The evaluation of suitable landfill sites is a complex process and requires various legislative, technical, social, and environmental criteria. Therefore, this study provides a management tool for identifying suitable sites for landfills through the integrated use of the analytic hierarchy process (AHP), geographic information systems (GISs), and remote sensing (RS). Accordingly, fourteen subcriteria were identified and grouped into physical (7), environmental (3), and socioeconomic (4) criteria and were weighed using pairwise comparison matrices (PCMs). The weighted linear combination (WLC) approach of maps allowed us to generate models and submodels of land suitability. From the territory of the districts of Chachapoyas and Huancas, 0.9% (1.71 km2), 71.1% (141.89 km2), 21.0% (41.86 km2), 0.0%, and 7.7% (14.21 km2) have highly suitable, moderately suitable, marginally suitable, unsuitable, and restricted conditions, respectively, for a landfill site. Twelve highly suitable sites were identified, of which three were selected based on their shape and the minimum area required for the operation of the landfill until 2040. In fact, this study proposes a management tool for decision-makers (DMs) that improve the process of selecting landfill sites, supported by engineering and its applications for territorial sustainability.

The generation of large volumes of solid waste is linked to the exponential demand for food, urbanization, and global overpopulation [1]. It is expected that in the following 30 years, the generation of solid waste will increase by 70% [2]. Therefore, this is a global environmental problem and adequately addressing, managing, and disposing of this waste pose enormous challenges [3–5]. In this context, solid waste management plays a vital role in urban planning [6, 7]; in developing countries, the most economical technique for the final disposal of solid waste is landfills [8, 9]. Namely, 54.4% of the population in Latin America and the Caribbean dispose of their waste in landfill sites [10]. In Peru, in 2018, 7 342 713 tons of municipal solid waste (MSW) were generated, of which only 1.05% were revalued, and only 49.16% were disposed of in authorized final disposal infrastructure (FDI) [11]. In particular, in Peru (for more than 32 million inhabitants), there are only 58 FDIs (6 secure landfills, 49 landfills, and 3 mixed landfills) and are located in only 19/25 regions [12].

Currently, the best and common technique for the final disposal of solid waste is the establishment of landfills [13–15]. However, determining the location of a landfill is a highly complex and tedious task, mainly because there are many factors and strict regulations involved in the selection process [3, 16]. The inadequate selection of the disposal site and poor management of the landfill infrastructure can become a problem with strong environmental, social, and economic impacts that threaten public health [17, 18]. The inappropriate placement of a landfill site is the main factor influencing the biophysicochemical characteristics of the environment and the ecology of the surrounding area [19–21].

The proper selection of a site for a landfill is a process that requires the evaluation of various environmental, financial, social, and technical criteria [17, 18, 22]. Methodologies have arisen that incorporate geographic information systems (GISs), remote sensing (RS), and multicriteria decision analysis (MCDA) through the analytic hierarchy process (AHP) [13, 23–28]. GISs are efficient in collecting, manipulating, interacting, and analyzing spatial data (many of which come from RS) that apply to the criteria for the selection of the ideal site [29–32], while the AHP is one of the most commonly used MCDA techniques for determining the relative importance of the criteria [26, 33–35]. These tools have been integrated into several studies [9, 36, 37] because they are effective in simplifying the selection of the optimal landfill sites [7, 28, 38–41]. For Amazonas, one of the 10 poorest regions of Peru [42], this methodology represents an important management tool for the proper beginning of MSW management.

This study identified suitable sites for landfills in the districts of Chachapoyas and Huancas (Amazonas, NW Peru) with the MCDA through the AHP in a GIS-RS environment, thus facilitating the planning of the territory in accordance with the physical, environmental, and socioeconomic criteria in order to avoid future negative impacts. Moreover, it also considered a 20-year projection of a landfill site based on the population growth rate, MSW volume, minimum area, and landform. In effect, this study not only provides a local management tool for decision-makers (DMs), but it may also be used in other regions since the methodology can be easily replicated with the necessary complements adjusted to the local reality.

The districts of Chachapoyas and Huancas are located in the province of Chachapoyas, which is in the Amazonas region in the NW of Peru (Figure 1; 6°27′45″–6°48′07″ S and 77°43′05″–77°49′51 ″W). The total surface area represents approximately 199.67 km2, with an average annual temperature of 15.6°C and annual accumulated rainfall of 811 mm. Chachapoyas is the second most populated city and the administrative center in the region. It has high economic, commercial, and tourist activities. The city is expanding at a rapid pace, with new areas of human settlement. This is due to the creation of the National University Toribio Rodríguez de Mendoza de Amazonas (Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas—UNTRM) in 2000, which has influenced the migration of the rural population. As a consequence, the population of Chachapoyas increased from 32,026 to 42,350 inhabitants in the period of 1997–2017 [43]. Chachapoyas generates 0.434 kg/inhab-day of MSW (the national average of 2014–2018 was 0.520 kg/inhab-day [44]), with a total of 5169.86 MT/year [45]. This MSW is disposed of in “El Atajo” or “Rondón” open waste disposal sites (OWDS) (Figure 1; 1.12 ha; 6°13′10″S and 77°50′21″W), located 3.81 km (in a straight line) from the city center. This OWDS has been open for more than 40 years and is considered one of the 1585 areas degraded by MSW in Peru, and this area is still getting 31.81 MT/day of MSW [46]. This OWDS poses a great challenge for the population and DMs due to the environmental impact on the air (this site has constantly conducted burning operations for 10 years), landscape, soil, and water (this site has direct contact with El Atajo Creek) [47]. Therefore, the selection of possible sites for a controlled and authorized landfill is essential and of great importance for the main towns and cities, especially in developing countries such as Peru. This study seeks to provide the authorities with an alternative for selecting potential sites to establish a sanitary landfill in order to curb the current negative impacts by the current OWDS impacts on natural resources. In the selection process, we integrate the territory of the Huancas district to strategically form the Chachapoyas-Huancas community because, in the national strategy, the FDI-MSWs are only built for commonwealths rather than municipalities or districts.

Figure 2 shows the procedure used to model the suitability of the territory to identify a landfill suitable site. In summary, (i) criteria and subcriteria were identified and evaluated for a landfill location, (ii) a GIS database of the criteria and subcriteria was constructed, while the importance of each criterion was weighted by MCDA-AHP, (iii) submodels and the final suitability model were generated from weighted superposition, and finally, and (iv) the very adequate sites were evaluated according to future requirements of the area and shape.

The selection of the appropriate criteria is the main task for any type of analysis of land suitability, and it differs according to the objectives and the geographical scope [31]. Through a literature review, hierarchy levels were established based on three criteria and 14 subcriteria: seven physical, three environmental, and four socioeconomic (Figure 3). The shape and type of terrain are important for preventing contamination from a landfill, as well as for reducing the costs of drilling and leveling irregular terrain [48]. Therefore, the physical variables considered were as follows: the elevation, slope, soil texture, proximity to geological faults, and geology type as well as the meteorological variables such as rainfall and average annual temperature [36, 49], which are very important for landfills due to the influence of these parameters on the generation of leachates and the decomposition rate of MSW [48]. Within environmental subcriteria [17, 50], the proximity to surface waters, type of geology, proximity to protected natural areas (PNAs), and land use/land cover (LULC) were considered. Finally, for socioeconomic subcriteria, which is based on criteria established by the Ministry of Environment (MINAM) [51], it was considered proximity to access to roads, urban areas, and airports, and apart from that, it was included the proximity to tourist centers due to the increase in tourism in the city of Chachapoyas.

The cartographic data included maps of the 14 subcriteria, which included physical, environmental, and socioeconomic criteria. The thematic maps, based on vector and raster formats, were generated in ArcGIS 10.5, QGIS 3.10, Google Earth Pro 7.3, and Google Earth Engine (GEE). All maps were standardized to raster format with a spatial resolution of 30 m.

The altitude and slope subcriteria were generated in ArcGIS from the digital elevation model (DEM ALOS PALSAR, 12.5 m), downloaded from the Distributed Active Archive Center (DAAC) of the Alaska Satellite Installation (https://search.asf.alaska.edu/#/). The soil texture was generated based on the layers of the sand, silt, and clay content of the global soil data system SoilGrids (https://soilgrids.org/) [52]. From SoilGrids, layers of three depths (0–5, 5–10, and 10–15 cm) were averaged, with a spatial resolution of 250 m. The geological faults and types of geology subcriteria were obtained from the National Geological Chart 13 h (scale 1 : 50 000) of the Geological, Mining, and Metallurgical Institute (Instituto Geológico, Minero y Metalúrgico-INGEMMET) [53]. The rainfall and temperature were downloaded to the WorldClim Climate Geodatabase version 2 (http://worldclim.org), with a spatial resolution of 30 seconds (∼1 km) [54].

The rivers of the National Charter 13 h (scale 1 : 100 000) of the National Geographic Institute (Instituto Geográfico Nacional-IGN), downloaded from the website of the Ministry of Education (Ministerio de Educación-MINEDU), were used [55] and updated by manual mapping in Google Earth Pro. The LULC was generated in GEE with Sentinel 2B satellite images (ID = COPERNICUS/S2_SR), which were filtered with a cloud percentage of 13.3% in the period from 1 July 2019 to 6 June 2020. The average mosaic, with a cloud mask, was classified by a random forest (RF) algorithm and 102 training points in five classes (water, urban area, grasslands, forest, and scrubland) [56]. The LULC classification obtained an overall accuracy of 0.83 based on 45 verification points. In addition, the PNAs were updated in 2020 by the National Service of Natural Areas Protected by the State (Servicio Nacional de Áreas Naturales Protegidas por el Estado-SERNANP) [57].

The road layer was obtained from the Ministry of Transport and Communications (MTC) [58] and was updated by manual mapping in Google Earth Pro. Similarly, by manual digitization, tourist sites, urban areas, and airports were identified.

The layers of each subcriterion were standardized through reclassification, where a codification of land suitability alternatives was generated under thresholds that were determined by a literature review [59] (Table 1). The subcriteria were reclassified and scored in four alternatives: unsuitable (S1), marginally suitable (S2), moderately suitable (S3), and highly suitable (S4). Additionally, the alternative of restricted territory (S0), which includes the urban territory, the airport, and, additionally, the PNAs, is included in the analysis.

The MCDA facilitates the structure, design, evaluation, and prioritization of alternatives in decision problems, and the AHP is one of the most commonly used techniques by DMs and researchers [62]. The AHP generates a pairwise comparison matrix (PCM), and important weights for the subcriteria and criteria using a rating scale of 1 to 9 are obtained from a group of experts (Table 2) [63]. In addition, the subjectivity of the experts who complete the PCM can be evaluated through the consistency ratio (CR) [6, 64].

The CR was determined by dividing the consistency index (CI) of the matrix in question by the random index (RI) of a matrix with the same number of “n” criteria (Table 3). If CR ≤ 10%, the degree of consistency is satisfactory, while if CR > 10%, the PCM is inconsistent and must be revised [6]. The CI serves as a measure of the logical inconsistency of the evaluation of experts during pairwise comparisons of criteria [65]. CI is calculated according to the order of the matrix (n x n) so that the greatest eigenvalue (λ max) is always greater than or equal to the number of rows and columns:

With the standardized subcriteria and their respective weights of importance, weighted linear combination (WLC) was applied [66]. Raster layers were integrated for each hierarchical group (Figure 3) to generate submodels for physical, environmental, and socioeconomic suitability. By integrating submodels, the final suitability model was obtained.

The areas of the highly suitable (S4) polygons in the final suitability map were calculated and compared with the minimum area required for the disposal of the MSW generated by the population of Chachapoyas. The minimum area was calculated with a projection of 20 years (2040) to prioritize and ensure the useful life of the landfill. For this calculation, the procedures of MINAM [51, 67] and Silveira and Gonçalves [27] were followed. Table 4 shows the data and equations used to estimate the minimum area for the landfill of the study area.

In addition, the shape of the highly suitable (S4) polygons was analyzed with the compactness coefficient (Kc) so that polygons with more regular shapes could be chosen [70]. The Kc was calculated from the perimeter (P) and the area (A) of each polygon (Equation (2)). Values of Kc close to 1 indicate that the polygons resemble a circle; when the values approach 1.75, the polygons tend to be elongated, and values greater than 2 indicate irregular polygons [71].

Finally, to validate the highly suitable sites (S4) meeting the requirements of area and shape, photogrammetric flights were performed with a Phantom 4 RTK drone.

The weights of the average importance of each criterion and subcriterion are shown in Table 5. These weights were determined by a panel of seven experts, which included local DMs and landfill specialists. The coherence index (CR) was