Use of Satellite Data for Air Pollution Modeling in Bulgaria

Air pollution continues to be of concern for Bulgarian cities, mainly due to particulate matter of aerodynamic diameter smaller than 10 μm (PM10). There is public and expert interest in the improvement of two operational air quality modeling systems: the Bulgarian Chemical Weather Forecast System (BgCWFS) and the Local Air Quality Management System (LAQMS) for the city of Plovdiv. The aim of the study is to investigate the effects of satellite data assimilation in BgCWFS on surface concentrations over Bulgaria (resolution 9 km), to downscale BgCWFS output to LAQMS (resolution 250 m), and to examine effects on PM10 in Plovdiv. Data from the Global Ozone Monitoring Experiment-2 (GOME-2) (MetOP satellites) for aerosols, nitrogen dioxide (NO2), and sulfur dioxide (SO2) were assimilated in BgCWFS using objective analysis. Simulation experiments with and without satellite data were conducted for a summer and a winter month. The comparison to surface observations in the country showed improvement of results when using satellite data, especially in the summer due to mineral dust events captured by satellites. The decrease in the normalized mean bias (NMB) over the two months was 43% (PM10) and 73% (SO2). The LAQMS estimated background contributions to PM10 in the city as 32%. The absolute NMB by LAQMS decreased by 38%.

Association between Exposure to Noise and Air Pollution and Metabolic Syndrome

Background: In addition to the classical risk factors environmental pollution such as traffic noise and air pollution are suggested to be a risk factor for Metabolic Syndrome (MetS). In this study, we examined the between exposure to noise and air pollution (PM2.5) and MetS.Methods: This study was performed on 3513 participants in a prospective study on the health of the people of Yazd (YaHS). A wide range of demographic, anthropometric and blood biochemistry data were gathered during 2014-2015. Data on personal level noise and air pollution (PM2.5) at residential address were collected through air pollution maps. Air and noise pollution modeling was performed using Kriging model. Using weighted logistic regressiARAon, we reported the odds ratio (95% confidence interval) of MetS for a unit increase in exposure to the pollutants. Results: The prevalence of MetS in the total population was 43.7%. No association was found with MetS between noise exposures in the range (54.1-62.3) dB (A). There was a positive relationship between air pollution exposure in the range (32.82 - 16.38) micrograms per cubic meter and MetS in the raw model and after adjusting for the effect of age and sex, increased air pollution exposure, chance The incidence of MetS increases by 8% (95% CI = 1.06 - 1.10), while the fully adjusted model did not find a positive and significant relationship. Conclusion: We did not find any association between noise and air pollution with MetS in the fully adjusted model.

A Visualization of Air Pollution Distribution over an Observed Area Surrounded by Mountains: A Computational Approach

We study the propagation of pollutants emitted from a single generator such as a factory chimney located between 2 mountains as well as its effects on an observed area such as a village or agricultural land. The problem is formulated as a system of partial differential equations, composed of Navier-Stokes equations and an advection-diffusion equation, and is solved by the finite element method. We visualize the propagation of the pollutants for several variants of the problem depending on the heights of the mountains and investigate their negative effects on the observed area by computing an average concentration of the pollutants over the observed area. We found that the observed area between the two mountains experienced a long-term negative effect compared with those located on flat land. This is because the mountain on the side, where the wind is blowing, obstructs the wind resulting in air recirculation. In contrast, the other mountain bounces some pollutants back to the observed area, preventing them from leaving the domain. The higher the mountains, the longer the time the pollutants remain in the observed area. If the heights of the mountains encircling the observed area are not equal, the residual remains in the area longer if the taller mountain is on the windward side. HIGHLIGHTS Air Visualization of air pollution between two mountains Air pollution propagation modeling A system of partial differential equations for air pollution modeling with FEM GRAPHICAL ABSTRACT

Comparison of the Air Pollution Mathematical Model of PM10 and Moss Biomonitoring Results in the Tritia Region

Knowing the relationship between pollution sources and air pollution concentrations is crucial. Mathematical modeling is a suitable method for the assessment of this relationship. The aim of this research was to compare the results of the Analytical Dispersion Modelling Supercomputer System (ADMOSS), which is used for air pollution modeling in large areas, with the results of moss biomonitoring. For comparison purposes, air pollution mathematical modeling and the collection of moss samples for biomonitoring in the Czech–Polish–Slovak border area in the European Grouping of Territorial Cooperation (EGTC) Tritia were carried out. Moss samples were analyzed by multi-element instrumental neutron activation analysis (INAA). The INAA results were statistically processed using the correlation-matrix-based hierarchical clustering and correlation analysis of the biomonitoring results and ADMOSS results. Biomonitoring using bryophytes proved to be suitable for the verification of mathematical models of air pollution due to the ability of bryophytes to capture the long-term deposition of pollutants and the resulting possibility of finding the real distribution of pollutants in the area, as well as identify the specific chemical elements, the distribution of which coincides with the mathematical model.

Machine Learning Methods Performance Evaluation*

In this paper, we describe an approach for air pollution modeling in the data incompleteness scenarios, when the sensors cover the monitoring area only partially. The fundamental calculus and metrics of using machine learning modeling algorithms are presented. Moreover, the assessing indicators and metrics for machine learning methods performance evaluation are described. Based on the conducted analysis, conclusions on the most appropriate evaluation approaches are made.

Air Pollution Modeling: Local, Regional, and Global-Scale Applications

The Atmosphere Special Issue entitled “Air Pollution Modeling: Local, Regional, and Global-Scale Applications” comprises nine original papers [...]