Environmental and economic assessment of waste collection and transportation using LCA: A case study.

The present study is aimed to evaluate the environmental and economic burden associated with current waste collection practices in Dhanbad city, Jharkhand, India. In this study various alternatives were suggested to mitigate these impacts by optimizing resource utilization and maximizing material recovery using life cycle approach. The functional unit adapted is the daily collection service provided for 180 tonnes of municipal solid waste generated in the study area. GaBi 10.6.1 software was used for impact assessment and impacts were assessed for five scenarios in terms of five different impact categories. This study assessed the collection services and treatment options jointly. Baseline scenario (S1) representing the current collection system had the highest impacts on all the impact categories and landfilling contributed the highest (67%) to the overall impacts on the environment. Scenario S2 involved the provision of material recovery facility and considered recycling of plastic wastes having sorting efficiency of 75% which reduced the overall impacts significantly (∼971%) compared to the baseline scenario. Scenario S3 considered composting of food waste (80% food waste sent for composting) and offered further overall impacts reduction (∼1052%) compared to the baseline scenario. In scenario S4, use of electric tippers was accounted which did not offer any significant impacts reductions. Scenario S5 considered the future electricity mix at grid (2030) in India which increased the benefits of using electric tippers. S5 had the least environmental impacts providing overall reductions of ∼1063% compared to baseline scenario and provided maximum economic benefits. Sensitivity analysis results found that variation in recycling had significant change in the environmental impacts. Considering the decrease in recycling rate from 100% to 50%, the impacts on abiotic depletion fossil increased by 136%, acidification by 176%, global warming by 11%, human toxicity by 172% and terrestrial ecotoxicity by 56%.

Evaluation of the recycling potential of obsolete mobile phones through secondary material resources identification: A comprehensive characterization study.

The growing concern over the management of e-wastes has generated an interest in the recovery of resources from these wastes under the concept of urban mining and circular economy. However, in the absence of accurate knowledge of the physico-chemical compositional structure of these wastes makes the recycling process difficult. Thus, the present study conducted a recycling-oriented characterization of waste mobile phones (WMPs) for the identification of secondary materials and estimated their recycling potential. The characterization was performed using ICP-OES and FTIR techniques after dismantling WMPs for the determination of elemental composition and the polymeric fractions respectively. Dismantling of the WMPs revealed that enclosures, batteries, display modules, and PCBs consist of 35.33 wt%, 28.9 wt%, 19.44 wt%, and 16.31 wt% respectively. Of these components, PCBs constitute the highest economic recovery potential with an estimated potential revenue generation of more than 50,000 US $ per ton of waste PCBs. Copper showed the highest recovery potential (234.39 tons/year) with an economic value of approximately 3317 US $/ton of WPCBs followed by Sn (27.37 tons/year) and Ni (24.64 tons/year). Among different precious metals, Au was found to have the highest percentage of economic value (76.22%) followed by Pd (8.16%) and Ag (3.13%). The display modules and enclosures were found to have relatively lower contributions than WPCBs in the overall recycling potential due to lower metal contents and mixed polymeric fractions. The findings in the study indicate that WMPs could serve as a promising new source for sustainable secondary mining of rare and valuable metals. Further, the study will help the policymakers in designing effective e-waste management strategies through the promotion of sustainable recovery of materials.

Landfill site selection in a mining dominated area using a fuzzy AHP and DRASTIC vulnerability based multicriteria decision analysis: a case study of Dhanbad, India.

Landfilling is the least preferred method in the hierarchy of solid waste management options, but it is the most widely practiced option. Thus, identification of environmentally and economically suitable landfill sites should be of prime importance. The main objective of this study is to identify environmentally and economically suitable landfill sites using fuzzy analytical hierarchy process-based weighted linear combination model within a GIS environment. This study also used the DRASTIC-based groundwater vulnerability index and distance of landfills from densely populated areas to protect groundwater and reduce cost of transportation of solid waste which were not considered by the previous studies. Using the previously reported methods, a total of 132 landfill sites were found environmentally suitable in the study area. But, after applying DRASTIC-based groundwater vulnerability index, the number of environmentally suitable sites reduced to 95. When the proximity of the 95 sites to densely populated areas was considered to reduce waste transportation cost, the number of selected sites further reduced to 21 site and they can be considered the most environmentally and economically suitable landfill sites. This study will help the policy makers and the concerned SWM authorities to construct the engineered landfills at environmentally and economically suitable landfill sites in the study area and in other similar areas.

Evaluation of methane generation rate and energy recovery potential of municipal solid waste using anaerobic digestion and landfilling: A case study of Dhanbad, India.

In this study, two most common biological waste to energy conversion techniques have been analysed and compared on the basis of methane generation and energy recovery potential. The biogas generation potential has been estimated using anaerobic co-digestion experiment. The main substrate used for this study was food waste, while cow dung and anaerobic digester sludge were used as co-digestion substrates. Food waste was used because of its maximum composition (36% of total wastes) in the study area (Dhanbad city, India) with high biogas generation potential. Cow dung acted as a natural buffer, while anaerobic digester sludge was a source of active inoculum. Based on the maximum biogas yield of 402 mL g-1 VS and annual food waste generation rate in the study area, the energy recovery potential using anaerobic digestion was estimated to be 6.59 × 106 kWh year-1. Presently, the wastes in the mixed form are being dumped on a large abandoned land with an approximate area of 93 ha in the outskirts of Dhanbad city in an uncontrolled manner. The annual landfill gas generation from the existing landfill has been estimated using Landfill Gas Emissions Model (LandGEM) based on the waste characteristics, anticipated landfill life and other region-specific parameters of the present study area. The maximum electrical energy recovery potential of 44.62 × 105 kWh from landfill gas has been estimated for the year 2041. Although, the results are based on the waste generation and characteristics of Dhanbad city, the comparison methodology can be applied to other cities.

A critical review of the pre-processing and metals recovery methods from e-wastes.

E-wastes being potential sources of numerous valuable metals are promoted to undergo recycling and recovery under the umbrella of urban mining and circular economy. Thus, the present study provides a critical review of the technological details of different metal recycling processes, pre-treatment methods, and the advancements made in these techniques. Critical evaluation of different metal recovery techniques has also been presented based on the available life cycle assessment (LCA), techno-economic, and industrial-scale studies. The study revealed that the integrated metal recovery techniques serve better in terms of recovery efficiency and environmental performance than any single recovery technique. Also, scaling up of biometallurgical, electrochemical, and super critical fluid extraction methods needs to be promoted due to their better environmental performances.

Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis.

Forests play a vital role in maintaining the global carbon balance. However, globally, forest ecosystems are increasingly threatened by climate change and deforestation in recent years. Monitoring forests, specifically forest biomass is essential for tracking changes in carbon stocks and the global carbon cycle. However, developing countries lack the capacity to actively monitor forest carbon stocks, which ultimately adds uncertainties in estimating country specific contribution to the global carbon emissions. In India, authorities use field-based measurements to estimate biomass, which becomes unfeasible to implement at finer scales due to higher costs. To address this, the present study proposed a framework to monitor above-ground biomass (AGB) at finer scales using open-source satellite data. The framework integrated four machine learning (ML) techniques with field surveys and satellite data to provide continuous spatial estimates of AGB at finer resolution. The application of this framework is exemplified as a case study for a dry deciduous tropical forest in India. The results revealed that for wet season Sentinel-2 satellite data, the Random Forest (adjusted R2 = 0.91) and Artificial Neural Network (adjusted R2 = 0.77) ML models were better-suited for estimating AGB in the study area. For dry season satellite data, all the ML models failed to estimate AGB adequately (adjusted R2 between -0.05 - 0.43). Ensemble analysis of ML predictions not only made the results more reliable, but also quantified spatial uncertainty in the predictions as a metric to identify its robustness.

Reduction of spatial distribution of risk factors for transportation of contaminants released by coal mining activities.

It is reported that water-energy nexus composes two of the biggest development and human health challenges. In the present study we presented a Risk Potential Index (RPI) model which encapsulates Source, Vector (Transport), and Target risks for forecasting surface water contamination. The main aim of the model is to identify critical surface water risk zones for an open cast mining environment, taking Jharia Coalfield, India as the study area. The model also helps in feasible sampling design. Based on spatial analysis various risk zones were successfully delineated. Monthly RPI distribution revealed that the risk of surface water contamination was highest during the monsoon months. Surface water samples were analysed to validate the model. A GIS based alternative management option was proposed to reduce surface water contamination risk and observed 96% and 86% decrease in the spatial distribution of very high risk areas for the months June and July respectively.

Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands.

The objective of the present study is to monitor reclamation activity in mining areas. Monitoring of these reclaimed sites in the vicinity of mining areas and on closed Over Burden (OB) dumps is critical for improving the overall environmental condition, especially in developing countries where area around the mines are densely populated. The present study evaluated the reclamation success in the Block II area of Jharia coal field, India, using Landsat satellite images for the years 2000 and 2015. Four image processing methods (support vector machine, ratio vegetation index, enhanced vegetation index, and normalized difference vegetation index) were used to quantify the change in vegetation cover between the years 2000 and 2015. The study also evaluated the relationship between vegetation health and moisture content of the study area using remote sensing techniques. Statistical linear regression analysis revealed that Normalized Difference Vegetation Index (NDVI) coupled with Normalized Difference Moisture Index (NDMI) is the best method for vegetation monitoring in the study area when compared to other indices. A strong linear relationship (r(2) > 0.86) was found between NDVI and NDMI. An increase of 21% from 213.88 ha in 2000 to 258.9 ha in 2015 was observed in the vegetation cover of the reclaimed sites for an open cast mine, indicating satisfactory reclamation activity. NDVI results indicated that vegetation health also improved over the years.

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Municipal Solid Waste Management using Geographical Information System aided methods: a mini review.

Municipal Solid Waste Management (MSWM) is one of the major environmental challenges in developing countries. Many efforts to reduce and recover the wastes have been made, but still land disposal of solid wastes is the most popular one. Finding an environmentally sound landfill site is a challenging task. This paper addresses a mini review on various aspects of MSWM (suitable landfill site selection, route optimization and public acceptance) using the Geographical Information System (GIS) coupled with other tools. The salient features of each of the integrated tools with GIS are discussed in this paper. It is also addressed how GIS can help in optimizing routes for collection of solid wastes from transfer stations to disposal sites to reduce the overall cost of solid waste management. A detailed approach on performing a public acceptance study of a proposed landfill site is presented in this study. The study will help municipal authorities to identify the most effective method of MSWM.

Assessment of the capability of SWAT model to predict surface runoff in open cast coal mining areas.

The hydrological response of watersheds affected by large-scale coal mining activities is complex and difficult to simulate. The present study aims to bridge this gap by simulating the effects of land-use and topographical changes due to coal mining on surface runoff in the Jamunia basin of Jharkhand, India. The derivatives of digital elevation model (DEM) have been used to simulate the changes in topography of the study area and the runoff has been calculated using Soil and Water Assessment Tool (SWAT) hydrological model. The study results revealed significant increase in surface runoff (mm) during the simulation period. The findings of this study established that unplanned mining activities can reduce the water holding capacity of downstream reservoirs and increase the runoff. The outcome of the study will be helpful for mine planners to design sustainable mining operations which will have less adverse impact on the hydrological regime of the watershed.

Efficient loading of nano Mn particles on calcined laterite soil (Lt-nMn) for higher removal of As(III) ions from groundwater: adsorption and eco-scale analysis.

Occurrence of arsenic in the groundwater has become a cause for concern in many countries. The presence of As(III) species in the groundwater had been one of the biggest challenges for the water workers especially in the southeast Asian countries. Nano based adsorption techniques are gaining attention among researchers for the removal of arsenic ions. However, synthesis of nano-adsorbents is a costly affair. Therefore, the present study utilized the cheap and readily available laterite soil as a base material for nano coating. Nano sized manganese (nMn) particles were synthesized by chemical reduction method and later coated on calcined laterite (Lt-nMn). Coating with 0.1 M nMn provided the best As(III) removal efficiency. The prepared material was characterised for its morphological and surface properties. Phase analysis using XRD (X-ray diffraction) showed the presence of zero valent manganese species, which assisted in adsorption. Adsorption studies were conceded by selecting the different affecting parameters such as contact time, concentration, dose, temperature, and pH. Very high removal in less time regime led to the investigation of the oxidation mechanism. Phosphate and sulphate anions insignificantly reduced the removal efficiency. Langmuir model for the sorption isotherms and pseudo-second order kinetic model for the sorption kinetics symbolized the experimental data. Sustainability studies in terms of eco-scaling and cost analysis were performed for the applied method of production of the nano materials. Adsorber design analysis was also conducted to estimate the required amount of Lt-nMn particles for achieving the desired equilibrium As(III) concentration.

Use of adsorption-influencing parameters for designing the batch adsorber and neural network-based prediction modelling for the aqueous arsenate removal using combustion synthesised nano-alumina.

Removal of arsenic from water is of utmost priorities on a global scenario due to its ill effects. Therefore, in the present study, aluminium oxide nano-particles (nano-alumina) were synthesised via solution combustion method, which is self-propagating and eco-friendly in nature. Synthesised nano-alumina was further employed for arsenate removal from water. Usually, pre-oxidation of arsenite is performed for better removal of arsenic in its pentavalent form. Thus, arsenate removal as a function of influencing parameters such as initial concentration, dose, pH, temperature, and competing anions was the prime objective of the present study. The speciation analysis showed that H2AsO4- and HAsO42- were co-existing anions between pH 6 and 8, as a result of which higher removal was observed. Freundlich isotherm model was well suited for data on adsorption. At optimal temperature of 298 K, maximum monolayer adsorption capacity was found as 1401.90 µg/g. The kinetic data showed film diffusion step was the controlling mechanism. In addition, competing anions like nitrate, bicarbonate, and chloride had no major effect on arsenate removal efficiency, while phosphate and sulphate significantly reduced the removal efficiency. The negative values of thermodynamic parameters ΔH° (- 23.15 kJ/mol) established the exothermic nature of adsorption, whereas the negative values of ΔG° (- 7.05, - 6.51, - 5.97, and - 5.43 kJ/mol at 298, 308, 318, and 328 K respectively) indicated the spontaneous nature of the process. The best-fitted isotherm was used to design a batch adsorber to estimate the required amount of aluminium oxide nano-particles for achieving the desired equilibrium arsenate concentration. Nano-alumina was also applied to treat the collected arsenic-contaminated groundwater from actual field. Experimental data were used to develop a neural network-based model for the effective prediction of removal efficiency without carrying out any extra experimentation.

As(V) removal using biochar produced from an agricultural waste and prediction of removal efficiency using multiple regression analysis.

Arsenic contamination in drinking water is a matter of concern for many countries. An efficient and low-cost solution for this hazard is essentially needed on urgent basis. Therefore, in this study, banana pith (an agricultural waste) was used for biochar production and later it was modified with iron and applied for arsenic adsorption from aqueous solution. Produced biochar was characterized for proximate, ultimate, and surface analyses. Interestingly, after iron impregnation, the surface area of biochar increased (31.59 m2/g) by nearly 8 times. Morphological analysis showed that iron particles firmly held within the pores after impregnation. Arsenate (As(V)) adsorption behavior of iron-impregnated banana pith biochar was evaluated through a batch study by considering various parameters like dose, concentration, pH, temperature, and competing anions. Compared to impregnated biochar, raw biomass and its biochar showed a lesser affinity for arsenate in aqueous solution. The adsorption isotherm of As(V) on banana pith biochar was covered in the temperature range of 298 to 318 K, and kinetic data of adsorption was experimentally generated at 298 K. Langmuir model for the sorption isotherms and pseudo-second-order kinetic model for the sorption kinetics represented the experimental data. The thermodynamic study showed negative Gibb's free energy (- 46.88 kJ/mol at 298 K, - 48.58 kJ/mol at 308 K, - 50.73 kJ/mol at 318 K) that suggested spontaneity of the adsorption process. Negative enthalpy (ΔH° = - 10.55 kJ/mol) showed exothermic nature of adsorption of arsenic, while negative entropy (ΔS° = 0.123 kJ/mol.K) suggested enthalpy-driven adsorption process. Mechanism of arsenic adsorption onto iron-impregnated banana pith biochar has also been discussed in detail. Based on the experimental observation, a predictive model for arsenate removal has been developed in this study. The findings of the present study elucidated that iron-impregnated banana pith biochar can be used as a low-cost adsorbing material for As(V) from aqueous solutions.

Environmental impact assessment of municipal solid waste management options using life cycle assessment: a case study.

The goal of this study is to use life cycle assessment (LCA) tool to assess possible environmental impacts of different municipal solid waste management (MSWM) scenarios on various impact categories for the study area Dhanbad City, India. The scenarios included in the present study are collection and transportation (denoted as S1); baseline scenario consisting of recycling, open burning, open dumping, and finally unsanitary landfilling without energy recovery (denoted by S2); composting and landfilling (denoted by S3); and recycling and composting followed by landfilling of inert waste without energy recovery (denoted by S4). One ton of municipal solid waste (MSW) was selected as the functional unit. The primary data were collected through sampling, surveys, and literatures. Background data were obtained from Eco-invent data of SimaPro 8.1 libraries. The scenarios were compared using the CML 2 baseline 2000 method, and the results indicated that the scenario S1 had the highest impact on marine aquatic ecotoxicity (1.86E + 04 kg 1,4-DB eq.) and abiotic depletion (2.09E + 02 kg Sb eq.). S2 had the highest impact on global warming potential (9.42E + 03 kg CO2 eq.), acidification (1.15E + 01 kg SO2 eq.), eutrophication (2.63E + 00 kg PO43- eq.), photochemical oxidation (2.12E + 00 kg C2H4 eq.), and human toxicity (2.25E + 01 kg 1,4-DB eq.). However, S3 had the highest impact on abiotic depletion (fossil fuels) (2.71E + 02 MJ), fresh water aquatic ecotoxicity (6.54E + 00 kg 1,4-DB eq.), terrestrial ecotoxicity (3.36E - 02 kg 1,4-DB eq.), and ozone layer depletion (2.73E - 06 kg CFC-11 eq.). But S4 did not have the highest impact on any of the environmental impact categories due to recycling of packaging waste and landfilling of inert waste. Landfilling without energy recovery of mixed solid waste was found as the worst disposal alternative. The scenario S4 was found as the most environmentally suitable technology for the study area and recommended that S4 should be considered for strategic planning of MSWM for the study area.