Multi-Factor GIS Modeling for Solid Waste Dumpsite Selection in Lilongwe, Malawi

Abstract

Solid waste disposal is an essential component of any waste management system, but finding suitable solid waste disposal sites is seen as the main challenge. The goal of this study was to locate solid waste disposal facilities in Lilongwe that would not present logistical or operational difficulties. We used a hybrid approach involving qualitative and quantitative methods. Primary and secondary data were collected, through interviews, questionnaires, and GPS for coordinates. The study considered 10 suitability factors of slopes, rivers, soil types, built-up areas, airports, forests, wetlands, current dumpsites, roads, and railways weighted using the analytic hierarchy process (AHP). We analyzed site selection techniques, evaluated the current dumpsite, and created a multi-factor geographic information system (GIS) model. This study found that the current dumpsite is dangerous for human health and is close to homes. Additionally, this research reveals that the most popular techniques for choosing the location of a solid waste disposal facility are the AHP and weighted linear combination (WLC) coupled with GIS. Out of 46,283 Ha in Lilongwe, 84.07% are unsuitable, 14.97% are suitable, and 0.96% are highly suitable for solid waste disposal sites. Six sites with capacities ranging from 28 Ha to 94 Ha were identified as optimal to reduce operational costs in areas 55, 44, 58, and 61. Further waste recycling initiatives are recommended to reduce solid waste volume and promote sustainable waste management practices.

1. Introduction

Waste, material discharged from human activities, poses a significant threat to human health and the environment [1]. It includes various materials such as leaves, food remnants, paper, textile materials, bones, and dead animals [2]. Waste production is expected to rise to 2.8 billion metric tons by 2050 worldwide [3]. This rise creates enormous cost constraints for collection, landfills, and recycling [3], with the Sub-Saharan Africa region producing around 62 million tons of waste annually [3]. The development of smart waste management is required, leveraging the Internet of Things (IoT)-enabled services and other smart technologies to support real innovation in waste management. With a projected growth in the global population expected to reach 9.75 billion by 2050 (a 22.5% increase from the population of 7.96 billion in 2022), it is anticipated that the Sub-Saharan Africa region will be producing around 62 million tons of waste annually [4]. In terms of environmental pollution, social inclusion, and economic sustainability, solid waste (SW) mismanagement and misgovernance in cities, mining, and the wood industry among others are global issues that call for integrated evaluations and holistic measures to address them [3,5,6,7,8,9]. Cities [3] and many nations worldwide, including Cambodia [9], Brazil [8], India [7], and Chile [5], among others, face significant economic challenges when it comes to waste management. These challenges include a lack of coordination between various data sources and an integrated data cloud that can be shared among various stakeholders; a lack of environmental awareness on the part of citizens and the implementation of technologies in recycling, reverse logistics, and support of environmental regulations [3]; illegal dumping (open dumping) [9]; a lack of a global agreement regarding the best practices for tailings management and governance [5], limited financial resources, inadequate technologies; and a lack of a policy framework [9].

Malawi generates half a kilogram of waste per capita daily, resulting in 633 fifteen-ton trucks of waste produced every 24 h [10]. Lilongwe in Malawi, the largest city in Malawi, has been one of the most waste-generating areas, causing a steady decline in the quality of solid waste management (Figure 1 and Figure 2).

Solid waste management (SWM) is a global environmental problem in both developed and developing countries like Malawi. Traditional site selection methods, such as topographical maps, are often used in developing countries, but an appropriate landfill site should have the least negative effect on an area’s economic, sociological, and environmental aspects [11]. Geographic information systems (GIS) has become a valuable tool in environmental and engineering sciences for efficient site suitability analysis, including dumpsite identification and site/location identification. GIS provides efficient data manipulation and presentation, while multi-criteria evaluation (MCE)/multi-criteria decision analysis (MCDA) calculates factors’ weights of landfill sites based on criteria of importance [12].

Lilongwe faces challenges in managing waste due to an increasing urban population and limited public resources. The city’s current dumpsite, located 30–40 km from most areas, poses logistical and operational challenges for the Lilongwe City Council (LCC). This study aims to determine suitable locations for dumpsites. Specifically, this study assessed the current dumpsite’s suitability, examined waste disposal site selection methods, and developed a multi-factor GIS model for identifying additional suitable dumpsites in Lilongwe. This will help reduce operational costs and improve waste management efficiency. The solid waste collection rate in Lilongwe is currently low with 70% of waste lying unmanaged and disposed of in undesignated places, and thus increasing health risks [13].

This waste is due to the inadequate capacity of the Lilongwe City Council (LCC) to collect and dispose of solid waste at the dumpsite. The council has only four refuse vehicles, which is inadequate due to the amount of waste generated and the long distances from collection points to the dumpsite [13]. The council is struggling to generate about MWK 14.5 million per month for waste collection alone, which could increase if additional issues like waste disposal are added [14].

The emergence and increase in private waste collection/management companies suggests that the LCC is overwhelmed and has failed to manage solid waste in the city. These private companies are also challenged by increasing operational costs due to the long distance from waste collection points to the single dumpsite. The situation worsens as the population increases. To address LCC’s logistical and operational challenges, this research proposed using GIS, integrated with MCE techniques, to identify alternative sites for locating solid waste dumpsites and reduce operational costs. Previous studies have focused on dumpsite selection methods, but this research focused on identifying suitable dumpsites that did not pose logistical and operational challenges to improve waste management efficiency.

Solid waste management (SWM) is a critical discipline in urban areas worldwide, as urban populations continue to grow, leading to an increasing quantity of domestic solid waste and decreasing disposal space [15]. Globally, 2 billion tons of MSW are generated, with 33% remaining uncollected by municipalities [16]. The main SWM techniques include recovering, recycling, reusing, composting, incineration, and landfilling. Landfilling is a common method in developed countries, affecting the environment and human habitat [17]. Malawi, with an estimated population of 18.6 million and a population expected to double by 2038 [18], faces a significant challenge in SWM. The four major cities in Malawi generate over 1000 tons of solid waste daily, and the waste management system and public awareness are inadequate to cope with the amount of waste generated [19]. Lilongwe faces significant challenges in managing waste due to lack of adequate human resources and capacity, as well as lack of guiding documents on waste management. The LCC has developed an SWM plan to achieve a clean and environmentally sustainable city, addressing illegal dumping and spending MWK 80 million per annum on clearing illegal dumping [20].

GIS is a computerized system that aids in efficient solid waste management planning by capturing, storing, analyzing, managing, and presenting data linked to locations [21]. It is a promising approach for analyzing complex spatial phenomena, as it can store, retrieve, and analyze large amounts of data from various sources. GIS has been used in various environmental applications, such as assessing water pollution and identifying forest fire susceptibility. It has also been used to improve municipal solid waste management, such as predicting generation and composition patterns, improving collection and transport, and identifying landfill siting areas [22]. Multi-criteria decision analysis (MCDA) is a valuable tool for solving complex decisions, particularly those involving multiple factors in solid waste management [23]. Integrating MCDA with GIS enhances analysis effectiveness, accuracy, and decision-making reliability [22].

The analytical hierarchy process (AHP), developed by Saaty in the 1970s, is an MCDA method component used in planning [22]. It provides a hierarchical structure by reducing multiple variable decisions into pair comparisons and developing subjective priorities based on user judgment [24]. The integration of GIS and AHP is a powerful tool for selecting landfill sites, as it provides efficient data manipulation and consistent ranking of potential landfill areas [25]. AHP is built on three principles: decomposition, comparative judgments, and synthesis of priorities. In the decomposition principle, decision-making problems are divided into hierarchical forms, while the comparative judgment principle constructs pairwise comparisons [26]. The third principle involves an overall priority rating to produce composite weight [17]. The relative importance between two criteria is measured using a numerical scale of 1 to 9 (

Table 1. The fundamental scale of AHP.

Intensity of Importance	Definition	Explanation
1	Equal importance	Contribution to the objective is equal
3	Moderate importance	An attribute is slightly favoured over another
5	Strong importance	An attribute is strongly favoured over another
7	Very strong importance	An attribute is very strongly favoured over another
9	Extreme importance	Evidence favoring one attribute is of the highest possible order of affirmation
2, 4, 6, 8	Intermediate values (2, weak or slight; 4, moderate plus; 6, strong plus; 8, very, very strong)	When compromise is needed

) [27].

AHP is a technique used to measure consistency in decision-making processes, thereby determining the consistency ratio (CR) by comparing the consistency index (CI) to the random index (RI), varying in the number of criteria. The CR is calculated using the equation CR = CI/RI, where CI is the consistency index, calculated using CI = (λmax − n)/(n − 1) and n is the number of parameters [21,22]. An acceptable CR is less than 0.10 (10%); otherwise, judgments may be inconsistent and should be reassessed [22].

Equation (1)

$$\mathit{CR}=\raisebox{1ex}{$\mathit{CI}$}\!\left/ \!\raisebox{-1ex}{$\mathit{RI}$}\right.$$

(1)

Equation (2)

$$\mathit{CI}=\frac{\lambda \mathit{max}-n}{n-1}$$

(2)

The integration of AHP or MCDA with GIS has been widely used in dumpsite or landfill site selection studies [28]. Weighted linear combination (WLC), a GIS multi-criteria evaluation technique, is used to evaluate suitable areas for dumpsite or landfill site locations [29]. Decision-makers use this technique to assign criteria weights based on the relative importance of each criterion’s suitability map and combine the reclassified criteria maps to obtain an overall suitability score. The WLC method involves defining the set of evaluation criteria and alternatives, standardizing each evaluation criterion/map layer, defining the criterion’s weight, constructing weighted standardized map layers, generating scores for every alternative, and ranking alternatives based on the overall score [17].

The selection of the most suitable solid waste disposal site necessitates a thorough assessment of site conditions, considering economic, environmental, health, and social impacts [9,16]. Factors such as distance to urban centers, water bodies, airports, infrastructures, soil permeability, and proximity to residential and industrial areas must be considered [22]. A dumpsite should be located far enough from main roads, but not too far, to avoid increased costs and transportation. A distance of less than 500 m is considered unacceptable, while 1000–2000 m is optimal [30]. The complex process of selecting a dumping site can be complicated, but models like AHP and WLC can be effective when combined with GIS for site selection or suitability analysis.

2. Materials and Methods

This study employed a blended approach to research dumpsite selection in Lilongwe. The qualitative approach involved semi-structured interviews with LCC staff, content analysis, and quantitative data collection using a questionnaire. The AHP scale was used for the quantitative analysis, which included descriptive analysis in maps.

This study used both primary and secondary data, including interviews, questionnaires, and GPS receivers for spatial data collection (

Table 2. Datasets and sources.

Data	Source
Land use (built-up area)	Sentinel-2 (of 2021) https://livingatlas.arcgis.com/landcover/ https://www.dof.gov.mw/resources/geospatial-data accessed on 14 November 2022
River	OpenStreetMap
Road, city boundary, railway, wetland	Ministry of Lands
Airport, River	OpenStreetMap
Slope	https://earthexplorer.usgs.gov/ accessed on 13 November 2022
Soil	https://www.masdap.mw/ accessed on 26 January 2023
Current dumpsite	Google Earth/Maps

). Interviews were conducted with LCC staff, experts in dumpsite selection, and households around the current dumpsite. The questionnaire suggested selection criteria for dumpsites, and experts ranked them using the AHP scale of 1 to 9 (Table 1). Physical observations and GPS coordinates were also collected. Secondary data were gathered from various sources, such as reports, the Internet, books, journals, departments/institutions, and other documents. Factor maps such as land use maps and road network maps were used to select suitable solid waste dumpsites. Various software and tools were used, including ArcMap 10.7.1 for map generation and suitability analysis, QGIS 3.28.4 for map generation, Excel 2019 for geometric mean calculation, the SpiceLogic 4.1.5 analytic hierarchy process for consistency index and ratio calculations, and Google Earth Pro 7.3.4 for satellite image visualization and site verification.

This study aimed to determine suitable locations for dumpsites in Lilongwe by identifying siting criteria from the literature and expert knowledge. Factors considered included environmental, economic, accessibility, and social safety factors. Environmental factors included slopes, rivers, wetlands, and soil types, while economic factors included land uses (built-up areas, forests/plantations). Accessibility factors included roads and railways, and social safety factors included distance from the airport and the current dumpsite. The datasets were georeferenced to WGS 84/UTM zone 36S, buffer distances were calculated and reclassified with weights, and they were clipped based on the study area boundary.

Slope is an important criterion in dumpsite selection, as it determines the runoff of the site [31]. A mild slope of less than 12% is suitable for dumpsites [32], as steep slopes increase excavation costs and leachate pollution [31]. A buffer distance of 500 m must be established around rivers or surface water bodies to prevent leachate pollution [33]. Roads and railways are crucial for transporting wastes [34], and the selected site should be away from primary and secondary roads to minimize transportation costs [29,34,35]. Built-up areas should be carefully assessed, as dumpsites can cause harm from various factors, including odors, noise, and health issues [32]. A distance of more than 1000 m is considered appropriate [36]. Soil type should be properly evaluated when selecting dumpsites, with clay soil being one of the best for solid waste disposal siting [31]. Existing dumpsites should be located far enough from surrounding residents and water bodies, and a distance of more than 1000 m is considered safe [31,32,37,38]. Wetlands should be avoided when allocating dumpsites to minimize landfill leachate, and a distance of more than 500 m from the wetland area should be considered [37]. Forests should be at a distance of at least 500 m [39] and airports at 3000 m, as birds are attracted to waste and their presence is a real danger to airplanes [40].

Table 3. Criteria and buffer distances.

Criteria	Buffer Distance (m)	Reference
Airport	3000	[40]
River	700	[35]
Road	700	[35]
Built-up area	1000	[36]
Wetland	500	[41]
Forest/Plantation	500	[39]
Railway	500	[42]
Existing dumpsite	1000	[38]

and

Table 4. Factors and siting criteria.

Built–Up Area Distance (m)	Suitability Class	Score Value
0–250	Not Suitable	1
250–500	Less Suitable	2
500–750	Moderately Suitable	3
750–1000	Highly Suitable	4
>1000	Very Highly Suitable	5
Wetland Distance (m)	Suitability Class	Score Value
0–200	Not Suitable	1
200–300	Less Suitable	2
300–400	Moderately Suitable	3
400–500	Highly Suitable	4
>500	Very Highly Suitable	5
Road Distance (m)	Suitability Class	Score Value
0–200	Not Suitable	1
200–300	Less Suitable	2
300–400	Moderately Suitable	3
400–500	Highly Suitable	4
>500	Very Highly Suitable	5
River Distance (m)	Suitability Class	Score Value
0–200	Not Suitable	1
200–400	Less Suitable	2
400–600	Moderately Suitable	3
600–700	Highly Suitable	4
>700	Very Highly Suitable	5
Railway Distance (m)	Suitability Class	Score Value
0–200	Not Suitable	1
200–300	Less Suitable	2
300–400	Moderately Suitable	3
400–500	Highly Suitable	4
>500	Very Highly Suitable	5
Forest Distance (m)	Suitability Class	Score Value
0–200	Not Suitable	1
200–300	Less Suitable	2
300–400	Moderately Suitable	3
400–500	Highly Suitable	4
>500	Very Highly Suitable	5
Airport Distance (m)	Suitability Class	Score Value
0–500	Not Suitable	1
500–1000	Less Suitable	2
1000–2000	Moderately Suitable	3
2000–3000	Highly Suitable	4
>3000	Very Highly Suitable	5
Land Slope (%)	Suitability Class	Score Value
0–12	Very Highly Suitable	5
12–16	Highly Suitable	4
16–20	Moderately Suitable	3
20–30	Less Suitable	2
>30	Not Suitable	1
Soil Type	Suitability Class	Score Value
Not Clay	Not Suitable	0
Sandy Clay Loam	Highly Suitable	1
Clay	Very Highly Suitable	2
Current Dumpsite (m)	Suitability Class	Score Value
0–250	Not Suitable	1
250–500	Less Suitable	2
500–750	Moderately Suitable	3
750–1000	Highly Suitable	4
>1000	Very Highly Suitable	5

present the factors with their siting criteria.

Buffering is a way of producing areas or regions of numerically calculated distances from a feature, such as a point, line, or polygon. Factors were assigned weights or ranked using the AHP scale based on expert judgment, and the consistency index (CI) and consistency ratio (CR) were calculated using Equations (1) and (2), respectively. The weights from experts were summarized in Microsoft Excel using the geometric mean. A weighted overlay was applied to reclassified layers to produce a common measurement scale of values, standardizing all combined factors and producing an overall dumpsite suitability map. This was performed by applying a weight to each factor followed by a summation of the results to yield a suitability map, using the following equation.

Equation (3):

$$S=\sum w_i{x}_i$$

(3)

where S represents the result of the WLC, $w_i$ is the weight of factor $i$, and $x_i$ is the criterion score of factor $i$. Figure 3 highlights the conceptual model.

3. Results and Discussion

3.1. The Suitability of the Current Dumpsite

The current dumpsite was chosen based primarily on the availability of vacant land, which at the time, consisted entirely of bushland or bare land with no developments except for subsistence farming occupying 26.3 Ha. Soil samples were tested to check their suitability and being clay soil with low permeability that limited water passage, it was deemed suitable. The siting also incorporated the slope of the area to afford construction, accessibility, and maintenance.

This study reveals that diseases such as cholera, malaria, and stomach aches are prevalent in the area around the dumpsite. Flies lay eggs on animal feces and garbage, spreading diseases like food poisoning and dysentery. Four people have died from cholera, possibly related to the dumpsite’s condition [43]. Mosquitoes also cause diseases in the area due to stagnant water in empty containers and tires. Environmentally, residents report unpleasant odors from the mixed waste. The dumpsite has led to some students resorting to scavenging for school supplies and dropping out of school. Collectors often dump waste along the road connecting 6 miles and Area 24, making it difficult for residents to pass in dry seasons and impassable during rainy seasons. Some also dump waste at night near residents’ gates (see Figure 4). The site’s location has been impacted by factors like land use changes and infrastructure development, making it an unsuitable location for the community. Residents are now calling for its permanent closure and relocation.

3.2. Dumpsite Selection in Lilongwe

Researchers often combine GIS, AHP, and WLC to rank alternatives in site selection [16,23,32,38,39,40,41,44,45,46,47,48,49,50]. AHP provides a structured framework for determining a criterion’s importance, while WLC integrates these criteria mathematically. Therefore, this study combined GIS, AHP, and WLC to identify solid waste disposal sites in Lilongwe, considering both quantitative and spatial factors in a systematic and transparent manner. This approach improves decision-making and provides a methodical, quantitative, and spatially informed approach to dumpsite selection.

3.2.1. Individual Suitability Factors

Several factors were considered in determining the suitability of an area in locating a dumpsite. We present the individual factors considered in discussing pre-selection criteria at the factor level.

Slope: An area whose steepness results in a high cost of dumpsite construction is not recommended. The study area is dominated by a slope of 0–12°, which is acceptable for the development of dumpsites, with 59.65% of the area being very highly suitable (see Figure 5). A total of 23.26% of the area is highly suitable, which ranges from 12 to 16° and 16 to 20°, representing 10.76% of the area that is moderately suitable, 5.86% of the area is less suitable, ranging from 20 to 30°, and 0.48% of more than 30° not being suitable for the construction of dumpsites. These findings also indicate that the current dumpsite is situated on a moderately suitable area of land.

Built-up area: To minimize negative effects on human health and society, such as offensive odors, diseases, and flies, dumpsites should be placed at an appropriate distance from urban areas, businesses, and other built-up areas. An amount of 77.59% of the total area is unsuitable for solid waste disposal. Furthermore, 5.09% is moderately suitable, and 2.7% and 1.4% (with a buffer distance of more than 1000 m) are highly suitable and very highly suitable, respectively (see Figure 6).

Soil: The study area has three soil types: Chromic Luvisol, Eutric Cambisol, and Leptosol. Chromic Luvisol, which makes up 99.1% of the total area, is the dominant type due to its high clay accumulation and low permeability. It is highly suitable for solid waste disposal sites due to its low permeability (Figure 7) [51].

Road and Rail: For transit convenience and reduced logistical and operational costs, 41.02% of the area is at >500 m, making it very highly suitable (Figure 8). With limited rail connectivity, over 94% of the area is very highly suitable (Figure 9).

Protected areas: Wetlands and forests must not be selected for dumpsites [39,41]. At a buffer distance of 500 m, 78.32% and 97.65% are very highly suitable in terms of wetland and forest cover buffering (Figure 10 and Figure 11).

Airport: A distance of more than 3000 m is considered very highly suitable for solid waste dumpsites [40] for airport clearance, for which 90.03% is very highly suitable (Figure 12).

River: Dumpsites ought to be located far from water sources for which we prescribed a distance of more than 700 m (Table 3). At this threshold, 6.02% is highly suitable (Figure 13).

This study suggests that solid waste dumpsites should be placed away from roads and rail to reduce operational costs. The distance should be 700 m for roads and 500 m for rail. The proximity to rivers is also crucial for environmental reasons. The current dumpsite is located in an unsuitable location near homes, rivers, and streams, and on a moderate slope, causing negative impacts on human and environmental health. Proper buffering is necessary for proper disposal site location.

3.2.2. Criteria Evaluation Using AHP and WLC

AHP is a widely used multi-criteria evaluation (MCE) methodology that assigns weights to factors based on empirical data and subjective judgements. It helps decision-makers make informed decisions based on expert judgements and the literature. The weighting process used a 1–9 scale to determine the importance of a criterion over another (Table 1). The WLC method, which sums weights to 1, was used to determine the suitability index values of potential areas (Table 5).

Table 5. Normalized comparison matrix.

	BU	RV	RD	AP	WL	SP	SL	CD	FP	RW	Priority	Priority %
BU	1	7	5	3	5	5	3	7	5	3	0.303	30.3
RV	0.143	1	4	3	1	5	3	5	3	2	0.163	16.3
RD	0.2	0.25	1	3	3	3	1	2	3	2	0.114	11.4
AP	0.333	0.333	0.333	1	2	2	1	1	2	2	0.08	8.0
WL	0.2	1	0.333	0.5	1	3	2	3	2	1	0.084	8.4
SP	0.2	0.2	0.333	0.5	0.333	1	1	2	2	1	0.052	5.2
SL	0.333	0.333	1	1	0.5	1	1	2	1	1	0.063	6.3
CD	0.143	0.2	0.5	1	0.333	0.5	0.5	1	1	1	0.041	4.1
FP	0.2	0.333	0.333	0.5	0.5	0.5	1	1	1	1	0.044	4.4
RW	0.333	0.5	0.5	0.5	1	1	1	1	1	1	0.056	5.6

Key: BU, built-up area; RV, river; RD, road; AP, airport; SP, slope; SL, soil; FP, forest/plantation; CD, current dumpsite; WL, wetland; RW, railway.

Table 5. Normalized comparison matrix.

	BU	RV	RD	AP	WL	SP	SL	CD	FP	RW	Priority	Priority %
BU	1	7	5	3	5	5	3	7	5	3	0.303	30.3
RV	0.143	1	4	3	1	5	3	5	3	2	0.163	16.3
RD	0.2	0.25	1	3	3	3	1	2	3	2	0.114	11.4
AP	0.333	0.333	0.333	1	2	2	1	1	2	2	0.08	8.0
WL	0.2	1	0.333	0.5	1	3	2	3	2	1	0.084	8.4
SP	0.2	0.2	0.333	0.5	0.333	1	1	2	2	1	0.052	5.2
SL	0.333	0.333	1	1	0.5	1	1	2	1	1	0.063	6.3
CD	0.143	0.2	0.5	1	0.333	0.5	0.5	1	1	1	0.041	4.1
FP	0.2	0.333	0.333	0.5	0.5	0.5	1	1	1	1	0.044	4.4
RW	0.333	0.5	0.5	0.5	1	1	1	1	1	1	0.056	5.6

Key: BU, built-up area; RV, river; RD, road; AP, airport; SP, slope; SL, soil; FP, forest/plantation; CD, current dumpsite; WL, wetland; RW, railway.

S = 0.3 * [BU] + 0.16 * [RV] + 0.11 * [RD] + 0.08 * [AP] + 0.08 * [WL] +0.05 * [SP] + 0.06 * [SL] + 0.04 * [CD] + 0.04 * [FP] + 0.06 * [RW] = 1

The consistency ratio (CR) was found to be 0.078, which is less than 10%, indicating a respectable consistency level. Built-up areas were found to be the most significant component in choosing a location for solid waste disposal, with a factor weight of 30.3%. Environmental features, particularly rivers and accessibility, were essential in selecting sites for solid waste disposal. Rail, forest, slope, and soil had little influence due to limited coverage in cities and the homogeneity across areas (see Table 5). All factors were included in a weighted overlay analysis, and 5.78%, 78.28%, 14.97%, and 0.96% (446 Ha) of the total 46,283 Ha were classified as very low suitable, unsuitable, suitable, and highly suitable for solid waste disposal sites (Figure 14 and Figure 15). Out of these sites, 24 were highly suitable, ranging from 1 to 94 Ha.

The longevity of a dumpsite is determined by its rate of waste generation and a city’s growing population. Lilongwe generates 553 tons of waste daily [52] and needs a larger dumpsite than the current one 26.3 Ha in size. Six optimal locations were identified in areas 61, 58, 44, and 55 with ground truthing and Google Earth imagery conducted (

Table 6. Proposed sites in order of lifespan.

Site Name	Capacity	Estimated Lifespan
Site 3	94 Ha	18 years and 2 months
Site 2	81 Ha	15 years and 6 months
Site 1	50 Ha	9 years and 7 months
Site 4	44 Ha	8 years and 5 months
Site 6	34 Ha	6 years and 6 months
Site 5	28 Ha	5 years and 4 months

and Figure 16). Multiple sites running concurrently could increase dumpsite longevity and minimize operational costs when geographically well distributed. For instance, sites 1, 2, and 3 with the longest lifespans are geographically located in the northern part of the city, which may face similar high operational cost issues as the current dumpsite located in the southern part. Therefore, multiple sites running concurrently could be considered for optimal results. That notwithstanding, the public must be made aware of the environmental and health risks associated with improper waste management practices [53]. To complement awareness, fences should be built around the dumpsites to prevent unauthorized access, hold some of the flying litter, and keep animals out of the dump [53]. Furthermore, routine inspections, plans for biodegradable overpackaging including cardboard boxes, and a plan for handling electronic waste, such as outdated computers, communication devices, and batteries, should be made [54].

Dumpsite selection is a complex spatial phenomenon for decision-makers, requiring a lot of datasets [22]. With GIS’s ability to store, retrieve, and analyze large amounts of data from multiple sources and display the results spatially, this study demonstrates an aid for Lilongwe in addressing the problem of identifying an optimal location for a dumpsite. Similar approaches have been used in studies aimed at enhancing municipal solid waste management, including forecasting waste generation and composition patterns, enhancing waste collection and transportation, choosing sites for waste transfer stations, and pinpointing landfill siting areas [22]. Additionally, AHP offers a method for assessing how consistently results are reached during the decision-making process [27], which was normalized using the WLC method in this study.

While AHP required the acquisition of precise and comprehensive data, it was imperative that this study balanced between its efforts and resources. This entailed making economic choices on the scope of the study, narrowing the study results to preemptive rather than conclusive suggestions. Secondly, the subjectivity of the experts consulted limits the applicability of the results, and hence scalability, to jurisdictions of a similar size.

4. Conclusions

This study aimed at finding new viable sites for solid waste dumping in Lilongwe to alleviate the current logistical and operational problems. The current dumpsite is unsuitable due to its proximity to residents and health risks. This study recommends relocation to more suitable locations using multi-criteria decision-making, subjective and objective weighting, geographical analysis, and flexibility. Out of the 46,283 Ha in Lilongwe, 84.07% are unsuitable, 14.97% are suitable, and 0.96% are highly suitable for solid waste disposal sites. Six sites with capacities ranging from 28 to 94 Ha were identified as optimal to reduce operational costs. Waste recycling initiatives are advised to reduce the volume of solid waste at disposal sites and promote sustainable waste management practices. The current dumpsite has been overtaken by events such as population growth and land use change, despite not having an elaborate decision process to site it. To enhance long-term planning and sustainability, it is important to integrate temporal and dynamic factors (e.g., population growth and land use changes among others) into the model. Thus, further research should be conducted to examine the climate change resilience of solid waste dumpsites in Lilongwe using multi-factor GIS modeling, analyzing their vulnerability to extreme weather events such as cyclones (e.g., Tropical Cyclone Freddy, which hit Blantyre in March 2023), floods, storms, and heatwaves. These findings are a step in the process toward streamlining city operations to minimize transportation costs and enhance the overall efficiency in waste disposal. Furthermore, an assessment of the potential environmental impact of the proposed sites on groundwater contamination and air quality degradation should be undertaken.