Intelligent Environmental Data Monitoring for Pollution Management

By Siddhartha Bhattacharyya

Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. Thus, the book ushers in a new era as far as environmental pollution management is concerned. It reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, followed by a review of intelligent tools and techniques which can be used in this direction. In addition, it discusses novel intelligent algorithms for effective environmental pollution data management that are on par with standards laid down by the World Health Organization.

• Introduces novel intelligent techniques needed to address environmental pollution for the well-being of the global environment
• Offers perspectives on the design, development and commissioning of intelligent applications
• Provides reviews on the latest intelligent technologies and algorithms related to state-of-the-art methodologies surrounding the monitoring and mitigation of environmental pollution
• Puts forth insights on future generation intelligent pollution monitoring techniques

• Language: English
• Publisher: Academic Press
• Release date: Oct 22, 2020
• ISBN: 9780128199244

Book preview

Intelligent Environmental Data Monitoring for Pollution Management

First Edition

Siddhartha Bhattacharyya

CHRIST (Deemed to be University), Bangalore, India

Naba Kumar Mondal

The University of Burdwan, Burdwan, India

Jan Platos

VŠB - Technical University of Ostrava, Czech Republic

Václav Snášel

VŠB - Technical University of Ostrava, Czech Republic

Pavel Krömer

VŠB - Technical University of Ostrava, Czech Republic

Series Editor

Fatos Xhafa

Universitat Politècnica de Catalunya, Spain

Table of Contents

Cover image

Title page

Copyright

Dedication

Contributors

Preface

1: Batch adsorption process in water treatment

Abstract

1: Introduction

2: Batch experiments

3: Factors affecting adsorption process

4: Mechanism in batch adsorption

5: Adsorption isotherm

6: Adsorption kinetics

7: Thermodynamics

8: Desorption studies

9: Conclusion

2: Removal of heavy metals from industrial effluents by using biochar

Abstract

Acknowledgment

1: Introduction

2: Industrial effluents and heavy metal pollution

3: Conventional processes of removal heavy metals from effluent

4: Biochar: The adsorbent

5: Preparation of biochar

6: Properties of biochar

7: Removal of heavy metals by biochar

8: Conclusion

3: Nanoparticles: A new tool for control of mosquito larvae

Abstract

Acknowledgments

1: Introduction

2: Nanoparticle synthesis

3: Nanoparticle characterizations

4: Application

5: Research gap

6: Conclusion

4: Biosorption-driven green technology for the treatment of heavy metal(loids)-contaminated effluents

Abstract

1: Introduction

2: Heavy metal(loid)s

3: Conventional treatment process for metal(loid)s removal from wastewater

4: Biosorption

5: Mechanisms of biosorption

6: Advantages of biosorption process

7: Factors affecting biosorption

8: Biosorption isotherm and model

9: Biosorbent

10: Modification of biosorbent

11: Instrumentation involved in analysis of biosorption

12: Conclusion

5: A comprehensive review of glyphosate adsorption with factors influencing mechanism: Kinetics, isotherms, thermodynamics study

Abstract

1: Introduction

2: Adsorption study

3: Comparative study of glyphosate in recent published paper

4: Possible mechanism of adsorption

5: Lack of area of glyphosate adsorption

6: Conclusion

6: Dyes and their removal technologies from wastewater: A critical review

Abstract

Acknowledgment

1: Introduction

2: Dye removal technologies

3: Comparison between different treatment processes

4: Conclusion and future perspective

7: An intelligent estimation model for water quality parameters assessment at Periyakulam Lake, South India

Abstract

1: Introduction

2: Materials and methods

3: Statistical analysis for water quality assessment

4: Proposed methodology

5: Experimental observations and discussions

6: Conclusion

8: Recent trends in air quality prediction: An artificial intelligence perspective

Abstract

1: Introduction

2: Preliminary information

3: Neural network-based prediction models

4: Deep learning models for air quality prediction

5: Conclusion

9: Optimization of absorption process for exclusion of carbaryl from aqueous environment using natural adsorbents

Abstract

1: Introduction

2: Characterization of adsorbents

3: Adsorption study

4: Conclusion

10: Artificial neural network: An alternative approach for assessment of biochemical oxygen demand of the Damodar River, West Bengal, India

Abstract

Acknowledgments

1: Background

2: Material and methods

3: Results and discussion

4: Conclusion

11: Codesign to improve IAQ awareness in classrooms

Abstract

1: Introduction

2: Related work

3: Methodology

4: System design and implementation

5: Understanding system use

6: Discussion

7: Conclusion

12: Data perspective on environmental mobile crowd sensing

Abstract

Acknowledgments

1: Introduction

2: Taxonomy

3: Challenges of MCS

4: Related work/projects

5: Potential solutions

6: Conclusion and research perspectives

13: A survey of adsorption process parameter optimization related to degradation of environmental pollutants

Abstract

1: Introduction

2: Motivation and contributions

3: Neural networks

4: Path analysis

5: Neurofuzzy network analysis

6: Response surface methodology

7: Random forest model

8: Stochastic gradient boosting

9: Conclusion

Index

Copyright

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Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

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Dedication

Siddhartha Bhattacharyya would like to dedicate this book to Dr Fr Benny Thomas, Professor, Department of Computer Science and Engineering & Information Technology and Dr Fr Joseph Varghese, Personnel Officer and Professor, Department of Mathematics, CHRIST (Deemed to be University), India.

Naba Kumar Mondal would like to dedicate this book to his sweet and loving father and mother, whose affection, love, encouragement, and prayer of day and night made him achieve such success and honor.

Jan Platos would like to dedicate this book to his wife Daniela and his daughters Emma and Margaret.

Contributors

Jinat Aktar     Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, Assam, India

Anindya Banerjee     Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India

Jatindra N. Bhakta     Department of Ecological Studies & International Center for Ecological Engineering, University of Kalyani, Kalyani, West Bengal, India

Mariem Brahem     DAVID Lab, University of Versailles Saint-Quentin-en-Yvelines, Paris-Saclay University, Versailles, France

Mohamed Chachoua     LASTIG, University Gustave Eiffel, EIVP, Paris, France

Adriane Chapman     University of Southampton, Southampton, United Kingdom

Soumya Chattoraj     University Institute of Technology, General Science and Humanities, The University of Burdwan, Bardhaman, West Bengal, India

Priyanka Debnath     Environmental Chemistry Laboratory, Department of Environmental Science, The University of Burdwan, Bardhaman, West Bengal, India

T.T. Dhivyaprabha     Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India

Apurba Ratan Ghosh     Department of Environmental Science, The University of Burdwan, Burdwan, West Bengal, India

Manash Gope     Department of Chemistry, National Institute of Technology Durgapur (NITD), Durgapur, West Bengal, India

Metehan Guzel     Department of Computer Engineering, Gazi University, Ankara, Turkey

Hafsa E.L. Hafyani     DAVID Lab, University of Versailles Saint-Quentin-en-Yvelines, Paris-Saclay University, Versailles, France

G. Jayashree     Northeastern University, Boston, MA, United States

Zoubida Kedad     DAVID Lab, University of Versailles Saint-Quentin-en-Yvelines, Paris-Saclay University, Versailles, France

Ibrahim Kok     Department of Computer Science, Gazi University, Ankara, Turkey

M. Krishnaveni     Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India

Ahmad Ktaish     DAVID Lab, University of Versailles Saint-Quentin-en-Yvelines, Paris-Saclay University, Versailles, France

Bradley McLaughlin     University of Southampton, Southampton, United Kingdom

Souheir Mehanna     DAVID Lab, University of Versailles Saint-Quentin-en-Yvelines, Paris-Saclay University, Versailles, France

Arghadip Mondal     Environmental Chemistry Laboratory, Department of Environmental Science, The University of Burdwan, Bardhaman, West Bengal, India

Naba Kumar Mondal     Environmental Chemistry Laboratory, Department of Environmental Science, The University of Burdwan, Bardhaman, West Bengal, India

Suat Ozdemir     Department of Computer Engineering, Hacettepe University, Ankara, Turkey

Anirudha Paul     Department of Ecological Studies & International Center for Ecological Engineering, University of Kalyani, Kalyani, West Bengal, India

Avedananda Ray     Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN, United States

Cyril Ray     The Naval School, IRENAV, Brest, France

Mouni Roy

Department of Chemistry, National Institute of Technology Durgapur (NITD), Durgapur, West Bengal

Department of Chemistry, Banasthali University, Banasthali, Rajasthan, India

Ritabrata Roy     Hydro-Informatics Engineering Department, National Institute of Technology Agartala, Agartala, Tripura, India

Rajnarayan Saha     Department of Chemistry, National Institute of Technology Durgapur (NITD), Durgapur, West Bengal, India

Palas Samanta     Department of Environmental Science, Sukanta Mahavidyalaya, Dhupguri, West Bengal, India

N. Santhi     Department of Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India

Tarun Sasmal     Department of Geography, Panskura Banamali College, Purba Medinipur, West Bengal, India

Kamalesh Sen     Environmental Chemistry Laboratory, Department of Environmental Science, The University of Burdwan, Bardhaman, West Bengal, India

Tarakeshwar Senapati     Department of Environmental Science, Directorate of Distance Education, Vidyasagar University, Midnapore, West Bengal, India

Ramesh Sivanpillai     Department of Botany, University of Wyoming, Laramie, WY, United States

Stephen Snow     University of Queensland, Brisbane, QLD, Australia

P. Subashini     Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India

Yehia Taher     DAVID Lab, University of Versailles Saint-Quentin-en-Yvelines, Paris-Saclay University, Versailles, France

Laurent Yeh     DAVID Lab, University of Versailles Saint-Quentin-en-Yvelines, Paris-Saclay University, Versailles, France

Karine Zeitouni     DAVID Lab, University of Versailles Saint-Quentin-en-Yvelines, Paris-Saclay University, Versailles, France

Preface

Siddhartha Bhattacharyya, CHRIST (Deemed to be University), Bangalore, India

Naba Kumar Mondal, The University of Burdwan, Burdwan, India

Jan Platos, VŠB - Technical University of Ostrava, Czech Republic

Václav Snášel, VŠB - Technical University of Ostrava, Czech Republic

Pavel Krömer, VŠB - Technical University of Ostrava, Czech Republic

Efficient monitoring of environmental data for the purpose of pollution management has been at the forefront of affairs given the vast amount of pollutants injected into the environment due to industrial affluent. The state-of-the-art methods rely on time-intensive and costly batch procedures. As a result, environmental scientists and researchers are engaged in devising newer, cost-effective methods. The latest additions in this direction are inspired by computational intelligence that relies on intelligent tools like neural networks and evolutionary intelligence.

This book focuses on evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. It may be noted that there are many potential applications and prototypes in this field, yet very few real-life applications are known so far. This is mainly due to the fact that most pollution management procedures still rely on the time-consuming batch processes and field study. Thus, this book will usher in a new era as far as environmental pollution management is concerned. This book reviews the fundamental concepts of gathering, processing, and analyzing data from batch processes, supplemented by a review of intelligent tools and techniques that can be used in this direction. This book also covers novel intelligent algorithms for the purpose of effective environmental pollution data management at par with the standards laid down by the World Health Organization.

The book is organized into 13 well-versed chapters on different aspects of environmental data monitoring, both traditional and intelligent.

The batch adsorption process remains one of the most practical approaches used to adsorb pollutants from a liquid solution for the purification of water. Chapter 1 presents a literature survey of different adsorption types with their characteristics, including an overview of different factors influencing the batch process. This chapter also helps to understand the removal mechanism of the batch process through bulk solution transport, film diffusion transport, pore transport, and adsorption on available adsorption sites.

The term heavy metal is commonly envisaged to be those whose density surpasses 5 g/cm³. An enormous number of elements belong to this group, but Cd, Cr, Cu, Ni, As, Pb, Hg, and Zn are those of relevance in the environmental context. Heavy metals cause serious health issues such as organ injury, nervous system impairment, reduced growth and development, cancer, and in extreme cases, death. Therefore, it is essential to treat metal-contaminated wastewater prior to its discharge into the environment. Among the various types of treatment processes in vogue, adsorption is acknowledged a financially cheap practice for heavy metal expulsion from wastewater as it is economical, simple to deal with, and exceedingly productive. Chapter 2 explains the adsorption technique by using biochar, which is a carbon-rich item obtained when biomass, for example, wood, fertilizer, or leaves, is heated in a shut vessel with next to zero accessible oxygen. In a more methodical term, biochar is obtained by thermal disintegration of organic material under partial or no source of oxygen (O2) and at comparatively low temperatures (< 750°C). The use of biochar in industrial wastewater can potentially diminish the heavy metal’s mobility due to the porous assemblage, large surface area, and high adsorption capacity of biochar.

Nanoscience is an interdisciplinary subject that has been one of the most dynamic disciplines in material science. Key features of nanoparticles are clusters of atoms in the size range of 1–100 nm. Metal nanoparticles can be synthesized by physical, chemical, and biological routes. But the green or biological route for nanoparticle synthesis from biological origin is of more interest than the other two ways because of its environmental friendliness, economically cheap, feasibility, and applications in various fields. Several analytical tools are used for structure determination and characterization of synthesized nanoparticles. Chapter 3 focuses on the power of biomolecules for the synthesis of silver, gold, zinc, and copper nanoparticles and their applications toward the effect on mosquito larvicidal mortality.

The generation of heavy metal(loids) and their derivative compounds (such as oxides, carbides, sulfides, etc.) in increasing rates by various anthropogenic activities is of major global concern. To control these problems, the removal of metal(loids) from effluents is essential. Although several physicochemical methods (adsorption, electrodialysis, floatation, ion exchange) are available, the effectiveness of these methods are poor and of high cost. Therefore, eco-friendly and green technology is required in this regard. Among various green synthesis techniques, biosorption drew great attention for scientific research as a novel method for industrial wastewater treatment to reduce or neutralize metal content. Chapter 4 reviews the state-of-art works carried out in this regard by different scientists. Different parameters of biosorbent materials along with optimum treatment conditions have also been considered in this chapter. This can be helpful for future studies in exploring novel biosorbents to treat industrial wastewater using biosorption technology.

Glyphosate [N-(phosphonomethyl) glycine] pollution, mainly due to industrial drainage and unnecessarily used for weed control of agricultural and residential purposes, creates ecosystem and environmental toxins. There are very important research fields needed for decontamination in a sustainable way. In Chapter 5, a review on the adsorption process of glyphosate from aqueous solution is reported with influencing mechanisms like kinetics, isotherms, and thermodynamics. Reported results are depicted by consequence factors, namely pH, contact time, initial taken concentration, doses, etc.

Today, dyes, highly colored organic compounds, find widespread applications in industries like textile, food technology, leather tanning, paper, etc., for coloring products, light-harvesting solar-cells, photoelectrochemical cells, etc. However, the large usage of these dyes leads to their release as effluents, which eventually contaminate drinking water sources, hampering the ecological system. The contaminated wastewater effluent exhibits detrimental effects related to people and environmental concerns. Thus, a requirement for a suitable, cost-effective treatment method for contaminated wastewater seems to be indispensable. Chapter 6 aims to compose various information about dyes, dye effluents, and existing treatment technologies for wastewater.

Water is an essential natural resource for the survival of living beings in the world. Freshwater bodies significantly support ecological and economic activities in each state or country, especially India, which has a large number of rivers, lakes, ponds, and wetlands. Notably, lake water is critically polluted due to industrial effluents, encroachment, eutrophication, garbage dumping, poor drainage, and climate changes that have a severe effect on water quality. Hence, monitoring and assessment of water quality over time is important for minimizing the negative effects to the ecosystem, and it greatly helps to sustain valuable aquatic species. In Chapter 7, an intelligent estimation model is developed using the Kalman filter integrated with a Synergistic Fibroblast Optimization (SFO) algorithm for forecasting water quality parameters. The developed predictive model is evaluated with real-time water samples collected from Ukkadam Periyakulam Lake, Coimbatore, and the promising results provide qualitative information to enhance water quality in this lake.

Many countries are suffering from air pollution due to imbalanced urbanization, unregulated increase in transportation, and inorganic industrialization. Air pollution directly affects the ecosystem and climate thereby compromising the well-being of citizens and cities. For this reason, predicting air pollution in advance has great importance in supporting proactive plans and environmental management actions for decision-makers. In the literature, many research efforts focus on predicting air pollutant concentrations. In Chapter 8, the authors present recent artificial intelligence-based air pollution prediction approaches. In this context, the authors present a comprehensive review of the literature and have categorized the proposed prediction methods based on feature selection methods, air pollutant types, and learning models.

Chapter 9 examines the equilibrium adsorption of carbaryl insecticide by several easily available natural adsorbents like Alluvial soil, Pistia Stratiotes biodust, Lemna major biodust, Neem Bark dust (NBD), and Eggshell powder to remove carbaryl from an aqueous environment. The batch experiments are archived out as different operating parameters. For overall validity of the process, response surface (RSM) and artificial neural network (ANN) models are used. The maximum amount of carbaryl adsorbed onto different adsorbents is presented in this study. Adsorption isotherms, kinetics, and thermodynamics of the process have also been provided. Finally, results show that NBD has the maximum adsorption capacity of carbaryl. As NBD is easily prepared, it can be a used as an effective adsorbent for removal of the insecticide from contaminated water.

Biochemical oxygen demand (BOD), representing the biodegradable organic load in water, is a prime parameter to assess water quality. Estimation of BOD requires prolonged incubation of water samples. Thus, it is a time-consuming as well as energy-consuming process. Therefore, it is not possible to respond rapidly for mitigation if the BOD level goes beyond the permissible limit. In Chapter 10, a study is presented on River Damodar; the BOD value is predicted from electrical conductivity (EC), turbidity, and chloride, using an artificial neural network (ANN)-based empirical model. Because predictor parameters of these models are rapidly measurable, the BOD value can also be quickly predicted. As all the predictor parameters are highly correlated (correlation coefficient, 0.9 or more) with BOD, the models are valid for prediction of BOD. Additionally, for further validation of the models, a portion of field data (20%) was used for model testing. As the model predictions are close to the actual values (r = 0.98, MAE = 0.43, RMSE = 0.57), the model, developed in this study, can be considered as successful in BOD prediction.

The effective monitoring and management of indoor-sourced pollutants is vital, given that poor indoor air quality (IAQ) reduces the academic performance of school students and contributes to short- and long-term health effects. Despite this, 66% of classrooms are found to exceed UK government IAQ guidelines, and there is not yet a requirement for real-time IAQ monitoring in UK classrooms. Chapter 11 describes the design and deployment of a visual iPad display of classroom IAQ called Airlert. Findings inform a discussion of which visual aspects of IAQ feedback devices hold the best potential for empowering teachers to improve understanding of IAQ in their classroom, to employ healthier ventilation practices. Implications for the design of IAQ feedback devices and necessary data management considerations are discussed, along with suggestions for future research.

The advent of the new generation of low-cost, lightweight, and connected sensors makes a paradigm shift in environmental studies. In particular, nomadic sensors allow for a very precise personalized measurement, by continuously quantifying the individual exposure to air pollution components. Moreover, a broad dissemination among volunteers of these devices, or their deployment on vehicle fleets, is becoming a credible solution. Another major interest of such sensor deployment is to densify the air quality monitoring network, indoor, as in the outdoor, which is today restricted to sparse nodes. However, this high spatiotemporal resolution raises several issues related to their analysis. After an overview of the projects relying on this technology, Chapter 12 points out the remaining challenges to be addressed. Part of these challenges constitute the research program of the ongoing project Polluscope in France.

In Chapter 13, the authors aim to tabulate the statistical repertoire used to predict adsorption of contaminant from an aqueous solution. The effects of various parameters like pH, temperature, adsorption time, initial concentration, and adsorbent dosage have been analyzed, and their optimization by an ANN, RSM, and Path analysis model has been presented. A comparison of predictions of the experimental data with the adsorption efficiency shows the sustainability of the different models of the adsorption process under different conditions.

This book is envisaged to offer benefits to researchers and several categories of students for some part of their curriculum. The editors would feel good about their attempt if the academic and scientific community finds benefit from this novel venture.

1: Batch adsorption process in water treatment

Jinat Aktar    Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, Assam, India

Abstract

The batch adsorption process is one of the most practical approaches used to adsorb pollutants from the liquid solution for the purification of water. This chapter presents literature of different adsorption types with its characteristics, including an overview of different factors influencing the batch process. This chapter also helps to understand the removal mechanism of the batch process through bulk solution transport, film diffusion transport, pore transport, and adsorption on available adsorption sites. Several experimental studies and mathematical modeling have been used to optimize the factors affecting the efficiency of batch process. Here, mathematical models of the pseudo-first-order and second-order kinetics model, Eovich's model, and the adsorption isotherm involve the concept of batch adsorption infinite bath and infinite bath.

Keywords

Batch process; Adsorption isotherm; Kinetics; Thermodynamics

Acknowledgments

The author would also like to acknowledge Mr. Abhishek N. Srivastava, research scholar IIT Delhi, and Mr. Sounak Bhattachariya, research scholar IIT Guwahati, for checking the draft.

Funding

The author wants to thank the Department of Science and Technology (DST)-INSPIRE, Government of India for providing fellowship no. DST/INSPIRE Fellowship/2016/ IF 160093.

1: Introduction

Wastewater gets released from different types of industries and is a significant source of different heavy metals, dyes, detergents, and other contaminants that can consume dissolved oxygen of accommodating water stream, can alter chemical and biological characteristics of the water, and poses environmental hazards by endangering ecosystems and human health [1]. Hence, wastewater treatment is prerequisite before its discharge into the ecosystem. Although there are multiple water treatment options are being used frequently, adsorption process is still unrivaled because of its effectiveness, low energy consumption, and easiness to perform; therefore, the versatility of adsorption makes it extensively used while removing contaminants from wastewater [2]. Mainly, the adsorption process is used for the removal of a highly toxic or low concentrated compound, which is not readily treated by biological processes [3]. The batch process is promptly used for adsorption treatment of wastewater. The batch process occurs in a closed system containing the optimum amount of adsorbent in contact with the predetermined volume of adsorbate solution. In a closed vessel, agitation is provided by rotating stirrers for the full mixing of adsorbent materials with the contaminated solution. Well-designed batch processes are highly efficient, which results in a high-quality recyclable effluent after treatment (Fig. 1). Moreover, batch adsorption process can be cost effective if low-cost adsorbents are used or regeneration is feasible [4]. Furthermore, the batch adsorption process can also be used for abatement of pollutants at source and quality improvement of industrial and other wastewater [5].

Fig. 1 Scheme of adsorption of pollutants by the batch process.

Literature in Table 1 shows that different heavy metals like cadmium, copper, lead, chromium present in wastewater are treated by the batch process (Table 1). The batch process can be used for the treatment of textile discharge containing different dyes, detergents, and other contaminants [5,34] and also used in the pharmaceutical industrial water treatment like acetaminophen and ibuprofen removal [2].

Table 1

The purpose of this study is to develop the detailed view of the batch process and its affecting factors, mechanism of pollutant removal in the batch process, the significance of various mathematical models to predict the performance of the batch process, and analyze the surface nature and pore size of the adsorbent. This information could help to optimize the factors for the efficient removal of pollutants in the batch process.

This book chapter is organized in multiple sections: beginning with introduction, which discussed the problem caused by wastewater, role of adsorptive removal while treating wastewater, various adsorbents used for removal of particular contaminants from wastewater. Further, Section 2 comprised the concepts of batch experiments along with its working principle(s), which were discussed in detail. Moreover, types of adsorption were also covered in brief in this section. Section 3 focused mainly on factors influencing adsorption process. The detailed information discussed here may help in optimizing the factors to achieve maximum removal of pollutants from wastewater. One of the most important aspects of adsorption process is mechanism followed during the adsorption, which is discussed in Section 4. Here, bulk diffusion transport, pore transport, film diffusion transport, and surface diffusion were summarized. The investigation of theoretical uptake capacity of the material is obligatory; therefore, various kinds of adsorption isotherms, including Langmuir, Freundlich, Sips, Brunauer Emmett and Teller (BET), and Extended Langmuir models are discussed in Section 5. The mechanism of adsorption could be well understood by adsorption kinetics; therefore to study the contaminant removal rate, the pseudo-first-order, pseudo-second-order, and Elovich model, intraparticle diffusion model and kinetics of finite bath and infinite bath experiments are illustrated in Section 6. In Section 7, significance of thermodynamics in adsorption reactions is mentioned. Lastly, chapter concludes with providing briefs regarding regeneration capacity of adsorbent and its recyclability after adsorption in Section 8. Various desorption studies are also presented in tabular format.

2: Batch experiments

In the batch tank, adsorbent is mixed with wastewater for a predetermined time and pH; after that, the final concentration of adsorbate is measured. After the batch process, separation of the solution is done by either centrifugation, sedimentation, or filtration process. In the Industrial area, multiple batches or crossflow systems are established, and large quantities of adsorbent are required. In industry, the batch process is easily scaled up-scale or scale-down, depending on requirements. At the end of the steps, a finite amount of the final product is produced, and after desorption, the adsorbent can be used for another batch process sequence. In the batch tank, adsorbent comes with contact with pollutants present in solution and adsorption occurs. Two types of adsorption may happen: physical adsorption and Chemisorption. In physical adsorption, Van der Waals forces work as a force of attraction, and the resulting adsorption is reversible. In Chemisorption, strong chemical bonding acts as a force of attraction, and this adsorption is irreversible. In Fig. 2, the framework of batch process is described properly. In a properly designed batch tank, the adsorbent can be reused after the desorption process.

Fig. 2 The framework of pollutant removal by the batch process.

3: Factors affecting adsorption process

pH: pH is one of the most significant parameters that can directly affect the removal efficiency of pollutants or adsorbate by adsorbents [35]. pH can affect the ionization as well as the speciation of adsorbate in solution and also the surface nature of adsorbent material. In solution, hydrogen ions (H+) and hydroxide ions (OH−) interact with activated site of adsorbents. Hence, the adsorption process gets affected by the pH of the solution.

Fig. 3 shows variation in chemical speciation of chromium with respect to pH, which also affects the batch process. Redox potential (Eh) plays an essential role in the conversion of different valence states of chromium [37]. In lower pH, Cr(VI) has a high positive redox potential value, which acts as a strong oxidizing agent. Cr(OH)² + is the principal species at pH 5, although Cr(OH)3 present at pH 8. The chromate ion is present at pH > 7, though CrO4− 2 is dominating species at pH < 6 [38]. In case of Cu(II) removal, the effect pH was measured in 2 to 6 pH range, because above pH 6 precipitation of Cu(OH)2 occurs, which causes sharp drop of Cu(II) removal [33,39].

Fig. 3 Speciation of chromium in water environment [36 , 37] .

pHzpc: Surface charge of adsorbent plays an important role in the batch processes and also helps to understand the sorption mechanism. The pH at which the surface charge of the adsorbent is zero, considered as the point of zero charges (pHpzc). This allows hypothesizing that below the point of zero charges, the material surface is positive, and it can adsorb negatively charged pollutants [40].

Among several techniques of the determination of pHpzc of adsorbent, immersion technique and mass technique have been used widely [22]. The plot shown in Fig. 4 for initial pH versus delta pH and the point zero charge value by the immersion process was found to be at pH 4.7. The pHzpc value of the mass titration technique was 4.6. The values of both techniques are quite close; therefore, the value of pHpzc was relevant. The electrostatic attraction between oppositely charged adsorbent species increases the rate of adsorption.

Fig. 4 A plot of immersion technique and mass titration technique.

Adsorbent dose: The active adsorption sites increase with increase in the adsorption dose, which helps in more removal of contaminant(s). However, with the increase of the adsorption dose, the total uptake of pollutants (qe) per unit mass of an adsorbent reduces due to the unsaturated site that exists in adsorption process. Literature showed increase of removal percentage of copper (II) from 47% to 87% with the rising dose from 0.5 to 8 g/L; however, the uptake capacity decreased from 9.6 to 1.1 mg/g [39]. Baral et al. examined the effect of sawdust dose on chromium (VI) removal and found that the percentage removal increased from 20% to 100% with dose of 0.2 to 1.6 g/L, though the Chromium adsorption capacity was reduced from 2.72 to 1.7 mg/g [41]. Furthermore, the increase in removal percentage of copper (II) from 47% to 87% with the rising dose from 0.5 to 8 g/L was reported by Aydın et al., but the adsorption capacity was reduced from 9.6 to 1.1 mg/g [39].

Temperature: In the batch process, the temperature can affect the characteristics of adsorbents, the stability of adsorbate, and adsorbate-adsorbent interaction. With the rising temperature, the viscosity of the solution decreases, which helps in the transfer of pollutants from bulk solution to the surface of material [42]. The thermodynamic parameters help to estimate the characteristic of the batch adsorption process such as exothermic or endothermic, spontaneous or random nature and also signifies the favorability of temperature in the batch adsorption process. The spontaneity of adsorption is accounted by the negative values of Gibbs free energy (ΔG⁰) and the enthalpy (ΔH⁰) indicates the nature of the process whether it is exothermic or endothermic process.

According to Le chatelier's principle, the magnitude of adsorption decreases with increase in temperature. In Chemical adsorption, the removal first rises with increasing temperature then decreases (Fig. 5). Malana et al. [43] studied that with rising temperature, the adsorption capacity of polymeric gels decreases, indicating the exothermic nature for Bromophenol blue, Malachite green, and, Methylene blue dye removal.

Fig. 5 Effect of temperature on (A) physical adsorption and (B) chemical adsorption.

Pressure: In Adsorption Isotherm, with the increasing pressure, adsorption also increases up to a certain level, until the saturation of adsorbent occurs. After reaching the equilibrium level, no more uptake takes place no matter how high the pressure is applied. In Fig. 6, Ps is the saturation pressure; after that the graph becomes linear, as further adsorption of pollutants is not possible.

Fig. 6 Effect of pressure on batch adsorption.

Hu et al. [44] studied the effect of adsorbate pressure (methylene blue concentration) and concluded that with the rising methylene blue concentration from 100 mg/L to 140 mg/L, the adsorption capacity increased from 137 to 139 mg/g, so it reached its