**1. Introduction**

Unlike other natural resources such as wind and solar, water resources are limited and can include surface water, ground water, rain water and saltwater. Of all the water resources present on earth, only 2.5% exists as freshwater. Fresh water can exist in the form of ground water, surface water and water present in glaciers and ice. A major fraction of fresh water is present in glaciers with only a tiny fraction available as surface water.

Increasing population and urbanization has increased our water demands and place undue stress on the existing water resources. Studies indicate that by 2030 there will be a 40% deficit between the world water demand and the available water resources [1]. People in the developing world are most vulnerable to climate changes; changes in rainfall pattern as a consequence of climate change have largely affected the world in recent years. Extreme changes in the rainfall patterns have increased the frequency of floods and droughts. Exploitation of the available water resources affects all aspects of human existence in addition to disrupting the fine balance in the ecosystem. Erratic and unseasonal rainfall largely affects agricultural activity which mainly sustains the economy of most of the developing countries. Sustainable water management practices are therefore critical to ensure conservation of the existing water resources. Development of policies on smart water practices and its implementation need to go hand in hand to promote conservation of water resources. Many countries around the world have adopted different strategies to reduce the pressure on their available water resources and interesting case studies on their success stories exist.


**New Insights Regarding the Potential Health Benefits of Isoflavones**

Corina Danciu, Diana Simona Antal, Florina Ardelean, Aimée Rodica Chiş, Codruţa Şoica, Florina Andrica and Cristina Dehelean

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/67896

#### **Abstract**

Isoflavones are a class of plant secondary metabolites, with an estrogen‐like structure presenting a plethora of biological activities. The chapter discusses important facts about this class of phytoestrogens, from biosynthesis to the latest research about their health benefits. The following major points discussed are: biosynthesis, regulation, isolation, metabolism and bioavailability, isoflavones in diet and intake, and new insights regarding the therapeutic effect including cancer chemoprevention. The chapter ends with a mini review of own research of the anti‐inflammatory and chemopreventive activity of iso‐ flavonoid genistein alone and incorporated in modern pharmaceutical formulations. The chapter updates the interested researchers in the field with the latest progress regarding potential health benefits of isoflavones.

**Keywords:** isoflavones, biosynthesis, regulation, isolation, metabolism, bioavailability, therapeutic effect

### **1. Introduction**

Isoflavonoids, a class of secondary metabolites including over 1000 structures [1], are polyphe‐ nolic derivatives of 1,2‐diphenylpropane, as opposed to the larger group of flavonoids having a 1,3‐diphenylpropane skeleton. They encompass several subgroups, the most prominent being the isoflavones, the rotenoids, the pterocarpans and the coumestans (**Figure 1**). The mul‐ tiplicity of isoflavonoid structures is accounted by different oxidation levels of the backbone, the presence of additional heterocyclic rings and the diversity of substituents. These substances occur

© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Figure 1.** Flavonoid and isoflavonoid backbones. 1: Flavones, 2: Isoflavones, 3: Rotenoids, 4: Pterocarpans and 5: Coumestans.

mostly in free forms as aglycones (**Figure 2**); glycosides are formed with glucose, rhamnose or apiose as the sugar moiety and are mainly O‐glycosides.

Isoflavonoids are a major biochemical marker of the Fabaceae, especially of the subfamily Faboideae [2]. As much as 95% of the isoflavonoid aglycones and about 90% of isoflavones were reported in this family [3]. In recent years, the distribution of isoflavonoids has reliably been proven in a variety of nonlegumes, including bryophytes, gymnosperms and angiosperms. In flowering plants, they occur in over 50 families, and their distribution pattern is unrelated with the degree of phylogenetic closeness. Such families include among monocots such as the Iridaceae, Liliaceae, Asphodelaceae, Poaceae, Zingiberaceae and Cyperaceae as well as a large number of dicots such as Asteraceae, Apiaceae, Malvaceae, Rosaceae, Rutaceae or Solanaceae [1, 4]. Among plant organs, isoflavonoids are mainly present in underground parts, wood and bark as compared to flowers and leaves [1]. *In planta*, they act as anti‐microbial compounds synthesized in response to the attack of pathogens, representing the first identified type of phytoalexins. Isoflavonoids may either be preexistent in plant tissues before microbial attacks or be produced only upon exposure to infection or environmental stressors. The prominent distribution of these secondary metabolites

**Figure 2.** Main isoflavone aglycones: 1: Genistein, 2: Daidzein, 3: Glycitein, 4: Biochanin A and 5: Formononetin.

in legumes is related to their physiological role in nodulation. The isoflavonoids present in root exudates are associated with the attraction of symbiotic *Rhizobium* bacteria and promotion of their growth within root nodules [5], leading to an improved nitrogen uptake.

### **2. Biosynthesis and regulation**

mostly in free forms as aglycones (**Figure 2**); glycosides are formed with glucose, rhamnose or

**Figure 1.** Flavonoid and isoflavonoid backbones. 1: Flavones, 2: Isoflavones, 3: Rotenoids, 4: Pterocarpans and 5:

Isoflavonoids are a major biochemical marker of the Fabaceae, especially of the subfamily Faboideae [2]. As much as 95% of the isoflavonoid aglycones and about 90% of isoflavones were reported in this family [3]. In recent years, the distribution of isoflavonoids has reliably been proven in a variety of nonlegumes, including bryophytes, gymnosperms and angiosperms. In flowering plants, they occur in over 50 families, and their distribution pattern is unrelated with the degree of phylogenetic closeness. Such families include among monocots such as the Iridaceae, Liliaceae, Asphodelaceae, Poaceae, Zingiberaceae and Cyperaceae as well as a large number of dicots such as Asteraceae, Apiaceae, Malvaceae, Rosaceae, Rutaceae or Solanaceae [1, 4]. Among plant organs, isoflavonoids are mainly present in underground parts, wood and bark as compared to flowers and leaves [1]. *In planta*, they act as anti‐microbial compounds synthesized in response to the attack of pathogens, representing the first identified type of phytoalexins. Isoflavonoids may either be preexistent in plant tissues before microbial attacks or be produced only upon exposure to infection or environmental stressors. The prominent distribution of these secondary metabolites

**Figure 2.** Main isoflavone aglycones: 1: Genistein, 2: Daidzein, 3: Glycitein, 4: Biochanin A and 5: Formononetin.

apiose as the sugar moiety and are mainly O‐glycosides.

Coumestans.

258 Flavonoids - From Biosynthesis to Human Health

The biosynthesis of isoflavonoids occurs as a branch of the phenylpropanoid pathway, involved in the biologic obtainment of all flavonoids [6]. The departing point is represented by the amino acid phenylalanine, sequentially converted to cinnamate, p‐coumarate and p‐coumaroyl‐CoA by the relevant enzymes of each step (phenylalanine ammonia‐lyase, cinnamic acid‐4‐hydroxylase, and 4‐coumarate‐CoA ligase, respectively). Subsequently, chalcone synthase is involved in the creation of the 15‐carbon flavonoid backbone from p‐coumaroyl‐CoA; several derivatives may be produced via different branch pathways. The crucial enzyme for isoflavone biosynthesis is isoflavone synthase, a cytochrome P450 monooxygenase, which catalyzes the migration of the aryl moiety and transforms flavanones to isoflavones [7]. The reaction involves keto‐enol tautomerism of flavanones and epoxidation, followed by dehydration [8].

Relevant metabolic pathways for the biosynthesis of the major isoflavonoids genistein (pre‐ cursor naringenin) and daidzein (precursor liquiritigenin) have been well‐studied in soybean (*Glycine max* (L.) Merr.). Their understanding opened possibilities of metabolic engineering, leading to the development of soybean lines with an increased content of isoflavones in comparison to wild‐type seeds. On the other hand, the health benefits of isoflavones prompted strategies to induce the synthesis of these compounds by non‐legume plants (broccoli and tomatoes). This approach could be achieved in plants with an active phenylpropanoid path‐ way [9]. In fact, the flavanone naringenin is not only the substrate of isoflavone synthase, but as well that of flavanone‐3‐hydroxylase, involved in the biosynthesis of flavonols, antho‐ cyanins and condensed tannins. The introduction of the isoflavone synthase gene in plants that do not express this enzyme was able to trigger genistein production in corn [7] and the model cruciferous *Arabidopsis thaliana* [10]. The disadvantage was represented by low isofla‐ vone content, related to the competition between the flavonoid and the isoflavone pathways. Blocking the alternative flavonoid/anthocyanin branch of the phenylpropanoid pathway while upregulating the synthesis of isoflavones by introducing foreign transcription factors yielded very favorable results. Yu et al. could increase up to fourfold the level of isoflavones in soy by introducing the transcription factors C1 and R from corn in soybean (involved in the regulation of the phenylpropanoid pathway genes), and by co‐suppressing flavanone‐3‐ hydroxylase, hence diverting the whole substrate to the isoflavonoid pathway [11]. An addi‐ tional regulation of isoflavone biosynthesis involves the enzymes catalyzing the glycosylation of aglycones, as the majority of isoflavonoids accumulate in conjugated form. In soybean, the malonylglycosides are predominant, followed by glucosides. These conjugates are obtained upon catalysis by malonyl transferase and glycosyl transferase, respectively, in specific posi‐ tions. The engineered obtainment of isoflavonoids in nonlegumes is crucially related to the presence of isoflavonoid‐specific malonyl transferases and glycosyl transferases [12]. In soy, the content and composition of isoflavonoids are subjected to polygenic regulation and highly variable in response to drought, temperature, fertilization, carbon dioxide content and genetic factors [13]. The level of isoflavonoids is higher in wild‐growing populations than in cultivated soybean; this situation is thought to be a consequence of domestication [14].


Having a large set of critical water quality indicators means that it is not possible to operate with just one technology system of water treatment processes. For the retention reservoir considered in this paper, the critical indicators were water temperature, water

 The suggested methodology for creating and verifying ANN models permits the development of an optimised configuration for a particular ANN type to best forecast

Bardossy, G.; Halasz, G. and Winter, J. (2009). Prognosis of urban water consumption using

Brion, G.M. and Lingireddy, S. (2003). Artificial neural network modelling: a summary of

Camarinha-Matos, L.M. and Martinelli, F.J. (1998). Application of machine learning in water

distribution networks. *Intelligent Data Analysis* 2, pp. 311-332.

hybrid fuzzy algorithms. *Journal of Water Supply: Research and Technology-AQUA*

successful applications relative to microbial water quality. *Water Science &*

A review and analysis of the literature has led to the following conclusions:

determine the water treatment technology system to apply.

colour, pH and the general hardness of the water.

the quality indicators of the water held in the reservoir.

reservoir water and the observed ones was undertaken using Microsoft Access 2007.

**5. Conclusions**

variables were defined.

purposes.

**6. References**

58(3), pp. 203–211.

*Technology* 47(3), pp. 235–240.

Zimoch, I. and Kłos, M. (2003). Use of IT to forecast eutrophication of surface water, "Dobczyce" reservoir as an example. *Environment Protection* 25(3), 73-76.

346 Cutting Edge Research in New Technologies

Zimoch, I. and Kłos, M. (2003). Use of IT to forecast eutrophication of surface water, "Dobczyce" reservoir as an example. *Environment Protection* 25(3), 73-76.

### *Edited by Constantin Volosencu*

The book "New Technologies - Trends, Innovations and Research" presents contributions made by researchers from the entire world and from some modern fields of technology, serving as a valuable tool for scientists, researchers, graduate students and professionals. Some practical applications in particular areas are presented, offering the capability to solve problems resulted from economic needs and to perform specific functions. The book will make possible for scientists and engineers to get familiar with the ideas from researchers from some modern fields of activity. It will provide interesting examples of practical applications of knowledge, assist in the designing process, as well as bring changes to their research areas. A collection of techniques, that combine scientific resources, is provided to make necessary products with the desired quality criteria. Strong mathematical and scientific concepts were used in the applications. They meet the requirements of utility, usability and safety. Technological applications presented in the book have appropriate functions and they may be exploited with competitive advantages. The book has 17 chapters, covering the following subjects: manufacturing technologies, nanotechnologies, robotics, telecommunications, physics, dental medical technologies, smart homes, speech technologies, agriculture technologies and management.

Photo by anyaberkut / iStock

New Technologies - Trends, Innovations and Research

New Technologies

Trends, Innovations and Research


**1. Introduction**

There is no doubt that aerial imagery is a source of valuable information about geographical space. The rapid development of remote sensing technology supported by a significant improvement in access to remote sensing imagery [1] led to an increased interest in the potential use of the collected material among academia, government, and private sector representatives in areas such as urban planning, agriculture, transport, etc. Substantial quantities of image data have become available in recent years thanks to opening public access to images acquired by satellites such as Landsat 8 [2], Sentinel-2

K.; Majchrowska, A. Aerial Imagery Feature Engineering Using Bidirectional Generative AdversarialNetworks: A Case Study of the PilicaRiver Region, Poland. *Remote Sens.* **2021**, *13*, 306. https://doi.org/ 10.3390/rs13020306

**Citation:** Adamiak, M.; B ˛edkowski,

Received: 20 December 2020 Accepted: 14 January 2021 Published: 17 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

A/B [3], and Pléiades [4]. Furthermore, due to the epidemiological situation in Poland, the government decided to open access to national orthophoto resources [5]. Access to highquality and properly curated image repositories undoubtedly promotes the development of new ideas and contributes to the emergence of various methods and techniques for analyzing collected data.

The use of aerial and satellite images in the basic task of remote sensing that deals with land cover and land use classification is indisputable. At an early stage of remote sensing development, the possibility of distinguishing certain spatial units by interpreting the spectral, textural, and structural features of the image was indicated. Ol ˛edzki postulated extracting homogenous fragments of satellite images called photomorphic units. These units were similar in terms of structure and texture and originated from natural processes and man-made transformations of the environment [6,7]. Descriptive definitions of image features were soon replaced by mathematical formulas [8]. Further development has led to the introduction of object classification procedures, in which, in addition to the brightness parameters of the image pixels, their neighborhood and the shape and size of the distinguished objects were taken into account [9]. Object-oriented analysis is based on databases and fuzzy logic. Probably the most popular implementation of this paradigm in remote sensing applications is in the one originally developed in eCognition [10]. These techniques have been successfully applied to research on landscape structure and forestry [11]. Referring to division units used in physico-geographic regionalization [12,13], Corine Land Cover [14,15], or for the purposes of ecological research [16,17], an additional meaning and hierarchical structure [18,19] can be also given to units distinguished from the landscape. At the same time, it is important to properly take care of the appropriate adjustments of the scale of the study and data relevant for the analyzed problem, so as not to overlook important features of the area that may affect the reliability of the analysis, i.e., to mitigate the issues connected with spatial object scaling and the scale problem [20]. What is important is that the separation of landscape units cannot be based only on image data [21]. It is also necessary to take into account data on lithology, morphogenesis, terrain, geodiversity [22], water, and vegetation. The latter is interesting due to the fact that vegetation is represented in remote sensing imagery to the greatest extent both in terms of properties and structure. Therefore, it has great potential for being utilized in landscape quantification [23]. It should be mentioned that the process of identifying landscape units is also affected by human activity and created by the cultural landscape. Another major achievement in remote sensing image classification is the introduction of algorithms based on neural networks [24].

The influence of machine learning and deep learning on contemporary remote sensing techniques and their support in geographical space analysis is undeniable [25]. There are multiple fascinating applications of machine learning (ML) and deep learning (DL) in the remote sensing domain like land use classification [26], forest area semantic segmentation [27], species detection [28], recognition of patches of alpine vegetation [29,30], classification of urban areas [31], roads detection [32], etc.

What a significant part of these studies have in common is the focus on utilizing convolutional neural network (CNN) architectures capable of solving problems that can be brought down to traditional computer vision (CV) tasks like semantic segmentation, instance segmentation, or classification. This is directly associated with the underlying mechanism that enables the network to encode complex image features. CNN's convolutional filters are gradually trained to gain the ability to detect the presence of specific patterns. Frequently, the training routine is performed in a supervised manner. The model is presented with target data and uses it to learn the solution. Supervised learning is capable of achieving extraordinary results but at the same time relies on access to manually labeled data. Another incredibly interesting approach is to train the neural network without any pre-existing labels to let it discover the patterns on its own. Although unsupervised learning algorithms like clustering are well-known among remote sensing researchers, utilizing convolutional neural networks is still to gain trust. The way of training a neural network can be even more intriguing when you exchange human supervision with machine

supervision, and let multiple neural networks control their learning progress and work like adversaries.

Generative adversarial networks (GANs) are constructed from at least one discriminator and one generator network. The main goal of these two networks is to compete with each other in the form of a two-player minimax game [33]. The generator tries to deceive the discriminator by producing artificial samples, and the discriminator assesses whether it is dealing with real or generator-originating samples. The generator network is producing samples from a specified data distribution by transforming vectors of noise [34]. This technique was successfully applied in multiple remote sensing activities from upsampling satellite imagery [35], deblurring [36] to artificial sample generation [37]. GANs' artificial data creation capabilities are not the only aspect that makes them interesting for remote sensing. When exploring the theory behind GANs, one should observe that, to perform its work, the generator retains all the information needed to produce a complex sample using only a much simpler representation called the latent code [33]. In terms of spatial analysis, this means that the network is able to produce a realistic image of an area using only a handful of configuration parameters as input. In the classic approach to GANs, this image recipe is reserved only for artificially generated samples. It was the introduction of bidirectional GANs and adversarial feature learning [38] that allowed to extract the latent code from ground truth (real) samples. The novelty of this approach when applied to aerial imagery is that it allows performing advanced spatial analysis using lower-dimensional representations of the orthophoto computed by a state-of-the-art neural network rather than utilizing raw image data. This method resembles algorithms like principal component analysis (PCA) but, instead of treating the image on the pixel level, it operates on the spatial features level and, therefore, offers a richer analysis context. The projection, a latent vector, serves as a lightweight representation of the image and holds information about the strength, occurrence, and interaction between spatial features discovered during the network training. This interesting capability opens up new possibilities for geographical space interpretation such as

The principal goal of our study is to evaluate the potential of bidirectional generative adversarial networks in remote sensing feature engineering activities and unsupervised segmentation. Therefore, the following hypotheses have been defined:

#### **2. Materials and Methods**

Figure 1 presents an overview of the proposed procedure composed of the following steps: preparing an orthophoto patches dataset, training the big bidirectional generative adversarial network (BigBiGAN), utilizing the network encoding module to convert orthophoto patches to their latent codes, enriching the data with geographical coordinates, and performing geospatial clustering on enriched latent codes.

**Figure 1.** Investigation overview.

#### *2.1. Research Area*

To be able to produce precise results, generative models need to be trained on highquality datasets. The dataset needs to be large enough to cover the variety of spatial features that the encoder will be able to utilize when interpreting the input image. The authors decided to utilize RGB orthophotos of the Pilica River and Sulejowski Reservoir regions in Poland. The area from which the samples have been obtained includes the Plica River valley between Maluszyn and Tomaszów Mazowiecki together with adjacent areas (see Figure 2).

According to the physico-geographical regionalization of Poland [13], the southern and eastern parts of the area are located in the province of Polish Uplands, the macroregion of the Przedbórz Upland, the mesoregions of Włoszczowa Basin, Radomsko Hills, Przedbórz-Małogoszcz Range, and Opoczno Hills. The northwestern part is located in the mesoregions of the Piotrków Plain and the Białobrzegi Valley, which are part of the South Mazovian Hills macroregion in the Central European Lowland Province. The consequence of the location in the border zone of the Polish Uplands and the Central European Lowland is the interpenetration of features characteristic of both provinces and the relative diversification of the natural environment of the area.

According to the tectonic regionalization [39], a fragment of the area located south of Przedbórz includes the Szczecin–Miechów Synclinorium, constructed mainly from Cretaceous rock formations. The rest of the area belongs to the Mid-Polish Anticlinorium, dominated by Jurassic carbonate rocks.

The axis of the selected area is the Pilica River valley. The chosen section of the valley is in a natural condition. The Pilica River flows in an unregulated, sinuous to a meandering channel that is not embanked along its entire length from Maluszyn to the vicinity of Sulejów. There, it reaches the Smardzewice dam waters of the Sulejowski Reservoir. The valley floor descends from 211 m above sea level in the south to 154 m above sea level in the north, and the stream gradient equals 0.51%. The width of the valley varies from about 300 m in the vicinity of Sulejów and Przedbórz to over 3 km near Ł ˛eg R ˛eczy ´nski. It reaches

its greatest width in places of well-formed levels of over-flood terraces occurring on both sides of the floodplain.

**Figure 2.** Research area, source: own elaboration based on physico-geographical regionalization of Poland [13] and Head Office of Geodesy and Cartography data [40]. Mesoregions codes are consistent with those introduced in the referenced paper and therefore enable precise localization of the research area within Polish mesoregions.

The valley cuts down the adjacent plain and undulating moraine uplands to about 20–25 m. These landforms were formed in the Quaternary, mainly during the Pleistocene glaciations of the Middle Polish Complex. Within the uplands on both sides of the Pilica River, the thickness of Quaternary sediments decreases from the north to the south. The surface area of Mesozoic outcrops increases, which is a result of the weakening of the landforming capacity of the ice sheets as they entered the uplands. Absolute heights of culminations, in the form of isolated hills built of Mesozoic Jurassic and Cretaceous rocks within the Radomsko and Opoczno Hills, are also increasing, e.g., Diabla Góra 272 m, Czartoria Range 267 m, B ˛akowa Góra 282 m, the form of ridges in the Przedbórz– Małogoszcz Range exceeding 300 m above sea level, or Bukowa Góra 336 m.

The varied topography and the near-surface geological structures, in addition to the humidity conditions, shape the mosaic of land cover types. The area is poorly urbanized. The ground moraine plateaus are dominated by arable land, fluvioglacial plains, and other sandy areas largely occupied by forests and abandoned arable lands. In the valley, the over-flood terraces are characterized by complex systems of arable land, fallow land, forests, and meadows. The floodplain is dominated by meadows and pastures, in many places overgrown with shrubs and trees after their agricultural use ceased [41].


*3.2. Plant Material*

The plants of *Sarcococca hookeriana* Baill. were collected in Hezhang County, Guizhou Province, China, in April 2012 and identified by Prof. Qingwen Sun, Guiyang College of Traditional Chinese Medicine. A voucher specimen (No.<PHONE_NUMBER>1) was deposited at the Key Laboratory of Miao Medicine of Guizhou Province, Guiyang College of Traditional Chinese Medicine.

#### *3.3. Extraction and Isolation*

The air-dried and powdered roots of *S*. *hookeriana* Baill. (2.5 kg) were extracted with 95% (25 L) EtOH under reflux three times, with an extraction time of 2 h. The combined extracts (443 g) were concentrated and suspended in H2O (3 L). The suspension was extracted with CHCl3 to obtain the CHCl3 fraction (94 g). The CHCl3 fraction was subjected to silica gel column chromatography (Si CC) and eluted with petroleum ether-diethylamine (100:1, 95:5, 9:1, 8:2) to yield four fractions (Fractions A−D). Fraction B (1.6 g) was subjected to Si CC and eluted with petroleum ether-diethylamine (100:2, 9:1) to yield the fractions B1−B3. Fraction B2 (330 mg) was chromatographed using Si CC and was developed with petroleum ether-diethylamine (100:2) to yield compounds **3** (21 mg) and **4** (35 mg). Si CC was performed on Fraction C (12.3 g) with a gradient eluent of petroleum ether-diethylamine (100:5, 9:1, 8:2) to yield four fractions (fractions C1−C4). Fraction C2 (1.4 g) was first subjected to Si CC (petroleum ether-diethylamine, 100:5), before being purified on Sephadex LH-20. This yielded compounds **2** (26 mg), **5** (30 mg) and **7** (31 mg). Fraction C4 (985 mg) was purified on Sephadex LH-20, before Si CC was used (petroleum ether-diethylamine, 9:1) to separate compounds **1** (33 mg) and **6** (19 mg).

## 3.3.1. Sarchookloide A (**1**)

This was a white amorphous powder with a HR-ESI-MS *m*/*z* of 461.3731 [M + H]+ (calculated for C28H49N2O3, 461.3738). The [α] 22 D was +52.99 (*c* 0.58, MeOH); UV (MeOH) had a λmax of 209.4 nm; and IR (KBr) had a νmax of 3424, 2931, 2867, 1662 and 1623 cm–1. The 1H and 13C NMR data are shown in Table 1.

#### 3.3.2. Sarchookloide B (**2**)

This was a white amorphous powder with a HR-ESI-MS *m*/*z* of 445.3775 [M +H]+ (calculated for C28H49N2O2, 445.3789). The [α] 22 D was +30.18 (*c* 0.57, MeOH); UV (MeOH) had a λmax of 205.8 nm; and IR (KBr) had a νmax of 3442, 2930, 2966, 1664 and 1629 cm–1. The 1H and 13C NMR data are shown in Table 1.

#### 3.3.3. Sarchookloide C (**3**)

This was a white amorphous powder with a HR-ESI-MS *m*/*z* of 429.3829 [M + H]+ (calculated for C28H49N2O, 429.3839). The [α] 22 D was +7.10 (*c* 0.62, MeOH); UV (MeOH) had a λmax of 207.2 nm; and IR (KBr) had a νmax of 3454, 2930, 2853, 1665 and 1625 cm–1. The 1H and 13C NMR data are shown in Table 1.

#### *3.4.  All cells were cultured in a RPMI-1640 or DMEM medium (Hyclone, Logan, UT, USA), which was supplemented with 10% fetal bovine serum (Hyclone) in 5% CO2 at 37 ◦C. The cytotoxicity assay was performed using the MTT method in 96-well microplates [22]. Briefly, the adherent cells (100 μL) were seeded into each well of 96-well cell culture plates and allowed to adhere for 12 h before the addition of the drug. The suspended cells were seeded just before the addition of the drug at an initial density of 1 × 105 cells/mL. Each tumor cell line was exposed to the tested compound at different concentrations for 48 h. The experiments were performed in triplicate. Adriamycin (Sigma, St. Louis, MO, USA) was used as a positive control. After treatment, cell viability was measured and the cell growth curve was plotted. The IC50 values were calculated by the Reed and Muench method [23].

#### **4. Conclusions**

We obtained three new pregnane-type steroidal alkaloids, sarchookloides A–C (**1**–**3**), along with four known compounds, pachysamine G (**4**), pachysamine H (**5**), sarcovagine B (**6**), and pachyaximine A (**7**), from the roots of *Sarcococca hookeriana*. The new compounds, sarchookloides A–C (**1**–**3**), were shown to possess a 3*α* substituent, which has rarely been reported. By performing a cytotoxic assay on Hela, A549, MCF-7, SW480 and CEM cell lines in vitro, all three amide substituted compounds

exhibited significant cytotoxic activities on all cells, which suggests that the three amide group of these compounds was the necessary group for the cytotoxicity. The most active compound, pachysamine H (**5**), inhibited all cancer cells with IC50 values in the range of approximately 1.05–2.23 μM. The results suggested that these types of steroidal alkaloids merit further biological evaluation of their cytotoxic activities and might have the potential to be studied for anticancer activity.

**Supplementary Materials:** The following 1H NMR, 13C NMR, 2D NMR, HR-ESI-MS spectra and the RAW data of the new compounds are available as supporting data. Supplementary materials are available online.

**Author Contributions:** K.H. and J.D. designed the experiments and revised the paper; K.H. and J.W. performed the experiments, analyzed the data, and wrote the paper; J.W. and J.Z. contributed to bioassay reagents and materials and analyzed the data; J.W., S.H. and J.D. revised the paper. All authors read and approved the final manuscript.

**Acknowledgments:** This research work was financially supported by the Science and Technology Foundation of Guizhou Province [grant number J (2013) 2071], the Science and Technology Cooperation Program of Guizhou Province [grant number LH (2015) 7277], the Youth Science and Technology Talent Project of Guizhou Province [grant number (2017) 5618], and the Open Project of the Key Laboratory of Miao Medicine of Guizhou Province [grant number K (2017) 004].

**Conflicts of Interest:** We wish to confirm that there are no known conflicts of interest associated with this publication and that there has been no significant financial support for this work that could have influenced its outcome.


**1. Introduction**

The ecosystem plays a very paramount role in support of human life. This is manifested in the four forms established by the 2005 Millennial Assessment of Ecosystems (TMAE), namely: 1. Provision of habitat, food, fiber and water; 2. Regulation of climatic conditions, water and air quality; 3. Support system for the formation of soils, recycling of soil nutrients, as well as soil health and quality and preservation of biodiversity; and 4. Cultural support involves cultural beliefs, tourism and recreation as well as herbal benefits [1, 2]. It is moreover, emphasized by [2] that ecosystems are essential for human life, with which without, there is no life. It can further be argued that an ecosystem is an anchor to human life. Ecosystem refers to the community of living organisms, their diverse physical environment, and their interrelationships within a unit space [3]. It is that environment that supports the flow of energy. As per [4], ecosystems aid in carbon sequestration as well as

the preservation of both biotic and abiotic organisms. This, therefore, is evidence enough to argue that forests support human life and their management is a policy matter.

However, growing scientific concerns point to the fact that the support and services humanity receives from the world's ecosystems are on a rapid decline due to forest degradation [1, 2, 5]. Forest refers to a complex ecological system with interdepended, interrelated and interconnected elements of both the biotic and abiotic organisms largely dominated by trees [1]. Furthermore, this may not be limited to the only current dominance of trees, as geographers argue that forests exist in the past, the now and the future. However, 31% of the worldwide land is under threat of degradation and this has negative implications for human life [6]. Therefore, this threat calls for proper forest management so that future generations will benefit from the diverse services and support of the same forest and ecosystems we benefit from now. Moreover, the degradation of our global forests is a result of factors such as climate change, veld fires, pests and diseases, air pollution, land pollution, forest fragmentation, and soil erosion and degradation [1–3].

Forest management is the practical application of efforts in controlling the use and exploitation of our forests by means of policy, administration, social, economic, environmental and technical aspects [6]. It is furthermore, a critical process which determines the existence and preservation of our forests for continued and/ or sustainable development across the world. Hence, the concept of Conservation Agriculture (CA), seeks to preserve and where possible improve the natural resources humanity has access to, for the production of food and fiber. Conservation Agriculture refers to a farming system that aid in the preservation of arable lands and natural resources while improving degraded lands [7]. This is possible when the three principles of CA are practiced across the Agriculture and Forestry sectors. The three pillars of CA are Minimal soil disturbance, permanent soil cover and crop diversification [7, 8]. CA has the potential to help achieve Sustainable Development Goals 13 and 15, namely: Climate Action and Life on Land, respectively. Therefore, this paper delineates the concept of CA in the context of forestry, highlights the CA practices for forest management, and presents the implications of CA in forest management. On the implications of CA in forest management, the chapter is mainly focused on the environmental aspects instead of a broader scope around social, economic and environmental aspects.

#### **1.1 Delineation of conservation agriculture**

Conservation Agriculture (CA) was first introduced in 1930, aiming at improving agricultural production and performance, profitability as well as sustainable farming and food security to fight poverty across the world [7]. Significantly, CA helps to further achieve Sustainable Development Goals 1, 13 and 15, that is Zero Hunger, Climate Action and Life on Land, respectively. Minimal soil disturbance, optimal soil cover and crop diversification are the three main pillars of CA [7, 8]. According to [8–11], CA is a resource-saving farming mechanism. Therefore, forestry cannot be an exception in the use of this beneficial farming system. This is to be done in line with the aim of forest management outlined in the introduction of this chapter, which is to ensure the preservation of forests and improve degraded forests across the world. **Figure 1** illustrates the three pillars of CA. The CA pillars are very practical, even in forest management. These come with enormous benefits such as reduced production costs, reduces soil compaction, improves soil health and structure, reduces soil

*The Implications of Conservation Agriculture in Forests Management against Soil Erosion… DOI: http://dx.doi.org/10.5772/intechopen.109755*

**Figure 1.** *The pillars of conservation agriculture.*

erosion, minimized soil water evaporation, enhanced soil nutrient status, enhanced biological and microbial activities, improves soil water-holding capacity, improves Cation Exchange Capacity, improves the management of weeds, pests and diseases and leads to improved livelihoods and food security [9, 12–14].

Conservation Agriculture (CA) is an agricultural system with the power to conserve natural resources, reduction of greenhouse gas emissions as well as enhancement of soil health and fertility [10]. In the quest to conserve nature, the powerful, agricultural system (CA), uses its three pillars (already highlighted in sections above) as guiding principles to be practiced simultaneously. Conservation agriculture, through the application of the three pillars, prohibits the burning of the crop remains, while promoting the use of integrated pest management and the use of green manure which has the ability to produce residue soil cover. It further limits the movements of humans and machinery on agricultural soils which also leads to soil compaction [9, 12]. According to [15, 16] minimal soil disturbance refers to the tilling of the ground or turning of the soil with an aim of not disturbing the microbial life, without exposing the soil to harsh climatic conditions (especially too high temperatures) and erosion (either by wind or water).

Moreover, this practice involves the conservation tillage practices such as zero tillage and reduced tillage. Zero tillage and minimal tillage both involve direct seeding [17]. Minimal soil disturbance is the first pillar of conservation agriculture, and it helps to retain moisture in the soil, increases soil organic matter content, improves soil health and also helps to mitigate the effects of high climatic conditions [17–19]. On the other hand, permanent soil cover is mainly practiced through the organic mulch, green manure, retention of crop residue as well as plastic mulch. However, conservation agriculture promotes organic mulch as well as the retention of crop residue [7, 8, 20]. Soil cover protects the soil from harsh climatic conditions (such as the sun and rain). Furthermore, permanent soil cover helps to enhance the microbial activities in the soil, enhances soil fertility, and conserves soil moisture and nutrients. Permanent soil organic cover plays a huge role also in suppressing weeds and controlling pests and diseases [13, 19]. Moreover, it positively contributes to the improvement of soil's physical, chemical and biological properties.

Remarkably, an improvement in the soil's chemical, physical and biological properties improves the structure of the soil [18]. The more the soil structure is improved, the more soil fertility is improved. Therefore, permanent soil cover, significantly, benefit farmers in terms of soil water conservation, weeds, pests and diseases management, soil fertility enhancement, as well as the protection of the soil from climatically harsh conditions. Crop rotation assists in the improvement of crop nutrition, improves the farming system's resilience, manages pests, diseases and weeds, and improves microbial activities as well as enhancing the farm's agronomic output and economic efficiency [9, 13, 17–19]. Therefore, the three pillars of CA practiced simultaneously can yield good results. It is also evident, that CA is the vehicle farmers require, in order to achieve sustainable agriculture and adapt to the ever-changing climate. It can never be overemphasized that this powerful farming system is important for forest management for both sustainable development and nature conservation.

#### *1.1.1 Delineation of conservation agriculture in the context of forestry*

Agroforestry (AF) and Conservation Agriculture (CA) cannot be separated because they complement each other for the success of nature conservation and the Sustainable Development Goals (SDGs) 1, 13 and 15 as articulated in the introduction of this chapter. Agroforestry refers to a farming system that seeks to manage the land by means of combining agriculture, shrubs and trees [18]. Agroforestry also comes with some known benefits for both the environment and human life, such involves improved soil health, higher yields and chances of maximizing income for improved livelihoods [18, 21]. Forests are a support system for the ecological systems (ecosystems) and they do this through the continuous production and conservation of the soils, water and air [1, 21]. The conservation and enhancement of the soil and water in our forests through Forest Conservation Management Practices [FCMP] guarantees the prevention of land degradation, and desertification [21]. These further reduce the already existing risks of natural disasters such as floods, droughts and landslides. Conservation Agriculture is key to ensuring proper or conservative management of our forests across the world. Therefore CA in the context of forestry refers to a farming system which applies conservation principles in the management of forests to ensure the sustainable development and conservation of natural resources, simultaneously. These are made possible by the key principles of CA, namely: optimal ground or soil cover and minimal soil disturbance.

Ground cover is a strategy employed by forest managers, both in natural and planted forests to ensure reduced surface runoff and erosion in forests in order to achieve optimal forest performance [3, 5, 6]. On the other hand, minimal soil disturbance is the strategy to produce similar results as ground cover, however, it restricts ground tillers to turn the soils deep [7]. Ground cover, therefore, refers to conservation agricultural practice that involves allowing low-growing shrubs and/or planting low-growing plants, shrubs, grasses and wildflowers in order to prevent soil erosion [3–7]. While minimal soil disturbance, by definition refers to the farming practice that involves all ways of farming which avoids tilling the ground or soil [13]. This in the agricultural sector can be understood as direct seeding, which happens mostly, without disturbing the soil with the use of mechanical implements. Both these principles of Conservation Agriculture are essential to yield soil preservation, which then assures forest conservation. In the absence of soils, there can be no trees or forests. Hence, the CA practices aid to conserve and improve the soils in our forests.

*The Implications of Conservation Agriculture in Forests Management against Soil Erosion… DOI: http://dx.doi.org/10.5772/intechopen.109755*


**1. Introduction**

The rapid industrialization and intensive agricultural activities over the last few decades have resulted in accumulation of various pollutants in the environment, which are distributed over wide areas by means of air and water. This has caused visible detrimental effects to the ecosystem and consequences to human health. Today, many soils throughout the world have undesirably high concentrations of heavy metals. These include lead (Pb), cadmium (Cd), zinc (Zn), mercury (Hg), arsenic (As), silver (Ag), chromium (Cr), copper (Cu), iron (Fe), and the platinum group elements. At low or background concentrations, heavy metals are not pollutants. They occur naturally in the environment due to their presence in bedrocks. Some heavy metals such as Zn and Cu are also essential micronutrients for living organisms. Therefore, the term *heavy metal pollution* refers to heavy metal levels that are abnormally high relative to normal background levels. All heavy metals at high concentration have strong toxic effects and are regarded as environmental pollutants.

Some heavy metals (like Fe, Zn, Ca and Mg) have been reported to be of bio-importance to man and their daily medicinal and dietary allowances have been recommended. However, some others (like As, Cd, Pb, and methylated forms of Hg) have been reported to have no known bio-importance in human biochemistry and physiology and consump‐ tion even at very low concentrations can be toxic [1]. Even for those that have bio-impor‐ tance, dietary intakes have to be maintained at regulatory limits, as excesses result in poisoning or toxicity [2]. Although individual metals exhibit specific signs of their toxici‐ ty, the following have been reported as general signs associated with Cd, Pb, As, Hg, Zn,

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cu and Al poisoning: gastrointestinal disorders, diarrhoea, stomatitis, tremor, ataxia, paralysis, vomiting and convulsion, depression, and pneumonia when volatile vapours and fumes are inhaled [3]. The nature of effects could be toxic (acute, chronic or sub-chronic), neurotoxic, carcinogenic, mutagenic or teratogenic.

Pb, Zn, Cu, Co, Mn, Fe, Cr and Cd have been found in the streams and rivers of the Americas, Europe, Asia, Africa and Australia [4-9]. In India, presence of heavy metals has been reported in the Brahmaputra [10]; the Kali and Hindon [11]; and more recently, in the Gomti [12]; the Cauvery [13]; and the Ganga [14].

The Yamuna (also Jamuna or Jumna) is the largest tributary of the Ganga in northern India, having the total length of about 1376 km. The source of Yamuna is Yamunotri in the Uttarak‐ hand Himalaya, which is north of Haridwar in the Himalayan mountains. Yamuna river flows through the states of Uttarakhand, Delhi, Haryana and Uttar Pradesh and finally merges with river Ganges at a sacred spot known as Triveni Sangam in Allahabad. A number of prominent cities such as Delhi, Mathura and Agra lie on the bank of river Yamuna. Over 57 million people depend on the Yamuna waters. Just like the Ganges, the Yamuna too is highly venerated in Hinduism and worshipped as goddess Yamuna, throughout its course.

Due to high density population growth, rapid industrialization, today Yamuna is one of the most polluted rivers in the world, especially around New Delhi, where 15 drains discharge waste water into the river. The city dumps ~58% of its waste into it. When the river enters the city, it is already contaminated with 7500 coliform content per 100 ml. when it leaves the city, it carries with a dangerously high coliform content of 24 million per 100 ml. Even the ground water has been affected by leachates that pass down from the dumping sites. According to the Central Pollution Control Board (CPCB), 70% of the pollution in river is from untreated sewage and the remaining 30% is from industrial sources, agricultural run-off, garbage etc. The water quality of Yamuna River falls under the category "E" which makes it fit only for recreation and industrial cooling, completely ruling out the possibility for underwater life. Almost every year mass death of fishes is reported. Biological Oxygen Demand (BOD) load increased by 2.5 times between 1980 and 2005: from 117 tonnes per day in 1980 to 276 in 2005.

Although the government of India has spent nearly \$500 million to clean up the river, the river continues to be polluted with garbage while most sewage treatment facilities are underfunded or malfunctioning. The Ministry of Environment and Forests (MoEF) of the Government of India (GOI) took measures to curb pollution in 12 towns of Haryana, 8 towns of Uttar Pradesh, and Delhi under an action plan (Yamuna Action Plan-YAP) which is being implemented since 1993 [15]. However in 2009, the Union government admitted the failure of the Ganga Action Plan (GAP) and the Yamuna Action Plan (YAP), saying that "rivers Ganga and Yamuna are no cleaner now than two decades ago" despite spending over Rs 1, 700 crore to control pollution [16]. In August 2009, Delhi Jal Board (DJB) initiated its plan for resuscitating a 22 km stretch of the Yamuna in Delhi by constructing intercep‐ tor sewers, at the cost of about Rs 1, 800 crore [17].

There are three main sources of pollution in the river, namely household and municipal disposal sites, agricultural run-off, and industrial effluents and run-off. Urban runoff and agricultural runoff are mainly non–point sources. The major sources of pollution from agriculture are fertilizers containing superabundant nutrients such as nitrogen and phospho‐ rus, and heavy metals such as Cd, Cu, Pb and Zn. Water quality may also be altered by other factors, such as livestock manure, human waste, and atmospheric deposition. Atmospheric pollutants are often the largest source of waterborne metals. It is estimated that 70% of lead in water and over 50% of many of the other trace metals in the Great Lakes (USA) are derived from atmospheric transfer. In general, freshwater ecosystems have low natural background metal levels and therefore tend to be sensitive to even small additions of most trace metals. Heavy metal contamination of soils and water from industrial and traffic sources in urban environments has been studied in North America and Europe [18-22]. Agencies like the World Health Organization (WHO) and the United states Environment Protection Agency (USEPA) have set stringent standards for maximum permissible limits of heavy metals, but there is a paucity of detailed studies on heavy metal pollution and its remediation within industrial zones in developing countries. Yamuna outnumbers any other river in the number of indus‐ tries on its bank. This is because it passes through many major industrial cities. About 22, 42, and 17 large and medium industrial units in the states of Haryana, Delhi, and Uttar Pradesh have been identified as polluting the river in the action plan area. In addition, the water in this river remains stagnant for almost 9 months in a year aggravating the situation.

Cu and Al poisoning: gastrointestinal disorders, diarrhoea, stomatitis, tremor, ataxia, paralysis, vomiting and convulsion, depression, and pneumonia when volatile vapours and fumes are inhaled [3]. The nature of effects could be toxic (acute, chronic or sub-chronic),

Pb, Zn, Cu, Co, Mn, Fe, Cr and Cd have been found in the streams and rivers of the Americas, Europe, Asia, Africa and Australia [4-9]. In India, presence of heavy metals has been reported in the Brahmaputra [10]; the Kali and Hindon [11]; and more recently, in the Gomti [12]; the

The Yamuna (also Jamuna or Jumna) is the largest tributary of the Ganga in northern India, having the total length of about 1376 km. The source of Yamuna is Yamunotri in the Uttarak‐ hand Himalaya, which is north of Haridwar in the Himalayan mountains. Yamuna river flows through the states of Uttarakhand, Delhi, Haryana and Uttar Pradesh and finally merges with river Ganges at a sacred spot known as Triveni Sangam in Allahabad. A number of prominent cities such as Delhi, Mathura and Agra lie on the bank of river Yamuna. Over 57 million people depend on the Yamuna waters. Just like the Ganges, the Yamuna too is highly venerated in

Due to high density population growth, rapid industrialization, today Yamuna is one of the most polluted rivers in the world, especially around New Delhi, where 15 drains discharge waste water into the river. The city dumps ~58% of its waste into it. When the river enters the city, it is already contaminated with 7500 coliform content per 100 ml. when it leaves the city, it carries with a dangerously high coliform content of 24 million per 100 ml. Even the ground water has been affected by leachates that pass down from the dumping sites. According to the Central Pollution Control Board (CPCB), 70% of the pollution in river is from untreated sewage and the remaining 30% is from industrial sources, agricultural run-off, garbage etc. The water quality of Yamuna River falls under the category "E" which makes it fit only for recreation and industrial cooling, completely ruling out the possibility for underwater life. Almost every year mass death of fishes is reported. Biological Oxygen Demand (BOD) load increased by 2.5 times

Although the government of India has spent nearly \$500 million to clean up the river, the river continues to be polluted with garbage while most sewage treatment facilities are underfunded or malfunctioning. The Ministry of Environment and Forests (MoEF) of the Government of India (GOI) took measures to curb pollution in 12 towns of Haryana, 8 towns of Uttar Pradesh, and Delhi under an action plan (Yamuna Action Plan-YAP) which is being implemented since 1993 [15]. However in 2009, the Union government admitted the failure of the Ganga Action Plan (GAP) and the Yamuna Action Plan (YAP), saying that "rivers Ganga and Yamuna are no cleaner now than two decades ago" despite spending over Rs 1, 700 crore to control pollution [16]. In August 2009, Delhi Jal Board (DJB) initiated its plan for resuscitating a 22 km stretch of the Yamuna in Delhi by constructing intercep‐

There are three main sources of pollution in the river, namely household and municipal disposal sites, agricultural run-off, and industrial effluents and run-off. Urban runoff and

Hinduism and worshipped as goddess Yamuna, throughout its course.

between 1980 and 2005: from 117 tonnes per day in 1980 to 276 in 2005.

tor sewers, at the cost of about Rs 1, 800 crore [17].

neurotoxic, carcinogenic, mutagenic or teratogenic.

Cauvery [13]; and the Ganga [14].

546 Environmental Risk Assessment of Soil Contamination

According to the Agra District Industrial Centre officials, there were 226 iron foundries and about 340 metal casting units functioning in Agra in the decade of 1990-2000. Before the revised pollution control directives put the Agra diesel generator manufacturing industry off its track, the foundry industry of this town ranked among the country's largest assemblies of metal casting industrial units, generating business of over Rs 6, 000 crores. The ban on coking coal in the blast furnaces utilized by the foundry and metal-casting industry was a serious setback and the number of industrial units reduced drastically. In August 1999, the Supreme Court ordered the closure of 53 iron foundries and 107 other factories in Agra. In September 2010, it again ordered the closure of 212 of the 1, 715 small industries that had failed to disclose their toxic emission levels to the Uttar Pradesh Pollution Control Board (UPPCB). Another 299 were required to install pollution controlling devices, failing which they too would face closure. However, the ground realities are still nowhere near the reduced pollution levels targeted in Yamuna and its adjacent areas whether Agra or elsewhere, after it leaves the Himalayan foothills. The status quo, thus, ultimately leaves much to be desired.

Phytoremediation is an emerging technology that employs the use of green plants for the clean up of contaminated environment. It takes the advantage of the fact that a living plant acts as a solar-driven pump, which can extract and concentrate certain metals from the environment [23]. This remediation method maintains the biological properties and physical structure of the soil. The technique is environmentally friendly, cost-effective, visually unobtrusive, and offers the possibility of bio-recovery of the metals. In the case of heavy metal contamination in soil, phytoremediation techniques are narrowed down to *Phytoextraction,* where plants remove metals from the soil by concentrating them in their harvestable parts [24], and *Phytostabilization*, where plants reduce the mobility and bioavailability of pollutants by immobilization [25].

Phytoremediation is becoming possible because of the successful basic and applied research much of it conducted with the productive interdisciplinary cooperation of plant biologists, soil chemists, microbiologists and environmental engineers. Extensive progress has been made in characterizing and modifying the soil chemistry of the contaminated site to accelerate phy‐ toremediation. The greatest progress in phytoremediation has been made with metals [26, 27]. Phytoremediation leaves the topsoil in usable condition and it is aesthetically pleasing. It requires minimal equipment and less energy inputs as plants do most of the work using solar energy. Thus, it is an eco-friendly process. The plants used can later be harvested, processed and disposed off in an environmentally sound manner. This technology has been receiving attention lately as an innovative, cost-effective alternative to the otherwise tedious and expensive methods in use which are not only a burden on the exchequer but also require efforts on recurring basis.

Phytoremediation employing indigenous species can be an ecologically viable option for sustainable and cost-effective management. Native plants often become adapted to locally elevated levels of metals in soil at contaminated sites, e.g. mines and industrial zones [28-30] and metal toxicity issues do not generally arise. Many native, well adapted plants have been investigated and even used for heavy metal bioindicatoring and phytoremedial purposes including lemongrass and other wild grasses, vetiver, *Sesbania*, *Avena*, *Crotalaria*, *Crinum asiaticum, Typha latifolia* and *Calotropis procera* etc. [31-35, 28]. Native wild species are also important to remediate soils in context of the studied area due to a remark (April, 2006) of the Supreme Court prohibiting the cultivation of plants requiring fertilizers and pesticides along the Yamuna. In the light of this limitation, native wild species are a viable option since these do not require agronomic inputs.

Since the river Yamuna is the life line of Mathura and Agra, the existing pollution level has posed a serious threat not only to the environment but also to the human population. Adjacent areas are highly polluted and are a sink for a variety of chemicals including heavy metals. The present study was undertaken: (i) to get a comprehensive profile of eight metals in water and adjacent soils of the river Yamuna within Mathura, Agra and Bateshwar; (ii) to get a qualitative and quantitative estimate of the species present at test sites through phyto-sociological surveys; and (iii) to inventorize species with potential for phytoremediation present on sites by comparing with those previously reported by the authors as suitable in this context.