Prediction of the NO2 concentration data in an urban area using multiple regression and neuronal networks

Dragomir, Carmelia; Voiculescu, Mirela; Constantin, Daniel-Eduard; Georgescu, L.

doi:10.1063/1.4937255

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…This study focuses on the dependence between meteorological factors including temperature, pressure, wind speed, wind direction, solar radiation, rainfall, and relative humidity and their influence on measured NO 2 concentration. The results indicated that MLP has a higher correlation coefficient than MLR in the forecasting of air quality and meteorological factors have an impact upon the NO 2 concentration 38 . Rahimi used MLP and MLR for prediction of the NO 2 and NO x concentrations according to meteorological variables.…”

Section: Discussionmentioning

confidence: 96%

“…Artificial Neural Network (ANN) models could be used as interpolation methods for complex and non-linear problems such as predicting and modeling air pollution 17,35,36 . Several research exhibits the performance of ANNs and traditional regression models to predict air pollutant concentrations 19,[37][38][39] . Dragomir et al compared the multiple linear regression and multilayer perceptron for the forecast of the NO 2 concentration in Romania.…”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Shams

Jahani²,

Kalantary

et al. 2021

Sci Rep

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Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Shams

Jahani²,

Kalantary

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…So, the concentrations of PM 10 , NO 2 and SO 2 , in the studied area, were decreased, respectively, by 75%, 96% and 49% within few days after implementation of Covid-19 countermeasures. Several studies (Ocak and Turalioglu, 2008;Dragomir et al, 2015) argued the effect of meteorology on the atmospheric concentrations of traffic-related pollutants. While the role of the meteorological parameters is quite evident in this study, they are not quantified.…”

Section: Discussionmentioning

confidence: 99%

Impact of Covid-19 lockdown on PM10, SO2 and NO2 concentrations in Salé City (Morocco)

Otmani

Benchrif

Tahri

et al. 2020

Science of The Total Environment

310

188

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“…Additional parameters such as daily air temperature, solar radiation and air pressure might need to be included in the model to be able to estimate PM 2.5 concentration above 20 µg/m 3 . In 2015, a study was conducted to estimate the NO 2 concentration in Romania from 2009 to 2013 (5 years) by multiple linear regression (MLR) and artificial neural networks (ANNs), focusing on the dependence between meteorological parameters (such as air temperature, air pressure, wind speed, wind direction, solar radiation, rainfall and relative humidity) and their influence on measured NO 2 concentration [20]. Their results show that meteorological parameters have an impact on the NO 2 concentration although their estimation models have relatively low accuracy, which could have resulted from the measurement errors of the meteorological parameters.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations

Hong

Koh

2022

IJERPH

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Despite extensive research on air pollution estimation/prediction, inter-country models for estimating air pollutant concentrations in Southeast Asia have not yet been fully developed and validated owing to the lack of air quality (AQ), emission inventory and meteorological data from different countries in the region. The purpose of this study is to develop and evaluate two machine learning (ML)-based models (i.e., analysis of covariance (ANCOVA) and random forest regression (RFR)) for estimating daily PM2.5 and PM10 concentrations in Brunei Darussalam. These models were first derived from past AQ and meteorological measurements in Singapore and then tested with AQ and meteorological data from Brunei Darussalam. The results show that the ANCOVA model (R2 = 0.94 and RMSE = 0.05 µg/m3 for PM2.5, and R2 = 0.72 and RMSE = 0.09 µg/m3 for PM10) could describe daily PM concentrations over 18 µg/m3 in Brunei Darussalam much better than the RFR model (R2 = 0.92 and RMSE = 0.04 µg/m3 for PM2.5, and R2 = 0.86 and RMSE = 0.08 µg/m3 for PM10). In conclusion, the derived models provide a satisfactory estimation of PM concentrations for both countries despite some limitations. This study shows the potential of the models for inter-country PM estimations in Southeast Asia.

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Prediction of the NO2 concentration data in an urban area using multiple regression and neuronal networks

Cited by 11 publications

References 14 publications

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Impact of Covid-19 lockdown on PM10, SO2 and NO2 concentrations in Salé City (Morocco)

Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations

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