Identification and verification of PCDD/Fs indicators from four typical large-scale municipal solid waste incinerations with large sample size in China
文献类型：期刊论文
| 作者 | Liu, Lijun2; Chen, Xichao2,3; Yin, Wenhua2; Wu, Hao1; Huang, Junbin1; Yang, Yanyan2; Gao, Zhiqiang2; Huang, Jinqiong2; Fu, Jianping2; Han, Jinglei2 |
| 刊名 | WASTE MANAGEMENT
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 172页码:101-107 |
| 关键词 | MSWI Emissions Indicator Traditional statistical method Machine learning method |
| ISSN号 | 0956-053X |
| DOI | 10.1016/j.wasman.2023.10.016 |
| 英文摘要 | Monitoring PCDD/Fs emissions from municipal solid waste incinerations (MSWIs) is of paramount importance, yet it can be time-consuming and labor-intensive. Predictive models offer an alternative approach for estimating their levels. However, robust models specific to PCDD/Fs were lacking. In this study, we collected 190 PCDD/Fs samples from 4 large-scale MSWIs in China, with the average PCDD/Fs levels and TEQ levels of 0.987 ng/m(3) and 0.030 ng TEQ/m(3), respectively. We developed and evaluated predictive models, including traditional statistical methods, e.g., linear regression (LR) as well as machine learning models such as back propagation-artificial neural networks (BP ANN) and random forest (RF). Correlation analysis identified 2,3,4,7,8-PeCDF, 1,2,3,6,7,8-HxCDF, 2,3,4,6,7,8-HxCDF were better indicator congeners for PCDD/Fs estimation (R-2 > 0.9, p < 0.001). The predictive results favored the RF model, exhibiting a high R-2 value and low root mean square error (RMSE) and mean absolute error (MAE). Additionally, the RF model showed excellent prediction ability during external validation, with low absolute relative error (ARE) of 10.9 %-12.6 % for the three indicator congeners in the normal PCDD/F TEQ levels group (<0.1 ng TEQ/m(3)) and slightly higher ARE values (13.8 %-17.9 %) for the high PCDD/F TEQ levels group (>0.1 ng TEQ/m(3)). In conclusion, our findings strongly support the RF model's effectiveness in predicting PCDD/Fs TEQ emission from MSWIs. |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001104475300001 |
| 源URL | [http://ir.gig.ac.cn/handle/344008/79146]  |
| 专题 | 有机地球化学国家重点实验室 |
| 通讯作者 | Han, Jinglei |
| 作者单位 | 1.Shenzhen Energy Environm Co LTD, Shenzhen 518055, Peoples R China 2.Minist Ecol & Environm, South China Inst Environm Sci, Guangzhou 510000, Peoples R China 3.Chinese Acad Sci, State Key Lab Organ Geochem, Guangzhou Inst Geochem, Guangzhou 510640, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Lijun,Chen, Xichao,Yin, Wenhua,et al. Identification and verification of PCDD/Fs indicators from four typical large-scale municipal solid waste incinerations with large sample size in China[J]. WASTE MANAGEMENT,2023,172:101-107. |
| APA | Liu, Lijun.,Chen, Xichao.,Yin, Wenhua.,Wu, Hao.,Huang, Junbin.,...&Han, Jinglei.(2023).Identification and verification of PCDD/Fs indicators from four typical large-scale municipal solid waste incinerations with large sample size in China.WASTE MANAGEMENT,172,101-107. |
| MLA | Liu, Lijun,et al."Identification and verification of PCDD/Fs indicators from four typical large-scale municipal solid waste incinerations with large sample size in China".WASTE MANAGEMENT 172(2023):101-107. |
入库方式： OAI收割
来源：广州地球化学研究所
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