Effect of ageing on waste characteristics excavated from an Indian dumpsite and its potential valorisation

Process Safety and Environmental Protection 134 (2020) 24–35

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Effect of ageing on waste characteristics excavated from an Indian dumpsite and its potential valorisation Ayush Singh, Munish K. Chandel ∗ Environmental Science and Engineering Department, Indian Institute of Technology Bombay, India

a r t i c l e

i n f o

Article history: Received 5 July 2019 Received in revised form 3 October 2019 Accepted 21 November 2019 Available online 26 November 2019 Keywords: Physicochemical characteristics Legacy waste Remediation Landfill mining Municipal solid waste Indian dumpsite

a b s t r a c t This paper assesses the effect of ageing on physicochemical characteristics of excavated municipal solid waste from Mulund dumpsite in Mumbai, India. Based on disposal year, waste was excavated from different zones in the dumpsite. The excavated waste was screened into five different size categories and further sorted into different streams. Physicochemical characteristics, i.e., pH, bulk density, ultimate and proximate analysis, calorific value and heavy metal concentration of excavated waste were also determined. The results indicate a change in the characteristics and composition of waste with age. The particle size distribution of waste revealed that waste above 80 mm was mostly plastic and textile, whereas <4 mm (fine fraction) composed of soil-like material. Above 80 mm fraction shows a decreasing trend with age and depth, suggesting particle size reduction with time. Parameters like volatile matter, total and organic carbon and calorific value of excavated waste also reduced with the age. A significant portion of waste was fine fraction (∼45%) emphasising on its valorisation for success of landfill mining. Furthermore, metal content in the dumpsite was <1%. The findings from this study can be used to reclaim dumpsites and suggest possible valorisation routes for excavated waste in developing countries like India. © 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

1. Introduction Open dumpsites had been a popular municipal solid waste (MSW) disposal choice in India. Most of the towns, villages and cities practised open dumping in the sites that had no to very few sanitary measures (Joshi and Ahmed, 2016; Kumar et al., 2017). This dumped waste has a negative impact on the environmental and societal level. The decomposition of waste in dumpsites generate harmful gases like methane, ammonia and mercaptans as well as leachate, which contains heavy metals and organic pollutants. Studies have indicated the percolation of leachate infiltration from landfills in groundwater in India (Pujari and Deshpande, 2005; Mor et al., 2006). Furthermore, there are also various other issues with the existing dumpsites. In India, most of the dumpsites/landfills have exhausted their capacity and are serving beyond their operational life (Sharholy et al., 2008). Sites like Deonar in Mumbai, Ghazipur, Bhalaswa, and Okhla in Delhi have exhausted their capacity a long time ago, but are still operational due to lack of landfill space (Kumar, 2013). Over dumping of the waste in such sites leads to problems like slope instability, which can cause the slope failure

∗ Corresponding author. E-mail address: [email protected] (M.K. Chandel).

leading to landfill collapse (Koelsch et al., 2005). The maximum permissible limit for the height of garbage dump in India is 20 m above ground level, which most of the landfills have already crossed. For instance, Ghazipur landfill in Delhi stands at the height of more than 50 m, which is way above the permissible limit (Vyawahare, 2018). The increasing MSW generation requires more land for waste disposal, stating that the land requirement for unscientific dumping of MSW will not go in near future (Kumar et al., 2017). One of the solution to mitigate environmental issues from open dumps is capping. The capping of Gorai and Malad dumpsite in Mumbai, India has been carried out in past. However, leakage of harmful gases like methane, and mercaptans were reported for Malad dumpsite, which was developed into a residential area after capping. Capping along with a bottom liner may be a better option; however entire waste needs to be excavated to install a bottom liner and provide further sanitary provisions which in turn would increase the remediation cost (Dubey et al., 2016). While capping and fixing a bottom liner, dumpsites resources can be recovered to gain some benefit from it. Solid waste management rules (2016) in India, makes it necessary to investigate and analyse all old and operational dumpsites to determine their potential and feasibility for bioremediation, reclamation, and biomining and take necessary actions to biomine or remediate the dumpsites (MoEFCC, 2016). The potential extraction of waste materials has been conceptu-

https://doi.org/10.1016/j.psep.2019.11.025 0957-5820/© 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

4.70 17.50 13.40 54.00 10

– 23.00 7.50 2.30 – 7.30 7.10 – 1.50 43.00 20 10 22.40 5.10 3.10 4.60

– 9.30 1.80 2.50 7.20 0.70 – 8.40 – 70.10 5 ∼ 10 2.40 – 0.10 0.40 0.60 0.50 28.30 – 67.80 20 Weighted average value of the coarse and medium fraction. middle layer. § plastic includes rubber and foam (if present). ‡

†

8–10 12.70 0.05 0.38 1.19 0.95 3.04 29.73 2.44 49.53 4 Age (years) Plastic§ (%) Paper/Cardboard (%) Metal (%) Glass (%) Textile (%) Wood (%) Stone (%) Others (%) Fine fraction (%) (Cut-off dia. in mm)

17-22 5.52 9.73 1.73 0.28 2.30 11.05 13.70 0.74 54.49 18

23-25 2.13 2.27 1.41 0.93 0.00 1.96 19.10 0.23 71.30 18

10–14 25.00 14.00 2.20 0.50 3.10 4.10 2.00 4.10 45.00 10

10–15 2.10 – 0.10 – – 0.60 31.50 – 63.50 8

∼ 10 25.50 – 0.20 0.80 2.30 11.60 18.50 – 40.10 20

3–5 29.66 3.33 6.42 6.51 7.64 7.97 3.27 – 33.81 25

Kuopio Kudjape Nanqijia Sai Noi Kodungaiyur Perungudi Deonar Mulund (zone E) Landfill

Masalycke

Gladsax

Remo (Location 6)

Finland Estonia China Thailand India India Europe India Country

Europe

Bhatnagar et al., 2017 Rong et al., 2017 Prechthai et al., 2008† Kurian et al., 2003 Manfred and Bhattacharyya, 1995 Quaghebeur et al., 2013 This study

Hogland et al., 2004

25

Reference

Table 1 Comparison of composition of the excavated waste (zone E from this study) with different literature studies.

alised through different mining ideas, for example, urban mining, bio-mining, technospheric mining and waste mining (Ayres et al., 2001; Brunner, 2011; Johansson et al., 2013). Landfill mining is one of such concepts that can be defined as a strategy to recover secondary resources from an active or closed landfills with the help of unit operations like excavation, screening, sorting and processing (Hogland et al., 2004; Jones et al., 2013; Krook et al., 2012; Quaghebeur et al., 2013). Landfill mining was first conceptualised in Israel in 1953 to excavate the waste of Hiriya landfill and process the soil fraction to be used as compost (Savage, 1994). Until the early 1980s, this initiative remained the single documented study of landfill mining. Increased concerns over the limitations of space and environmental footprints marked the stage for further landfill mining initiative. Landfill mining projects (LFM) had various objectives like landfill remediation, extraction of secondary resources, controlling the pollution created by landfill, void-space recovery and increasing landfill life. Within landfill mining studies, waste characterisation is the most covered topic and an important one too. Most of the characterisation studies done primarily consists of screening the waste based on size and then manual or mechanical separation of the coarse particle into different categories such as plastic, paper, textile, wood, metal, glass and inert. Prechthai et al. (2008) found major concentration of fine fraction and plastic in the waste 19–39% and 35–51%, respectively. Rong et al. (2017) also identified major concentration of fine fraction, plastic and stone, i.e., 52.4%, 13.9% and 13.2%, respectively. Similar trend for high amount of fine fraction, plastic and stone was found in other studies (Bhatnagar et al., 2017; Kaartinen et al., 2013; Quaghebeur et al., 2013) (Table 1). Characterisation studies also assess the physical and chemical characteristics of excavated waste. The physicochemical characteristics are necessary for checking the feasibility of landfill mining project. For example, determining the capacity of recovery and recycling facility bulk density is an important parameter. Similarly, moisture content of excavated waste is crucial to determine the valorisation route (thermal, recycling, or biological treatment) of the waste fraction and depends on several parameters like location, climatic conditions, leachate generation and waste type. Major outcome of the literature review could be summarised as 1) landfills waste composition varies with the location and topography of the region (Jones et al., 2013). 2) old landfills can be the primary target of a landfill mining project (because of no clear segregation policies in the past, most of the resources were directly dumped in the landfills). 3) landfills mainly consists of fine fraction accounting for almost 50% of total dumped waste (Burlakovs et al., 2016; Kurian et al., 2003; Mönkäre et al., 2016). However, minimal reported literature is available regarding landfill mining in India. The first reported initiative for landfill mining in India was for Deonar dumpsite, which was a trial project to recover fine fraction to be used as compost and increase landfill space (Manfred and Bhattacharyya, 1995). Kurian et al. (2003) studied the degradation status in Kodungaiur and Perungudi landfill using physicochemical parameters analysis. In recent years, the valorisation aspect of landfilled waste has garnered attention. Ranjan et al. (2014) studied the potential use of mined waste for refuse-derived fuel production. While Somani et al. (2018) assessed the use of fine fraction as cover material or geotechnical application. In Panchvati (Maharashtra), a project for waste stabilisation was carried out by spreading bio-culture on aerobic windrow prepared from mined waste for waste volume reduction (NSWAI, 2010). In Kumbakonam (Tamil Nadu) waste stabilisation and segregation of dumped waste was carried out to clear the dumpsite (Patel, 2015). The projects carried out in India are majorly for void-space recovery, while few studies have assessed the valorisation potential. Currently, there is a push from the government in terms of policies to reclaim old dumpsites. The projects carried out for landfill reclamation and remediation in India can

Kaartinen et al., 2013‡

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be further integrated with the recycling of segregated fraction to improve economic viability of LFM project (Frändegård et al., 2015). For that, a proper methodology needs to be formulated and pilot studies need to be carried out to get a definitive idea for waste characteristics for dumpsites in India. The potential of landfill mining depends on the resource and energy recovery from the lying resources in a landfill. Hence, for developing any LFM project, it is necessary to conduct a preliminary characterisation study to have an in-depth analysis of the site (Burlakovs et al., 2017). Since most of the old landfills in India are typically dumpsites and may have different waste characteristics, inferences from the global studies may not be entirely valid for India. The objective of this study is to investigate the composition of aged waste obtained from MSW dumpsite in India and assess the influence of age on its characteristics. Based on the initial assessment, the study will also identify different valorisation option based on literature for the recovered streams. The results from the study can be used to assess the processing, utilisation and disposal of excavated waste while planning LFM project. 2. Materials and methods 2.1. Site description Mumbai is a metropolitan city and capital of Maharashtra state and also known as the financial capital of India. It is the most populated city of India and generates ∼9000 M T of waste per day (MPCB, 2016). There are four known dumpsites in Mumbai, namely Deonar, Mulund, Gorai (closed), and Malad (closed) and one sanitary landfill in Kanjurmarg. Mulund dumpsite is the second largest dumpsite in Mumbai and was selected for this study to assess its potential for landfill mining. This site was in operation since 1968 and is located in eastern suburbs of Mumbai with an area of ∼25 ha. The Mulund dumpsite has been shut down for services in year 2018. The waste was dumped at the site in an unscientific manner (MPCB, 2014). In Mulund dumpsite, the dumping is carried out in a closed circuit, i.e., the waste is disposed in one location for few months then next and so on. Hence, the most aged waste would be in the bottom layer of the dumpsite. The dumpsite has mounds with height varying from 10–30 m above ground. The dumpsite is 61 m away from the nearest habitation and is located adjacent to a creek. 2.2. Excavation procedure For characterisation studies, the sampling method (excavation equipment, number of sampling locations and number of samples) is determined by preliminary assessment of the landfill/dumpsite. In general, the sampling method varies from case to case and majorly depends on the objective of the study (ASTM Standard D6009, 2012). We carried out literature review regarding sample collection for landfill mining based studies. In studies performed internationally, the amount of waste excavated for analysis varied from 30−600 kg (Hogland et al. (2004): ∼70−170 kg, Quaghebeur et al. (2013): 30 l (∼30 kg). Kaartinen et al. (2013): ∼600 kg) and waste excavation in landfills was carried out either by drilling or excavation. Though drilling has an advantage of reaching to the bottom of landfill it has also disadvantage of particle size reduction and high operating cost. On the other hand, excavation has low operating cost with limitation of reaching maximum depth up to 4–5 metres (Burlakovs et al., 2017). Based on the reconnaissance survey and the help of available data (municipality officials and Google map), five zones were selected for detailed study (zone A being the youngest and zone E being the oldest). Accounting for waste dumping year, logistics and cost involved during sample collection and processing, total three samples from 1 m interval depth (0−1 m,

1−2 m, 2−3 m) were collected using JCB excavator from each zone. We could collect more than 300 kg of sample from top layer (∼1 m) of dumpsite. However, as we go deeper into the dumpsite, the sample collection became harder due to limitations of excavator. To maintain homogeneity in analysis after reaching the required depth two excavator bucket worth waste was collected separately. The oversized particle and big debris were removed and the waste accounted was approximately between 60−80 kg in most cases for one depth and overall 180−240 kg in one zone. The sampling was carried out in March 2017. The year of waste disposal for selected zones was: zone A – 2015–2016, zone B – 2013–2014, zone C – 2012–2013, zone D – 2010–2012, zone E – 2007–2009. The storage time at selected zones varied between 1–10 years. A representative sample of 10−15 kg was collected from each depth by using quartering and coning method. Also, ∼1 kg sample was collected in an airtight bag to analyse moisture content, bulk density, and pH. The sample was stored in a cold room at −4 ◦ C before analysis to stop any microbial activity and changes in physicochemical parameters. During excavation, the temperature of excavated waste, as well as the surface temperature, was measured using a temperature probe. The bagged and marked samples were further analysed in the laboratory. 2.3. Sorting and characterisation The excavated waste was dried in a hot air oven at a temperature of 80 ± 5 ◦ C for 48 h. As for most of the analysis, excavated waste needs to be dried at low temperatures (Quaghebeur et al., 2013). For physical characterisation, the dried sample was screened manually into categories of >80 mm, 80−40 mm, 40−20 mm, 20−4 mm and <4 mm. Subsequently, the screened fractions were further manually segregated, except <4 mm size, into seven categories (plastic, paper/cardboard, metal, glass, textile, wood, stone and others). The others category indicates the material which could not be classified. 2.4. Analytical procedures Analysis of the physicochemical properties of excavated waste was carried out to understand the effect of ageing. To determine bulk density, pH and moisture content of excavated waste, airtight bagged sample were used. Water extract was obtained by mixing bagged excavated waste and deionised water in 1:1 (w/v) ratio and further filtering the mixture using a 0.45 ␮m filter membrane. The pH was measured from the filtrate of water extract using a pH probe (EPA 9045D). Since the temperature inside the landfill can reach up to 90 ◦ C (Quaghebeur et al., 2013), the moisture content was determined by heating the crushed and sieved bagged sample at 90 ◦ C for 24 h. For the rest of the analysis, a representative sample of excavated waste was crushed and further screened to get a homogeneous sample of <1 mm. Proximate analysis of the excavated waste was carried out using loss on ignition method (Dean, 1974; USEPA, 2001). The sieved sample was first heated at 550 ◦ C in a muffle furnace until a constant weight was achieved. The weight loss in the sample during this step indicates the volatile matter present in the sample. The fixed carbon and inorganics were further determined by measuring weight loss from burning the residue obtained in the previous step at 990 ◦ C in the muffle furnace. Ultimate analysis was carried out with CHNS analyser (FLASH EA 1112, Thermo Finnigan) and oxygen was calculated by difference. For determination of total organic carbon (TOC), the dried sample was first acidified using phosphoric acid for removal of inorganic carbon and then element analyser was used to detect the CO2 formed after combustion (Kaartinen et al., 2013). For heavy metal estimation, ∼0.5–0.1 g of the waste sample was digested in 10 ml of acid (HNO3 , HCl, HF in proportion 3:1:1) using microwave digester. The

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surement of gross calorific value, automated bomb calorimeter (IKA C 200 calorimeter) was used. The gross calorific value was reported from the calorimeter in MJ/kg. 2.5. Statistical analysis

Fig. 1. Particle size distribution of excavated waste in different size categories at different zones and depths in Mulund dumpsite.

digested sample was then further diluted up to 25 ml and filtered. The heavy metal concentration was measured using Inductively Coupled Plasma — Atomic Emission Spectroscopy (ICP-AES ARCOS, SPECTRO analytical). Merck ICP multi-element standard solution IX was used for calibration of the method in accordance with DIN EN ISO/IEC 17,025. Line selection was carried out for the elements based on the least interference and high sensitivity. For the mea-

Statistical analysis was applied using OriginPro 2018 software. The data was checked thoroughly before the analysis. We used the Pearson correlation coefficient and linear regression to study the relationship between different physicochemical characteristics. Pearson correlation coefficient was employed to detect the patterns in different parameters. The correlation coefficient varies between −1 and +1 and indicates negative and positive correlation between two parameters. The p-value indicates the statistical significance of the correlation between two parameters. To further deduce the relationship between different physicochemical characteristics, parameters were selected for linear regression analysis that had a correlation coefficient >0.60 and p-value < 0.01. 3. Results and discussion 3.1. Composition of excavated waste The excavated waste was categorised into different particle sizes, i.e., >80 mm, 80−40 mm, 40−20 mm, 20−4 mm and <4 mm using sieve analysis on dry basis (Fig. 1). The >80 mm fraction

Fig. 2. Composition of the excavated waste (plastic, paper/cardboard, textile, wood, metal, glass, stone) in the sieved samples of different size categories a) >80 mm b) 80−40 mm c) 40−20 mm and d) 20−4 mm.

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showed a significant decreasing trend from zone A (15%) to zone E (4%), indicating particle size reduction with degradation. It also depicted a decreasing trend with depth for zone A, zone B and zone C and was more or less similar in all depths for zone D and zone E. However, for zone A, the coefficient of variation was >98% suggesting the composition varied greatly with depth. Particle size category 80−40 mm, 40−20 mm and 20−4 mm depicted no trend with zone and had almost similar values for all zones and ranged between 14–17 %, 11–14 % and 18–21 %, respectively in the dumpsite. On the other hand, <4 mm fraction depicted an increasing trend from zone A to zone E, indicating an increment with age. Furthermore, the classification of waste into different streams was carried out with respect to particle size category (Fig. 2). We observed that >80 mm fraction mostly comprised of plastic (∼3057%), followed by textile and wood. For particle size 80−40 mm and 40−20 mm the plastic content significantly decreased in all zones compared to >80 mm fraction. The 20−4 mm particle category consisted of pebbles, fine gravel and unidentifiable matter. It was hard to characterise the 20−4 mm fraction manually. The <4 mm fraction mostly consisted of soil, microplastic, and unidentifiable degraded matter which could not be separated manually. The composition analysis of <4 mm would require advance separation technology and is not covered in the current study. Based on the observations of particle size distribution, it can be argued that the excavated waste could be classified into three categories 1) Coarse fraction (>20 mm) 2) Middle fraction (20−4 mm) and 3) Fine fraction (<4 mm). The coarse fraction is easily identifiable into different categories and can be sorted manually. However, for the middle and fine fraction, distinguishing between different types of waste would be hard to do manually and will require screening equipment. The particle size distribution results will help in selection criteria of screening equipment (trommels, vibrating screens, disc screens) and selection of cut-off diameter for the screen. The <4 mm (fine fraction) particle size was highest for all zones with 45% average in the dumpsite (Fig. 3). The <4 mm fraction also depicted increase with depth indicating that the waste has degraded more in the bottom layers. These results are in agreement with study done by Kaartinen et al. (2013) which reported that fine fraction (<20 mm) for the middle and the bottom layer was 44% and 47%. The fine fraction in zone E which is ∼8–10 years old was 49.5% (Table 1). In the Indian context: ∼10 years old waste of Perungudi and Kodungaiyur landfill had fine fraction (<20 mm) ∼40.1% and ∼67.8% respectively; 10–15 years old waste of Deonar dumpsite had fine fraction (<8 mm) ∼63.5% (Table 1). While for international studies: 10–14 years old waste the fine fraction (<10 mm) in REMO landfill, Europe was ∼45%; 10 years old waste the fine fraction (<40 mm) for Kudjape landfill, Europe was 54% (Table 1).

Fig. 3. Composition of excavated waste in different categories (plastic, paper/cardboard, textile, wood, metal, glass, stone, others and < 4 mm fraction) at different zones and depths.

The above reported results indicate that the definition of fine fraction is not uniform and varies with the study. Even for the same aged waste, the fine fraction varies in the landfill. The variation in the fine fraction can be attributed to the difference of waste composition, climatic condition and the cut-off diameter selected for the study. In the sorted fraction, after fine fraction, the most dominant fraction was stones, accounting ∼30% on average in each zone in Mulund dumpsite. Plastic was the third highest fraction in waste contributing to ∼12% by mass. The wood and textile accounted for an average of 3.5% and 4%, respectively. The paper/cardboard showed a decreasing trend with zone A to zone E, indicating a decrease with degradation over the years. The total combustible fraction of the waste ranged from 11 to 28% and shows a decrement from zone A to zone E. The metal content in the dumpsite was <1%. Comparison of the average composition of excavated MSW for different studies is enumerated in Table 1. The composition of waste would vary with the location due to the different lifestyle of people, legislation and waste management practices being followed in the country (Miezah et al., 2015; Quaghebeur et al., 2013). However, some common inferences could be drawn from the studies. It could be seen that with the age, amount of fine fraction increases in the landfill. The Gladsax landfill, which is the oldest in the reported table has maximum waste as fine fraction (71.5%). It could also be observed that the % plastic followed an increasing trend over the decades in landfills.

Table 2 Basic physicochemical characteristics of the excavated waste from Mulund dumpsite at different zones and depths. Depth 0–1 m 1–2 m 2–3 m 0–1 m 1–2 m 2–3 m 0–1 m 1–2 m 2–3 m 0–1 m 1–2 m 2–3 m 0–1 m 1–2 m 2–3 m

Location Zone A (2015–2016) Zone B (2013-2014) Zone C (2012–2013) Zone D (2010–2012) Zone E (2007–2009)

Temperature (◦ C) 55.0 56.4 57.5 56.0 56.5 56.7 49.0 51.0 52.0 41.3 46.0 46.5 41.0 44.0 45.0

pH 6.47 6.75 6.92 6.47 6.75 6.84 6.88 7.02 7.05 7.87 6.95 7.67 7.01 7.07 7.31

Bulk density (kg/m3 )

TOC (% ± SD)

Gross calorific value (MJ/kg ± SD)

850 1062 1200 967 1012 1220 835 845 843 1089 1077 1300 963 1194 1113

16.7 ± 0.7 8.0 ± 0.9 3.9 ± 0.2 11.2 ± 0.9 10.2 ± 0.4 6.8 ± 0.7 12.0 ± 0.1 8.8 ± 0.2 10.8 ± 0.6 6.6 ± 0.2 5.3 ± 0.2 3.6 ± 0.1 5.6 ± 0.4 5.0 ± 0.1 6.6 ± 0.1

16.23 ± 2.11 13.96 ± 0.16 2.89 ± 0.22 11.82 ± 1.02 10.00 ± 0.47 5.50 ± 0.73 6.44 ± 0.29 7.68 ± 0.86 6.39 ± 0.40 7.96 ± 0.70 4.90 ± 0.66 3.85 ± 0.37 7.02 ± 0.59 3.88 ± 0.43 4.96 ± 0.49

± ± ± ± ± ± ± ±

2−3 m

17.0 13.0 6.0 63.9 10.5 1.3 0.4 12.7 0.4 1.0 0.1 1.0 0.6 0.08 0.07 0.7 ± ± ± ± ± ± ± ±

1−2 m

14.5 7.6 3.7 64.1 7.3 1.2 0.2 16.2 0.2 0.3 0.0 0.0 1.8 0.10 0.08 1.9 ± ± ± ± ± ± ± ±

0−1 m

14.3 13.9 6.4 65.2 10.0 1.4 0.3 11.9 0.5 0.4 0.2 0.6 0.2 0.22 0.02 0.4 ± ± ± ± ± ± ± ±

2−3 m

12.6 14.2 6.8 66.2 6.1 1.0 0.2 16.8 0.0 0.2 0.1 0.3 2.3 0.24 0.01 2.5 ± ± ± ± ± ± ± ±

1−2 m

10.6 15.4 4.1 69.7 12.1 1.6 0.6 7.5 0.6 1.0 0.1 0.1 4 0.68 0.34 2.1 ± ± ± ± ± ± ± ±

0−1 m

10.3 18.1 3.0 68.4 12.4 1.4 0.7 9.7 0.1 0.8 0.4 0.6 0.7 0.01 0.08 0.3 ± ± ± ± ± ± ± ±

2−3 m

17.6 15.3 6.7 60.3 15.1 1.8 0.9 9.9 0.1 0.4 0.2 0.5 1.2 0.06 0.09 1.2 ± ± ± ± ± ± ± ±

1−2 m

11.0 15.1 6.8 66.8 13.9 1.9 0.6 8.3 0.4 0.6 0.4 1.0 3.4 0.05 0.09 3.7 ± ± ± ± ± ± ± ±

0−1 m

16.4 14.5 5.7 63.2 14.3 2.0 0.8 7.0 0.6 0.1 0.2 0.1 0.4 0.12 0.02 0.4 ± ± ± ± ± ± ± ±

2−3 m

19.5 13.3 5.0 61.9 10.1 1.5 0.4 10.7 12.9 29.0 8.4 49.4 29.6 3.4 1.0 8.9

0.6 0.4 0.6 0.7 4.3 0.45 0.15 4.3

15.5 20.4 9.0 54.9 14.7 1.4 1.0 17.6

0.2 0.3 0.3 0.6 4.1 0.11 0.11 4.1

21.7 9.0 4.2 64.9 7.2 1.1 0.3 8.4

± ± ± ± ± ± ± ±

0.3 0.2 0.4 0.2 1.3 0.16 0.05 1.3

15.0 22.4 5.8 56.6 8.5 1.3 0.7 22.7

± ± ± ± ± ± ± ±

0.1 0.1 0.2 0.3 0.1 0.04 0.06 0.2

0−1 m

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Ultimate analysis (dry basis)

Proximate analysis (as received)

Depth

Moisture content (%±SD) Volatile matter (%±SD) Fixed carbon (%±SD) Ash (%±SD) Carbon (%±SD) Hydrogen (%±SD) Nitrogen (%±SD) Oxygen (%±SD)

2−3 m 1−2 m 0−1 m

1−2 m

± 0.7 ± 0.4 ± 0.1 ± 0.3 ± 2.3 ± 0.16 ± 0.19 ± 2.2

Zone E (2007–2009) Zone D (2010–2012) Zone C (2012–2013) Zone B (2013–2014) Zone A (2015–2016) Location Parameter

Table 3 Proximate and ultimate analysis of excavated waste from Mulund dumpsite at different zones and depths. Means and standard deviations (n = 3) are presented.

The surface (ambient) temperature during excavation was ∼39.8 ◦ C. The temperature inside dumpsite was higher than the ambient temperature and decreased from zone A to zone E. The temperature of waste during excavation, was notably higher for zone A and zone B ∼55–57.5 ◦ C, while comparatively lower for zone D and zone E ∼41–46.5 ◦ C (Table 2). The high temperature in zone A and zone B can be attributed to the fact of ongoing degradation as it had relatively fresh waste compared to other zones. The temperature also showed an increasing trend with increasing depth of the waste. However, the coefficient of variation within zones was <6%. The rise in temperature with depth was ∼1 ◦ C/m, which is similar as reported in the literature (Hull et al., 2005). Furthermore, zone A, zone B and zone C had pH in the acidic range, i.e., 6.7, 6.6 and 6.9 on average respectively indicating incomplete biodegradation of the waste. Whereas, zone D and zone E had pH in the alkaline range, i.e., 7.4 and 7.1 respectively, which could be associated to waste disposed of more than five years old and could be near the end of the methanogenic phase (Kaczala et al., 2017). The density of the excavated waste ranged from 850 to 1300 kg/m3 . The average density of waste was ∼1030 kg/m3 which is similar to a study by Kurian et al. (2003), i.e., 965 kg/m3 for Perungudi and 1106 for kg/m3 for Kodungaiyur. The total organic carbon (TOC) showed a reduction from zone A to zone E, except zone C. The decrease in TOC could be contributed to the production of landfill gas from carbon-rich waste (Smidt and Lechner, 2005). The gross calorific value for the mixed waste ranged from 3−18 MJ/kg for dry basis (Table 2). The calorific value decreased with depth and age of waste. The study by Kaartinen et al. (2013) observed a net calorific value of 22 MJ/kg for the combustible fraction which indicates segregated combustible fraction can yield much higher calorific value and can be used for refuse-derived fuel (RDF) production. The TOC and calorific value showed a significant positive correlation (p < 0.01) with each other. The proximate and ultimate analysis of the excavated waste was carried out on an as-received and dry basis, respectively (Table 3). The moisture content of the waste ranged between 10–22%. Further, moisture content of the waste shows an increasing trend with the depth for zone A, zone B, zone D and zone E. For zone C, no relation between moisture content and depth was found. The moisture content was also affected by the location of waste in the landfill. The moisture content in zone C & zone D is comparatively less than other as its location was isolated than other zones. Moisture content is a crucial factor for waste degradation and should be around 40–70% inside a landfill for optimum microbial activity (Hull et al., 2005; Kjeldsen et al., 2002). The volatile matter (food, vegetables, yard trimmings) decreases over the year in a landfill as it is turned into a soil-like material. The volatile matter showed a decreasing trend from zone A to zone E in the dumpsite. Zone A had the highest volatile matter content ∼20% whereas zone D had the lowest volatile matter content ∼14% (on average). For zone A, zone B and zone D the volatile matter had a clear decreasing trend with depth. However, zone E had no trend and varied randomly with depth and can be attributed to the fact of heterogeneity in the dumpsite. TOC and volatile matter had a significant positive correlation (p < 0.01) and are often used as a parameter to assess potential degradation status of landfilled waste (Hull et al., 2005). The total carbon content decreased from zone A to zone E, as expected due to the degradation of organic matter. However, no trend was observed over depth. The high carbon content in zone A shows ongoing degradation. The nitrogen content also shows a decreasing trend from zone A to zone E. The sulphur content in the excavated waste was below the detection limit (<0.1%). The coefficient of variation in zone A for properties like TOC, calorific value, volatile matter and total carbon >50%. This finding suggests that the waste deposited in deeper layer (2−3 m) could

17.6 20.5 4.0 57.7 12.4 1.4 0.7 15.3

3.2. Physicochemical characteristics of excavated waste

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0.1 0.1 0.1 0.2 1.4 0.05 0.04 1.3

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be more aged then the suggested period for the zone A. While for other zones the coefficient of variation was <25% for properties like TOC, calorific value, volatile matter and total carbon indicating that the waste excavated could be from a similar period. In general, the heavy metals present in excavated waste followed an increasing trend from zone A to zone E, indicating increase with age (Fig. 4). Similar trend of increase in heavy metals concentration with increase in storage time of MSW was observed by Quaghebeur et al. (2013). For Cd, Cr, Cu, Ni, and Pb mean heavy metal concentration in waste increased significantly with age, while Zn showed an increasing trend from zone A to zone C and then decreasing trend from zone C to zone E. No trend was observed with change in depth for heavy metals in excavated waste. The concentration of Pb and Zn was comparatively lower in younger zones (zone A and zone B). Based on average concentration of heavy metal in excavated waste following order was observed: Zn > Cu > Cr > Pb > Ni > Pb > Cd. Heavy metals in the MSW can be attributed to batteries, paints, alloys, leather, textile, inks in paper/cardboard, pesticides and fertilisers in agricultural residue and waste (Long et al., 2011). Compared to coarser fraction, the heavy metal concentration decreased in fine fraction expect for Cd (Fig. 5). A similar decreasing trend is observed in heavy metal content of fine fraction by Quaghebeur et al. (2013). The association of heavy metals needs to be studied to understand the fate and mobility of heavy metal in dumped waste. 3.3. Statistical analysis Bivariate Pearson correlation coefficient was calculated for studied physicochemical parameters using Origin 2018 (Table 4). Parameters like TOC, calorific value, volatile matter, total carbon and nitrogen correlated with most of the parameters (r>0.60). Whereas parameters like oxygen content and moisture content of the waste showed no significant correlation with other parameters. TOC and calorific value had a significant correlation with 6 out of 10 parameters. The highest correlation is observed between calorific value and volatile matter at correlation coefficient 0.89 and TOC and total carbon at correlation coefficient 0.83, which is expected as all the parameters depend on carbon content of the waste. Bulk density also had a significant correlation with TOC value (r = 0.79, p < 0.01). Although temperature had no correlation with most parameters, it had a significant negative correlation with pH (r = −0.7, p < 0.01). During degradation, the temperature in the landfill increases in acidogenesis phase and reduces at the end of methanogenic phase. Whereas pH reduces during acidogenesis phase due to formation of volatile fatty acids and increases upon their degradation signifying the negative correlation between pH and temperature. The correlation analysis suggests that for preliminary characterisation of the excavated waste, TOC can be used as a surrogate parameter to determine other parameters like calorific value, volatile matter, total carbon, nitrogen and bulk density, hence saving time and cost of analysis. The linear regression of parameters with a correlation coefficient greater than 0.75 was further studied and is shown in Fig. 6. 3.4. Assessment of combustible fraction The calorific fraction of the waste includes fractions like plastic, paper, cardboard, textile, rubber, and wood (Fig. 7). Around 75–90 % of total plastic was recovered in the size fraction of greater than 40 mm and decreased significantly in the lower particle size categories. A similar trend was observed for the textile fraction. For paper/cardboard and wood, the distribution of the fraction in different particle size had no trend and was available in all particle size category in a significant proportion.

Combustible fraction constituted an average of 21% of total waste. The paper/cardboard and textile fraction on an average was ∼2% and 4% respectively and were not suitable for recycling purposes due to a high level of contamination. Recycling of plastic depends on the pretreatment of the fraction. The fraction needs to be cleaned and washed before recycling. Also, the fraction should meet the permissible standards for heavy metals. Overall recycling of plastic will require an initial investment which may not be the economically viable solution. New frontiers are being developed for the reuse of contaminated plastic, one of that involves the use of plastic aggregate in the construction sector. Most feasible option for the combustible fraction could be waste to energy. The thermal treatment of the waste will produce heat, gas and ash, depending on the treatment technique used. Currently, various thermochemical treatments are available and widely used in the world like torrefaction, pyrolysis, gasification, incineration and plasma pyrolysis/gasification (Bosmans et al., 2013). Most of the studies recommended the use of plastic as fuel by thermal treatment (Bhatnagar et al., 2017; Rong et al., 2017). Quaghebeur et al. (2013) also suggested that for the combustible fraction, waste to energy treatment is the most effective method for valorisation. This consideration is made on account of material impurities and high level of contamination, which makes the recycle operation difficult. Kaartinen et al. (2013) also suggested that ∼40–45% of recovered waste from manual sorting can be used as solid recovered fuel (SRF). New advancements and improvements are being made in the field to increase the efficiency of the thermal process, control of the emissions released due to thermal treatment and the commercial viability of the process (Ford et al., 2013). The residue obtained from thermal treatment can also be utilised in the construction sector. The vitrified slag obtained from plasma gasification/pyrolysis has glass-like properties and can be used as gravel/aggregate replacement (Jones et al., 2013). 3.5. Assessment of non-combustible fraction The metal content in the dumpsite was less than 1% on an average (Fig. 7). It could be true for most of the old landfills in India as very few percentage of scrap metal enters the landfills as it is sold directly to scrap dealers from household or collected from landfill by rag pickers. The study by Kurian et al. (2003) and Manfred and Bhattacharyya (1995) also reported the metal content 0.1–0.2% and 0.4% respectively in Indian landfill. The glass stream was also below 1.5% in the excavated waste. One of the significant fraction observed in Mulund dumpsite was stone and ceramic material. This fraction can be utilsed in the construction industry after pretreatment (cleaning and sizing). The crushed stones and gravel obtained can be used as coarse and fine aggregate during construction. Quaghebeur et al. (2013) suggested that the fraction like glass/ceramics, metals, stones, and other inerts can be recycled if the effective separation of material is possible in waste to material facility. 3.6. Assessment of fine fraction The fine fraction accounts for ∼45% of the total dumped waste. Most of the previous studies on landfill mining focus on either recycling or energy recovery from coarse fraction whereas, the fine fraction is generally sent back to the landfill with or without treatment (Bhatnagar et al., 2017; Hernández Parrodi et al., 2018). For a successful landfill mining project, the fine fraction needs to be valorised. The fine fraction exhibits soil like properties with higher nutrient content than farm soils. According to Indian standards, the required compost size is <4 mm. Based on the usage of the fine fraction as compost, 4 mm was selected as cut off diameter. The usage of the fine fraction as compost depends on various factors. The phys-

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Fig. 4. Average concentration of heavy metal with the standard deviation in excavated waste at different zones in Mulund dumpsite. a) Cadmium, b) Chromium, c) Copper, d) Nickel, e) Lead, f) Zinc.

Table 4 Pearson correlation coefficient between physicochemical properties of excavated waste.

Temperature pH Bulk density TOC GCV VM C N O Moisture content

pH

Bulk density

TOC

GCV

VM

C

N

O

Moisture content

−0.7b 1

−0.12 0.44 1

0.42 −0.59a −0.79b 1

0.42a −0.55a −0.52a 0.76b 1

0.25 −0.45 −0.4 0.73b 0.89b 1

0.26 −0.42 −0.65b 0.83b 0.73b 0.69b 1

0.41 −0.42 −0.71b 0.78b 0.76b 0.62b 0.75b 1

0.16 −0.14 0.29 −0.07 0.27 0.33 −0.36 −0.11 1

−0.03 0.24 0.01 −0.13 −0.36 −0.4 −0.28 −0.13 −0.03 1

Abbreviations: Total organic carbon (TOC), Gross calorific value (GCV), Volatile matter (VM), Carbon (C), Nitrogen (N), Oxygen (O). Pearson correlation. a Correlation is significant at the level 0.05 (2-tailed). b Correlation is significant at the level 0.01 (2-tailed).

ical and chemical properties (pH, bulk density, primary nutrients, secondary nutrients and micro nutrients) of the fine fraction needs to be assessed prior to application (not covered in present study). The heavy metal present in fine fraction should satisfy the Indian standard to be used as compost (Fig. 5). The heavy metals Cr, Cu, Ni and Pb are higher than limits prescribed by Indian standard for MSW compost (IS 16556, 2016). Therefore, to use the fine fraction as compost, the heavy metals content needs to be reduced. Various physicochemical remediation methods are available such as

soil washing to mitigate heavy metal in soil (Dermont et al., 2008). Also, the microplastic present in the fine fraction can be a problem as it would develop a film in soil over time and prevent the percolation of water in the soil. Hence, further research is required to find suitable and economic pretreatment methods on a large scale, for remediation of fine fraction. Another usage of fine fraction could be as a construction material. Prior research has extensively investigated the application of waste materials obtained from industrial waste (e.g., slag, ash) and

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Fig. 5. Indian MSW compost standard limits and the average concentration of heavy metal with the standard deviation in the fine fraction at different zones in Mulund dumpsite. a) Cadmium, b) Chromium, c) Copper, d) Nickel, e) Lead, f) Zinc.

agricultural waste (e.g., bagasse) to be used in the construction sector (Pappu et al., 2007; Shakir et al., 2013). However, little research has been conducted to show the use of MSW waste as a construction material in terms of fired or geopolymer bricks or aggregates. Goel and Kalamdhad (2017) studied the substitution of soil with degraded MSW in different proportion of 5–20%. They concluded that 20% mix of degraded MSW with soil fired at 900 ◦ C had maximum durability. Somani et al. (2018) studied the use of fine fraction for geotechnical applications and suggested the use of fine fraction as landfill cover soil. In developing countries like India, where topsoil is used for manufacturing fired bricks, the requirement of topsoil can be substituted with the fine fraction obtained from excavated waste. Application of fine fraction as a construction material could also help in the stabilisation of heavy metal. Therefore, further research and pilot studies are required for testing applicability of fine fraction in the construction sector. 3.7. Challenges and economics for landfill mining The cost associated with landfill mining projects are majorly due to excavation, material sorting, transport, recovery/treatment plants and plant operations and maintenance. Van Der Zee et al. (2004) assessed benefits and cost for reclamation of a landfill. The costs are majorly divided into capital cost (site prepara-

tion, equipment rental or purchase, material handling facility) and operational costs (labour, maintenance, safety, hauling and final disposal). While the benefits are majorly due to revenue generated from recyclable, combustibles, recovered landfill space and avoided costs. The cost and benefit will also depend on closure and aftercare requirements, remediation necessity, waste characteristics, waste decomposition status and local economics (cost of recyclables, land value, labour costs among others). For most cases, the capital and operational cost exceed the revenue generated from extracted materials (Frändegård et al., 2015; Maheshi et al., 2015; Van Passel et al., 2013; Wolfsberger et al., 2016). Furthermore, the cost and benefit for landfills and dumpsites would vary with each case (Burlakovs et al., 2017). Typical LFM costs (total expenses-attainable revenue), in case of international cases, for sanitary landfills varied from 30 to 60 D m−3 in literature (Wolfsberger et al., 2016). However, no literature is available for Indian scenario, whether landfill or dumpsite. For Indian waste characteristics, major revenue sources would be from landfill space recovery and combustible fraction (Dubey et al., 2016). One of the revenue sources found in most of the international literature was metal fraction, which is very low in case of Indian dumpsites. Most of the benefits from landfill mining, whether nationally or internationally, would be indirect in terms of environmental remediation and social aspect. Moreover, landfill mining can also reduce

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Fig. 6. Linear fit between physicochemical parameters with correlation coefficient <0.75. [a) Calorific value vs TOC, b) Bulk density vs. TOC, c) Calorific value vs Volatile matter, d) TOC vs Nitrogen, e) TOC vs Carbon, f) Calorific value vs Nitrogen].

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Fig. 7. Overall Composition of excavated waste from Mulund dumpsite (Combustible fraction: plastic, paper/cardboard, textile, wood; Non-combustible fraction: metal, glass, stone; Fine fraction: <4 mm).

carbon footprint by valorisation of excavated material, create jobs and prevent further environment contamination. One major push can be provided by the government through subsidies to encourage entrepreneurs to take landfill mining projects. Although landfill mining has various benefits, there may be certain issues like logistics management, safety of personnel, smell and odour released during LFM activities and not in my backyard attitude of the society. One major challenge is finding appropriate market for the excavated materials. Before the initiation of any LFM project, the market for the project deliverables should be located to establish its economic feasibility. The study provides possible valorisation routes which can be adopted to get maximum recovery from a dumpsite. However, technological challenges for implementation, economics and environmental effects for such techniques need to be studied in details for the Indian context. 4. Conclusion - The plastic content in the dumpsite has increased over the decades while fine fraction dominates the dumpsite with ∼45% average content. Moreover, the metal content in the dumpsite is >1%, indicating very less metal fraction enters into the dumpsite. - The parameters like volatile matter, total carbon, total organic carbon (TOC) shows a decreasing trend with age. The study shows a strong correlation of TOC with other parameters which suggests use of TOC as surrogate parameters to calculate other parameters in the dumpsite. - Heavy metals (cadmium, chromium, copper, nickel and lead) in the excavated waste depicts increment with age except for zinc. The heavy metal concentration decreases in fine fraction (except for Cd) when compared with excavated waste. - One major component in the dumpsite was combustible fraction 11–28% indicating potential for generating refuse-derived fuel. - From this study, it can be said that landfill mining can be used as a tool to deal with the old and unscientific landfill. However, to increase the viability of secondary resources and get maximum recovery and benefits, some pretreatment like mechanical and biological treatment, washing would be required.

Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors Declarations of Competing Interest None. Acknowledgements The authors would like to thank the Municipal Corporation of Greater Mumbai (MCGM) for providing access to the site, excavation equipment and assistance in sample collection. We would also like to thank Bhupendra Kumar Sharma (PhD scholar) for assisting with the sampling and processing of samples in the dumpsite. We also thank Sophisticated Analytical Instrument Facility (SAIF), IIT Bombay for providing required facilities for analytical procedures. We would also like to thank Prof. Sanjay Mahajani, IIT Bombay for providing access to bomb calorimeter (IKA C 200) in his laboratory facility. References ASTM Standard D6009, 2012. Standard Guide for Sampling Waste Piles 1. West Conshohocken, PA, http://dx.doi.org/10.1520/D6009-12.2. Ayres, R.U., Holmberg, J., Andersson, B., 2001. Materials and the global environment: waste mining in the 21st century. MRS Bull. 26, 477–480, http://dx.doi.org/10. 1557/mrs2001.119. Bhatnagar, A., Kaczala, F., Burlakovs, J., Kriipsalu, M., Hogland, M., Hogland, W., 2017. Hunting for valuables from landfills and assessing their market opportunities A case study with Kudjape landfill in Estonia. Waste Manag. Res. 35, 627–635, http://dx.doi.org/10.1177/0734242X17697816. Bosmans, A., Vanderreydt, I., Geysen, D., Helsen, L., 2013. The crucial role of Wasteto-Energy technologies in enhanced landfill mining: a technology review. J. Clean. Prod. 55, 10–23, http://dx.doi.org/10.1016/j.jclepro.2012.05.032. Brunner, P.H., 2011. Urban mining a contribution to reindustrializing the city. J. Ind. Ecol. 15, 339–341, http://dx.doi.org/10.1111/j.1530-9290.2011.00345.x. Burlakovs, J., Kaczala, F., Vincevica-Gaile, Z., Rudovica, V., Orupõld, K., Stapkevica, M., Bhatnagar, A., Kriipsalu, M., Hogland, M., Klavins, M., Hogland, W., 2016. Mobility of metals and valorization of sorted fine fraction of waste after landfill excavation. Waste Biomass Valorization 7, 593–602, http://dx.doi.org/10.1007/ s12649-016-9478-4. Burlakovs, J., Kriipsalu, M., Klavins, M., Bhatnagar, A., Vincevica-Gaile, Z., Stenis, J., Jani, Y., Mykhaylenko, V., Denafas, G., Turkadze, T., Hogland, M., Rudovica,

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