and restoration was generated. Our EHM scheme outperformed all single models in terms of habitat suitability, scoring ∼0.95 for both true statistic skill (TSS) and area under the curve (AUC) performance criteria. Machine learning methods outperformed profile, regression and classification methods. Regarding model explanatory variables, overall, topographic characteristics such as depth (DEP) and seafloor slope (SSL) are the most significant factors determining the distribution of eelgrass. The EHM predicted that the overlapping area was almost 90% of the current eelgrass habitat. Using results from our EHM, a LOESS regression model for the relationship of the habitat suitability to both the biomass and density of Z. marina outperformed better than the classic Ordinary Least Squares regression model. The EHM is a promising tool for supporting eelgrass protection and restoration areas in temperate lagoons as data availability improves.

The challenge of setting restoration targets for macroalgal forests under climate changes.

The process of site selection and spatial planning has received scarce attention in the scientific literature dealing with marine restoration, suggesting the need to better address how spatial planning tools could guide restoration interventions. In this study, for the first time, the consequences of adopting different restoration targets and criteria on spatial restoration prioritization have been assessed at a regional scale, including the consideration of climate changes. We applied the decision-support tool Marxan, widely used in systematic conservation planning on Mediterranean macroalgal forests. The loss of this habitat has been largely documented, with limited evidences of natural recovery. Spatial priorities were identified under six planning scenarios, considering three main restoration targets to reflect the objectives of the EU Biodiversity Strategy for 2030. Results show that the number of suitable sites for restoration is very limited at basin scale, and targets are only achieved when the recovery of 10% of regressing and extinct macroalgal forests is planned. Increasing targets translates into including unsuitable areas for restoration in Marxan solutions, amplifying the risk of ineffective interventions. Our analysis supports macroalgal forests restoration and provides guiding principles and criteria to strengthen the effectiveness of restoration actions across habitats. The constraints in finding suitable areas for restoration are discussed, and recommendations to guide planning to support future restoration interventions are also included.

Siting MPAs for multiple protecting purposes by co-consideration of ecological importance and anthropogenic impacts.

The global marine ecosystem has been significantly altered by the combined effects of multiple anthropogenic impacts. Systematic planning of marine protected areas (MPAs) is of paramount importance in alleviating conflicts between humans and the sea. Existing approaches, however, merely integrate both ecological and anthropogenic factors for multiple conservation purposes. By combining the three main anthropogenic impact factors with two main ecological importance factors, this study used a GIS-based AHP-OWA method to identify different levels of priority protection for MPAs in Zhejiang, China. Our results proved that: 1) the multi-objective MPA siting issues can be addressed by the GIS-based AHP-OWA method through scenario simulation; 2) the best locations for MPAs are in the northeast, central, and southern marine areas of Zhejiang; 3) considering the trade-off degree, spatial conservation efficiency, and spatial heterogeneity, an optimized MPA siting scheme can be developed for decision-makers. The proposed MPA siting method and case study may provide an effective technical reference for solving regional marine spatial planning (MSP) issues in the future.

A method of gas-related pollution source layout based on multi-source data: A case study of shaanxi province, China.

The location and layout of enterprises have an important impact on local air quality. However, a few studies on exploring of the optimal layout of gas-related enterprises from the perspective of optimizing the layout of air pollution sources. This study developed a method for the evaluation of air pollution source layout based on air pollutant emission inventory data, atmospheric self-purification capacity data, and satellite remote sensing air quality data. Taking Shaanxi Province as an example, the Moran's I index and GIS spatial analysis techniques were used to evaluate the layout of air pollution sources, analyze the spatial variation characteristics of air pollution sources, and propose specific countermeasures to optimize the layout of air pollution sources. Results showed that northern Shaanxi and Guanzhong Plain are the most unsuitable for the distribution of NOx and CO sources, accounting for 13.78% and 21.77% of the total area, respectively. The most suitable area for the distribution of NOx is southern Shaanxi, accounting for 65.77% of the total area, mainly concentrated in Hanzhong and Ankang regions. The most suitable area for the distribution of CO is southern Shaanxi, accounting for 40.97% of the total area, mainly concentrated in Hanzhong and Shangluo regions. The findings of this study could supplement and improve the evaluation of the layout of industrial enterprises in China from technical and methodological aspects, and provide new insight for local governments to adjust and optimize the layout of air pollution sources.

Multi-criteria decision analysis for wind farm location selection in Bahir Dar City and its surroundings, Northwestern Ethiopia.

While there is a consensus on the multifaceted advantages of wind farms, only a handful of developing countries harness it to the fullest potential. Among the various factors that contributed to the low development of wind farms in those countries, the dearth of methods for identifying a suitable site is the leading one. Studies conducted elsewhere on wind farm industry identifies criteria such as slope, wind speed, land use/land cover, geology, distance from electric transmission grids, roads, and settlements. However, the relative magnitude of each factor to the optimum location of the wind farm is inadequately examined. This study focuses on the combined use of geographic information systems (GIS) and spatial multi-criteria decision analysis (MCDA) for selecting the most appropriate sites for wind-farm development. Using a pairwise comparison method of the analytical hierarchical process, we computed the relative importance and weight of parameters affecting wind farm sites in a weighted overlay technique. Based on the analysis of pertinent data collected from Bahir Dar City and its surroundings, northwestern Ethiopia, we categorized the study area into five suitability classes ranging from extremely suitable to not suitable. Our result, revealed a spatial pattern of the potential sites suitable for wind farm investments that are economical, environmentally sound, and aesthetically amenable. We recommend replicating the method developed in this study in other areas of developing countries where transitioning to renewable energy sources through windfarm development is sought.

Site selection for construction and demolition waste landfill using catastrophe theory: the case of Bonab city, Iran.

Rapid urbanization and physical development have produced a significant quantity of construction and demolition waste in Bonab, a city in northwest Iran. In order to achieve a high level of accuracy and minimize subjective bias, catastrophe theory (CT) principles were combined in the geographic information system (GIS) environment for identifying suitable locations for safe disposal of the waste. Site selection process for construction waste landfill (CWLF) in Bonab city involved preparation and integration of 12 maps representing elevation, slope, geology, soil type, land use, vegetation, distance to population centers, valuable ecosystems, river, groundwater, and access to the main roadways with the CT to reduce uncertainty associated with subjective evaluations. Evaluation of relative importance of the variables showed that elevation, slope, distance to the city, vegetation cover, and distance to communication networks have greater importance in site selection for construction waste landfill. On the contrary, distance to the river, groundwater level, distance to villages, and valuable ecosystems have the least impact. Suitable areas were identified in parts of the north and west of the city. The identified sites have a gentle slope, low agricultural potential, good access to the communication networks, suitable distance to the city, and no limitations regarding the groundwater level, geological formations, and vegetation cover.

Land Suitability Assessment for Pistachio Cultivation Using GIS and Multi-Criteria Decision-Making: A Case Study of Mardin, Turkey.

Site selection for pistachio orchards is an important issue for sustainable agricultural policies, crop productivity, agricultural planning, and communities. This study aims to investigate suitable places for pistachio in the Mardin Province (SE Turkey) by considering several variables, such as meteorological data, topographic conditions, economic factors, and soil characteristics, using Geographic Information System (GIS) and Multi-Criteria Decision Analysis. Pistachio farmers, expert opinions, and literature data were used to determine the requirements for pistachio cultivation. Four main assessment criteria (thirteen sub-criteria), sixty value ranges, and fourteen exclusion criteria were determined for the pistachio land suitability assessment. The weighting of the evaluation criteria was calculated using the Analytical Hierarchy Process (AHP). Farmers and experts have stated that meteorological factors are more important than soil, topography, and economic factors. All data were transferred to the GIS environment, and a land suitability map was created using the weighted linear combination method. The results show that Mardin province has very suitable lands for pistachio cultivation. The resulting map determined that the 228,891.59 ha area in Mardin province is very suitable for pistachio. To evaluate the accuracy of the land suitability map generated for pistachio, the Receiver Operating Characteristic (ROC) curve was used. The value of the area under the curve (AUC) was calculated to be 0.806, which indicates that the study is consistent. The created suitability map will be an essential data source for developing sustainable agricultural strategies in the Southeastern Anatolia region.

Combined use of BWM-TOPSIS methods in the selection of thermal power plant installation site in the Karapinar/Turkiye Region, at risk of sinkhole formation.

With the rapidly increasing world population and the need for industrialization, energy supply has become an important global problem. A significant part of the world's energy needs is provided by fossil fuels. About half of all global coal deposits are low-quality coals, including lignite. Karapinar/Konya, also the study area, has Turkiye's second richest lignite reserve. The region's lignite reserve can be used in thermal power plants for electricity generation in terms of its nature and the amount is an important opportunity to meet the energy demand for both region and country. The region contains many sinkholes, and the potential for the formation of new sinkholes makes the site selection for thermal power plants in the region an even more strategic decision. This study aims to propose the most suitable thermal power plant site for the region by using Multi-Criteria Decision Making methods and Geographic Information Systems in an integrated way. Within the scope of the study, a total of twelve sub-criteria were taken into consideration under the main criteria of Geological, Economic and Environmental. The Best-Worst Method was applied to determine the criteria weights, and by using the weights, a suitability map for the thermal power plant installation site was produced and candidate regions were determined. TOPSIS was applied to determine the most suitable location among the candidate regions. The Candidate Region in the easternmost part of Karapinar district was chosen as the most suitable site for the thermal power plant installation.

A mass vaccination site selection problem: An application of GIS and entropy-based MAUT approach.

Coronavirus disease (COVID-19) was recognized in December 2019 and spread very severely throughout the world. In 2022 May, the total death numbers reached 6.28 million people worldwide. During the pandemic, some alternative vaccines were discovered in the middle of 2020. Today, many countries are struggling to supply vaccines and vaccinate their citizens. Besides the difficulties of vaccine supply, mass vaccination is a challenging but mandatory task for the countries. Within this context, determining the mass vaccination site is very important for recovering, thus a five-step approach is generated in this paper to solve this real-life problem. Firstly the mass vaccination site selection criteria are determined, and secondly, the spatial data are collected and mapped by using Geographical Information System (GIS) software. Then, the entropy weighting method (EWM) is used for determining the relative importance levels of criteria and fourthly, the multiple attribute utility theory (MAUT) approach is used for ranking the potential mass vaccination sites. Lastly, ranked alternative sites are analyzed using network analyst tool of GIS in terms of covered population. A case study is conducted in Gaziantep city which is the ninth most population and having above-average COVID-19 patients in Turkey. As a result, the fourth alternative (around the Sehitkamil Monument) is chosen as the best mass vaccination site for the city. It is believed that the outcomes of the paper could be used by city planners and decision-makers.

Application of a sustainable location index approach to landfill site selection in Monterrey, Mexico.

Landfilling is the main method to manage municipal solid waste (MSW) in Latin America due to the economic, technological and political characteristics of the region. The disposal of MSW in landfill sites may affect the quality of the environment and compromise a considerable share of the municipal budgets. The selection of suitable sites reduces the environmental and economic impact of landfills. In the present study the sustainable location index (SLI) is proposed as a methodology to assess environmentally, and economically, sanitary landfill site selection in the Metropolitan Area of Monterrey, a representative large-size city of Latin America. EVIAVE methodology was modified to include administrative and economic dimensions, which are assessed as a sustainable approach using together the SLI integrated with geospatial and multicriteria analysis tools. The assessment showed that the zones with the lowest SLI changes drastically when the importance of the economic or environmental factor varies. This result suggest that the inclusion of sustainability in landfill site selection decision-making is complex and it may requires the inclusion of local particularities such as municipal budgets, policies of MSW management and public perception about environmental deterioration.