Industrial emissions effect into atmospheric air quality: mathematical modeling

Abstract

This paper presents the results of modeling the distribution process of industrial emission components at specified distances from the emission source along the normal. The model uses a system of differential diffusion equations to compute the concentration profiles of aerosols, industrial gases, and fine particles in the atmosphere. In order to investigate the regularity of the emitter propagation into the atmosphere, a theory of impurity dispersion was developed. The model is constrained by the effect of particle interactions. The partial derivative equations are presented to calculate the concentrations of aerosols and fine particles under the turbulent airflow in the atmosphere, dispersion of inert impurities, and distribution of chemically active compounds. The adequacy of the mathematical model for a series of theoretical calculations was checked by contrasting the data of the atmospheric air monitoring for the cities of Almaty, Ust-Kamenogorsk, Pavlodar, Atyrau, Krasnodar, Chelyabinsk, Beijing, and Shanghai. Air monitoring data included PM10, SO2, and NO2 levels. The mathematical model solutions for the relative values of the emitter concentration in the direction along the normal of the pollution source at the surface were obtained. Graphical interpretation of the calculation results over the 0…200 m distance for time intervals ranging from 3 to 600 min was provided. According to the multiple factor cluster analysis, the critical values of SO2 concentrations in Atyrau exceeded MPC in 26.2% of cases. The level of NO2 for Shanghai was 15.6%, and those for PM10 concentrations in Almaty and Atyrau amounted to 16.4%. A comparison of theoretical values and results obtained from official sources showed arithmetic mean of 49.4 mg/m3 and maximum value of 823.0 mg/m3. Standard deviation comprised 48.9 mg/m3. Results were considered statistically significant at p≤0.005. The mathematical model developed in this study can be used to predict the status of atmospheric air.

Spatial Distribution of PM2.5 and Pm10 Pollutants in Residential Area of Pavlodar, Kazakhstan

“…The results of the analysis of the air quality correlate with the conclusions published in the Information bulletin on the state of the environment of the Republic of Kazakhstan for September 2023 (Kazhydromet, 2003) that the level of air pollution in Pavlodar is increased. The results of the study are also consistent with previously published works (Kakabayev et al, 2023;Lukyanets et al, 2023). For example, the annual average level of PM2.5 exceeds the WHO recommended limit (10 µg m -3 , (WHO, 2006)) in six out of seven monitoring stations.…”

The Supply of Macro- and Microelements to Cotton Plants at Different Distances from a Fertilizer Production Factory

“…Empirical models for the sulfur contents in roots and leaves are given in Table 5. Model (9), which describes the change in the sulfur content in the plant roots depending on the distance from the pollutant, is not statistically significant. Furthermore, there were no significant changes, on average, over the entire measurement interval.…”

“…The problem of air pollution from industrial emissions is exacerbated in arid regions [1,5,8]. The pollution level peaks with increasing distance from the emission source and then gradually decreases [9,10]. Dry deposition accounts for up to 50% of pollutants in the atmosphere [11], or 63% of PM10 removal [12].…”

Numerical Modeling for Enhanced Pollutant Transport Prediction in Industrial Atmospheric Air

“…Lukyanets et al [12] presents the results of modeling the process of distribution of industrial emissions in the atmosphere. Using a system of differential diffusion equations, the model allows calculating the concentration level of aerosols, gases and small particles along the normal from the emission source.…”