A GIS Based Study Of Generation And Disposal Management Of Demolition Waste

Ongoing urbanization prompted a massive urban development and renovation activities, resulting in a huge number of demolition waste(DW) during the process of demolition and reconstruction of buildings. To optimize the DW management, systematic research on managing process should be conducted at first, and followed by the accurately obtain DW’ life cycle information. However, through the in-depth literature review, it show that there is a lack of research regarding the waste lifecycle management process and key links, and the basic inventory data according to the generated and handed DW are also scarce. Meanwhile, there is also a lack of more reasonable method to estimating and forecasting the temporal and spatial distribution of generated waste, and a lack of effective measures to assessing the economic resumes and environmental impacts of DW. Only by addressing these fundamental issues, the management effectiveness then would be improved.First, this study discuss the flow and management process of DW from the perspective of life cycle management, and employs the Process Mapping Approach(PMA) to identifying the DW’ life cycle process and link-depth characterization. The results show that the life cycle of DW management mainly consists of process like waste generation, on-site management, waste transportation, treatment and disposal; and that covers management activities like building demolition, waste collection, sorting, classification, pretreatment, loading, etc. and disposal methods such as recycling, landfill, pit backfill, roadbed laying and illegal dumping; involves many stakeholders including contractors, demolition companies, removal companies, recycling companies, landfills, government departments and scavengers.Second, that using the Geographic Information System(GIS) to combing the waste generation attribute information with the urban building GIS data to predicting the generation of DW. To be specific, take the Nanshan District in Shenzhen City as a case study by forecasting the generation and temporal distribution of DW in Nanshan between 2015 and 2060. The results show that there are 135 million tons DW should generated in the study area during the study period, of which concrete contributes 63 million tons, accounting for 46% of the total; From 2015 to 2027, the generation amount maintains a low level, while from the year 2028 onward, the average annual generation of DW should increase dramatically to 400 million tons; For regional distribution, three zones that generate the largest amount of DW are Yuehai(28 million tons), Shahe(18 million tons) and Nantou(17 million tons).Third, scenarios analysis is conducted to illustrating the resource recycling value and landfill need of DW generated in study area. The results show that the resource recycling value under the three scenarios were 27 billion yuan(worst), 32.6 billion yuan(reality) and 42 billion yuan(ideal) respectively. If the current recycling rate could increase to the desired level, then resource recycling value should increase 30%; When considering the average value of three scenarios, the recycling value of steel was reaches 26 billion yuan(77.4% of the total) with the weight of 5% of all wastes, followed by brick/block(2.6 billion, 7.7%) and concrete(2.2 billion yuan, 6.4%). However, the increasing potential of steel is not large sine it’s recycling rate already very high, therefore, interests should focus on improving recycling rate of concrete and brick/block waste, by improving the recycling methods to improve unit resource recycling value of particular product; Under the worst scenario, the demand for landfill space should be 50 million which means, from 2033 onwards, it is need to build landfills compacting 10 million m3 in every 6.25 year. Besides, with the promotion of recycling rate, the demand for landfill space can be reduced up to 80%.What’s more, those typical process and key links of DW management system identified in this study provide useful information for optimizing waste management, the model could employed to evaluating the temporal and spatial distribution data of DW management from generation to disposal, of which the results can provide useful data to recycling and landfill industry as well as government department regarding waste management. In addition, a large number of detailed generation and composition data of DW was obtained during the study, which could provide important information for related research.