Environmental Assessment Issues for Combating Wind and Sandy Weather in Ecological Zones

Abstract

For The environmental conditions of Saihanba before and after restoration were analyzed（Problem.1）, this article first analyzes several indicators (table 1) that reflect the environmental conditions of Saihanba, and from the nine three-level indicators collected from the data, first, use the spss software to export the scatter plot (figure 1), and preliminarily analyze whether there is a linear relationship in the indicators, use the Pearson correlation coefficient method to calculate the correlation coefficient between multiple variables (figure 2), and selects 4 indicators with larger correlation coefficients (table 2) and plot the change trends of the four indicators of Saihanba(figure 3), then adopts TOPSIS and entropy weight method calculates comprehensive environmental indicators and normalizes the indicators. The results show (figure 4) that with the advancement of the Saihanba project, the environmental quality index of Saihanba has increased by about 10% to 20% each year, showing an upward trend. To quantitatively evaluate the effect of Saihanba on the wind-sand resistance of Beijing(Problem.2), this article adopts a similar method to question 1. Through the Pearson correlation coefficient and entropy weight method, we get the weight of the benefit and cost index(table 4). Through the RSR comprehensive evaluation analysis method to calculate the sand resistance capacity of Beijing from 2004 to 2017 (table 5), and perform the regression model. The regression coefficients are a=1.603 and b=-0.2073, which shows that from 2004 to 2017, the Saihanba project has improved Beijing’s ability to resist sand and dust year by year. To establish a mathematical model to determine the appropriate location of China's ecological zone(Problem.3), in order to select similar forest farms, we divided the country into three levels according to the degree of disaster(table 6), by comparing the correlation coefficients (table 7) under the same disaster. Three potential cities were selected. The KMO test and Bartlett were carried out. The results show there is a correlation between the variables. The principal component analysis is valid, the degree is suitable. After that, we normalized the indicators and performed the principal component analysis to get the weights. Xingtai’s and Beijing’s scores are the closest. So we choose Xingtai. Repeat the above steps. List the score table(table 13) for each location in Xingtai. Finally, we determined the Hebei Ningjin Economic Development Zone as a forest farm area. By developing mathematical models, we assess the impact of the ecoregions on GHG uptake and carbon emission reduction(Problem.4), based on the comprehensive consideration of the environment in the Asia-Pacific region, Australia was selected as the analysis object. Based on the statistics of Australia’s comprehensive data, we adopted a descriptive analysis: through the analysis of the indicators, the overall descriptive analysis of the statistical indicators; use Matlab visualizes the data and explains the problem in a more graphical way.