基于β射线吸收法的PM2.5测量技术的研究

针对空气中粒径小于或等于2.5μm的可入肺颗粒物对人体的严重危害,设计了基于β射线吸收法实时监测PM2.5浓度值的监测仪器。利用粉尘粒子吸收β射线的量与粉尘粒子的质量成正比关系的原理,通过测量β射线通过干净滤纸和含有粉尘滤纸前后强度的变化,测量出PM2.5的浓度值。通过与标准过滤称重法的对比校准,验证了测量仪器的准确性。

Multi-Scale Variation Prediction of PM2.5 Concentration Based on a Monte Carlo Method

Haze concentration prediction,especially PM2.5,has always been a significant focus of air quality research,which is necessary to start a deep study.Aimed at predicting the monthly average concentration of PM2.5 in Beijing,a novel method based on Monte Carlo model is conducted.In order to fully exploit the value of PM2.5 data,we take logarithmic processing of the original PM2.5 data and propose two different scales of the daily concentration and the daily chain development speed of PM2.5 respectively.The results show that these data are both approximately normal distribution.On the basis of the results,a Monte Carlo method can be applied to establish a probability model of normal distribution based on two different variables and random sampling numbers can also be generated by computer.Through a large number of simulation experiments,the average monthly concentration of PM2.5 in Beijing and the general trend of PM2.5 can be obtained.By comparing the errors between the real data and the predicted data,the Monte Carlo method is reliable in predicting the PM2.5 monthly mean concentration in the area.This study also provides a feasible method that may be applied in other studies to predict other pollutants with large scale time series data.

基于生成对抗网络模型的小样本PM_(2.5)预测

针对目前数据驱动的方法在小样本下PM_(2.5)预测准确率较低的问题,提出一种基于生成对抗性网络(GAN)模型PME-GAN,用于在线预测PM_(2.5)浓度值。在生成器中加入长短期记忆网络(LSTM)并用于提取输入数据的时序特征,在判别器中加入多层感知机网络(MLP),通过生成器对PM_(2.5)浓度值进行预测。与LSTM、GRU、CNN-LSTM和CNN-GRU 4种模型进行对比实验,结果表明,该方法在小样本数据集上具有更高的预测准确率,对保定测试集的后25%数据开始预测,预测效果很好。