Mahmud Tanvir Shahrier, Ng Kelvin Tsun Wai, Hasan Mohammad Mehedi, An Chunjiang, Wan Shuyan
Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan S4S 0A2, Canada.
Department of Building, Civil, and Environmental Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec H3G 1M8, Canada.
Sustain Cities Soc. 2023 Sep;96:104685. doi: 10.1016/j.scs.2023.104685. Epub 2023 May 28.
There is currently a lack of studies on residential waste collection during COVID-19 in North America. SARIMA models were developed to predict residential waste collection rates (RWCR) across four North American jurisdictions before and during the pandemic. Unlike waste disposal rates, RWCR is relatively less sensitive to the changes in COVID-19 regulatory policies and administrative measures, making RWCR more appropriate for cross-jurisdictional comparisons. It is hypothesized that the use of RWCR in forecasting models will help us to better understand the residential waste generation behaviors in North America. Both SARIMA models performed satisfactorily in predicting Regina's RWCR. The SARIMA D model's performance is noticeably better during COVID-19, with a 15.7% lower RMSE than that of the benchmark model (SARIMA B). The skewness of overprediction ratios was noticeably different between jurisdictions, and modeling errors were generally lower in less populated cities. Conflicting behavioral changes might have altered the residential waste generation characteristics and recycling behaviors differently across the jurisdictions. Overall, SARIMA D performed better in the Canadian jurisdiction than in U.S. jurisdictions, likely due to the model's bias on a less variable input dataset. The use of RWCR in forecasting models helps us to better understand the residential waste generation behaviors in North America and better prepare us for a future global pandemic.
目前北美地区缺乏关于新冠疫情期间居民垃圾收集情况的研究。本研究构建了季节性自回归整合移动平均模型(SARIMA),用于预测北美四个辖区在疫情之前及期间的居民垃圾收集率(RWCR)。与垃圾处理率不同,RWCR对新冠疫情管控政策和行政措施的变化相对不那么敏感,这使得RWCR更适合进行跨辖区比较。研究假设,在预测模型中使用RWCR将有助于我们更好地了解北美地区的居民垃圾产生行为。两个SARIMA模型在预测里贾纳市的RWCR时均表现良好。在新冠疫情期间,SARIMA D模型的表现明显更好,其均方根误差(RMSE)比基准模型(SARIMA B)低15.7%。不同辖区的预测比率偏度存在显著差异,且人口较少城市的建模误差通常较低。不同辖区内相互矛盾的行为变化可能对居民垃圾产生特征和回收行为产生了不同影响。总体而言,SARIMA D模型在加拿大辖区的表现优于美国辖区,这可能是由于该模型对变化较小的输入数据集存在偏差。在预测模型中使用RWCR有助于我们更好地了解北美地区的居民垃圾产生行为,并让我们更好地为未来的全球大流行做好准备。