Abstract
A spatial autocorrelation analysis method is adopted to process the spatial dynamic change of industrial Chemical Oxygen Demand (COD) discharge in China over the past 15 years. Studies show that amount and intensity of industrial COD discharges are on a decrease, and the tendency is more remarkable for discharge intensity. There are large differences between inter-provincial discharge amount and intensity, and with different spatial differentiation features. Global spatial autocorrelation analysis reveals that Global Moran’s I of discharge amount and intensity is on the decrease. In space, there is an evolution from an agglomeration pattern to a discretization pattern. Local spatial autocorrelation analysis shows that the agglomeration area of industrial COD discharge amount and intensity varies greatly in space with time. Stringent environmental regulations and increased funding for environmental protections are the crucial factors to cut down industrial COD discharge amount and intensity.
Highlights
Chemical Oxygen Demand (COD), a specific comprehensive index to characterize the organic pollution in the environmental water sample, is mostly used as the important basis for determining the relative content of organic water pollutants in environmental monitoring and environmental impact assessments
Using a spatial autocorrelation analysis method, this study analyzes the spatial dynamics of industrial COD discharge intensity in China in the past 15 years and probes into its spatial heterogeneity and development rules
In order to identify and measure the strength of spatial patterns, showing how the industrial wastewater COD discharge were correlated in these provinces, Moran’s I values was calculated and assessed by testing a null hypothesis
Summary
Chemical Oxygen Demand (COD), a specific comprehensive index to characterize the organic pollution in the environmental water sample, is mostly used as the important basis for determining the relative content of organic water pollutants in environmental monitoring and environmental impact assessments. Spatial autocorrelation analysis is an important method for the quantitative study of various problems involving spatial relations within natural, economic and social fields, as well as an effective measure to analyze spatial patterns Using a spatial autocorrelation analysis method, this study analyzes the spatial dynamics of industrial COD discharge intensity in China in the past 15 years and probes into its spatial heterogeneity and development rules. It provides a reference for industrial development strategies and relevant water environmental protection policies, a basis for environmental protection macro strategy, which is currently studied and implemented by the state, and a beneficial reference for the construction of an environment-friendly society and ecological civilization
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