Article Preview
Top1.Introduction
The study of the state of the environment is based on a multitude of data on the values of the parameters of this environment. However, a lot of parameters can be chosen for the study of the state of the environment. Among these parameters of the state of the environment, one should choose a subset of significant parameters necessary for assessing the state of the environment. Significant parameters are parameters which changing has the most significant effect on the change in the environment. For example, to assess the state of the aquatic environment, important parameters are the concentrations of toxic substances, oxygen, and the content of bottom sediments (Ma et al., 2019). Important parameters for assessing soil and air conditions can also be mentioned (Jin et al., 2019).
Different models are used for studying the state of the environment. A lot of attention is given to the development of mathematical models for assessing and predicting the state of the environment. Models can be of different types. The classification of models is shown in the fig 1.
Figure 1. The classification of models
Stochastic models have been widely used to study the condition of the environment. The explanation is that the occasional factors (impacts) influence the ecological condition of the environmental objects. The use of the linear regression model in the work (Fernández-Caliani et al., 2019) let to assess health risks due to the concentration of trace elements in the soil. For example, in (Krupina et al., 2019) a regression model was developed which imitates the temperature difference between the outer surface of a building’s wall, between a wall covered with vegetation, and an open wall. The temperature mode of the residential premises refers to the environmental parameters of buildings and modelling of the thermal effects of the green facade concerns environmental problems.
In (Blanco et al., 2019), it was pointed out that in studying of factors influencing CO2 gas emissions in China, it is not enough to use linear methods based on data from time series or cross sections. A nonparametric additive regression approach is proposed to study the main factors affecting CO2 emissions in China. The studies of metal concentrations using correlation models were described in (Wen & Shao, 2019). The research results have revealed the fact that for zinc there is a significant correlation between its concentrations in the environment and in the sea grass.
In (Chernova & Shulkin, 2019), the influence of concentrations of heavy metals (Cd, Ni, and Pb) in cocoa beans from nine Ecuadorian provinces on the growth of risks to human health was studied. The methods of probability theory were applied, the results of regression analysis were obtained, the values of the Pearson correlation coefficient and the results of the variational inflation coefficients were highly estimated and should be taken into account by health and trade authorities to establish acceptable levels of Ni and Pb.
In the paper (Romero-Estévez et al., 2019), the use of the hidden Markov model (HMM) for modelling the internal and external correlations of ecosystem condition by establishing the relationships between the internal level of environmental health and the condition of external observation combination (to simulate the internal-external correlations of ecosystem status through) was investigated establishing the relationships between internal ecological health level and combination state of external observation.). The results of the studies showed that the use of HMM can successfully monitor the state of the ecosystem and provides a new research approach for the study of ecosystem health assessment of urban agglomerations.
A lot of works can be mentioned with the results of applying the analytical and stochastic models. The use of these models gives good results, but the use of verbal models is of particular interest. Verbal models are created using expert knowledge. Attention should be paid to the results of using these models to study the condition of the environment.