Statistical study of particulate matter (PM10) air contamination in the city of Vidin, Bulgaria

Veleva, E.; Filipova, M.; Zheleva, Ivanka

doi:10.1063/5.0101245

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…Seasonal concentrations were most lacking in spring and highest in winter. In the same year, Veleva et al (2022) studies the concentrations of PM10 in Vidin, Bulgaria. The town of Vidin is in north-western Bulgaria, on the south bank of the river Danube in the north Bulgarian border with Romania.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model

Bunnag

2024

IJEEP

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This paper analyzes the relationship between air pollutants and the amount of PM10 measured in Bangkok. It forecasts the amount of PM10 in Bangkok by using the SARIMA and SARIMA-GARCH models to formulate policies to reduce the occurrence of PM10 and guidelines for further prevention. PM's data is from January 2008 to July 2023. First, the process is to build the SARIMA Model and SARIMA-GARCH Model Estimation. We perform model comparisons that SARIMA (3,1,3)(1,1,2)12 and SARIMA(3,1,3)(1,1,2)12-GARCH(1,1), which model gives lower MAE and RMSE values, which indicates good prediction accuracy than another model. The results show that the MAE and RMSE predictions of the SARIMA (3,1,3) (1,1,2)12 model are 15.303 and 20.839 better than those of the SARIMA (3,1,3) (1,1,2)12-GARCH (1,1) model are 17.280 and 22.677. Therefore, the SARIMA (3,1,3) (1,1,2)12 forecast results are better precise. Thus, in summary, we will choose the first model to use in forecasting for policy making. Moreover, in the study results, we found the relationship between air pollutants and PM10 in Bangkok and found that the elements of NO2 and O3 will require quite a lot of attention because they affect the relationship with PM10 at a moderate level.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model

Bunnag

2024

IJEEP

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“…For this purpose, both classic and the most avant-garde computer-based methods and algorithms with machine learning (ML) are developed and applied. Classical methods such as multiple linear regression (MLR), Principal component analysis (PCA), stochastic autoregressive integrated moving average (ARIMA), seasonal ARIMA, numerical simulation modeling, gaseous air contamination modeling and others are used, for example, in [5][6][7][8][9][10]. The authors of [5] analyze and model the tropospheric ozone in Warsaw depending on its precursors for selected heat waves using PCA and MLR.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [5] analyze and model the tropospheric ozone in Warsaw depending on its precursors for selected heat waves using PCA and MLR. Stochastic Box-Jenkins ARIMA and MLR models of PM10 are developed in [6,7], gaseous air pollution is studied in [8] for some Bulgarian cities. Computational fluid dynamics model capable of simulating the dispersion of pollutants in a large urban environment is reported in [9].…”

Section: Introductionmentioning

confidence: 99%

Temporal-causal modeling of air pollution in the city of Plovdiv, Bulgaria: a case study

Ivanov,

Gocheva-Ilieva,

Stoimenova-Minova

2023

J. Phys.: Conf. Ser.

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High levels of air pollution are dangerous to human health, which is a current problem for densely populated cities worldwide. Studying this problem can help detect pollutants’ time dependencies on basic meteorological measurements and other factors for future prediction and elaborate corresponding alarms when official upper pollution limits are exceeded. In this work, time-causal models based on previous daily time observations and meteorological measurements in the city of Plovdiv, Bulgaria, are applied. Vector-type temporal-causal models are constructed and analyzed for carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine dust particles below size 10, 2.5, and 1 micron (PM10, PM2.5, and PM1), respectively. Pollution levels are predicted seven days ahead.

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“…To analyse and model concentrations of the main air pollutants, such as O3 -ground-level ozone, CO -carbon monoxide, NO -nitrogen monoxide, SO2 -sulfur dioxide, NO2 -nitrogen dioxide, PM10 -fine particulate matter below 10 microns and others different statistical and numerical methods were applied. The classical linear stochastic ARIMA (Auto Regressive Integrated Moving Average) approach and its variants [4] have been used in [5][6][7][8][9]. In [5], a stationary stochastic ARMA/ARIMA modelling approach has been considered to forecast the daily mean air pollutants O3, CO, NO, and NO2…”

Section: Introductionmentioning

confidence: 99%

Stochastic modelling of daily air pollution in Burgas, Bulgaria

Koleva,

Gocheva-Ilieva,

Kulina

2023

J. Phys.: Conf. Ser.

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Exceeding the norms and limits of atmospheric air pollution causes enormous damage to the population’s health and the environment. Determining the factors affecting air quality is a current task in a local, regional, and global scale. In this study, we use daily time series data for the main air pollutants in Burgas, Bulgaria – O3, NO, NO2, CO, SO2, and PM10, to analyze, model, and forecast these levels depending on meteorological factors. For this purpose, the stochastic ARIMA method and ARIMA with transfer functions are applied. Results are obtained for univariate and multivariate time series. Particular attention is paid to the concentrations of the secondary pollutant ground-level ozone (O3), which are modelled as a function of all variables considered. Results were evaluated using root mean square error, mean absolute percentage errors, and the coefficient of determination. Short-term forecasts have been obtained for seven days ahead. Model accuracy up to 84% has been established.

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Statistical study of particulate matter (PM10) air contamination in the city of Vidin, Bulgaria

Cited by 3 publications

References 24 publications

Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model

Forecasting PM10 Caused by Bangkok’s Leading Greenhouse Gas Emission Using the SARIMA and SARIMA-GARCH Model

Temporal-causal modeling of air pollution in the city of Plovdiv, Bulgaria: a case study

Stochastic modelling of daily air pollution in Burgas, Bulgaria

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