Mathematical modeling of sulfur dioxide concentration in the western part of Romania

A performance comparison study on PM2.5 prediction at industrial areas using different training algorithms of feedforward-backpropagation neural network (FBNN)

Presence of particulate matters with aerodynamic diameter of less than 2.5 μm (PM_2.5) in the atmosphere is fast increasing in Malaysia due to industrialization and urbanization. Prolonged exposure of PM_2.5 can cause serious health effects to human. This research is aimed to identify the most reliable model to predict the PM_2.5 pollution using multi-layered feedforward-backpropagation neural network (FBNN). Air quality and meteorological data were collected from Department of Environment (DOE) Malaysia. Six different training algorithms consisting of thirteen various training functions were trained and compared. FBNN model with the highest coefficient correlation (R²) and lowest root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were selected as the best performing model. Levenberg Marquardt (trainlm) is the best performing algorithms compared to other algorithms with R² value of 0.9834 and the lowest error values for RMSE (2.3981), MAE (1.7843) and MAPE (0.1063).

Mathematical modeling in the health risk assessment of air pollution-related disease burden in China: A review

This review paper covers an overview of air pollution-related disease burden in China and a literature review on the previous studies which have recently adopted a mathematical modeling approach to demonstrate the relative risk (RR) of air pollution-related disease burden. The associations between air pollution and disease burden have been explored in the previous studies. Therefore, it is necessary to quantify the impact of long-term exposure to ambient air pollution by using a suitable mathematical model. The most common way of estimating the health risk attributable to air pollution exposure in a population is by employing a concentration-response function, which is often based on the estimation of a RR model. As most of the regions in China are experiencing rapid urbanization and industrialization, the resulting high ambient air pollution is influencing more residents, which also increases the disease burden in the population. The existing RR models, including the integrated exposure-response (IER) model and the global exposure mortality model (GEMM), are critically reviewed to provide an understanding of the current status of mathematical modeling in the air pollution-related health risk assessment. The performances of different RR models in the mortality estimation of disease are also studied and compared in this paper. Furthermore, the limitations of the existing RR models are pointed out and discussed. Consequently, there is a need to develop a more suitable RR model to accurately estimate the disease burden attributable to air pollution in China, which contributes to one of the key steps in the health risk assessment. By using an updated RR model in the health risk assessment, the estimated mortality risk due to the impacts of environment such as air pollution and seasonal temperature variation could provide a more realistic and reliable information regarding the mortality data of the region, which would help the regional and national policymakers for intensifying their efforts on the improvement of air quality and the management of air pollution-related disease burden.

Exploratory research on influential factors of China's sulfur dioxide emission based on symbolic regression

The amount of China's sulfur dioxide emission remains significantly large in recent years. To further reduce sulfur dioxide emission, the key is to find out the leading factors affecting sulfur dioxide emission and then take measures to control it accordingly. In order to investigate the influential factors of sulfur dioxide emission of various provinces, the data of sulfur dioxide emission of 30 provinces in China from 2001 to 2020 were collected. We established the symbolic regression model to explore the relationship between the GDP (x₁), total population (x₂), total energy consumption (x₃), thermal power installed capacity (x₄), and sulfur dioxide emission (dependent variable) for each province. The results show that the amount of China's total sulfur dioxide emission and sulfur dioxide emission in most provinces meet the environmental Kuznets curve (EKC). The influential degree of the factors affecting China's sulfur dioxide emission are GDP, total energy consumption, thermal power installed capacity, and total population. The provinces with the primary factor of GDP have the lowest average total energy consumption and average thermal power installed capacity, and their average sulfur dioxide emissions are also relatively low. The provinces with the primary factor of GDP do not show obvious geographical characteristics, but the provinces with the primary factor of total energy consumption are all distributed in southern China. Based on the research results, some control measures are also put forward.

Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series

Nowadays, observing, recording, and modeling the dynamics of atmospheric pollutants represent actual study areas given the effects of pollution on the population and ecosystems. The existence of aberrant values may influence reports on air quality when they are based on average values over a period. This may also influence the quality of models, which are further used in forecasting. Therefore, correct data collection and analysis is necessary before modeling. This study aimed to detect aberrant values in a nitrogen oxide concentration series recorded in the interval 1 January–8 June 2016 in Timisoara, Romania, and retrieved from the official reports of the National Network for Monitoring the Air Quality, Romania. Four methods were utilized, including the interquartile range (IQR), isolation forest, local outlier factor (LOF) methods, and the generalized extreme studentized deviate (GESD) test. Autoregressive integrated moving average (ARIMA), Generalized Regression Neural Networks (GRNN), and hybrid ARIMA-GRNN models were built for the series before and after the removal of aberrant values. The results show that the first approach provided a good model (from a statistical viewpoint) for the series after the anomalies removal. The best model was obtained by the hybrid ARIMA-GRNN. For example, for the raw NO2 series, the ARIMA model was not statistically validated, whereas, for the series without outliers, the ARIMA(1,1,1) was validated. The GRNN model for the raw series was able to learn the data well: R2 = 76.135%, the correlation between the actual and predicted values (rap) was 0.8778, the mean standard errors (MSE) = 0.177, the mean absolute error MAE = 0.2839, and the mean absolute percentage error MAPE = 9.9786. Still, on the test set, the results were worse: MSE = 1.5101, MAE = 0.8175, rap = 0.4482. For the series without outliers, the model was able to learn the data in the training set better than for the raw series (R2 = 0.996), whereas, on the test set, the results were not very good (R2 = 0.473). The performances of the hybrid ARIMA–GRNN on the initial series were not satisfactory on the test (the pattern of the computed values was almost linear) but were very good on the series without outliers (the correlation between the predicted values on the test set was very close to 1). The same was true for the models built for O3.

Arsenic and SO2 hotspot in South-Eastern Europe: An overview of the air quality after the implementation of the flash smelting technology for copper production

This extensive study considered the air pollution data after the flash smelting technology for copper production had become fully operational. The assessment of the air quality after the implementation was significantly important, since the modernisation was necessary for reducing the environmental contamination in one of the most polluted regions in South-Eastern Europe. The concentrations of SO₂, PM₁₀ and toxic elements (As, Pb, Cd, Ni) in PM₁₀ samples were monitored at different sites, with respect to the copper smelter, in the period 2016-2019. The air quality evaluation was performed concerning the corresponding limit and target values defined by the Serbian and European legislation, as well as the World Health Organization Air Quality Guidelines (WHO AQG). The measured SO₂ concentrations indicated frequent exceedances of the defined daily and annual limit values, at both national and European level. Although exceedances were not as pronounced as in the period before the implementation of the new technology, the episodes of extreme air pollution with SO₂ persisted on the daily basis. The maximum daily SO₂ concentration of 2125 μg m^-3 was more than 100 times higher compared to the WHO AQG, but lower compared to the period before the implementation of the flash smelting technology. The air quality considering PM₁₀ and especially As levels in PM₁₀ samples was notably poorer after the modernisation. The annual target value for As, defined by the European and Serbian Regulation, was exceeded at all the measuring sites, with maximum exceedance of more than 90 times at the suburban site during 2019. The frequent exceedances of the corresponding annual limit and target values were also denoted for Pb and Cd in PM₁₀ samples. The analysed data emphasised that the Bor area could still be characterised as an environmental hotspot in Serbia and beyond.

New approaches for modeling the regional pollution in Europe

Generally, official statistical reports provide information on the pollution extent over a region using the average records from all the observation sites. In the outliers' presence, the average is not a good choice. Therefore, in this article, we propose two alternatives for replacing the average series by most significant regional series, obtained by two selection procedures. The first algorithm chooses the candidates to be utilized for the regional estimation of pollution by a data segmentation that provides the most representative value for a given time interval. Since the number of segments to be used should be prior introduced, the second algorithm proposes a version of the selection procedure based on the k-means algorithm. The performances of these methods are verified on three groups of series (carbon oxides, sulfur oxides, and nitrogen oxides) recorded in the EEA33 countries during a period of 28 years. Both algorithms give better results than the average series, in terms of mean standard errors (MSE) and mean absolute errors (MAE).

The relationship between the development of global maritime fleets and GHG emission from shipping

International shipping currently accounts for about 3% of total global greenhouse gas (GHG) emissions, but would continue to rise as transport capacity expands. If the shipping industry aims at delivering its proportionate contribution to curbing global warming under the Paris agreement, the sector has to, inevitably, promote energy conservation and emission reduction. A rapidly growing oceangoing fleet size and correspondingly rising GHG emissions on a global scale raise an interesting research question: could a certain relationship between the two be characterized as a function so that further emissions can be forecast based on the model? The paper adopts an allometric approach based on biological scaling laws to explore the potential relationship between the fleet size and corresponding GHG emissions from shipping. The results show that both the slowdown of the navigation speed and the current implementation of the Energy Efficiency Design Index (EEDI) and Energy Efficiency Operation Index (EEOI) are effective on the whole. By employing the model, the development trends of GHG emissions from shipping in the future can be better understood. Through model applications and result analysis, numerical results validate the effectiveness of this method. The paper not only studies the development of GHG emissions from shipping in the past, but aslo evaluates its specific emission quantities in the future which is in line with the GHG emission reduction targets proposed by IMO on the 72nd IMO meeting, which will be helpful for policy decisions on the quota of GHG emissions to the International Maritime Organization (IMO) and port administrators.