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A GIS Based Assessment of Waste Storage System and Identification of Waste Bins




CASE STUDY AREA
Two blocks (Sutluj and Chenab) of Allama Iqbal Town were considered as study area. Allama Iqbal Town is a thickly populated commercial and residential locality in the south-western Lahore. The area of Satluj and Chenab Blocks is 329465.474871 sq. meter. There are 530 dwelling units with population of the 3482 persons. There are five waste containers placed by solid waste management department, out of which two are of 5m3 capacity and remaining three of 10m3 making total capacity of 40 m3.


Figure 1: Case Study Area

Table 1: Population on the basis of Socioeconomic Structure


MATERIALS AND METHODS
The study methodology consisted of the following

• Digitizing / demarcation of the existing roads, residential area, Hospitals, clinics, Commercial Plazas and Public buildings on 0.6 m satellite imagery. Using this, the development of landuse classification map.
• Identification of existing location of Municipal bins/ through GPS coordinates.
  
  Table 2: Solid waste Containers GPS Coordinates with Location
  
  
• Designing and conducting Questionnaire survey for Households, Commercial areas as well as for Hospitals and Clinics.
• Division of study area into three economic classes (low, middle and high) based on plot sizes (1 kanal, 10, 5, 7 Marlas).
• Use of GIS for the analysis of present and proposed solid waste storage system.

SAMPLE COLLECTION AND SEGREGATION
For this study both the primary and secondary data were collected. Stratified sampling was used to calculate the generation rate as well as determining the physical composition.

There were total 530 dwelling units; out of it 50 houses were selected for detailed sampling. From 50 houses, 5 from high income, 20 from middle income and 25 from low income groups were selected. These houses were selected randomly on the basis of socioeconomic groups and cover the 10% of the residential area. On the same basis 6 shops were selected out of 60 shops for detailed sampling (Parks and Brockman, 2000). Sample of waste was also collected from 3 clinics, 1 hospital and 2 offices.

There were 5 Municipal bins.3 of them were considered to be serving residential area due to their close proximity to houses and 2 were near commercial markets so they were supposed to serve the commercial area.so 200 kg waste sample was collected from residential Municipal bins as well as from commercial bins(ASTM Standards).

     
Figure 2: Sample collection and weighting in field    Figure 3: Sample collection from Municipal bin

After collecting the sample from different sources, the waste was segregated manually into 11 different physical components like paper, polythene bags, bottles, wood etc. Each of these categories of waste was weighed to determine its fraction in the total solid waste collected. Segregation of household was done as well as commercial and municipal bin waste.


Figure 4: (a & b) Segregation of sample in lab (c) weighting Sample after segregation

LABORATORY SAMPLE ANALYSIS
Moisture Content
After collection and segregation, 5kg of organic waste was collected in polythene bag from both houses and municipal bins as sample for analyzing moisture content. It was put in drying oven at 105oC for 24 hours. After drying, the waste was weighted at electric analytical and precise balance. The moisture content is the percent sample weight lost in drying. It can be calculated by using following formula.

M=100 X W-D/W

W= Sample wet weight
D= Sample dry weight


Figure 5: Organic Waste with Moisture Figure 6: Organic Waste without Moisture


Figure 7: Grinding of Dry Waste

RESULTS AND DISCUSSION
Results derived from Waste Composition


Table 3: Total amount of waste generated in the area


Table 4: Amount of waste w.r.t Socioeconomic Structure


Table 5: Composition of waste from Municipal bins (near houses)

Table 6: Composition of waste from Municipal bins (near Commercial Area)

Results derived from Laboratory Sample Analysis

Moisture Content
Considerable amount of organic waste was recorded from household as well as from commercial. Household organic waste had 57% moisture content whereas communal bin near residential area had 70% moisture content and municipal bin near commercial area had 67% moisture content. The variation in moisture content reveals that household waste is collected daily by private sweepers that’s why it has moisture content less as compared to moisture content of municipal bin waste. This shows that the household waste is thrown into nearby Municipal bin on the daily basis but the waste in the Municipal bin remains for a long time.

Generation Rate
The total population of the area is 3482 and the amount of waste is 804.53 kg/day (0.804 ton/day), so the per capita generation rate is 0.2 kg/cap/day or 1.46 kg/house/day.

Statistical analysis of Socio-economic and Commercial waste data

Classification of area according to Plot Size

The results of field survey showed that most of the residents living in this area are having plot size 10 Marla and belongs to middle income group.



Methods of Waste Collection
From the field survey, it was recorded that 72% houses employ the private sweeper to collect the waste from their residents. Further 28% households throw the waste by themselves.


Table 7: Collection of waste


Convenient Distance
From the field survey, it was recorded that the preferable distance of municipal bin for the households is 100 meters. In other words people have to travel 4 minutes to throw the wastes and come back to home. But some people argued about the 200 m (8 minutes) distance that it could be difficult to travel such a distance with bags full of waste.



Status of Organic Waste
During field survey it was noted that most of the residents employ private Sweepers so their household waste is to be collected from their houses.18% of household throw their solid waste to nearby communal bin by themselves and 10% residents throw the waste in the street.