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As direct prospecting data, geochemical data play an important role in modelling prospect potential. Geochemical element assemblage anomalies are usually reflected by the correlation between elements. Correlation coefficients are computed from the values of two elements, which reflect only the correlation at a global level. Thus, the spatial details of the correlation structure are ignored. In fact, an element combination anomaly often exists in geological backgrounds, such as on a fault zone or within a lithological unit. This anomaly may cause some combination of anomalies that are submerged inside the overall area and thus cannot be effectively extracted. To address this problem, we propose a local correlation coefficient based on spatial neighbourhoods to reflect the global distribution of elements. In this method, the sampling area is first divided into a set of uniform grid cells. A moving window with a size of 3×3 is defined with an integer of 3 to represent the sampling unit. The local correlation in each unit is expressed by the Pearson correlation coefficient. The whole area is scanned by the moving window, which produces a correlation coefficient matrix, and the result is portrayed with a thermal diagram. The local correlation approach was tested on two selected geochemical soil survey sites in Xiao Mountain, Henan Province. The results show that the areas of high correlation are mainly distributed in the fault zone or the known mineral spots. Therefore, the local correlation method is effective in extracting geochemical element combination anomalies. [ABSTRACT FROM AUTHOR]

Synergistic governance for reducing pollution and carbon emissions can generate savings in policy costs and respond to ecological and environmental change pressures effectively. For exploring the temporal-spatial evolution of pollution and carbon reduction, and probing the synergistic of promoting pollution and carbon reduction at the urban level, this study utilizes panel data from 275 Chinese cities between 2006 and 2019, we examine the spatial-temporal evolution characteristics of pollution and carbon reduction, the synergistic effects and spatial spillover effects of national eco-industrial demonstration parks (NEDP) on pollution and carbon reduction. And we do this work by using spatial analysis tools such as spatial autocorrelation, local cold-hot spot analysis, kernel density analysis, standard deviation ellipses, as well as multi-period double difference and spatial double difference models. We find that: (1) Carbon dioxide emissions and sulfur dioxide emissions display a geographical clustering trend during the study period, with high-density emission areas spreading from coastal regions to central and western ones. The center of emissions is located in Henan Province and shifting towards the northwest of China. (2) The development of NEDP in cities may greatly cut carbon dioxide and sulfur dioxide emissions. (3) National eco-industrial demonstration parks may significantly increase the synergistic impact of pollution and carbon reduction in the neighboring cities due to their significant spatial spillover effect. To enhance the synergistic effect of pollution and carbon reduction in the future, we need actively explore the construction of NEDP, and lay out these parks based on the level of resource endowment, industrial structure, and economic development. • Explore the spatial-temporal laws of the reduction of pollution and carbon emissions. • National eco-industrial demonstration parks can enhance the synergy between pollution and carbon emissions reduction. • Spatial correlation of carbon emissions reduction and pollution reduction. • The construction of national eco-industrial demonstration parks has policy spillover effect. [ABSTRACT FROM AUTHOR]