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J Environ Manage

Title:		Genetic algorithm (GA) - Artificial neural network (ANN) modeling for the emission rates of toxic volatile organic compounds (VOCs) emitted from landfill working surface
Author(s):		Li R; Xu A; Zhao Y; Chang H; Li X; Lin G;
Address:		"School of Environment, Beijing Normal University, Beijing, 100875, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin, 300191, PR China. School of Environment, Beijing Normal University, Beijing, 100875, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin, 300191, PR China. Electronic address: yanzhao@bnu.edu.cn. School of Environment, Beijing Normal University, Beijing, 100875, PR China"
Journal Title:		J Environ Manage
Year:		2022
Volume:		20220105
Issue:
Page Number:		114433 -
DOI:		10.1016/j.jenvman.2022.114433
ISSN/ISBN:		1095-8630 (Electronic) 0301-4797 (Linking)
Abstract:		"Volatile organic compounds (VOCs) emitted from the working surface of landfills have received increasing attention due to the potential risks to human health. Quantifying the emission rates of risky VOCs is important to their health risk assessment but is also challenging because of their high variation and complicated relationship between the emission rates and various influencing factors. In this study, a continuous nine-month sampling of VOCs was conducted on a landfill working surface to identify dominant VOCs that are risky to human health and to construct artificial neural network (ANN) models for their emission rates by involving 105 datasets. Among the 63 detected VOCs, ethanol presented the highest emission rate (885.28 +/- 1398.10 mug.m(-2).s(-1)), and the dominant compounds with high emission rates and detection frequencies were characterized in each category. According to the human toxicity impact scores calculated with USEtox method, carbon tetrachloride, ethanol, tetrachloroethylene, 1, 2-dichloroethane, benzene, ethylbenzene, and chloroform were identified as the dominant carcinogenic VOCs, and acrolein, carbon tetrachloride, and 1, 2-dichloropropane were the dominant noncarcinogenic VOCs. ANN models were established for the emission rates of six typical risky VOCs, with meteorological conditions and waste compositions as input parameters and emission rates as output parameters. With the structure optimization and genetic algorithm, all the ANN models achieved good performance and excellent prediction capability with high R(2) and low root mean square error (RMSE) values. The emission rates under a 95% probability were predicted for each risky VOCs via the established ANN models, by randomly sampling the input parameters under their data distribution. The approach proposed and results obtained can provide scientific methodology and important information for the monitoring, prediction, and health risk assessment of the VOCs emitted from MSW landfills"
Keywords:		"*Air Pollutants/analysis Benzene Environmental Monitoring Humans Neural Networks, Computer *Volatile Organic Compounds/analysis Waste Disposal Facilities Ann Emission rate Human health risk Landfill VOCs;"
Notes:		"MedlineLi, Rong Xu, Ankun Zhao, Yan Chang, Huimin Li, Xiang Lin, Guannv eng England 2022/01/08 J Environ Manage. 2022 Mar 1; 305:114433. doi: 10.1016/j.jenvman.2022.114433. Epub 2022 Jan 5"