The long-term stressful utilization of forests and grasslands has led to ecosystem degradation and C loss. Since the late 1970s China has launched six key national ecological restoration projects to protect its environment and restore degraded ecosystems. Here, we conducted a large-scale field investigation and a literature survey of biomass and soil C in China's forest, shrubland, and grassland ecosystems across the regions where the six projects were implemented (∼16% of the country's land area). We investigated the changes in the C stocks of these ecosystems to evaluate the contributions of the projects to the country's C sink between 2001 and 2010. Over this decade, we estimated that the total annual C sink in the project region was 132 Tg C per y (1 Tg = 10 g), over half of which (74 Tg C per y, 56%) was attributed to the implementation of the projects. Our results demonstrate that these restoration projects have substantially contributed to CO mitigation in China.

Measurements of trace metal species in situ in a softwater river, a hardwater lake, and a hardwater stream were compared to the equilibrium distribution of species calculated using two models, WHAM 6, incorporating humic ion binding model VI and visual MINTEQ incorporating NICA−Donnan. Diffusive gradients in thin films (DGT) and voltammetry at a gel integrated microelectrode (GIME) were used to estimate dynamic species that are both labile and mobile. The Donnan membrane technique (DMT) and hollow fiber permeation liquid membrane (HFPLM) were used to measure free ion activities. Predictions of dominant metal species using the two models agreed reasonably well, even when colloidal oxide components were considered. Concentrations derived using GIME were generally lower than those from DGT, consistent with calculations of the lability criteria that take into account the smaller time window available for the flux to GIME. Model predictions of free ion activities generally did not agree with measurements, highlighting the need for further work and difficulties in obtaining appropriate input data.