Hg emission flux from various land covers, such as forests, wetlands, and urban areas, have been investigated. China has the largest area of coalfield in the world, but data of Hg flux of coalfields, especially, those with coal fires, are seriously limited. In this study, Hg fluxes of a coalfield were measured using the dynamic flux chamber (DFC) method, coupled with a Lumex multifunctional Hg analyzer RA-915+ (Lumex Ltd., Russia). The results show that the Hg flux in Wuda coalfield ranged from 4 to 318 ng m?2 h?1, and the average value for different areas varied, e.g., coal-fire area 99 and 177 ng m?2 h?1; no coal-fire area 19 and 32 ng m?2 h?1; and backfilling area 53 ng m?2 h?1. Hg continued to be emitted from an underground coal seam, even if there were no phenomena, such as vents, cracks, and smog, of coal fire on the soil surface. This phenomenon occurred in all area types, i.e., coal-fire area, no coal-fire area, and backfilling area, which is universal in Wuda coalfield. Considering that many coalfields in northern China are similar to Wuda coalfield, they may be large sources of atmospheric Hg. The correlations of Hg emission flux with influence factors, such as sunlight intensity, soil surface temperature, and atmospheric Hg content, were also investigated for Wuda coalfield.
We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years. 相似文献
With the environmental carrying capacity reaching its limits and the decreasing margin benefits of traditional production factors, the green transformation and green development through technological innovations has been a major direction for the future development of Chinese industries. However, the characteristics and heterogeneities of various types of industries call for different approaches regarding technological innovations. How to choose the most effective mode of technological innovation according to the characteristics of a certain industry has been a key issue. This paper measures the green total factor productivity of 32 industrial trades using the Slacks Based Measure(SBM)-DDF method. The effects of three innovation modes in the green transformation of industrial industry, including the independent innovation(Ⅱ), the technology introduction(TI), and the government support(GS), are empirically analyzed based on industry heterogeneity. Results indicate that the green total factor productivities of different industries show significant differences if taking into account the energy input and the undesirable output of pollutant emissions. The green total factor productivities of traditional high input,high pollution, and high energy consumption industrial trades were significantly lower than those with obvious green features. The year of 2009 is a leap year for the industrial green transformation in China. For resource-intensive industries, the II and the GS are the important ways to achieve green transformation. For labor-intensive industries, the TI is the best path to achieve green transformation, while for technology-intensive industries, the II is the primary driving force for the promotion of green developments. In addition, the innovation-compensating effect of the current Chinese environmental regulations to the resource-intensive industries has been revealed. Improving the overall scale and the industrial concentration of the industries is also beneficial for the green transformation of the industries. 相似文献