STUDY AREA

6.0 Integrated Wastewater Management System
The treatment of domestic sewage in natural systems such as constructed wetlands and lagoons is being practiced in developing nations. Significant advantages are its construction and operation are simple and economically viable. Lagoon systems are associated with a high growth rate of phytoplankton that are beneficial and are caused by the influence of light and the continuous nutrient inflow. Algal growth contributes towards the treatment of wastewater by transforming dissolved nutrients into particle aggregates (biomass). Algal retention in the lagoon helps in the treatment, which has to be harvested at regular interval to ensure effective treatment. Wetlands consisting of reed-bed and algal pond help in the removal of nutrients (Mahapatra et al., 2011; 2013).

Emergent macrophytes (such as Typha) act as a filter in removing suspended matter and avoiding anaerobic conditions by the root zone oxidation and the dissolved nutrients would be taken up by the lagoon algae. This type of treatment helps in augmenting the existing treatment system in complete removal of nutrients and bacteria. The combination of wetlands (with macrophytes assemblages), algal lagoon and a sustained harvesting of algae and macrophytes would provide complete solution to wastewater treatment systems with minimal maintenance. Integrated wetland system at Jakkur provides an opportunity to assess the efficacy of treatment apart from providing insights for replicating similar systems to address the impending water scarcity in the rapidly urbanising Bangalore.

6.1 Insights to the efficacy of treatment: The treatment plant (1.6 Ha) with an  installed capacity of 10 MLD, comprises of an Upflow Anaerobic Sludge Balnket Reactor (UASB) with an extended aeration system for sewage treatment. The treatment effluent then gets into wetlands (settling basin) of  spatial extent ~4.63 hecatres consisting of diverse macrophytes such as  Typha sp., Cyperus sp., Ludwigia sp., Alternanthera sp., Water hyacinth sp., etc.  in the shallow region (with an area of ~1.8 hectares) followed by  deeper algal basin (covering an area of about 2.8 hecatre). This being the significant functional component with macrophytes and algae jointly helps in the nutrient removal and wastewater remediation. The water from the settling basin flow pases through three sluices of which only the middle one is functional (with moderate flow). This water flows into Jakkur lake that spans over 45 hectares. There were notably less occurance of floating macrophytes, except near the outfalls (~0.5 Ha)  due to blockage of the ouflow channels by solid wastes and debris. These macrophytes are being managed by local fishermen. Water in the Jakkur lake is clear with abundant phytoplankton diversity and acceptable densities, which indicates of a healthy trophic status.
The nutrient analysis shows (illustrated in Figure 4), that treatment happens due to  immergent macrophytes of the wetlands and algae, which  removes  ~45% COD, ~66 % BOD, ~33 % NO3-N and ~40 % PO43—P.  Jakkur lake treats the water and acts as the final level of treatment which shown as stage two that removes ~ 32 % COD, ~23% BOD, ~ 0.3 %  NO3-N and ~34 % PO43—P.. The synergestic mechanism of sewage treatment plants followed by wetlands helps in the complete removal of nutrients to acceptable levels according to CPCB norms.

Table 5: Macrophytes of Wetlands at Jakkur

7.0 Land use (LU) Dynamics in Wetlands Catchment
Land use changes in the wetland catchments are the direct and indirect consequence of human actions to secure essential resources. These changes encompass the greatest environmental concerns of human populations today, including loss of biodiversity, pollution of water and soil, and changes in the climate.  Monitoring and mitigating the negative consequences of LU changes, while sustaining the production of essential wetlands resources has therefore become a major priority today.
Land use change analyses is done using Landsat MSS (1973), IRS P6 data (2013) and Google Earth (http://earth.google.com). The Landsat data is cost effective, with high spatial resolution and freely downloadable from public domains like GLCF (http://glcfapp.glcf.umd.edu:8080/esdi/index.jsp) and USGS (http://glovis.usgs.gov/). IRS P6 LISS-IV (Indian Remote Sensing Satellite, part of the Indian Space Programme) data was procured from the National Remote Sensing Centre, Hyderabad (http://www.nrsc.gov.in).
Remote sensing data obtained were geo-referenced, rectified and cropped corresponding to the study area. Geo-registration of remote sensing data (Landsat data) has been done using ground control points collected from the field using pre calibrated GPS (Global Positioning System) and also from known points (such as road intersections, etc.) collected from geo-referenced topographic maps published by the Survey of India. In the correction process numerous GCP’s are located in terms of their two image coordinates; on the distorted image and in terms of their ground coordinates typically measured from a map or located in the field, in terms of UTM coordinates as well as latitude and longitude. The Landsat data of 1973 are with a spatial resolution of 57.5 m x 57.5 m (nominal resolution), while IRS P6 are of 5.8 m.
Land use analyses involved (i) generation of False Color Composite  (FCC)  of  remote  sensing  data  (bands–green,  red  and  NIR). This composite image helps in locating heterogeneous patches in the landscape, (ii) selection of training polygons by covering 15% of the study area (polygons are uniformly distributed  over  the  entire  study  area) (iii)  loading  these  training polygons  co-ordinates  into  pre-calibrated  GPS,  (vi)  collection  of  the corresponding attribute data (land use types) for these polygons from  the  field.  GPS  helped  in  locating  respective  training  polygons in the field, (iv) supplementing this information with Google Earth and  (v)  60%  of  the  training  data  has  been  used  for  classification, while  the  balance  is  used  for  validation  or  accuracy  assessment. The land use analysis was done using supervised classification technique based on Gaussian maximum likelihood algorithm with training data (collected from field using GPS).
Classifier based on Gaussian Maximum Likelihood algorithm has been widely applied as an appropriate and efficient classifier to extract information from remote sensing data.  This approach quantitatively evaluates both the variance and covariance of the category spectral response patterns when classifying an unknown pixel of remote sensing data, assuming the distribution of data points to be Gaussian. After evaluating the probability in each category, the pixel is assigned to the most likely class (highest probability value). GRASS GIS (Geographical Resources Analysis Support System, http://ces.iisc.ernet.in/grass) a free and open source software with the robust support for processing both vector and raster data has been used for analyzing RS data. Temporal remote sensing data have been classified through supervised classification techniques by using available multi-temporal “ground truth” information. Earlier  time  data were classified using the training  polygon  along with  attribute  details  compiled  from  the  historical  published topographic  maps, vegetation  maps,  revenue  maps, land records available from local administrative authorities.

 

Temporal remote sensing data of Landsat (1973) and IRS data (2013) were classified into four land use categories (Figure 9): tree vegetation, built-up, water-bodies and others (agriculture, open area, etc.). The analyses show decline of tree vegetation by 50%  from 44.06% (1973) to 22.38% (2013), with an increase in built-up from 1.19 (1973) to 6.56% (2013). Details of land use analyses are listed in Table 6.