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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 35409

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Special Issue Editors

Prof. Dr. Ombretta Paladino

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Guest Editor

Department of Civil, Chemical and Environmental Engineering, University of Genova, Via Opera Pia 15, 16145 Genova, Italy
Interests: renewable energy; waste-to-energy; sustainable processes; biofuels; hydrogen; risk assessment; industrial hazards
Special Issues, Collections and Topics in MDPI journals

Dr. Mahdi Seyedsalehi

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Guest Editor

School of Environment, Tsinghua University, 100084 Beijing, China
Interests: wastewater treatment processes; green energy; biofuels

Special Issue Information

Dear Colleagues,

Despite the efforts made in recent years to reduce the production of solid waste and wastewater, including their recycle and reuse with a view to environmental sustainability, much can still be done in this field in terms of research and technological development. All the proposed treatment processes must operate while maintaining reduced risks for air, water and soil and prevent risks for the health of plants, animals and humans. Moreover, in light of sustainable development, treatment processes directly transforming solid and liquid waste into energy have to be promoted whenever waste and wastewater reuse and their transformation into fuels is not possible.

Solid waste treatment processes include how waste can be recovered, separated, recycled, reused and transformed, as well as where the final resulting waste can be sent for storage, final disposal, thermal or other industrial treatment. Interesting new processes are related to waste biomass coming from agro-food and the farming sector, and waste plastics.

Wastewater treatment processes have lately been approached from a total circular economy perspective, and recent technological research suggests the combination of new technologies for their reuse in integrated plants for biofuels production. Wastewater contributes to significant negative impacts not only on water bodies at a regional scale, but also on global energy, climate, and sustainability. In thinking holistically of water and wastewater management, energy recovery from wastewater becomes an appealing option to achieve greater resource recovery. The most common form of biofuels production from wastewater is anaerobic digestion; a wide range of anaerobic digestion technologies are converting livestock manure, municipal wastewater solids, food waste, high strength industrial wastewater and residuals, fats, oils and grease (FOG), and various other organic waste streams into biogas.

Although anaerobic digestion (AD) is a well-established process, the optimal design of anaerobic digesters for maximum methane production is still a challenge. Mathematical models are useful tools we can leverage to improve the design and efficiency of AD systems. It is generally accepted that well-developed models should describe the main aspects of a biological process, help to better understand the underlying phenomena and provide an accurate prediction of the AD performance as well as the optimization of operational parameters.

The aim of this Special Issue is to attract works in which new insights for solid-waste and wastewater treatment processes are proposed. This Special Issue invites you to submit innovative contributions in advanced treatment of plastics, micro- and nano-plastics, hazardous waste, emerging pollutants, high organic content wastewaters, and hospital waste. Works on biofuels production (methane, syngas, liquid biofuels) from waste are particularly welcome. Works related to combined treatment processes of municipal and industrial solid and liquid waste and to their environmental impact and human health risk assessment are also welcome. This Special Issue also seeks original contributions on monitoring, modeling and management of treatment plants. Our editorial process focuses on the robustness and validity of your research rather than making subjective decisions on your manuscripts.

Prof. Dr. Ombretta Paladino
Dr. Mahdi Seyedsalehi
Guest Editors

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Keywords

Waste-to-energy
Biowaste to biofuels
Solid-waste thermal treatment processes (gasification, pyrolysis, incineration)
Anaerobic digestion
Manure and sewage treatment
Hazardous waste treatment
Wastewater containing PAH, heavy metals, pesticides, PCBs, emerging contaminants
&Microalgae for wastewater treatment
Landfills
Plastics and micro-plastics
Particulate
Sustainable utilization of waste materials
Modeling and simulation

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Published Papers (8 papers)

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Research

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28 pages, 5423 KiB

Open AccessArticle

Solid Waste Management Scenario in India and Illegal Dump Detection Using Deep Learning: An AI Approach towards the Sustainable Waste Management

by Sana Shahab and Mohd Anjum

Sustainability 2022, 14(23), 15896; https://doi.org/10.3390/su142315896 - 29 Nov 2022

Cited by 19 | Viewed by 10264

Abstract

The study is presented in four sections. The first section defines the municipal solid waste and solid waste management system. The second section illustrates the descriptive statistical analysis of waste generation patterns in India. The average waste generation in India was 160,038.9 tons per day in 2021; 95% of this total waste was collected and transported to the disposal sites. Based on scientific studies and observations, the per capita waste generation rate in 2018 was 0.490–0.626 g per day. In the last one and a half decades (1999–2000 to 2015–2016), Delhi and Bangalore have shown the highest percentage growth of 2075% and 1750%, respectively, in total waste generation among the highest population cities. The analysis of waste generation patterns concludes urbanization is a major factor that highly influences the waste generation rate. The third section describes the major issues in current solid waste management services. Some of these issues are the unavailability of web portals for citizens, no real-time monitoring of bins, collection vehicles and illegal dumping. These issues are identified based on the survey performed in a city and analysis of related research studies and scientific reports. We determined that illegal dumping is one of these major concerns and needs a technological solution. In the fourth section, we propose a multipath convolutional neural network (mp-CNN) to detect and localize the waste dumps on streets and roadsides. We constructed our dataset to train and test the proposed model, as no benchmark dataset is publicly available to obtain this objective. We applied the weakly supervised learning approach to training the model. In this approach, mp-CNN was trained according to the image class; in our case, it is two (waste and non-waste). In the testing phase, the model showed the performance evaluation matrices 97.82% of precision, 98.86% of recall, 98.34% of F1 score, 98.33% of accuracy, and 98.63% of AUROC for this binary classification. Due to the scarcity of benchmark datasets, waste localization results cannot be presented quantitatively. So, we performed a survey to compare the overlapping of the mask generated by the model with the region waste in the actual image. The average score for the generated mask obtained a score of 3.884 on a scale of 5. Based on the analysis of model performance evaluation parameters, precision-recall curve, receiver characteristic operator curve, and comparison of mask generated by the model over waste with corresponding actual images show that mp-CNN performs remarkably good in detection, classification, and localization of waste regions. Finally, two conceptual architectures in the context of developing countries are suggested to demonstrate the future practical applications of the mp-CNN model. Full article

(This article belongs to the Special Issue Solid-Waste and Waste-Water Treatment Processes)

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20 pages, 3483 KiB

Open AccessArticle

Application of Event Detection to Improve Waste Management Services in Developing Countries

by Mohd Anjum, Sana Shahab and Mohammad Sarosh Umar

Sustainability 2022, 14(20), 13189; https://doi.org/10.3390/su142013189 - 14 Oct 2022

Cited by 2 | Viewed by 1706

Abstract

This study illustrates a proof-of-concept model to improve solid waste management (SWM) services by analyzing people’s behavior towards waste. A deep neural network model is implemented to detect and identify the specific types of events/activities in the proximity of the waste bin. This model consists of a three-dimensional convolutional neural network (3D CNN) and a long short-term memory (LSTM)-based recurrent neural network. The model was trained and tested over a handcrafted data set and achieved an average precision of 0.944–0.986. This precision is promising to support the implementation of the model on a large scale in the actual environment. The performance measures of all individual events indicate that the model successfully detected the individual events and has high precision for classifying them. The study also designed and built an experimental setup to record the data set, which comprises 3200 video files duration between 150–1200 s. Methodologically, the research is supported through a case study based on the recorded data set. In this case study, the frequencies of identified events/activities at a bin are plotted and thoroughly analyzed to determine people’s behavior toward waste. This frequency analysis is used to determine the locations where one of the following actions is required to improve the SWM service: (i) people need to be educated about the consequences of waste scattering; (ii) bin capacity or waste collection schedules are required to change; (iii) both actions are required simultaneously; (iv) none of the actions are needed. Full article

(This article belongs to the Special Issue Solid-Waste and Waste-Water Treatment Processes)

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17 pages, 2971 KiB

Open AccessEditor’s ChoiceArticle

Multilinear Regression Model for Biogas Production Prediction from Dry Anaerobic Digestion of OFMSW

by Elena Rossi, Isabella Pecorini and Renato Iannelli

Sustainability 2022, 14(8), 4393; https://doi.org/10.3390/su14084393 - 7 Apr 2022

Cited by 23 | Viewed by 4292

Abstract

The aim of this study was to develop a multiple linear regression (MLR) model to predict the specific methane production (SMP) from dry anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW). A data set from an experimental test on a pilot-scale plug-flow reactor (PFR) including 332 observations was used to build the model. Pearson′s correlation matrix and principal component analysis (PCA) examined the relationships between variables. Six parameters, namely total volatile solid (TVSin), organic loading rate (OLR), hydraulic retention time (HRT), C/N ratio, lignin content and total volatile fatty acids (VFAs), had a significant correlation with SMP. Based on these outcomes, a simple and three multiple linear regression models (MLRs) were developed and validated. The simple linear regression model did not properly describe the data (R² = 0.3). In turn, the MLR including all factors showed the optimal fitting ability (R² = 0.91). Finally, the MLR including four uncorrelated explanatory variables of feedstock characteristics and operating parameters (e.g., TVSin, OLR, C/N ratio, and lignin content), resulted in the best compromise in terms of number of explanatory variables, model fitting and predictive ability (R² = 0.87). Full article

(This article belongs to the Special Issue Solid-Waste and Waste-Water Treatment Processes)

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23 pages, 18924 KiB

Open AccessEditor’s ChoiceArticle

Sustainable Biodiesel Production by Transesterification of Waste Cooking Oil and Recycling of Wastewater Rich in Glycerol as a Feed to Microalgae

by Ombretta Paladino and Matteo Neviani

Sustainability 2022, 14(1), 273; https://doi.org/10.3390/su14010273 - 28 Dec 2021

Cited by 8 | Viewed by 5583

Abstract

The amount of solid and liquid organic waste and wastewater is continuously increasing all over the world. The necessity of their reuse and recycling is, therefore, becoming more and more pressing. Furthermore, the limited fossil fuel resources, in conjunction with the need to reduce greenhouse gas emissions, advocate the production of renewable fuels. In this work, we analyze a sustainable second-generation process to produce biodiesel by transesterification of waste cooking oil, coupled with a third-generation process in cascade for recycling the incoming wastewater. Since this latter is rich in glycerol, it is used as a feed for microalgae, from which oil can be extracted and added to the waste cooking oil to further produce biodiesel and close the cycle. We studied the influence of different factors like temperature, catalyst load, and reactants ratio on the kinetics of transesterification of the waste oil and estimated the kinetic parameters by different kinetic schemes. The obtained values of activation energies and pre-exponential factors at chosen conditions of

T

= 60 °C and catalyst load of 0.6% w/w in methanol are:

E_{a, direct}

= 35,661 J mol⁻¹,

E_{a, reverse}

= 72,989 J mol⁻¹,

k_{0, direct}

= 9.7708 [dm³ mol⁻¹]³ min⁻¹, and

k_{0, reverse}

= 24,810 [dm³ mol⁻¹]³ min⁻¹ for the global fourth-order reversible reaction scheme and

E_{a}

= 67,348 J mol⁻¹ and

k_{0}

= 2.157 × 10⁹ min⁻¹ for the simplified pseudo-first-order irreversible reaction scheme; both in strong agreement with literature data. Furthermore, we designed very efficient conditions for discontinuous and continuous operating mode, both at lab-scale and pilot-scale. The quality of the biodiesel produced from waste cooking sunflower oil is compared with that of biodiesel produced by different kinds of virgin vegetable oils, showing that the former possesses acceptable quality standards (Cetane number = 48 and LHV = 36,600 kJ kg⁻¹). Finally, the recycling of wastewater rich in glycerol as a nutrient for mixotrophic microalgae nurturing is discussed, and microalgae growing kinetics are evaluated (k₁ about 0.5 day⁻¹), endorsing the possibility of algae extraction each 4–5 days in a semi-continuous operating mode. The experimental results at the pilot scale finally confirm the quality of biodiesel, and the obtained yields for a two-stage process prove the competitiveness of this sustainable process on the global market. Full article

(This article belongs to the Special Issue Solid-Waste and Waste-Water Treatment Processes)

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18 pages, 6048 KiB

Open AccessArticle

Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique

by Aliasghar Azma, Esmaeil Narreie, Abouzar Shojaaddini, Nima Kianfar, Ramin Kiyanfar, Seyed Mehdi Seyed Alizadeh and Afshin Davarpanah

Sustainability 2021, 13(7), 3788; https://doi.org/10.3390/su13073788 - 29 Mar 2021

Cited by 15 | Viewed by 2790

Abstract

In arid and semi-arid lands like Iran water is scarce, and not all the wastewater can be treated. Hence, groundwater remains the primary and the principal source of water supply for human consumption. Therefore, this study attempted to spatially assess the groundwater potential in an aquifer in a semi-arid region of Iran using geographic information systems (GIS)-based statistical modeling. To this end, 75 agricultural wells across the Marvdasht Plain were sampled, and the water samples’ electrical conductivity (EC) was measured. To model the groundwater quality, multiple linear regression (MLR) and principal component regression (PCR) coupled with elven environmental parameters (soil-topographical parameters) were employed. The results showed that that soil EC (SEC) with Beta = 0.78 was selected as the most influential factor affecting groundwater EC (GEC). CaCO₃ of soil samples and length-steepness (LS factor) were the second and third effective parameters. SEC with r = 0.89 and CaCO₃ with r = 0.79 and LS factor with r = 0.69 were also characterized for PC1. According to performance criteria, the MLR model with R² = 0.94, root mean square error (RMSE) = 450 µScm⁻¹ and mean error (ME) = 125 µScm⁻¹ provided better results in predicting the GEC. The GEC map indicated that 16% of the Marvdasht groundwater was not suitable for agriculture. It was concluded that GIS, combined with statistical methods, could predict groundwater quality in the semi-arid regions. Full article

(This article belongs to the Special Issue Solid-Waste and Waste-Water Treatment Processes)

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17 pages, 2600 KiB

Open AccessArticle

The Investigation of TiO₂ NPs Effect as a Wastewater Treatment to Mitigate Cd Negative Impact on Bamboo Growth

by Abolghassem Emamverdian, Yulong Ding, Farzad Mokhberdoran, Zishan Ahmad and Yinfeng Xie

Sustainability 2021, 13(6), 3200; https://doi.org/10.3390/su13063200 - 15 Mar 2021

Cited by 13 | Viewed by 2323

Abstract

The recent emerging evidence reveals that titanium dioxide nanoparticles (TiO₂ NPs) can be used as a wastewater treatment. This study provides new information about the possible detoxification role of TiO₂ NPs as a wastewater treatment in plants under heavy metal stress, with an emphasis on the mechanisms involved. Here, we investigated the effects of TiO₂ NPs as one wastewater treatment on a bamboo species (Arundinaria pygmaea L.) under in vitro Cadmium (Cd) toxicity conditions. A factorial experiment was conducted in a completely randomized design with four replications of four concentrations of Cd (50, 100, 200, and 300 µM) alone and in combination with 100 and 200 µM TiO₂ NPs as two wastewater treatments, as well as a control treatment. The results indicated that TiO₂ NPs concentrations enhanced enzymatic and non-enzymatic antioxidant activities and proline accumulation as well as reducing hydrogen peroxide (H₂O₂), superoxide radical (O₂^•−), and malondialdehyde (MDA) levels, which led to improved photosynthetic parameters with an eventual increase in plant biomass as compared to the control treatment. Therefore, TiO₂ NPs improved the photosynthetic parameters of bamboo under Cd toxicity, which led to an increase in plant biomass. We concluded that the wastewater treatments of TiO₂ NPs improved bamboo biomass through the scavenging of reactive oxygen species (ROS) compounds (H₂O₂ and O₂^•−), which was induced by the stimulation of the antioxidant capacity of the plant. TiO₂ also protected cell membranes by reducing lipoperoxidation in bamboo under Cd toxicity. The concentration of 200 µM TiO₂ NPs had the most impact in reducing Cd toxicity. Full article

(This article belongs to the Special Issue Solid-Waste and Waste-Water Treatment Processes)

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Review

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19 pages, 1022 KiB

Open AccessReview

Efficacy of Electrocoagulation Treatment for the Abatement of Heavy Metals: An Overview of Critical Processing Factors, Kinetic Models and Cost Analysis

by Saif Ullah Khan, Mohammad Khalid, Khalid Hashim, Mehdi Hassanvand Jamadi, Milad Mousazadeh, Farrukh Basheer and Izharul Haq Farooqi

Sustainability 2023, 15(2), 1708; https://doi.org/10.3390/su15021708 - 16 Jan 2023

Cited by 9 | Viewed by 4341

Abstract

The electrocoagulation (EC) process introduces coagulants by electrochemical means, and is widely adopted for removing heavy metals, besides other contaminants, such as organic pollutants, suspended and dissolved solids, colloidal materials, etc. However, its capability can vary significantly, depending on the operating conditions. Although most of the investigations so far are limited at the laboratory level with artificially prepared solutions or industrial effluent lacking full- and field-scale studies, the success of the process depends a lot on optimizing the process variable. It has been found that the current density (typically 1–20 mA/cm²), type of electrode (generally aluminum or iron) and minimum electrolysis time are the key process parameters that influence performance. Furthermore, key mechanisms involved in the EC process, including charge neutralization, reduction-oxidation and precipitation/co-precipitation, are crucial for pollutant abatement. This review presents a detailed study undertaking all significant parameters that play a crucial role in the EC process, its mechanism, and improving the efficiency of this process by optimization of these parameters, along with suitable kinetic models. Full article

(This article belongs to the Special Issue Solid-Waste and Waste-Water Treatment Processes)

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21 pages, 946 KiB

Open AccessReview

Data Driven Modelling and Control Strategies to Improve Biogas Quality and Production from High Solids Anaerobic Digestion: A Mini Review

by Ombretta Paladino

Sustainability 2022, 14(24), 16467; https://doi.org/10.3390/su142416467 - 8 Dec 2022

Cited by 6 | Viewed by 2093

Abstract

Anaerobic Digestion (AD) is one of the oldest processes for producing biofuels from organic waste. Approximately 180 years have passed since the construction of the first modern plant, however, large prospects for improvement are still feasible, especially in regards to the quality and uniformity of the biogas produced. This work focalizes on the main quality issues and the available post-production treatment processes for biogas; subsequently, a mini-review on data-driven models and control strategies for biogas and bio-methane production plants is presented. Attention is focused on High Solids Anaerobic Digesters (HSADs), since these reactors present many interesting advantages, including a high number of operating variables which enable process optimization, high methane concentration in exit, reduced reactor volume and low water requirements. HSADs are the reactors with which Europe is aiming to rapidly increase the production of biogas and bio-methane, in order to carry out de-carbonization and reduce dependence on external methane imports. Crucial points for achieving these objectives include qualitative leaps in process operation and management, which, contrary to current practice in existing plants, require a significant increase in process automation, with control of product quality and reduction of stops due to death of bacteria at changing process parameters (such as temperature and pH). The most significant papers related to biogas quality, data-driven models and control strategies are briefly analyzed. Full article

(This article belongs to the Special Issue Solid-Waste and Waste-Water Treatment Processes)

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