引用本文:王月秋.考虑道路扬尘特性的环境空气质量在线监测方法[J].环境监控与预警,2022,14(6):52-56
WANG Yue-qiu.An Online Air Quality Monitoring Method for Atmospheric Environment Considering Road Dust Characteristics[J].Environmental Monitoring and Forewarning,2022,14(6):52-56
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考虑道路扬尘特性的环境空气质量在线监测方法
王月秋
唐山市生态环境局玉田县分局,河北 唐山 063000
摘要:
为判断环境空气污染的程度,提出一种考虑道路扬尘特性的环境空气质量在线监测方法。通过前端数据监测模块采集大气环境数据,基于光全散射法监测道路扬尘的质量浓度和密度;通过监测通信模块将监测结果传送至云服务器;云服务器利用基于极限学习机神经网络的预测模型,采用自适应粒子群优化算法,获取最佳的环境空气质量在线监测结果。结果表明,该方法学习速率的取值为0.5时,能够完成颗粒物浓度和密度的准确检测,且解释方差<2%,同时能够监测扬尘颗粒的扩散时间,确定适合活动的区域。
关键词:  道路扬尘  空气质量  在线监测  环境数据  光全散射法
DOI:
分类号:X831
基金项目:
An Online Air Quality Monitoring Method for Atmospheric Environment Considering Road Dust Characteristics
WANG Yue-qiu
Yutian County Branch of Tangshan Ecological and Environment Bureau, Tangshan, Hebei 063000, China
Abstract:
In order to judge the degree of ambient air pollution, an online monitoring method of ambient air quality considering the characteristics of road dust is proposed. The data monitoring module collects the atmospheric environment data, and monitors the mass concentration and density of road dust based by total light scattering method. The monitoring results are transmitted to the ECS through the monitoring communication module. The ECS uses the prediction model based on neural network of the extreme learning machine and the adaptive particle swarm optimization algorithm to obtain the best online monitoring results of ambient air quality. The results show that when the learning rate of the method is 0.5, the concentration and density of particles can be accurately detected, and the explained variance is less than 2%. At the same time, the diffusion time of dust particles can be monitored to determine the suitable for activity area.
Key words:  Road dust characteristics  Air quality  Online monitoring  Environmental data  Total light scattering method