scholarly journals Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

2021 ◽  
Vol 14 (12) ◽  
pp. 7411-7424
Author(s):  
Moritz Lange ◽  
Henri Suominen ◽  
Mona Kurppa ◽  
Leena Järvi ◽  
Emilia Oikarinen ◽  
...  

Abstract. Running large-eddy simulations (LESs) can be burdensome and computationally too expensive from the application point of view, for example, to support urban planning. In this study, regression models are used to replicate modelled air pollutant concentrations from LES in urban boulevards. We study the performance of regression models and discuss how to detect situations where the models are applied outside their training domain and their outputs cannot be trusted. Regression models from 10 different model families are trained and a cross-validation methodology is used to evaluate their performance and to find the best set of features needed to reproduce the LES outputs. We also test the regression models on an independent testing dataset. Our results suggest that in general, log-linear regression gives the best and most robust performance on new independent data. It clearly outperforms the dummy model which would predict constant concentrations for all locations (multiplicative minimum RMSE (mRMSE) of 0.76 vs. 1.78 of the dummy model). Furthermore, we demonstrate that it is possible to detect concept drift, i.e. situations where the model is applied outside its training domain and a new LES run may be necessary to obtain reliable results. Regression models can be used to replace LES simulations in estimating air pollutant concentrations, unless higher accuracy is needed. In order to have reliable results, it is however important to do the model and feature selection carefully to avoid overfitting and to use methods to detect the concept drift.

2020 ◽  
Author(s):  
Moritz Lange ◽  
Henri Suominen ◽  
Mona Kurppa ◽  
Leena Järvi ◽  
Emilia Oikarinen ◽  
...  

Abstract. Running large-eddy simulations (LES) can be burdensome and computationally too expensive from the application point-of-view for example to support urban planning. In this study, regression models are used to replicate modelled air pollutant concentrations from LES in urban boulevards. We study the performance of regression models and discuss how to detect situations where the models are applied outside their training domain and their outputs cannot be trusted. Regression models from 10 different model families are trained and a cross-validation methodology is used to evaluate their performance and to find the best set of features needed to reproduce the LES outputs. We also test the regression models on an independent testing dataset. Our results suggest that in general, log-linear regression gives the best and most robust performance on new independent data. It clearly outperforms the dummy model which would predict constant concentrations for all locations (mRMSE of 0.76 vs 1.78 of the dummy model). Furthermore, we demonstrate that it is possible to detect concept drift, i.e., situations where the model is applied outside its training domain and a new LES run may be necessary to obtain reliable results. Regression models can be used to replace LES simulations in estimating air pollutant concentrations, unless higher accuracy is needed. In order to have reliable results, it is however important to do the model and feature selection carefully to avoid over-fitting and to use methods to detect the concept drift.


Author(s):  
Laura Goulier ◽  
Bastian Paas ◽  
Laura Ehrnsperger ◽  
Otto Klemm

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.


Author(s):  
B. Yorkor ◽  
T. G. Leton ◽  
J. N. Ugbebor

This study investigated the temporal variations of air pollutant concentrations in Ogoni area, Niger Delta, Nigeria. The study used hourly data measured over 8 hours for 12 months at selected locations within the area. The analyses were based on time series and time variations techniques in Openair packages of R programming software. The variations of air pollutant concentrations by time of day and days of week were simulated. Hours of the day, days of the week and monthly variations were graphically simulated. Variations in the mean concentrations of air pollutants by time were determined at 95 % confidence intervals. Sulphur dioxide (SO2), Nitrogen dioxide (NO2), ground level Ozone (O3) and fine particulate matter (PM2.5) concentrations exceeded permissible standards. Air pollutant concentrations showed increase in January, February, November and December compared to other months. Simulation showed that air pollutants varied significantly by hours-of-the-day and days-of-the-week and months-of-the-year. Analysis of temporal variability revealed that air pollutant concentrations increased during weekdays and decreased during weekends. The temporal variability of air pollutants in Ogoni area showed that anthropogenic activities were the main sources of air pollution in the area, therefore further studies are required to determine air pollutant dispersion pattern and evaluation the potential sources of air pollution in the area.


2021 ◽  
pp. 045
Author(s):  
Jimmy Leyes ◽  
Laure Roussel

La surveillance réglementaire de la qualité de l'air en France est confiée aux associations régionales agréées de surveillance de la qualité de l'air (Aasqa) telles qu'Atmo Hauts-de-France. Elles s'appuient sur une palette d'outils et leur expertise pour mesurer les polluants dans l'air de leur territoire, alerter les populations en cas d'épisode de pollution, répondre aux exigences réglementaires de surveillance définies au niveau européen, tout en prenant en compte les spécificités régionales. Cet article présente les différents outils utilisés par les Aasqa, et plus particulièrement Atmo Hauts-de-France, pour surveiller et estimer la qualité de l'air. L'association régionale opère ainsi un ensemble de stations de mesures fixes et mobiles pour suivre en continu les concentrations de polluants réglementés ou non sur son territoire, et dispose d'outils de modélisation pour évaluer et prévoir la qualité de l'air en tous points de la région. Cet article présente également certains des paramètres météorologiques qui influencent la qualité de l'air de la région Hauts-de-France, particulièrement concernée par les épisodes de pollution aux particules. Regulatory air quality monitoring in France is performed by government-approved non-profit organisations called AASQAs, one of which is Atmo Hauts-de-France. These organisations rely on decades of accumulated air quality expertise and use several techniques to measure air pollutant concentrations, inform the public when pollutant levels are unhealthy, and comply with E.U. air quality monitoring regulations. This paper gives an overview of the tools used by AASQAs, and more particularly by Atmo Hauts-de-France, to monitor and forecast air quality. The year-round continuous monitoring of air pollutant levels at fixed sites is supplemented by short-term measurements made with fully-equipped vehicles or trailers and by modelling tools that forecast air quality and estimate pollutant levels where there are no measurements. AASQAs study pollutants which ambient concentrations are regulated by European air quality standards as well as other pollutants which are not regulated in this way. This work also discusses some of the meteorological factors, that affect air quality in the region Hauts-de-France, which is heavily impacted by particulate matter pollution.


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