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.