State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, China.
State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, China.
Waste Manag. 2022 May 1;144:445-453. doi: 10.1016/j.wasman.2022.04.024. Epub 2022 Apr 21.
Cities in mainland China are usually classified according to geographical locations. This traditional city classification system is limited to relative fixed factors, which lives out a gap in terms of the spatial differences of municipal solid waste (MSW). Developing a more comprehensive city classification system is essential for MSW generation prediction and waste management. In this study, six economic, social and climatic indicators that affect MSW generation: population, per capita GDP (PCGDP), environmental sanitation investment (ESI), average temperature, average precipitation, and average humidity, are selected. Weights were calculated for each indicator using a combination of CRITIC weight method and Pearson correlation coefficient prior to cluster analysis. The k-means clustering algorithm was used to classify all cities into four clusters, which differed significantly in the relationships between MSW generation and influencing factors. The results of Kruskal-Wallis test also show that cities in different clusters show different distributions in terms of the indicators selected. The cross-prediction results of the model further validate the reliability of the clustering results from a quantitative perspective. By establishing a city classification system, cities with similar relationships between MSW generation and influencing factors can be placed into one cluster. The model established in one certain city cluster can be used to predict the MSW generation for cities in the same cluster that lack historical data. This may also help to formulate appropriate regional policies according to different relationships between MSW generation and influencing factors, especially for the four city clusters in the mainland China.
中国内地的城市通常根据地理位置进行分类。这种传统的城市分类系统仅限于相对固定的因素，与城市固体废物（MSW）的空间差异不符。因此，建立一个更全面的城市分类系统对于 MSW 产生预测和废物管理至关重要。在本研究中，选择了六个影响 MSW 产生的经济、社会和气候指标：人口、人均 GDP（PCGDP）、环境卫生投资（ESI）、平均温度、平均降水量和平均相对湿度。使用 CRITIC 权重法和 Pearson 相关系数组合计算每个指标的权重，然后进行聚类分析。使用 k-均值聚类算法将所有城市分为四个聚类，这些聚类在 MSW 产生与影响因素之间的关系上存在显著差异。Kruskal-Wallis 检验的结果还表明，不同聚类中的城市在所选指标方面表现出不同的分布。模型的交叉预测结果从定量角度进一步验证了聚类结果的可靠性。通过建立城市分类系统，可以将具有相似 MSW 产生与影响因素关系的城市归入一个聚类。在一个特定城市聚类中建立的模型可以用于预测同一聚类中缺乏历史数据的城市的 MSW 产生量。这也有助于根据 MSW 产生与影响因素之间的不同关系制定适当的区域政策，特别是对于中国内地的四个城市聚类。
