Department of Environmental Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India.
Department of Environmental Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India.
Waste Manag. 2018 Sep;79:781-790. doi: 10.1016/j.wasman.2018.08.045. Epub 2018 Sep 1.
Plastic waste generation is an inevitable product of human activities, however its management faces challenges in many cities. Understanding the existing patterns of plastic waste generation and recycling is essential for effective management planning. The present study established a relationship between plastic waste generation rate and the identified socioeconomic groups, higher socioeconomic group (HSEG), middle socioeconomic group (MSEG), and lower socioeconomic group (LSEG) of the study area (Dhanbad, India). For identification of the socioeconomic groups, four different socioeconomic parameters were considered (total family income, education, occupation and type of houses). The information related to the identified parameters were obtained using questionnaire survey conducted in the selected households. One week plastic waste sampling was carried out in the households of all the socioeconomic groups. The plastic waste generated in the study area was 5.7% of the total municipal solid waste. In terms of total plastic waste generation rate, it was found that HSEG had maximum (51 g/c/d) and LSEG had minimum (8 g/c/d) generation rate. The present study area does not have any formal waste recycling system. Thus, the amount of plastic waste recovered and the revenue generated from recycling of plastic waste by the active informal recyclers (waste pickers, itinerant waste buyers and scrap dealers) in the study area have been evaluated. Additionally, three non-linear machine learning models i.e., artificial neural network (ANN), support vector machine (SVM) and random forest (RF) have been developed and compared for the prediction of plastic waste generation rate.
塑料废物的产生是人类活动不可避免的产物,然而,其管理在许多城市都面临挑战。了解现有的塑料废物产生和回收模式对于有效的管理规划至关重要。本研究建立了塑料废物产生率与研究区域(印度丹巴德)确定的社会经济群体(高社会经济群体、中社会经济群体和低社会经济群体)之间的关系。为了确定社会经济群体,考虑了四个不同的社会经济参数(家庭总收入、教育、职业和房屋类型)。使用在选定家庭中进行的问卷调查获得与确定参数相关的信息。在所有社会经济群体的家庭中进行了为期一周的塑料废物抽样。研究区域产生的塑料废物占城市固体废物的 5.7%。就总塑料废物产生率而言,高社会经济群体(HSEG)的产生率最高(51g/c/d),低社会经济群体(LSEG)的产生率最低(8g/c/d)。本研究区域没有任何正式的废物回收系统。因此,评估了活跃的非正式回收者(拾荒者、流动废品买家和废品经销商)在研究区域内回收的塑料废物量和回收塑料废物产生的收入。此外,还开发并比较了三种非线性机器学习模型,即人工神经网络(ANN)、支持向量机(SVM)和随机森林(RF),以预测塑料废物产生率。