eChapters

Small ruminants are grazing mammals and this group consists of sheep (Ovis aries) and goats (Capra hircus).Advantage of these animals is their low cost, small size and marginal land use, which convert into high quality animal products. Sheep is a grazing animal and goat is a browsing animal. They are generally kept in flock. Therefore flock management is more important than the individual animal. Though, procedure of history taking and physical examination is almost similar to other animals, some peculiarities warrant a separate discussion. Sheep The general methods of history taking and physical examination of sheep are similar to cattle and goat starting from owner’s complaint (history), signalment of the patient, history of the ailing

History and History Taking History in medical/ veterinary terminology is a record of information about the health, illness (past and present), diet, feeding, management,preventive health schedules (vaccination,deworming, dipping, sprays) and hygiene etc. History taking and physical examination is the important step in making a correct diagnosis as it provides valuable information to determine the cause of clinical manifestations. The main purpose of taking history is to get detailed information about the animal, its activities, behavior, diet, health care practices, nutritional status, housing, development of abnormal signs and treatment given, if any. The animal may be cared by owner himself or by an attendant. In the event of animal care given by an attendant, information obtained from the attendant may not be complete and fully reliable as sometimes attendant may not provide correct events and complete picture to hide his mistakes. For obtaining right information a good rapport is to be made with the owner or the attendant. A complete history not only suggest diagnostic possibilities but can also suggest diagnostic probabilities. For example a calf or heifer is unlikely to have Johne’s disease or an adult cow is more likely to suffer from parturient paresis than a first calf heifer. History can also provide a lead for further examination or which system is to be paid special attention.

Client communication is the key to success in practice as it is an important skill to narrow down the gap between owner of the animal and the veterinarian. In other words, it is a meaningful exchange of information between attending veterinarian and the owner of the ailing animal for improving health care outcome. In client centered world (where client satisfaction is in the core of practice), communication with client  has assumed greater significance whether it is related to educating him about preventive health care; explaining about disease diagnosis, prognosis and treatment options; or explaining about chronic disease condition and its outcome. Communication also influences the wellbeing of the patient. Therefore, development of trust in client-veterinarian relationship is crucial as it not only enhances the quality of history taking but also help in better clinical diagnosis and subsequent clinical care. Importance of Communication •     Client experience and feedback is important for providing good clinical

MANIFESTATIONS OF URINARY SYSTEM DISEASES Urinary tract diseases in farm animals  are generally clinically characterized by        pain    and        difficult    urination    (dysuria);    alterations    in    urine    volume    and    its composition; rupture of the renal pelvis, bladder and urethra; and inadequacy of nervous control of urination. Clinical Signs • Dysuria and abdominal pain are common expression of discomfort owing to urinary tract diseases. Acute abdominal pain is rarely seen in cattle due to sudden distension of renal pelvis (pyelonephritis), ureter or renal infarction. Abdominal pain from urethral obstruction leading to distension of urinary bladder is clinically manifested by tail switching, kicking at belly, straining at urination and grunting. Horses with acute tubular nephrosis may show signs of renal colic (arching back, rubbing perineum and tail head). Dysuria (frequent passage of small amounts of urine with pain) is associated with cystitis, vesicle calculus and urethritis • Uremia is commonly seen in cases of acute renal failure and is manifested

MANIFESTATIONS OF GASTROINTESTINAL TRACT DISEASES Diseases of gastro-intestinal tract may be acute or chronic. Dehydration and shock are the major effects of acute and hyper acute diseases of gastrointestinal tract respectively. While major effect of chronic dysfunction of gastrointestinal tract is exhaustion from lack of food and water owing to loss of appetite (anorexia,). Other manifestations of gastro-intestinal tract diseases are abnormalities of prehension, mastication and swallowing; salivation; diarrhea: constipation and tenesmus; ileus; vomiting; hemorrhage; abdominal pain; abdominal distension; and shock and dehydration. Abnormal Prehension • Prehension   abnormalities may arise from paralysis of jaw muscles and tongue, dentition defects, mouth pain

HYPERTHERMIA Hyperthermia is a condition characterized by elevated body temperature due to increased heat production or absorption, or reduced heat loss   owing to physical cause. Etiology • Physical causes such as exposure to high environmental temperature, severe and prolonged physical activity especially in hot humid condition or    animals    confined    with    poor    ventilation. • Spontaneous hemorrhage in hypothalamus can cause neurogenic hyperthermia. • Severe dehydration affects the heat loss by evaporation and thus can lead to hyperthermia. • Too much muscular activity.

The concept of health and diseases is essential to veterinary medicine. Understanding    of    such    concepts    provides    the    basis    of    disease    classification. But     these     concepts     are     rarely     given     precise     definition     in     the     textbooks     of veterinary medicine. HEALTH Health is generally considered as absence of the disease. But it is an incomplete definition.    The    concept    of    health    can    be    split    into    five    categories    viz.    health as normality, health as biological function, health as homeostasis, health as physical and psychological wellbeing, and health as productivity including reproduction. Therefore an animal is stated to be healthy only when it is in a complete state of physical, social and mental wellbeing and not

The word “Veterinary” has originated from Latin “veter?n?rius” concerning draught animal and “Medicine” from Middle English, Middle French, Old French and Latin words meaning “healing”. Veterinary medicine is the offshoot of the medicine dealing with the diagnosis, treatment and prevention of diseases/disorders of all animal species including domestic, wild and exotic, companion, draught and pack animals. CONTRIBUTION OF VETERINARY MEDICINE • Reduction in number of cases of tuberculosis and brucellosis in human. • Development of safe and effective vaccines to prevent and control of animal diseases such as Rinderpest, Foot and mouth, Hemorrhagic septicemia, Black quarter,  Pox, Parvo, Distemper,

Financial data visualization is the process of turning financial information into visual formats like charts, graphs, and dashboards. It helps finance teams interpret large datasets quickly, spot patterns or anomalies, and make betterinformed business decisions. Data visualization in finance is a craft of transforming complex financial numbers into accessible visual forms. Visual formats such as charts, graphs, dashboards, maps, and interactive displays serve as lenses through which data becomes visible and understandable. Finance practitioners navigate risk profiles, asset performance, expense trends, cash flows, and portfolio behavior more effectively when raw inputs become visual stories. Creation of visual representations hinges on careful selection of chart types, scaling, color, and annotation. Communicating precise meanings through visuals demands an understanding of data relationships, temporal changes, distributions, and comparisons.

Financial modeling using Python has emerged as a highly effective approach for modern finance, combining the flexibility of programming with the analytical rigor required for accurate forecasting and decision-making. Python is particularly valuable because it can handle large datasets, automate repetitive tasks, and integrate advanced statistical and machine learning techniques into financial models, surpassing the limitations of traditional spreadsheet-based approaches. Its extensive libraries, such as pandas, NumPy, scikit-learn, and matplotlib, make it an ideal tool for building robust and dynamic financial models. The foundation of financial modeling in Python begins with data acquisition and preprocessing. Financial data is often sourced from databases, APIs, or CSV files, and Python provides efficient tools to import and clean this information. The pandas library allows analysts to create dataframes, which are flexible structures for organizing, filtering, and transforming data. Preprocessing tasks, such as handling missing values, normalizing variables, and converting date formats, ensure that datasets are ready for accurate modeling. This step is critical, as errors or inconsistencies in the data can lead to incorrect forecasts and flawed financial decisions. Once the data is prepared, Python enables comprehensive exploratory data

SQL, or Structured Query Language, provides a powerful framework for querying, aggregating, and transforming financial data stored in relational databases, which is essential for constructing accurate and dynamic financial models. Unlike spreadsheet-based models, SQL enables automation, repeatability, and scalability, making it particularly useful when dealing with complex datasets or real-time financial information. The first step in financial modeling with SQL is understanding the structure of the database. Financial data is often stored across multiple tables, including transactions, accounts, customers, and market data. Effective financial modeling requires familiarity with relational database concepts such as primary keys, foreign keys, and normalization. By using SQL joins, analysts can merge these tables to create a unified dataset that captures all necessary information, such as revenues, expenses, and cash flows, which are essential for forecasting and valuation exercises. Data extraction in SQL

Financial modeling is one of the most highly valued, but thinly understood, skills in financial analysis. The objective of financial modeling is to combine accounting, finance, and business metrics to create a forecast of a company’s future results. A financial model is simply a spreadsheet, usually built in Microsoft Excel, that forecasts a business’s financial performance into the future. The forecast is typically based on the company’s historical performance and assumptions about the future and requires preparing an income statement, balance sheet, cash flow statement, and supporting schedules (known as a three-statement model, one of many types of approaches to financial statement modeling). Financial modeling skills are essential for anyone pursuing a career in finance. Models synthesize a vast amount of information to help users make better investment and business decisions. The modeler’s technical acumen

Dashboards play a crucial role in the fields of Machine Learning and Artificial Intelligence by providing a visual and interactive interface through which complex data, model outputs, and performance metrics can be easily understood and monitored. In data-driven environments, where vast amounts of information are processed continuously, dashboards serve as an essential bridge between technical systems and human decision-making. They transform raw data and analytical results into meaningful visual representations, enabling users to interpret insights quickly and effectively. In the context of Machine Learning and Artificial Intelligence, dashboards are used at multiple stages of the workflow. During data exploration and preprocessing, dashboards help visualize trends, patterns, and anomalies in datasets, allowing data scientists to make informed decisions about feature selection and data cleaning. Once models are developed, dashboards

A machine learning model is a program that can find patterns or make decisions from a previously unseen dataset. For example, in natural language processing, machine learning models can parse and correctly recognize the intent behind previously unheard sentences or combinations of words. In image recognition, a machine learning model can be taught to recognize objects - such as cars or dogs. A machine learning model can perform such tasks by having it ‘trained’ with a large dataset. During training, the machine learning algorithm is optimized to find certain patterns or outputs from the dataset, depending on the task. The output of this process - often a computer program with specific rules and data structures - is called a machine learning model. Machine Learning Model is a computational program that learns patterns from data and makes decisions or predictions on new, unseen data. It is created by training a machine learning algorithm on a

Data cleaning is one of the most important parts of machine learning. It plays an important role in building a machine learning model. Data quality is a significant aspect to train the ML model. Inaccurate data can have an impact on results. Data quality problems can occur anywhere in information systems. A technique that helps to convert improper data into meaningful data. Machine Learning is datadriven. With the data cleaning techniques, your Machine Learning model will perform better. So, it is important to process data before use. Without quality data, it is unwise to expect a correct output. Data cleaning refers to identifying and correcting errors in the dataset that may negatively impact a predictive model. It is used to refer to all kinds of tasks and activities to detect and repair errors in the data. These problems can be solved by using various data cleaning techniques. The process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data

SQL, or Structured Query Language, is a fundamental component in the domains of Machine Learning and Artificial Intelligence, serving as a powerful tool for managing, retrieving, and manipulating data stored in relational databases. In an era where data drives intelligent systems, SQL plays a crucial role in preparing and organizing datasets that form the backbone of machine learning models and AI applications. Its ability to efficiently handle large volumes of structured data makes it indispensable for data scientists, analysts, and engineers working in these fields. Originally developed in the 1970s at IBM by researchers Donald D. Chamberlin and Raymond F. Boyce, SQL was designed to provide a standardized way to interact with relational databases. Over time,

Python has emerged as one of the most popular and versatile programming languages in the contemporary world of computing. Developed by Guido van Rossum and first released in 1991, Python was designed with a clear emphasis on simplicity, readability, and ease of use. Its syntax closely resembles natural language, making it an ideal choice for beginners as well as experienced programmers. Over the years, Python has evolved into a powerful, high-level language that supports multiple programming paradigms, including procedural, object-oriented, and functional programming. One of the defining features of Python is its readability and clean structure, which allows developers to write clear and concise code. Unlike many other programming languages that rely heavily on complex syntax and punctuation, Python uses indentation to define code blocks, promoting better organization and reducing the likelihood of errors. This design philosophy not only enhances code clarity but also encourages good programming practices,

Workflow Optimization is the process of improving the way work is done by making it faster, more efficient, and more organized. In every organization, work is completed through a series of steps. These steps together form a workflow. If the workflow is not properly organized, it can lead to delays, errors, confusion, and increased costs. Workflow optimization helps in improving these processes so that work can be completed in less time, with fewer errors, and with better results. In the modern digital world, workflow optimization has become very important because organizations are handling large amounts of data and complex tasks. With the help of AI tools and automation software, workflows can be optimized and automated. Workflow optimization

Task Automation is one of the most important applications of Artificial Intelligence and modern digital technology. It refers to the process of using technology, software, or AI tools to perform repetitive and routine tasks automatically without continuous human involvement. In many workplaces, people spend a large amount of time doing repetitive tasks such as sending emails, entering data, preparing reports, scheduling meetings, organizing files, and updating records. These tasks are necessary but time-consuming. Task automation helps in completing these tasks automatically, allowing people to focus on more important and creative work. In the modern digital world, automation has become an essential part of offices, businesses, education systems, banks, hospitals, and even personal life. Earlier, automation was limited to industries and manufacturing, but now automation is used in office work, communication, marketing, research, and data management. With the help of AI tools, automation has become smarter

Artificial Intelligence (AI) tools have rapidly transformed the way we work, learn, communicate, and solve problems in the 21st century. From automating routine tasks to assisting in complex decision-making, AI tools are becoming an essential part of modern life and professional environments. These tools use technologies such as machine learning, natural language processing, computer vision, and data analytics to perform tasks that traditionally required human intelligence. As a result, AI is not only increasing productivity but also changing the nature of work across industries. AI tools are software applications or platforms that use artificial intelligence to perform specific functions such as content creation, data analysis, customer support, language translation, image generation, video editing, coding assistance, and workflow automation. Today, AI tools are used in education, healthcare, business, marketing, finance, engineering, journalism, and many other fields. For example, students use AI tools for research, writing, and learning new concepts; businesses use AI for customer service chatbots, data insights, and marketing automation; and designers use AI for image and video creation.

Business efficiency refers to the ability of an organization to use its resources— such as time, money, labor, technology, and materials—in the most effective way to achieve maximum output with minimum waste. In simple terms, business efficiency means doing work in the best possible way with the least amount of effort, cost, and time while maintaining high quality. Efficiency is not only about working faster; it is about working smarter. A business is considered efficient when it produces more results with fewer resources. For example, if a company can produce 100 products in 5 hours instead of 8 hours by using better machines or automation, then the company has improved its efficiency.

AI-based content creation refers to the use of Artificial Intelligence technologies to generate, edit, optimize, and publish content automatically or semi-automatically. Content can include text, images, videos, audio, presentations, reports, emails, and social media posts. AI content creation tools use technologies such as Machine Learning, Natural Language Processing (NLP), Computer Vision, and Deep Learning to create content that looks like it was created by humans. In the modern digital world, content plays a very important role in education, business, marketing, communication, and entertainment. Earlier, content creation was completely manual and required a lot of time, effort, and human resources. Writers, designers, editors, and analysts had to work for many hours to create a single report, article, or presentation. But now, with the help of AI tools, content can be created in minutes instead of hours

Productivity automation refers to the use of technology, software, and artificial intelligence to perform tasks automatically with minimal human effort. The main goal of productivity automation is to save time, reduce manual work, increase efficiency, and improve the quality of output. In simple words, productivity automation means using machines and digital tools to do work faster and more accurately. Productivity automation is used in many areas such as business operations, communication, data management, marketing, education, and personal productivity. For example, sending automatic emails, scheduling meetings, generating reports, managing tasks, and analyzing data are all examples of productivity automation. The concept of productivity automation is based on the idea that repetitive and routine tasks should be handled by machines so that humans can focus on creative,

Cost analysis enables organizations to understand cost behavior, improve efficiency, and support strategic decision-making. When integrated with data analytics, cost analysis becomes more precise, data-driven, and adaptable, allowing firms to optimize resource allocation and enhance profitability. The process begins with the collection and classification of cost data. Costs are typically categorized into fixed, variable, and semi-variable components. Fixed costs remain constant regardless of production levels, while variable costs fluctuate with output, and semi-variable costs exhibit characteristics of both. Data analytics tools assist in organizing and cleaning large volumes of financial data from accounting systems, ensuring accuracy and consistency. Proper classification is essential, as it forms the basis

efficiency, and support strategic decision-making. When integrated with data analytics, cost analysis becomes more precise, data-driven, and adaptable, allowing firms to optimize resource allocation and enhance profitability. The process begins with the collection and classification of cost data. Costs are typically categorized into fixed, variable, and semi-variable components. Fixed costs remain constant regardless of production levels, while variable costs fluctuate with output, and semi-variable costs exhibit characteristics of both. Data analytics tools assist in organizing and cleaning large volumes of financial data from accounting systems, ensuring accuracy and consistency. Proper classification is essential, as it forms the basis for meaningful cost analysis and modeling.q

Budgeting is a fundamental component of financial modelling programs and data analytics, serving as a structured approach to planning, allocating, and controlling financial resources within an organization. It involves estimating future revenues and expenses based on historical data, current trends, and strategic objectives. When combined with data analytics, budgeting becomes more dynamic, accurate, and responsive, enabling organizations to make informed financial decisions and adapt to changing conditions. The budgeting process begins with data collection and preparation. Historical financial data, including past revenues, operating costs, and capital expenditures, is gathered from various sources such as accounting systems and databases. Data analytics tools help clean, organize, and validate this information, ensuring consistency and reliability. This step is crucia

Financial modeling programs integrated with data analytics using SQL provide a strong foundation for managing, analyzing, and transforming financial data in a structured and scalable manner. SQL, or Structured Query Language, is essential in environments where large volumes of financial data are stored in relational databases, enabling analysts to build reliable and data-driven financial models. Its ability to efficiently query, aggregate, and manipulate data makes it a critical tool in modern financial analytics. The process begins with understanding the database architecture. Financial data is typically distributed across multiple tables such as transactions, accounts, customers, budgets, and market indicators. SQL enables the integration of these datasets through joins, allowing analysts to

Financial modeling programs integrated with data analytics using Python have transformed the way financial analysis, forecasting, and decision-making are conducted. Python provides a powerful, flexible, and scalable environment that enables analysts to move beyond traditional spreadsheet limitations and build sophisticated models capable of handling large and complex datasets. Its growing adoption in finance is driven by its ability to combine data processing, statistical analysis, automation, and visualization within a single ecosystem. The foundation of Python-based financial modeling lies in efficient data handling and preprocessing. Financial data is often sourced from multiple channels, including databases, APIs, and flat files, and must be cleaned and structured before analysis. Libraries such as pandas and NumPy provide robust tools for organizing data into structured formats,

In business people regularly have to deal with situations where they have to make some decisions after analyzing some data. In such cases they often build a spreadsheet model of their problems. Financial Modelling using spreadsheets like MS Excel are used in many fields like financial analysis, risk analysis, valuations, investment management, credit analysis, scenario analysis, budgeting, sales forecast, financial projections, project evaluation, etc. Along with the use of Excel as a tool for modelling, skills in recording and using Macros in Excel is also required. Excel modeling remains one of the most widely used approaches in financial modeling and data analytics, offering a flexible and intuitive platform for analyzing financial data, building forecasts, and supporting decision-making. Its popularity stems from its accessibility, powerful built-in functions, and ability to create structured models without requiring advanced programming

Kudumbashree, is the flagship poverty eradication and women’s empowerment programme, of the Government of Kerala, and is one of India’s most successful community development missions. Launched in 1998 by the Government of Kerala in collaboration with NABARD and the Government of India, Kudumbashree operates through a unique three-tier community network that connects millions of women across the state. Its model- built on economic empowerment, social inclusion, and participatory governance- has become a benchmark for women-led development both nationally and internationally. At the foundation of Kudumbashree lies its grassroots structure: the Neighbourhood Group (NHG) of 10–20 women; the Area Development Society (ADS) at ward level; and the Community Development Society (CDS) at the panchayat level. This federated network enables women to mobilize

2.1 Mulkanoor Cooperative Society: An Introduction The Mulkanoor Cooperative Rural Credit and Marketing Society, established in 1956, in Mulkanoor village of today’s Telangana state of India, stands as a beacon of rural transformation. Post-independence, Shri A.K. Vishwanatha Reddy- a freedom fighter- envisioned a better future for farmers. His vision materialized through the formation of the Mulkanoor Cooperative Society, initially with 373 members and a modest share capital of ?2,300. The society has now grown to encompass 7,650 members, ?18 crores in share capital, and ?45 crores in thrift deposits, and offers a wide

Agriculture remains the primary livelihood for much of South Asia, underpinning food security, rural incomes, and economic stability. Despite its pivotal role, the sector has historically been constrained by low productivity, fragmented supply chains, significant post-harvest losses, and limited access to markets and institutional finance (FAO, 2023).Over the last decade, a rapid expansion of technology-driven agritech startups across South Asia- particularly in India- has sought to address these systemic gaps. By deploying artificial intelligence (AI), the Internet of Things (IoT), machine learning (ML), blockchain, drones, and renewable energy, these enterprises are reshaping agricultural value chains, enhancing resilience, and creating measurable socio-economic impact. Following are some of the promising areas where startups have shaped rural economies

1. Introduction
It is the need of the hour that disposal of radioactive wastes be taken seriouslyand given appropriate attention. We house around one million gallons of liquidradioactive waste awaiting remediation at U.S. Department of Energy’s HanfordReservation alone (Parshley & McDermott, 2021). Not only the numbers are
but the increasing demand of nuclear energy paves the way forgeneration of even more radioactive waste in future. The conventional methodshave certain limitations. Ion-exchange resins have reported to bedegradedunder high radiation and chemical precipitation has proven to be ineffectivein treating low concentration of radionuclides found in groundwater, posingrisk for centuries (Zhang & Liu, 2020). Remediation methods including soil

1. Introduction
The environment and human health are seriously threatened by contaminants,both organic and inorganic, whose prevalence and persistence have beensteadily rising in recent years. In addition to propelling economic and scientificadvancement, the industrial revolutions of the 19th and 20th centuries have alsoled to widespread pollution (Wuana & Okieimen, 2011; Singh et al., 2013).Industrial activity has increased trace metal concentrations in the environment,with sediments acting as key sources and reservoirs for heavy metals. Thesemetals build up in soils and aquatic bodies as a result of scouring, erosion,rainfallleaching, wastewater discharge, and atmospheric deposition (AlonsoCastillo et al., 2013). Additionally, toxic contaminants like phenols, industrialeffluents, domestic wastewater, agricultural runoff, fly ash, fertilizers, andwaste from insecticides, pesticides, and pharmaceutical industries infiltratethe environment and food chain via mechanisms such as bioaccumulation,biomagnification, and bioconcentration (Arora & Bae, 2014; Azubuike et al.,2016).

1. Introduction to Biomedical Waste (BMW)
The waste produced at different medical facilities, including hospitals,laboratories, research centers, mortuaries, and autopsy centers is considered asBiomedical Waste (BMW). Some of the BMW is hazardous whereas some arenon-hazardous in nature. Hazardous waste includes, Infectious, Pathological,Microbiological, Pharmaceutical, Chemical, Cytotoxic and Radioactive wastematerial as described by World Health Organization (Fig. 1). Healthcarepersonnel and the general public are at risk from physical, chemical, andmicrobiological effects of hazardous waste. On the other hand, the nonhazardousBMW which are not harmful, include packaging material made upof plastic & thermocol, cardboards, paper waste etc. (Capoor & Parida, 2021).

1. Introduction
Plastic pollution is a global problem that requires immediate attention due toits negative impacts on ecosystems, infrastructure, (MacLeod, M., Arp et al.2021) society, and the economy (Honingh, D. et al.,2020).In terms of absolute emissions, we find that South-eastern part of Asia, andSub-Saharan part of Africa possess the greatest quantities of plastic pollution,with India emitting the most (9.3 Mt year−1), or about one-fifth of the world'stotal plastic pollution (Joshua W. Cottom et al.).
Future regulations pertaining to the manufacturer, usage, and getting ridof plastics will be influenced by the discussions for a worldwide plasticpollution treaty. A baseline with great resolution of waste flows and sourcesof plastic emissions will be useful for the stakeholders in order to identify
pollution hotspots and the reasons behind them (United Nations EnvironmentProgramme). Furthermore, in addition to the production of municipal waste,the pandemic of coronavirus illness has raised the usage of disposable polymerproducts, masks, tissues, gloves, and additional personal protection equipment,which made the plastic pollution disaster worse (De-la-Torre GE et al., 2021).Wasted plastics can break down and fragment into three different sizes: 1mm to 5 mm big microplastics, 1 μm to 1 mm microplastics, and 1 nm to1 μm nano plastics, (Waller et al., 2017; De-la-Torre 2020; Atugoda et al.,
2022) being exposed to ultraviolet (UV) light and other processes that causeenvironmental damage

1. Introduction
The term “plastic” is derived from the Greek word “Plasticos" whichmeans to be capable of molding into various sizes and shapes. Daily usedsynthetic plastics are categorized based on their physical and chemicalproperties, such as thermoplastic and thermosetting. (i) Thermoplastics: Astheir names suggest, thermoplastic polymers melt and turn into a plastic orrubbery state when exposed to heat. They have various numbers of polymericchains and are the most widely used plastics in day-to-day life. Examples ofthermoplastics are Polystyrene(PS), Polyvinylchloride (PVC), Polypropylene(PP), Polyethylene Terephthalate (PET), Polyethylene (PE) etc. (Massy,2017), (Sharma et al., 2017). (ii) Thermosetting polymers: As the namesuggests, they set or harden permanently when heated (Massy, 2017). Due totheirstrong crosslinked structure, they are non-recyclable polymers. Examplesinclude Polyurethanes(PUR), Phenol formaldehyde, Melamine formaldehyde,and Polyester Resin (PR) (Sharma et al., 2017). Different polymers and theirapplications are illustrated in Fig. 1.

1. Introduction
Since plastic was discovered, global plastic production for industrial scale usages has grown exponentially, causing a tremendous increase in plastic waste from a few tons in the 1950's to almost 400 million tons in 2018 (Syberg et al. 2021). Plastic is non-biodegradable and ubiquitous.

Environmental degradation of plastic operates through a mechanism ofphysical fragmentation, photodegradation and microbial degradation (Wright
et al., 2020), resulting in fragmentation of larger plastics into smaller plasticparticles, also called microplastics and nanoplastics (MNPs). These debris areserious environmental threats (Hartmann et al., 2019).

1. Introduction
Pharmaceutical pollution has emerged as a significant global concern, raising alarms about its potential impacts on ecosystems and human health. The journey of pharmaceutical compounds from their point of use to contamination of water bodies reveals a complex and interconnected web of anthropogenic activities. From manufacturing plants to individual households, these pollutants persistently infiltrate aquatic systems, creating a growing

1. Introduction
Water is the most essential resource for life, supporting the survival of allliving beings, including humans, and playing a crucial role in food productionand economic progress. Currently, many cities around theworld are facingsignificant water shortages. About 40 % of global food production dependson irrigation, and countless industrial operations rely heavily on water. Theavailability and quality of water, whether surface or groundwater, have amajor impact on environmental health, economic development, and societagrowth. Human activities like urban expansion, population increase, industrialoutput, and climate shifts are causing a decline in water quality. As nationsdevelop, industrial activities increasingly lead to the pollution of watersources, resulting in negative consequences such as widespread disease and
diminished beauty of water bodies. This pollution is a significant threat toboth the planet and its inhabitants. Higher temperatures are interfering with themetabolic processes of aquatic life, while changes in water pH create unstableconditions. Additionally, increased cloudiness in water reduces oxygen levels,affecting deep-water species and contributing to health issues for both animalsand humans. Polluted water also hinders growth and development in allliving organisms Haldar et al., 2015 and Arif et al., 2020. A study conductedby Halder et al. (2015) highlighted severe health challenges faced bylocalcommunities due to pollution in the Turag River, Dhaka, caused by thedischarge of untreated wastewater into the river. It was revealed that elevatedpH levels in the water, resulting from the use ofcaustic chemicals such assoda ash and caustic soda in industrial processes, were associated with skinirritation and sores. Dietary exposure to polluted crops and fish was found
to contribute to stomach ailments, including gastric ulcers. Concerns wereraised about potential groundwater contamination through the infiltration ofindustrial waste. Microbial contamination was identified as a likely cause ofdiarrhoea and dysentery. The use of river water for bathing and laundry wasobserved to facilitate the spread of waterborne diseases. Outbreaks of yellow
fever, cholera, dengue, malaria, and other epidemics were noted as prevalent inthe area. Respiratory problems and foul odours were attributed to air pollutioncaused by industrial discharge. Maternal and child health in riverbank slumsfound to be vulnerable, emphasizing the urgent need for interventions to addressthese public health concerns.Wastewater produced by industries, agriculture,and domestic activities contains high levels of nutrients, heavy metals, andchemicals, which harm the environment by polluting land, air, and water,

1. Introduction
Oil spills are defined as the unintentional release of liquid petroleum hydrocarbons into the environment, predominantly impacting marine ecosystems. This form of pollution, often caused by human activities, can also occur onterrestrial landscapes. Spills typically result from the discharge of crude oilfrom sources such as tankers, offshore platforms, drilling rigs, and wells, aswell as from refined petroleum products like gasoline and diesel (Smith etal., 2019). While such incidents are primarily accidental, they may arise fromvarious circumstances, including inadequate storage or poor maintenance,leading tocontainerleaks. Historically, one of the earliest documented oil spillsoccurred in 1907 in Batum, a region then part of the Russian Empire (moderndayBatumi, Georgia). This incident, involving a collision between an oil-ladenship and another vessel in the Black Sea, marked the beginning of recorded oilspill events, though historical records from that time remain limited (Johnsonet al., 2021). The consequences of oil spills are profound, affecting both theenvironment and the economy. On the ocean’s surface, oil disrupts aquaticecosystems by blocking sunlight penetration and depleting dissolved oxygenlevels, which are vital for marine life. Crude oil compromises the insulating andwaterproofing properties of fur and feathers, leading to hypothermia and deathin oil-coated birds and marine mammals. Additionally, ingesting oil can causeseveretoxicity, while damage to habitats and reproductive systems often delaysthe long-term recovery of affected species. Coastal vegetation, particularly inecosystems such as saltwater marshes and mangroves, is also highly vulnerable

1. Introduction
Dyes are compounds after addition to a substrate, for a moment give it a colorthrough a process that modifies the colored substance’s crystal structure (Bafanaet al., 2011). Such varieties of compounds with significant coloring capacityare widely employed in the textile, pharmaceutical, food, photographic, andpaper industries (Zollinger et al., 1987). Dyes can adhere to suitable surfacesthrough methods such as dissolving, forming covalent bonds, complexing withsalts or metals, physically adsorbing, or being mechanically retained (Kirkand Othmer, 2004). The chemical structure and application of dyes determine
their classification. The dye color is produced by a collection of atoms calledchromophores. Chromophore centres, which are responsible for color, consistof various functional groups like azo, anthraquinone,methine, nitro, arylmethane, carbonyl, and others. Additionally, substituents that either donateor withdraw electrons to enhance or create the color of these chromophoresare called auxochromes. Common examples of auxochromes include amine,carboxyl, sulfonate, and hydroxyl groups. There are three methods for dyingtextile materials: batch, continuous, and semi-continuous. Thekind of materialincluding yarn, fabric, fiber, clothing, and fabric structure as well as the generalfiber size of the dye lot, type, and quality standards of the dyed fabric, all playa role in determining the dyeing method used. Among these techniques, theprocess called the batch procedure is the most frequently employed for dyeingtextile materials. (Ogugbue et al., 2011).

1. Introduction
In recent decades, environmental contamination has emerged as a seriousthreat to our modern society. The relentless growth of urbanization andindustrialization leads to the ever-increasing production of hazardouschemicals. Safe disposal of these hazardous materials is crucial, as releasingthem into an already strained environment has severe consequences. Theexponential rise in human activity is directly related to the alarming risein local and global pollution levels, which severely disturbs the balance ofecosystems. Industrial activities, in particular, are a major source of highlyhazardous chemicals such as dyes, pigments, and aromatic compounds,which pose a serious threat to the environment. In India, the textile industry, amajor force driving the country’s economic growth, also ranks asthsecondlargestpolluter. The printing, finishing, and dyeing processes used in textileproduction generate significant waste (Dubey et al., 2018). Dyes, a majorsource of pollution in this industry, are syntheticand complex in structure. Thiscomplexity makes them poorly soluble in water, hindering biodegradation andposing a long-term environmental concern. The global textile industry utilizesa wide range of dyes, with estimates indicating over 100,000 commerciallyavailable options. The worldwide production of these dyes is estimated to be700,000 tons per year (Al-Tohamy et al., 2020). However, dye contaminationis a major challenge in this industry. Studies indicate that during synthesisand dyeing processes, a significant portion, ranging from 10% to 15% of thetotal dyestuffs used ends up released into global water bodies (Leal et al.,2020). A significant portion of textile dyes, ranging from 2% to 20%, aredirectly released into water reservoirs through wastewater effluents. This is

1. Introduction
The world’s ecosystem is facing significant challenges because of risingpopulations, more urbanization, and industrialization (Juwarkar et al.,2014). Significant environmental contamination has been caused by moderncivilizations, industrialization, population increase, intensive agriculturalpractices, human activities, and technological advancement (Lalevic et al.,2016).

1. Introduction
Metals pollution in water particularly in wastewater has become a center ofconcern due to social changes such as industrialization and urban development(Blaszczak-Boxe et al., 2023). It is in this context that most of the conventionalwastewater treatment fails to remove the heavy metals in question satisfactorilyand that there is a pressing need for new ideas that would be sustainable. Thischapter aims to discuss algal bioremediation as an environmentally suitableprocess for the decontamination of wastewater containing toxic metals.

1. Introduction
Bioremediation is an environmentally friendly process that uses numerousdifferent microbes (e.g., fungi & bacteria), green plants, or combinations
of them to weaken and detoxify dangerous pollutants in a sequential orsuccessional manner. The pollutant composites are converted by biologicalorganisms through a response that takes place as part of their metabolicprocesses. They convert poisonous pollutants into carbon dioxide (CO2) andwater (H2O), microbial biomass, and other products that are less poisonous.The organic wastes are biologically degraded under controlled, harmless, andsafe condition into inoffensive products below the admissible limits establishedby supervisory authorities.

1. Introduction
1.1. The Growing Problem of Pesticide Pollution
Pesticides are used in very great amounts in agricultural production for theprotection of crops against pests, diseases and weeds. They are also veryinstrumental in boosting agricultural productivity by ensuring food securityand preventing losses that may be caused by outbreaks of pests (Popp et al.,2013). However, the extensive use of pesticides has generally resulted in hugeenvironmental concerns. Improperlymanaged pesticide residues may remainin soil, water and even air for many years and create severe ecological andhealth hazards in the long run (Zhang et al., 2024). This widespread existenceof pesticides in the environment has raised a serious alarm concerning theiradverse impacts on biodiversity, soil fertility and quality of water, furtherbioaccumulation in the food chain that affects wildlife and human populations(Aktar et al., 2009).

1. Introduction
Agro-industrial waste plays a crucial role in bioremediation, the process of usingbiological organisms to remove or neutralize pollutants from contaminatedenvironments (Garg, 2020). With the rise of industrialization and agriculturalactivities, there’s a corresponding increase in waste production. However,these waste materials, often rich in organic compounds, can be repurposedas valuable resources as mentioned in scientific studies (Sahota and Sharma,2020; Yadav et al., 2022).

1. Introduction
One of the foremost environmental challenges the world faces today is airpollution. As industrialisation, urbanisation, and transportation continueunabated, harmful substance levels in the atmosphere have becomealarmingly high (Thanvisitthpon et al., 2024). This general contamination inthe air is not just a serious risk to humanity but also harms ecosystems whilcontributing to global climate change. Controlling airpollution has becomea critical necessity at this stage, with the need for innovative solutions inthis respect. Bioremediation is one of the innovative solutions, where certainmicroorganisms break down or neutralise pollutants in the air.

1. Introduction
Bioremediation is a process that utilizes biological agents, such asmicroorganisms, to eliminate pollutants, making it a natural and efficientmethod for environmental cleanup. This approach is considered cost-effectiveas it transforms harmful pollutants into less toxic substances through microbialdegradation or detoxification. Currently, many developing nations areprioritizing industrial growth, urbanization, and the use of agrochemicals inagriculture, which has heightened the importance of bioremediation and drawnsignificant attention to this practice. As a sustainable solution, bioremediationoffers a way to restore healthier ecosystems by removing pollutants from theenvironment (Fig. 1).