eChapters

19.1. Plant Available Fraction of Nickel, Cadmium, Lead and Chromium

The DTPA method was originally developed to extract Mn, Cu, Fe and Zn from slightly acidic to alkaline soils (Lindsay and Norvell, 1978), but in some cases it can be used for other metals also such as Nickel (Ni), Cadmium (Cd), Lead (Pb) and Chromium (Cr). Among them Ni is considered as an essential micronutrient. The DTPA is used to extract soil metals by forming chelates with free metals in solution, which lowers their ionic activities so that additional quantities of metal are released from the soil until equilibrium is attained. Triehanolamine (TEA) is used to buffer the extracting solution at pH 7.3 in an attempt to optimize the extraction of various metals. Calcium chloride is included in the extracting solution to minimize dissolution of CaCO₃.

Introduction
Energy is necessary for people, ecosystems, and living things to function. The effects of energy generation and use on the  environmentdetermine life quality. Our main source of energy comes from the sun. Energy is used by humans for running transportation, producing industrial items, farming, and residential use.
Definitions
Energy is the ability to do tasks.
Resource: Anything we obtain from our surroundings in order to fulfill our needs and wants. A natural resource is any substance or energy that comes from the environment and is utilized by all living things, including humans. Examples of natural resources include air, water, soil, minerals, plants, and wildlife.

Introduction to Energy Sources

Consumption of nonrenewable energy sources has caused more environmental harm than any other human activity during the last century. Electricity generated from fossil fuels like coal and crude oil has resulted in excessive levels of hazardous gases in the environment. As a result, several issues exist now, such as ozone depletion and global warming. Vehicle pollution has also been a significant issue.

As a result, alternative energy sources have become increasingly vital and relevant in today’s society. These sources, such as the sun and wind, can never be depleted and are hence referred to as renewable. They produce fewer emissions and are readily available locally. Their application has the potential to significantly reduce chemical, radioactive, and thermal pollution. They stand out as a promising source of infinite clean energy. These are also known as non-conventional energy sources.

Abstract

The groundwater behaviour in hard rock aquifer systems has always remained enigmatic owing to the heterogeneity of the aquifer systems. This is the reason why there are a lot of failures in groundwater targeting and management in hard rock aquifer systems even after the employment of many modern technologies. For precise groundwater targeting in hard rock aquifer systems various parameters like lithology, the type and the degree of structural deformation, the degree of weathering, geomorphology, etc., will have to be analysed which involves spatial analysis of voluminous data. In the sphere of spatial modelling, GIS plays an important role. Hence, a newer study has been carried out in parts of Madurai, Dindigul and Theni districts, Tamil Nadu, to identify the groundwater provinces and their functions using GIS. In the said study, GIS images were generated on the groundwater targets through GIS integration of transmissivity, permeability, storage co-efficient and water level. Such GIS image showing groundwater maxima was overlaid with GIS image of fracture maxima, subsurface geology maxima and the geomorphology maxima and thus by various GIS based spatial manipulations, aquifers controlled by structure, geomorphology, subsurface geology, and combinations of the above various variables were brought out. This has provided potential method for precise targeting of groundwater.

Energy, food production and various other natural resources are the limiting factors on this earth. The result of this bitter truth has been published in a book titled ‘The Limits to Growth’ in 1972. If the enormously growing population and the industrial process is not checked, then the earth’s civilization may come to an end within next hundred years.

By considering the space travel, we can very well understand about the Ecosystem. When we go away from the biosphere, it will be appropriate to develop unlimited environment through the energy accumulated from the sunlight in space. For the space travel for some days to moon and back to earth, we need not establish the entire ecological system as greater quantity of O₂ and food stuffs can be stored and CO₂ and other unwanted materials can be fixed for establishing space colonies, we have to build a special space ship with important abiotic components which can be recycled for use again and again. The vital processes of production, consumption and decomposition can be established and balanced through biotic components. In other words we are creating a complete mini human ecological machine. Before we established this system, we should be quite familiar about the space problems and its implications.

Key words : Remote Sensing, Ecosystem, Environmental Pollution, Biodiversity Conservation, Coastal Environmental Degradation, Wetland.

Introduction

National prosperity and human resources development are primarily shaped by the availability of natural resources and their use. None the less, the benefits are quite often negated by the resultant environmental degradation concomitant with their utilisation for economic values. The problems of environmental degradation have assumed critical dimensions in many parts of India due to resources exploitation; industrialisation and urbanisation, unchecked population growth and a never ending quest for energy to meet the increasing demands. Moreover, improper utilisation of resources has resulted in such environmental problems as soil erosion, waterlogging, floods, droughts and pollution of various types besides socio-economic disparity among people.

After the 1972 Stockholm conference on Human Environment and Development, there has been a phenomenal increase in the worldwide interest in the environmental issues associated with development. Consequently, the focus of development has shifted to environment friendly and sustainable developmental planning in recent years. The Rio Summit (June 3-14, 1992) further emphasised the fact that earth is an integral and interdependent system, which require sustainable developmental planning. During deliberations of the Rio Summit (Earth Summit), it was emphasised that satellite imagery will be quite helpful in monitoring earth’s environment.

India has a long tradition of environmental protection and has enacted laws during the last three decades to give further impetus to the conservation of natural resources and protection of environment. Some of the important laws are:

ABSTRACT

The estimation of the water requirement of crop is one of the basic needs for crop planning on the farm and for the planning of any irrigation project. Water requirements of crops include the losses due to Evapotranspiration (ET) plus the losses during the application of irrigation water (unavailable losses) and the quantity of water required for special operations such as transplanting and leaching of excess salts for crop growth. It is therefore necessary to estimate the ET and Crop Water Requirement (CWR) for the efficient use of water and increased crop production. In the present study, meteorological data were collected and the crop ET (ETc) and the CWR were estimated using FAO Penmann – Monteith method for the cropping pattern of the Buthangudy canal command area situated at a distance of 2Km from the popular village Sethiathope, Cuddalore district, of Tamil Nadu, India, which is located in between the Kumbhakonam – Chennai NH – 45 main road. The methodology combines the capability of GIS and CWR to produce maps showing spatial distribution of water resources over large areas.

Introduction

All spatial data stored in a GIS are associated with a map projection, either implicitly (so-called “unprojected” data stored with raw longitude, latitude coordinates) or explicitly (where the data have been transformed into a known map projection). Map projections are a particularly important feature of spatial data because they introduce error and distortion, since a sphere cannot be transformed to a plane without some stretching and twisting of the sphere’s surface.

The typical GIS package supports anywhere from 5 to 50 map projection types, and most projections may be modified or customized by varying their parameters. This flexibility is important for two reasons First, in order to use two or more layers together in a GIS for visualization or analysis, all data must be projected in the same way. Second, certain types of analyses, such as the measurement of area, are only valid when using certain types of projections (and conversely, are completely wrong when using an inappropriate projection).

Introduction

Spatial data is different from a non-spatial data in the sense that it always has a spatial reference system which is nothing but a pair of coordinate values and a system of reference for these coordinates. For example, the locations of benchmark soil series of India can be defined as pairs of longitude and latitude in decimal degree values with respect to the prime meridian at Greenwich and zero latitude at the equator. The World Geodetic System (WGS84) is a frequently used system of reference on the Earth. The different types of spatial data are: (i) point, which is a single point location, such as a GPS reading or a geocoded address (ii) line, which is defined as a set of ordered points, connected by straight line segments (iii) polygon, an area, marked by one or more enclosing lines, possibly containing holes and (iv) grid, a collection of points or rectangular cells, organised in a regular lattice. Among the above said spatial data, the first three are vector data models and represent entities as exactly as possible, while the fourth data model is a raster data model, representing continuous surfaces by using a regular tessellation. In this chapter, we discuss how a spatial data can be handled in R. There are several packages available in R in which spatial data analysis can be handled e.g. sp, rgdal, raster etc. All the contributed packages for spatial data analysis in R address two broad areas: moving spatial data into and out of R, and analysing spatial data in R. In the following section, the sp package for spatial data handling is briefly discussed.

Remote sensing images have an important characteristic known as spatial frequency. Spatial frequency is defined as the number of changes in pixel value per unit distance. Few changes in pixel or brightness value over an area in an image are known as low spatial frequency area like agricultural fields, forests, water bodies, etc. If the changes are large, then it is known as high frequency area like roads, built up areas, etc. Spatial filtering is an approach for extracting quantitative spatial information by taking into account the neighbouring pixel values along with the independent pixel value. Therefore, filtering is a technique for modifying or enhancing an image. Filtering is done to improve the interpretability of raster image or to extract certain information from them. It is a neighbourhood operation because the value of any pixel in the output image is determined by applying certain algorithm on the neighbourhood pixels of the corresponding input pixel. The nature of algorithm to be applied depends upon the information which user wants to derive from the raster image. Filtering operations are mainly done for smoothening, sharpening, noise removal and edge enhancement. A filter is a kernel of small array which is applied to input pixel and its neighbouring pixels in the raster. There are many types of filters which can be applied to a raster image in R. These types with their respective R – code examples are explained further.

The edible speciality mushrooms are mushrooms that are generally not grown and available commonly in the market. Any mushroom other than popularly grown white button mushroom (Agaricus bisporus) falls under the specialty category, and these mushrooms are generally not available over the counter or in departmental stores in India or other countries in the world. The mushrooms under this group include the different types of commonly grown Oyster mushrooms like grey, black, white, pink, yellow oyster mushrooms, King Oyster mushroom, Shiitake mushroom, Black Ear mushroom, Shimeji mushroom, Enokutake mushroom, Milky mushroom, the Portabella brown, Cremini mushroom , Straw mushrooms and others. This paper will describe in detail biology and cultivation of these mushrooms as grown under controlled environment conditions or under seasonal growing conditions, where ever possible.

13.1 Introduction
Having navigated the foundational statistical methods, advanced analytical techniques, visualization strategies, and the intricate world of Numerical Weather Prediction and Climate Data Analysis, we now arrive at a pivotal point in the chapter “Specialized Applications and Future Trends.” This chapter is dedicated to exploring how the comprehensive understanding and methodologies developed in previous chapters are being applied to address some of the most pressing environmental and societal challenges of our time. Furthermore, we will look ahead to the emerging technologies and interdisciplinary approaches that are shaping the future of atmospheric and climate science.
The atmosphere is not an isolated system; it profoundly influences, and is influenced by, human activities, energy systems, water resources, and urban environments. Therefore, the insights derived from meteorological and climate data are increasingly being tailored and integrated into highly specialized applications that directly impact public health, economic sustainability, and urban planning. This goes beyond traditional weather forecasting to provide actionable intelligence for complex, real-world problems.
In this chapter, we will delve into several key areas where atmospheric science is making critical contributions:
• Air Quality Analysis and Pollutant Measurements: We will explore how meteorological principles are applied to understand the transport, dispersion, and transformation of atmospheric pollutants. This includes the methods for measuring various pollutants and the sophisticated dispersion modeling techniques used to predict air pollution levels and their spread, crucial for public health warnings and environmental regulations.

Abstract

While multiple species interact with each other in variable environments, they employ a variety of interaction strategies to withstand variation in environmental conditions. Phenotypic plasticity is one such mean allowing species to cope with environmental challenges often posed by the variation in consumer pressure, disturbances, and predation risks. In response to these and other environmental cues, expressed phenotypes can alter the sign and strength of interactions among species as these responses are often reflected in increased or decreased species competitive or facilitative abilities. Besides phenotypic plasticity, species interactions also depend on continued variation in abundances or densities of interacting species commonly termed as density-dependent interactions. Such density-dependence follows the law of diminishing returns, which means that while an individual species adds a density-dependent weight to the performance of other species, with increasing accounts, at a certain level of interaction it provides diminishing returns resulting in decreased population growth of the species. Thus, species interactions in variable environments hold two interdependent opposing forces: the growth-escalating and the growth-diminishing forces. The first one is resulted from adaptive phenotypic responses to variable environments and the second one is resulted from dynamic changes of abundances or densities of interacting species. Based on this conceptual framework, we describe community dynamics that are shaped by the balance of these two forces in variable environment. By analyzing these dynamics, we then seek underlying mechanism of Grim’s classic humped-back model of productivity-diversity relationship and also provide an alternative explanation for the intermediate disturbance hypothesis.

Keywords: Competition, Facilitation, Functional niche, Interaction continuum, Phenotypic plasticity.

4.1. Data Set Generation for Modelling
We have generated 54 transfomed spectra by applying different combination of parameter. Now this spectrum is to be tagged with respective soil sample and desired soil properties for which spectral modeling has to be done. Here we are generaring data tagged with CaCO3.
For that we have to use cbind() fuction. Here we are keeping soil properties in first column and spectra after that column. We are storing data in R memeory and naming data in such way that we can identify properties and transfoemed spectra attached with that data. After generating data for modeling, data is divided into calibration and validation data set by using random sampling method or Kedard-
Stone method
# Data set for CaCO3 with raw spectra ####
Data.CaCO3.pc.113=cbind(data10nm_$CaCO3_equivalent_per, First_ derivative10nm113) #case 1st
Here in case 1st, CaCO3 indicating properties and 113 indcating without smoothed 1st derivative, 1st order polynomial 3rd point spectra is tagged with these soil samples. Same was followed with other cases also

For applying spectral data models with other machine learning and regression algorithms we have to follow all steps upto Chapter 4 till the partition of whole datainto calibration and validation data set. After that different algorithms is used to develop others models so that,the best model among the different algorithms can be identified and used for better perdiction of soil properties.
5.1 Principal Component Regression (PCR)
Principal Component Regression (PCR) is a regression technique that combines Principal Component Analysis (PCA) and multiple linear regression. It is particularly useful when dealing with multicollinearity in the predictor variables, as it transforms the predictors into a set of uncorrelated components before performing the regression.
set.seed(123)
pcr.CaCO3.pc.223SGS<- train(CaCO3.pc ~., Train_Data.CaCO3.pc.223SGS,
method =”pcr”, trControl = custom,
tuneLength=25, importance = TRUE)
Explaination for code train(CaCO3.pc ~ ., data = Train_Data.CaCO3.pc.223SGS, ...)
Formula (CaCO3.pc ~ .): Specifies the model formula where CaCO3.pc is the
dependent variable and . indicates that all other columns in Train_Data.CaCO3.
pc.223SGS are independent variables.
Data (data = Train_Data.CaCO3.pc.223SGS): The dataset used for training the model.
method = “pcr”:Specifies that the model to be used is Principal Component Regression (PCR).
trControl = custom:Specifies the training control method. custom should be an object of class trainControl created earlier, defining the resampling method (e.g., cross-validation).This example sets up 10-fold cross-validation. tuneLength = 25:Specifies the number of different values for the number of principal components to consider. The package caret will try 25 different values to find the optimal number of components.

Data pre-processing is a crucial step in data analysis and machine learning that involves preparing raw data for further processing and analysis. Proper preprocessing helps in improving the quality of the data, which in turn enhances the performance of machine learning models. Smoothening VIS-NIR spectra enhances signal quality by reducing noise, crucial for better spectral data interpretation. Key methods include Savitzky-Golay and moving average filters, wavelet transform (WT), and frational derivatives.
Savitzky-Golay Filter: The Savitzky-Golay filter is widely used for smoothing spectral data. It preserves the original shape and features of the signal better than simple averaging filters.Savitzky-Golay Filter fits low-degree polynomials to successive subsets of data points using linear least squares, effectively preserving the signal’s original shape and features, making it ideal for spectroscopy. Moving Average:Moving Average is a simple method that reduces random noise by averaging neighboring data points. While effective, it can sometimes blur sharp features.
Wavelet Transform: Wavelet transform is effective for denoising spectral data, especially when dealing with non-stationary signals.Wavelet Transform (WT) decomposes spectral data into multiple scales, providing both time and frequency information. It is particularly useful for identifying features like peaks and trends. WT includes Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Maximal Overlap Discrete Wavelet Transform (MODWT). Fourier Transform Filtering: Fourier transform can be used to remove highfrequency noise components from the spectra.
Median Filter: Median filtering is useful for removing outliers and preserving edges in the spectral data.
Fractional derivative:Fractional derivative generalizes traditional derivatives to non-integer (fractional) orders, allowing for arbitrary order derivatives. This provides a flexible, nuanced approach to modeling and analyzing complex, nonlinear, or memory-dependent behaviors.

Introduction

Soil is one of the vital resource, and is the basis of the existence of mankind. Management of this resource including its conservation and utilization is of crucial importance. It is a base on which all life depends. In the recent past, with burgeoning populations and the national goals of seeking self-sufficiency in food and fibre production, this resource base is slowly being stripped. While natural systems often adapt to stress in a remarkable fashion, some relationships - once destroyed - can never be restored. Soil resources can be assessed and monitored through its spectral signatures gathered through remote sensing.

The term ‘remote sensing’ is commonly restricted to methods that employ electromagnetic energy (such as lights, heat, micro wave) as means of detecting and measuring target characteristics. Sensors acquire data as various earth surface features reflect or e mit electromagnetic energy (EM energy). Remote sensing is the technology that is now the principal method (tool) by which the earth’s surface and atmosphere (as targets or objects of surveillance) are being observed, measured, and interpreted from vantage points. Remote sensing of the earth traditionally has used reflected energy in the visible and infrared, and emitted energy in the thermal infrared and microwave regions to gather radiation that can be analyzed numerically or used to generate images whose tonal variations represent different intensities of photons associated with a range of wavelengths that are received at the sensor.

Introduction

According to FAO estimates, about 50% total geographical area in India is under various degraded hazards and about 2.1 million ha of land is getting degraded and deforested annually (Sehgal et al. 1998). Out of that underground water in arid and semi-arid areas is saline or sodic in nature. While spices, medicinal and aromatic plants may be a boon for these areas, which not only withstand under adverse conditions but also could be productive and helps in mitigating these problems. However, other crops are remain less or unproductive in these areas.

Spices are low volume and high value crops and have tremendous export potential, which is witnessed by several invasions faced by this Indian peninsula during ancient time. Medicinal and aromatic plants have unequally market potential with the world demand of herbal products growing at the rate of 7 percent per annum (Aishwath and Tarafdar, 2007). Seed spices and aromatic plants are the natural source of flavour, perfumes and fragrance widely exploited by essential oil industries in the world. Owing to considerably diversity of edaphoclimatic conditions ranging from temperate to tropical and arid to sub-humid that prevails in India and an array of medicinal and aromatic flora occurs in wild state. Many exotic essential oil bearing plants, including mints, citronella, geranium, patchouli, lavender and chamomile have been naturalized in India. As the country has emerged as one of the main producers of seed spices and some of the essential oils, theses plants are significant foreign exchange earners. Some of the aromatic plants especially grasses can sustain both biotic as well as abiotc stresses, even in the areas unsuitable for conventional crops (Aishwath et al., 2008). Most of the seed spices crops are seasonal crops and comes up well with limited moisture and nutrients. Therefore, underutilized natural resources can be utilized profitably by crop diversification.