Particulate Matter (PM2.5) Concentration Forecasting through an Artificial Neural Network in Port City Environment

Macías-Hernández, Bárbara A.; Tello-Leal, Edgar; Ramirez-Alcocer, Ulises Manuel; Hernandez-Resendiz, Jaciel David

doi:10.3390/ecas2022-12856

Cited by 4 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 4 presents a comprehensive comparison of findings between the studies published in the literature and the technique utilized in this study 14,54,55,60–66 . It highlights key aspects such as the region, the specific neural network technique, and evaluation metrics.…”

Section: Resultsmentioning

confidence: 99%

Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Koçak

2024

Env Prog and Sustain Energy

View full text Add to dashboard Cite

This study analyzed the temporal variation and prediction of fine particulate matter (PM2.5) concentrations in Aksaray, Turkey, a city in Central Anatolia. The relationship between PM2.5 and meteorological parameters such as temperature, humidity, wind speed, and wind direction was investigated. An artificial neural network (ANN) model was developed to predict PM2.5 levels based on meteorological data and air pollutant information. Seasonal and diurnal patterns of PM2.5 concentrations were observed, with higher values recorded during the winter and lower values during the summer. Additionally, higher levels were observed in the morning and evening, while lower levels were recorded in the afternoon. The variations in meteorological parameters, especially temperature and wind speed, significantly influenced PM2.5 levels. To predict hourly PM2.5 concentrations, single and multiple data imputation techniques were employed in combination with resilient back‐propagation (RPROP‐ANN). The neural network was applied, consisting of one input layer comprising 11 parameters, one hidden layer with 20 neurons, and an output layer. The results indicate that the best forecasting performance for PM2.5 was demonstrated by the combination of the missForest imputation technique with the RPROP neural network, as assessed by the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The proposed model is characterized by a low RMSE of 5.94 and a high R2 value of 0.88, demonstrating exceptional predictive performance in air quality.

show abstract

Section: Resultsmentioning

confidence: 99%

Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Koçak

2024

Env Prog and Sustain Energy

View full text Add to dashboard Cite

show abstract

“…Currently, the application of neural networks has become more popular for predicting ground-level O 3 concentrations. A lot of researchers have effectively adopted ANN as a predictive tool to model O 3 concentration [21][22][23][24][25] and its precursor, NO 2 [26]. Even though many studies have been carried out in the world investigating the association between the weather and ozone concentration using MLR and ANN, there is still a shortage of work, especially in Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Effects of Weather and Anthropogenic Precursors on Ground-Level Ozone Concentrations in Malaysian Cities

et al. 2022

View full text Add to dashboard Cite

Ground-level ozone (O3) is a significant source of air pollution, mainly in most urban areas across the globe. Ground-level O3 is not emitted directly into the atmosphere. It results from photo-chemical reactions between precursors and is influenced by weather factors such as temperature. This study investigated the spatial and temporal analysis of ground-level ozone and analyzed the significant anthropogenic precursors and the weather parameters associated with ground-level ozone during daytime and nighttime at three cities in peninsular Malaysia, namely, Kuala Terengganu, Perai, and Seremban from 2016 to 2020. Secondary data were acquired from the Department of Environment (DOE), Malaysia, including hourly data of O3 with trace gases and weather parameters. The secondary data were analyzed using temporal analysis such as descriptive statistics, box plot, and diurnal plot as well as spatial analysis such as contour plot and wind rose diagram. Spearman correlation was used to identify the association of O3 with its precursors and weather parameters. The results show that a higher concentration of O3 during the weekend due to “ozone weekend effects” was pronounced, however, a slightly significant effect was observed in Perai. The two monsoonal seasons in Malaysia had a minimal effect on the study areas except for Kuala Terengganu due to the geographical location. The diurnal pattern of O3 concentration indicates bimodal peaks of O3 precursors during the peak traffic hours in the morning and evening with the highest intensity of O3 precursors detected in Perai. Spearman correlation analysis determined that the variations in O3 concentrations during day and nighttime generally coincide with the influence of nitrogen oxides (NO) and temperature. Lower NO concentration will increase the amount of O3 concentration and an increasing amount of O3 concentration is influenced by the higher temperature of its surroundings. Two predictive models, i.e., linear (multiple linear regression) and nonlinear models (artificial neural network) were developed and evaluated to predict the next day and nighttime O3 levels. ANN resulted in better prediction for all areas with better prediction identified for daytime O3 levels.

show abstract

Traffic volume and road network structure: Revealing transportation-related factors on PM2.5 concentrations

Yu,

Deng,

Qin

et al. 2023

Transportation Research Part D: Transport and Environment

View full text Add to dashboard Cite

Particulate Matter (PM2.5) Concentration Forecasting through an Artificial Neural Network in Port City Environment

Cited by 4 publications

References 26 publications

Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Effects of Weather and Anthropogenic Precursors on Ground-Level Ozone Concentrations in Malaysian Cities

Traffic volume and road network structure: Revealing transportation-related factors on PM2.5 concentrations

Contact Info

Product

Resources

About

Particulate Matter (PM2.5) Concentration Forecasting through an Artificial Neural Network in Port City Environment

Cited by 4 publications

References 26 publications

Prediction of daily fine particulate matter (PM2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Prediction of daily fine particulate matter (PM2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Effects of Weather and Anthropogenic Precursors on Ground-Level Ozone Concentrations in Malaysian Cities

Traffic volume and road network structure: Revealing transportation-related factors on PM2.5 concentrations

Contact Info

Product

Resources

About

Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network