Xu Ankun, Li Rong, Chang Huimin, Xu Yingjie, Li Xiang, Lin Guannv, Zhao Yan
School of Environment, Beijing Normal University, Beijing 100875, PR China; State Ecology and Environment 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.
Waste Manag. 2022 Feb 1;138:158-171. doi: 10.1016/j.wasman.2021.11.045. Epub 2021 Dec 9.
Landfills release significant odorous compounds from the working surface, and their emission rates are crucial for odor and health risk assessment. A total of 99 valid datasets of odor emissions from a landfill working surface were obtained from in situ monitoring for 9 months. Meteorological parameters (temperature, humidity, atmospheric pressure) and waste properties (contents of protein, lipid, carbohydrate, ash, and moisture) were used to construct artificial neural network (ANN) models for the emission rate prediction of typical compounds. The optimal structures and performance of the ANN models were determined by comparing and training with different structural configurations. The ANN models with genetic algorithm (GA) optimization show better performance than those without GA. With the data distribution of input parameters, the ranges of the emission rates of typical compounds were predicted by combining the established ANN models and the Monte Carlo approach. The sensitivity and uncertainty analyses revealed that temperature, atmospheric pressure, protein and lipid contents are parameters sensitive to emission rates, and meteorological parameters have significant impacts on the uncertainty. The established ANN models for the prediction of emission rates can provide scientific evidence and an approach to assess and control the odor and health risk in waste sectors.
垃圾填埋场从作业面释放出大量有气味的化合物，其排放速率对于气味和健康风险评估至关重要。通过9个月的现场监测，共获得了99个来自垃圾填埋场作业面的有效气味排放数据集。利用气象参数（温度、湿度、大气压力）和废物特性（蛋白质、脂质、碳水化合物、灰分和水分含量）构建人工神经网络（ANN）模型，用于预测典型化合物的排放速率。通过与不同结构配置进行比较和训练，确定了ANN模型的最优结构和性能。经过遗传算法（GA）优化的ANN模型比未经过GA优化的模型表现更好。结合已建立的ANN模型和蒙特卡罗方法，根据输入参数的数据分布预测了典型化合物排放速率的范围。敏感性和不确定性分析表明，温度、大气压力、蛋白质和脂质含量是对排放速率敏感的参数，气象参数对不确定性有显著影响。所建立的用于预测排放速率的ANN模型可为评估和控制垃圾处理行业的气味和健康风险提供科学依据和方法。
