Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables
Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables
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DOI：
10.3390/ijerph17062025
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发表时间：
2020-03-02
影响因子：
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通讯作者：
Klemm, Otto
中科院分区：
文献类型：
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作者：
Goulier, Laura;Paas, Bastian;Klemm, Otto
由于运营城市空气质量站不仅耗时而且成本高昂，而且空气污染物会导致严重的健康问题，本文采用人工神经网络（ANN）方法对明斯特街道峡谷中十种空气污染物浓度（CO2、NH3、NO、NO2、NOx、O-3、PM1、PM2.5、PM10和PN10）进行每小时预测。特别注意比较代表交通量的三个预测选项：我们将声学声音测量（声音）、车辆总数（交通）以及一天中的小时和一周中的某一天（时间）作为输入变量，然后比较它们的预测能力。这些模型经过训练、验证和测试以评估其性能。结果表明，气态空气污染物NO、NO2、NOx和O-3的预测与观测结果非常吻合，而颗粒浓度和NH3的预测不太成功，表明这些模型可以改进。所有三个输入变量选项（声音、交通和时间）都被证明是合适的，并且在模拟各种空气污染物浓度方面显示出明显的优势。
Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O-3, PM1, PM2.5, PM10 and PN10) in a street canyon in Munster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O-3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.