Using Precision Agriculture (PA) Approach to Select Suitable Final Disposal Sites for Energy Generation

Abstract

Severe environmental pollution and disease exposure are caused by poor waste management, specifically in urban areas due to urbanization. Additionally, energy shortage has threatened almost all parts of human life in the world. To overcome this problem, a precision agriculture approach using spatial mapping based on social environmental factors and sustainability principles can be used to find the variability of sites with respect to their suitability for waste disposal and energy generation. Therefore, this study aimed to develop a system for selecting suitable areas for municipal waste disposal and energy generation based on several structured criteria as hierarchical weighted factors. The system prototype was developed and tested in a case study conducted in an Indonesian Megapolitan area. The suitability map produced by the system for waste disposal and energy generation had an accuracy of 84.3%. Furthermore, validation was carried out by ground-checking at 102 location points. A future application of the proposed system is to provide spatial data-based analysis to improve regional planning and policy-making for waste disposal and energy generation in certain areas, particularly in Indonesia.

1. Introduction

Waste management is crucial to prevent environmental pollution and disease exposure, specifically in urban areas. One of the important tasks in this process is determining a suitable location for the final disposal of waste, which can vary from one location to another depending on ecological and environmental variability, economy, infrastructure, and social vulnerability, as well as natural disasters. Additionally, waste production increases every year due to population growth and lifestyle changes. The average solid waste disposal rate in 23 developing countries is 0.77 kg/person/day [1]. Disposal of municipal solid waste (MSW) worldwide is approximately 2 billion tons annually and expected to increase to 3 billion tons annually by 2025 [2].

In the case of Indonesia, better waste management is urgently needed, specifically in urban megapolitan areas. According to Aprilia [3] and Nanda, et al. [4], as urbanization increases, large urban centers in Indonesia generate up to 8 million tons of waste per day, which on average is dominated by organic waste (70%), compared to inorganic (30%). Population growth and changes in people’s consumption patterns have increased the volume, types, and characteristics of waste. This is coupled with poor waste management methods and techniques, which in turn can have a negative impact on public health and the environment. Meanwhile, the use of municipal waste as a source of electricity is in line with the Indonesian government’s program to foster the development and utilization of New and Renewable Energy (EBT), specifically bioenergy, to achieve the national target of 23% renewable energy by 2025.

MSW or urban waste is produced by cities, factories, agro-industry, and various household activities [5,6]. The most important step in waste management is selecting a suitable landfill site, which is unavoidable in big and small cities worldwide, specifically Jabodetabek. However, landfill selection is a complex and multi-disciplinary project due to various decision-making criteria, such as environmental, ecological, social, economic, and engineering. Several individuals have used different criteria according to their particular case studies. MSW management aims to reduce waste as well as unwanted impacts and utilize them as a renewable alternative energy source for electricity and cooking gas [7]. Furthermore, various alternative energy companies are developing new ways to recycle waste by generating electricity from landfill waste and pollution [8]. Figure 1 represents the basic options available for sewage treatment [5].

A new suitable site must be identified which is ideal for landfill and energy generation, specifically for the power grid. Standard waste energy generation technologies include biogas, incineration, pyrolysis, and landfill [9,10]. There are various considerations in selecting the waste-to-energy (WtE) technology, including MSW calorific value, biomass content, facility technology, residue, MSW quantity, distance from MSW to WtE facilities, and low-carbon generation incentives [11]. WtE technology, such as incineration and pyrolysis/gasification, is thermochemical technology requiring relatively high operating temperatures. Therefore, it is more suitable for MSW with a relatively high calorific value but low moisture and biomass content. For materials with high moisture content, thermochemical technology, such as pyrolysis require drying, and generally, the products produced are typically for high-quality fuels [9,12], which are usually not intended to enter the power grid directly. In addition, it also requires more complex facilities and better management experience.

Biogas and LFG are WtE technology suitable for most areas in developing countries such as Jabodetabek because the waste composition contains more organic matter and has a fairly high water content. Determining the location of an energy plant requires a comprehensive evaluation to identify the best available site while meeting various requirements, specifically government regulations. Various studies grouped important criteria that must be considered in determining the location of energy plants, such as environment, public health, engineering, urban, resources, culture, economy, social aspects, and safety [7,13,14,15,16,17,18,19,20]. The criteria for selecting a suitable site vary for each country and local community and are specific to certain regions. For example, the prohibited distances for biogas installations from agricultural land and power grids are <50 m and 0–50 m, respectively.

Determining the landfill location for energy generation is a complicated problem due to the various criteria in decision-making, namely the requirements for the location of the landfill and the energy plant. In general, all the studies on site selection matched the decision-making criteria in various countries pursuing common goals such as the desire for environmental sustainability, socio-economic, and technical aspects. To address this problem, Geographic Information System (GIS) offers a powerful tool to facilitate and expedite the location determination process as well as provide suitable solutions for planning and policy-making. More specifically, this approach contains spatial modeling-based analysis. It is an important instrument for conducting geospatial analysis to understand site characteristics and guide decision-making. Various predefined multidisciplinary criteria can be embedded in spatial modeling with the support of multi-criteria decision analysis (MCDA) to provide a real suitability map output [21,22]. According to Hariz et al. [16], MCDA is used to weigh criteria, while spatial modeling is used to identify candidate locations. MCDA has several unique advantages, including [23] simplicity, compatibility with GIS software for raster and vector data, ability to evaluate important criteria, high precision, fast operation, and the capacity to deal with uncertainty which are all characteristics of an ideal classification algorithm. Its technique includes various types of mathematics, such as analytic hierarchy processes (AHP), fuzzy linguistic hesitation (HFL), and fuzzy analytic network processes (FANP) [24,25,26]. Therefore, MCDA is an advanced technique to solve complex problems in waste management

Given that different geographical locations have varying characteristics and potential for energy generation, a site-specific selection method and recommendations for energy generation are needed. Precision agriculture is an approach to find variations or heterogeneity of an area or location for agricultural management [27,28]. In terms of energy generation management, the precision agriculture approach uses information technology to identify, analyze, and manage variability in the field by implementing all energy generation practices at the right location, time, and in the right way to optimize profitability as well as sustainability. This paper discusses the application of precision agriculture to select the most suitable location for energy generation from municipal solid waste. It will improve to address the variability assessment for planning and developing an appropriate on-site energy generation system with minimum pollution effects and maximum energy use. One of the significant waste administration challenges is selecting a suitable site to dispose of solid municipal waste [5].

The selection of landfill sites has been widely reported by previous studies in various areas such as Memari City, India [29], the capital city of Dodoma, central Tanzania [30], Samsun City, Turkey [31], and East Nusa Tenggara Province, Indonesia [32]. In general, the criteria for selecting a suitable landfill site include (i) environmental aspects including surface water, groundwater, land elevation, land use, elevation, soil type, soil drainage, geomorphology, and temperature; (ii) socio-economic such as distance from main roads, population density, land prices, distance from settlements; and (iii) safety criteria covering the epicenter. For example, the recommended landfill distance to settlements and surface water is >500 m and >200 m [29,32].

However, previous investigations only focused on selecting suitable landfill sites and did not consider site criteria for energy generation installations. This gap will be filled in this study, where the criteria for selecting a landfill site will be combined with those for selecting a location for an energy plant. The novelty is the use of a precision agriculture approach to select suitable sites for energy generation. This combination is very relevant as the main solution to complex problems in waste management in Indonesian megapolitan areas. The problem in this study is how to select a suitable location for energy generation from MSW based on several criteria and display the variability of suitability levels spatially at different specific locations. Therefore, this study aims to develop a system for selecting suitable sites for energy generation using a precision agriculture approach by mapping the spatial distribution and variability of the sustainable potential of bioenergy.

The Needs for Precision Agriculture

Precision agriculture (PA) aims to adapt and improve land and crop management to the needs in heterogeneous fields and balance production against environmental thresholds [23]. This approach uses energy maps, GIS, remote sensing, and sensors to identify the variability of potential energy generation in a given geographic area.

Opportunities for biomass energy are growing, although biomass energy is available in many forms. Almost all plant and organic wastes can be used to generate heat, electricity, or fuel. These renewable resources include solar, wind, biomass, and wave-generated power which have enormous potential for the agricultural industry [24]. Meanwhile, finding the most suitable location for energy generation is not a trivial task and involves many factors to be assessed and managed. PA revolves around three basic steps, namely capturing and analyzing variability as well as making decisions [25]. Determining the most suitable location for energy generation is very relevant to sustainability [24]. The selection of a suitable location for energy generation significantly impacts energy production costs. A favorable situation will culminate in significant cost savings and increased energy generation efficiency [32].

The use of precision agriculture for energy saving has been carried out and discussed in [33]. Nanda et al. [4] showed that these factors include environmental, social, security, and economic, requiring not only attributive (tabular) data but also spatial and temporal data. Furthermore, various factors influence the suitability of a particular site for energy generation from municipal solid waste. In this study, the criteria for selecting a landfill site were combined with those for selecting a location for an energy plant. The drawback in previous studies is that they only focused on testing the validity level of the weighting criteria, using evaluation metrics such as consistency ratios but there is no evaluation test related to the validation of field experience.

2. Materials and Methods

The development method used was SDLC (System Development Life Cycle) based on a systems approach to develop information system solutions [34]. SDLC could be viewed as an iterative, gradual process involving the following stages: (1) investigation, (2) analysis, (3) design, (4) implementation, and (5) maintenance. The investigative stage was related to the formulation of problems and solutions, while the analysis phase included functional and non-functional analyses of the system. The design phase included system architecture, database, computing (process), input-output, and user interface designs. Furthermore, the developed system was coded and constructed at the implementation stage, while the system evaluation and improvement were carried out at the maintenance stage. In this study, the system prototype has been previously implemented, tested, and evaluated.

2.1. System Analysis and Design

The proposed system architecture consisted of several functional components: (1) data acquisition; (2) user interface; (3) data & knowledge base; (4) data processing; and (5) display component, as shown in Figure 2. All of these components will be detailed in each of the following sub-sections.

2.2. Data Acquisition Component

The data acquisition component comprises all data representing the factors that determine the suitability of a location for energy generation from municipal solid waste. These factors include environmental, social and safety, and economic aspects [4], each of which is broken down into subfactors, as shown in Figure 3. All factors and subfactors have been weighted using AHP analysis and determined by suitability criteria as fully discussed in Nanda et al. [4]. This component also serves as a location input of interest in the form of a base map.

AHP has three main operations namely hierarchical construction, priority analysis, and consistency checking [23,26]. The decision maker must deconstruct some complex criteria choice problem into components in which all imaginable attributes are organized into various hierarchical levels. They should compare each cluster at the same level in pairs using their experience and knowledge. For example, each of the two criteria is compared simultaneously against the objective at the second level. Meanwhile, at the third level, any two attributes of the same criterion are simultaneously compared with the corresponding criteria. In this study, 14 subfactors related to the environment, society, safety, and economy are proposed to establish optimal sites for energy generation. However, this system test does not take into account several subfactors such as social acceptance, distance from the power grid, and land cost because these data are not openly accessible and require very strict government permits.

2.3. User Interface Component

This component facilitates dialogue or interaction between the user and the proposed system. Generally, there are two categories namely administrative, expert, and system users. Administrative users represent all the individuals assigned to maintain and manage the proposed system, while expert users refer to all those who are relevant to the development of the system and can update the knowledge base. Additionally, system users represent end users who directly or indirectly use and utilize system applications.

2.4. Data and Knowledge Base Component

A knowledge-based component was developed to facilitate storing and retrieval, as well as the site selection procedure for energy generation. It is made up of frames and rules. The rule describes the circumstances under which the selection procedure is conducted and also provides the constraints’ solutions. The knowledge-based component featured a structured description of the “basic information”, a business-oriented description, business-process templates, linkages between the components, and a flexible search capability [35].

2.5. Data Processing Component

This component performs computational tasks to process all the data obtained including pre-processing which comprises digitization and rectification, data analysis, map creation, map overlay, and overall map matching based on all factors. Furthermore, data analysis used the GIS and MCDA to create a land suitability map. Factors that influence site suitability were determined and weighed by the Analytical Hierarchy Process (AHP), as discussed in Nanda et al. [4] and Quadri and Dohare [5].

2.6. Display Component

This component produces output from the proposed system to users for various purposes. The main output is a map of the suitability of the observed areas which can be delivered to various platforms such as mobile or web platforms.

3. Results and Discussion

The prototype implementation is the construction and integration of the functional system components that comprise the proposed system. Figure 4 represents the system user interface, which provides login and registration facilities for registered and new users, respectively. A user must be registered and logged in to enter the application system and can select the role of an administrative, expert, or system user. Users are provided with the relevant menu after logging in, as shown in Figure 5. Afterward, they can select the layer to display or upload data, search for the target area, and draw the area for site suitability analysis, as shown in Figure 6. Several layers can be activated by users namely administration (from village level to city level), suitability layer, existing landfill location (represented as a point), and city. This makes the system scalable to the wider area of Jabodetabek in the vicinity of Indonesia’s capital as the case study area. Moreover, uploaded data on the system must be shapefile data in zip format.

The system administrator is responsible for managing, organizing, compiling, creating, updating, and deleting the system’s registered users, data, and knowledge base (Figure 7). The expert system is equipped with tools (Figure 8) to update data and content models for spatial data analysis in selecting suitable final disposal sites for energy generation from municipal solid waste.

Database conceptual development is based on the Calkins model [36], which is suitable for describing the relationship between spatial entity entities (spatial entity relationship diagram/SERD). The developed database model (Figure 9) was implemented using a PostgreSQL Ver. 12 database management system (DBMS) with the PostGIS extension for web-based platforms.

The display component provides spatially based information in the form of a map indicating suitable final disposal sites for energy generation from municipal solid waste, as shown in Figure 10, Figure 11, Figure 12, Figure 13 and Figure 14. Suitability maps can be displayed on a subfactor, factor, and overall factor basis, respectively.

3.1. System Testing and Evaluation

3.1.1. Case Study Area

To demonstrate the application and performance of the built system prototype, the system was tested with case studies in Jabodetabek, a megapolitan area consisting of Jakarta, Bogor, Depok, Tangerang, and Bekasi. Jabodetabek is Indonesia’s combined largest regional center with a dense population, strong economy, and geopolitical importance. It includes the administrative areas of three provinces, namely DKI Jakarta (Jakarta); West Java (Bogor, Depok and Bekasi) and Banten (Tangerang), as fully described in Figure 10. Furthermore, the area covers 6437.89 km² or 0.34% of the total area of Indonesia, with Bogor and Depok having the highest and lowest areas, respectively. The total population is 33.6 million people or around 12% of Indonesia’s population and is one of the most densely populated areas. In a more detailed demographic, Jakarta comprising 10.4 million people has the highest population compared to other regions, followed by Tangerang with 7.5 million people, Bogor 6.9 million people, Bekasi 6.5 million people and Depok with 2.5 million people. The population will increase every year due to high urbanization in Indonesia. The higher the population, the higher the waste generated. Therefore, this will affect the complexity of waste management.

3.1.2. Criteria-Based Suitable Sites

Land suitability maps are made using weights from the AHP process and then processed by overlaying with ArcGIS software. The results of suitable locations based on environmental, social and safety criteria, as well as economic factors are shown in Figure 11, Figure 12 and Figure 13, respectively. Based on the analysis, environmental factors are the most important aspect in selecting a suitable location for energy generation, followed by the social and economy aspects.

Figure 11 shows the appearance of the suitability map for all subfactors consisting of environmental factors. The suitability display for an environmental factor (Figure 11f) is obtained by superimposing all of its subfactors including (a) distance from settlements, (b) distance from rivers, (c) slope of land, (d) distance from agricultural areas and (e) climate.

The view of suitability maps of all subfactors comprising the social and safety actor is depicted in Figure 12. The suitability view of social and safety factors (Figure 12c) is obtained by overlaying all of its subfactors including (a) distance from low land and (b) trend of urban growth.

Figure 13 shows the suitability maps of all subfactors comprising the economic factor. The suitability view of the social and safety factors (Figure 13d) is obtained by overlaying all of its subfactors namely (a) distance from major road, (b) distance from local road, and (c) land use. The final suitability map representing the overlay of all factors including environmental, social, safety, and economic, is depicted in Figure 14.

Referring to Figure 14, the most suitable area is Bogor Regency, Southern Jabodetabek.

Table 1. Potential area in Jabodetabek for energy generation from MSW.

Region	A Potential Area for Energy Generation in Suitability Class (ha)
	Most Suitable	Suitable	Moderately Suitable	Less Suitable	Not Suitable	Restricted
Jakarta	0	104.37	1935.43	35,810.56	25,377.94	1062.27
Bogor	3096.87	67,205.90	106,120.99	83,860.91	48,761.59	455.26
Depok	0	0	219.66	7815.73	219.66	0
Tangerang	0	931.08	35,850.04	67,037.62	33,696.43	0.14
Bekasi	0	439.93	47,072.13	48,761.59	30,634.92	21.25
Total (ha)	3096.87	68,681.29	191,198.24	243,286.41	138,690.55	1538.93

shows more detailed information on the footprint area for energy generation from MSW (in ha) per suitability class. Based on the analysis, the most suitable area for energy generation is in the Bogor area, which is 3096.87 ha. One of these locations is in Nanggung District, Bogor. Compared to other regions, most of the Bogor area is still underdeveloped and dominated by rural areas, with vacant land which are suitable for energy generation. This implies that the TPA to generate energy from urban waste in the Greater Jakarta area must be located in Bogor.

3.1.3. System Evaluation

The evaluation is an important stage in assessing the reliability of a system, in general, field validation is very important in determining the location. Meanwhile, each location must be checked for conformity between the modeling results and the reality in the field. Modeling errors can occur due to data not being updated, sudden disasters, and major developments. This study compares the final overlay map and field validation, a total of 102 location points with 17 points for each class were ground-checked in the field regarding the level of conformity with the system output. The latest satellite imagery by Bing Aerial from Microsoft Bing was used to validate several parameters such as distance from settlements and rivers on environmental, social and security, as well as economic subfactors. This validation is displayed in a connection matrix (Figure 15) with an accuracy value and error as evaluation metrics. Based on the evaluation analysis, the suitability map for energy generation has an accuracy of 84.3% and an error of 15.7%. This error may be caused by using less updated map data. This study used the 2020 map, which allows land use changes to occur during that period, culminating in a discrepancy between the final map overlay and land validation. However, this system still has a fairly good performance and accuracy value.

3.2. Implementation Issues

Energy input in agriculture has increased rapidly and accounts for about 17% of the total energy consumed in the US. Meanwhile, precision agriculture involves a knowledge-based technical management system to optimize farm productivity, reduce input costs and increase crop yields while also reducing the harmful environmental impacts associated with the inefficient use of agricultural inputs [33]. Agro-industry produces agricultural organic waste that can be used for energy generation [5]. Therefore, selecting a suitable site for waste disposal can be very useful for improving waste management, agro-industry sustainability, and energy generation system sustainability, as well as reducing unwanted environmental impacts. Bioenergy production is a vital Ecosystem Service (ES) provided by natural and semi-natural ecosystems, which can achieve, for example, the target of 20% total energy production from renewable sources in the European Union (EU). There are environmental concerns associated with the use of bioenergy [35].

According to the World Bank, worldwide municipal waste generation will reach around 2 billion tons/year by 2020, where each person can produce an average of 0.79 kg/person/day of waste. Based on a well-known hierarchy, options for effective MSW management are sourcing reduction, recycling and composting, energy recovery and disposal, in order of preference. MSW is a sensitive issue that needs attention from the city government, city planners, and decision-makers as waste are not managed properly. More complex problems can be faced, specifically in developing countries with densely populated cities, due to unscientific waste management systems, low human awareness [13], and rapid population growth [15].

Various WtE technologies have been developed for MSW to generate electricity, WtE technology generally includes incineration (combustion), gasification, pyrolysis, anaerobic digestion (biogas) and landfill gas (LFG). Other technologies apart from the aforementioned ones above are generally not used to generate electricity, for example, RDF is used as a fuel to manufacture cement. According to UNEP, the development of new WtE facilities globally is dominated by combustion technology. However, waste in Indonesia has a fairly high organic matter content (60%) with a moisture content of up to 45%. To apply the various WtE technology above, except for LFG, pretreatment such as sorting and drying is required. The need for waste drying and segregation in LFG technology is minimal; hence, it seems that this technology is quite suitable for application in Indonesia. Assuming a landfill depth of 10 m, one ton of MSW requires 0.2083 m² of landfill.

Fortunately, there is support and policies from the Indonesian government pushing for more sustainable urban waste management nationwide. The government through the Ministry of Energy and Mineral Resources has fostered the participation of various parties in efforts to convert waste into energy. Moreover, to accelerate the development of the WtE sector, the government provides several incentives, including the establishment of a Feed in Tariff (FiT), namely the price paid for electricity generated from municipal waste.

4. Conclusions and Future Work

A system for selecting suitable landfills for energy generation from MSW has been developed and implemented on a prototype scale to test its applicability and performance. The system can display a spatial map that shows the variability of locations suitable for energy generation, which will improve the national management of MSW for sustainable energy generation in certain locations. Furthermore, compliance maps are displayed on thematic views that differ hierarchically from subfactors, factors, and overall factors, thereby allowing the reading of different sites’ suitability details. The system also provides updates in the data and knowledge modules by system administrators and expert users, making the system more extensible and adaptive to new computing requirements as well as knowledge enhancements.

Future investigations are needed to measure energy savings for agro-industry implementing precision agriculture practices and to assess the additional renewable energy that can be generated by agricultural waste in certain areas by applying the proposed system to select suitable sites. A promising future application of the proposed system is the provision of spatial data-based analysis to improve regional planning and policy making for waste disposal and energy generation in certain regions, particularly in Indonesia. To ensure the workability and sustainability of the proposed system, there should be a national policy to regulate the use of spatial sites for waste disposal for energy generation from urban organic waste.