Predicting PM2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City

Asadollahfardi, Gholamreza; Zangooei, Hossein; Aria, Shiva Homayoun

doi:10.5572/ajae.2016.10.2.067

Cited by 28 publications

(11 citation statements)

References 35 publications

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“…An artificial neural network coupled with an atmospheric trajectory model reached an RMSE of 19.8 µg/m 3 for 1-day PM 2.5 forecasting and was very efficient at predicting the high peaks observable in the Jing-Jing-Ji area, China [50]. Better performance was found in a study performed in Iran that reached correlations of 0.86-0.91 and an RMSE of 2.76-7.04 µg/m 3 for hourly PM 2.5 forecast, however, these results were obtained by using PM 10 as a predictor variable for PM 2.5 forecasting [51]. While neural networks show impressive performances in predicting air quality at complex urban sites, their main limitation is that they can hardly be generalized for broader areas, and obtaining predictions for locations without measurements requires an atmospheric chemistry model or interpolation between sites.…”

Section: Discussionmentioning

confidence: 94%

Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

et al. 2020

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Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest.

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Section: Discussionmentioning

confidence: 94%

Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

et al. 2020

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“…It was reported by another research group that, using the WS, WD, and temperature as input variables, and ANN, AN-FIS models have provided SO 2 prediction with R 2 values between 0.20 and 0.50 [32], and in another study with R 2 >0.70 [33]. Recent research have proposed using both the meteorological factors and air pollutant parameters as input variables and reported that the ANN model produced a PM 2.5 prediction with R 2 >0.92 [34]. Another study revealed that the use of NO X and meteorological parameters as input variables and the ANFIS model provided O 3 predictions with R 2 >0.94 [35].…”

Section: The Prediction Approachmentioning

confidence: 99%

Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ

Özkal

Arslan

2022

Environmental Research and Technology

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Air pollution-induced issues involve public health, environmental, agricultural and socio-economic aspects. Therefore, decision-makers need low-cost, efficient tools with high spatiotemporal representation for monitoring air pollutants around urban areas and sensitive regions. Air pollution forecasting models with different time steps and forecast lengths are used as an alternative and support to traditional air quality monitoring stations (AQMS). In recent decades, given their eligibility to reconcile the relationship between parameters of complex systems, artificial neural networks have acquired the utmost importance in the field of air pollution forecasting. In this study, different machine learning regression methods are used to establish a mathematical relationship between air pollutants and meteorological factors from four AQMS (A-D) located between Çerkezköy and Süleymanpaşa, Tekirdağ. The model input variables included air pollutants and meteorological parameters. All developed models were used with the intent to provide instantaneous prediction of the air pollutant parameter NOx within the AQMS and across different stations. In the GMDH (group method of data handling)-type neural network method (namely the self-organizing deep learning approach), a five hidden layer structure consisting of a maximum of five neurons was preferred and, choice of layers and neurons were made in a way to minimize the error. In all models developed, the data were divided into a training (80%) and a testing set (20%). Based on R 2 , RMSE, and MAE values of all developed models, GMDH provided superior results regarding the NOx prediction within AQMS (reaching 0.94, 10.95, and 6.65, respectively for station A) and between different AQMS. The GMDH model yielded NOx prediction of station B by using station A input variables (without using NOx data as model input) with R 2 , RMSE and MAE values 0.80, 10.88, 7.31 respectively. The GMDH model is found suitable for being employed to fill in the gaps of air pollution records within and across-AQMS.

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“…It has been employed in a wide range of applications such as modelling precipitation (da Silva et al, 2019 ; Schoof & Pryor, 2008 ), infrastructure deterioration (Baik et al, 2006 ), and wind speed (Sahin & Sen, 2001 ). Discrete-time Markov chains have also been employed to model air pollution (e.g., Asadollahfardi et al, 2016 ; Caraka et al, 2019 ; Mohamad et al, 2018 ; Romanof, 1982 ). Nebenzal and Fishbain ( 2018 ) found that, for forecasting long-term NO2 pollution, Markov chain models reduce the total error compared to other forecasting methods (e.g., multiple linear regression, moving average, exponential smoothing, Holt, and persistence methods).…”

Section: Introductionmentioning

confidence: 99%

“…Most air pollution studies that used Markov models focused on modelling specific contaminant concentrations such as particulate matter (Asadollahfardi et al, 2016 ; Caraka et al, 2019 ; Mohamad et al, 2018 ), nitrogen dioxide (Nebenzal & Fishbain, 2018 ), ozone (Rodrigues et al, 2019 ), and sulphur dioxide (Romanof, 1982 ), while few studies have focused on directly modelling an air pollution index similar to the AQHI (Alyousifi et al, 2018 ; Alyousifi et al, 2019 ; Zakaria et al, 2019 ). From a public health standpoint, directly modelling the AQHI is more informative since the AQHI categories are directly related to health risks and outdoor air quality advisories.…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time Markov Chain Modelling of the Ontario Air Quality Health Index

Holmes

Hassini

2021

Water Air Soil Pollut

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The Air Quality Health Index (AQHI) is an aggregate indicator of air pollution used to communicate to Canadians the health impact of short-term exposure to current air pollutant levels. Understanding the stochastic behaviour of the AQHI can aid public health officials in predicting air pollution levels, determining the likelihood and duration of air quality advisories, and planning for increased strain on the health care system during periods of higher air pollution. Previous research has applied discrete-time Markov chains to investigate stochastic behaviour of air pollution indices but only in a handful of regions and none with the same climatic characteristics as Canadian regions. In this study, we investigated the stochastic behaviour of AQHI risk categories in Ontario (34 air monitoring stations) for 5 years from 2015 to 2019. We employed discrete-time Markov chains using three of the AQHI risk categories (Low Risk, Moderate Risk, High Risk) as states to determine (1) the transition probabilities between these states, (2) the long-run proportion of time spent in each state, and (3) the mean persistence time of each state. These results were then used to assess spatial trends in the stochastic behaviour of AQHI risk categories and the likelihood and duration of air quality advisories. Overall, the air quality (as characterised by the AQHI) in Ontario tends to decrease as population density increases. Urban areas spent a greater proportion of time in higher risk categories, and tended to remain in the higher risk categories for longer before transitioning.

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Predicting PM2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City

Cited by 28 publications

References 35 publications

Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ

Discrete-Time Markov Chain Modelling of the Ontario Air Quality Health Index

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