Indian Scenario of Hazardous Waste Management | Ecology

After reading this article you will learn about the Indian scenario of hazardous waste management.

Identification of Hazardous Waste Generation:

The HW generation in Indian States is given in Table 22.3. The data shows that the HW genera­tion is maximum in Maharashtra (45.47%) fol­lowed by Gujarat (9.73%). Minimum HW is re­ported in Chandigarh (0.0069%). The number of industries that generate I1W are maximum in Maharashtra (30.38%) followed by Gujarat (22.93%). The data shows that, 13011 industries are generating 4415954 TPA of HW in India.

Waste Characterisation:

The HW in India is characterised and documented in literature. The HWs are categorized into three groups viz., Recyclable, Incinerable, and Dispos­able. The details are given in Fig. 22.1 for Maharashtra, Gujarat and India (total). It can be noted from Fig. 22.1 that, the HW generation trends in Maharashtra and India (total) are similar. The quantity of disposable HW (inorganic in na­ture to be disposed off in landfill) is high compared to the other two categories.

Quantification of Hazardous Wastes:

The quantity of HW generation reported in India is 4415954 TPA from 373 districts out of 524 dis­tricts. According to one estimate, the land required to dispose 5.3 million tones of HW in an engi­neered landfill, assuming the average density of waste to be around 1.2 tonnes/m³ and the depth of the landfill 4 m, would be around 1.08 km² every year. This information may be applied to future waste projections to arrive at future land requirements for the disposal of hazardous waste.

Identification of Sites for Disposal:

The number of sites identified for disposal of HW in India is 89 out of which 39 sites are notified. The State/Union Territory wise status of identifi­cation and notification of sites for disposal is given in Fig. 22.2.

The sites are ranked using a ranking methodology given in Guidelines (1991). The details of individual attributes are given in Table 22.4. The Site Sensitivity Indices (SSIs) are prepared for rank­ing the available sites with respect to thirty-four (34) selected attributes. These attributes are based on the migration, characteristics, waste manage­ment practices for the wastes to be disposed at the TSDF.

The Sensitivity Index (SI) for each attribute is evaluated on a four-level sensitivity scale ranging from 0 to 1 (0.0-0.25, 0.25-0.5, 0.5-0.75, and 0.75-1.0). The aspects to be considered for attribute mea­surement are identified depending on the impor­tance of the attribute. Based on the field data avail­able, this attribute can be graded on the four-level- scale for the particular site.

A total of 1000 points are divided among the four criteria of attributes @ 320, 280, 220, and 180 respectively using Delphi technique (Refer Table 22.4). Each of the 34 at­tributes is given weights based on the magnitude of its impact. The value of the SI multiplied by the corresponding weightage would give the at­tributed score for each attribute.

In the same way, score for all the attributes will be calculated and final attributed score for the site is obtained. This score is compared with the similar scores of the other sites available and all the sites are ranked as per the scores with the least score site given the top ranking.

The total scores (out of 1000) can thus be interpreted in terms of the sensitivity of the site as follows:

(i) Score below 300: Very low sensitivity

(ii) Score between 300-450: Low sensitivity

(iii) Score between 450—600: Moderate sensitivity

(iv) Score between 600—750: High sensitivity

(v) Score above 750: Very high sensitivity

A close examination of the options for rank­ing the sites has resulted in the following observa­tions:

1. The upper and lower limits for few attributes are not clearly defined.

2. The sensitivity scale distribution for some of the selected attributes is not clear, and also nonlinear when overall distribution is consid­ered.

3. The error/ambiguity in the prediction of SSI could lead to erratic ranking of the site desig­nated for TSDF.

Twelve attributes out of 34 attributes are iden­tified having the above limitations. They are listed in Table 22.5. A model based on Re­gression analysis is developed to address the above limitations. The data given in Guide­lines (Guidelines, 1991) is taken as reference for the entire analysis.

4. The analysis is carried out taking each attribute, case by case.

5. Regression analysis is carried out to find out the Best-Fit Mathematical Model (BFMM) suitable for the data points of each attribute. Additional points are also generated by plot­ting graphs wherever necessary, for accurate fitting.

6. An analysis is carried out by considering Lin­ear Interpolation Among the Intervals (LIAI) (i.e., 0.25-0.5 & 0.5-0.75) specified in the Guidelines for all the data points.

7. An additional analysis is also carried out for cross-checking by considering an Overall Lin­ear Distribution Model (OLDM) of all the data points i.e., linear variation from 0.25- 0.75.

8. The above three analyses viz., BFMM, LIAI and OLDM are compared and conclusions are drawn.