Abstract
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, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster 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 O3 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.
Highlights
The effect of air pollutant exposure on human health has been intensively studied; exposure increases the risk of several diseases for both children and adults [1,2,3]
The equivalent sound pressure level (LZeq), the total number of vehicles and the hour and day of the week were compared as three different input variable options representing the motor traffic intensity
Using all three input variable options, NH3 concentrations were predicted around the mean of observations without much variation
Summary
The effect of air pollutant exposure on human health has been intensively studied; exposure increases the risk of several diseases for both children and adults [1,2,3]. NOx , tropospheric O3 , PM, CO2 and particle number concentration (PN) are highly important in terms of climate change, leading the EU to commit to complying with the specified limit values for NO2 , O3 , PM2.5 and PM10 [6]. Another pollutant, ammonia, is an atmospheric precursor for fine inorganic aerosol formation, and it is toxic to the environment [7,8]. The LANUV operates background stations in many cities in North Rhine-Westphalia (NRW). Continuous data for physical sound pressure were recorded with an NTI Sound Level Meter XL2 at 24 Bit [31] and using a Class
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