Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method.

Physical composition of municipal solid waste (PCMSW) is the fundamental parameter in domestic waste management, however, high fidelity, wide coverage, upscaling, and year continuous datasets of PCMSW in China were insufficient. A traceable and predictable methodology for estimating PCMSW in China was established for the first time, by analyzing 503 PCMSW datasets of 136 prefecture-level cities in China. A hyperspherical transformation method was used to eliminate the constant sum constraint in statistically analyzing PCMSW data. Moreover, BP neural network methodology was applied to establish quantitative models between city-level PCMSW and its socioeconomic factors, including city size, per capita gross regional product, geographical location, gas coverage rate, and year. Results show that: (1) national-level PCMSW in 2017 was estimated as organic fraction (53.7%), ash and stone (8.3%), paper (16.9%), plastic and rubber (13.6%), textile (2.3%), wood (2.2%), metal (0.6%), glass (1.5%), and others (1.0%);(2) organic fraction, paper, and plastics showed an increasing trend from 1990 to 2017, while ash and stone decreased significantly; (3) organic fractions in East, North, and Central-south China were higher than those in other regions. It enables to fill the data gap in the practice of municipal solid waste management in China.