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Widespread soil acidification due to atmospheric acid deposition and agricultural fertilization may greatly accelerate soil carbonate dissolution and CO2 release. However, to date, few studies have addressed these processes. Here, we use meta-analysis and nationwide-survey datasets to investigate changes in the soil inorganic carbon (SIC) stocks in China. We observe an overall decrease in the SIC stocks in topsoil (0-30 cm) (11.33 g C m–2 yr–1) during the 1980 s and 2010 s. The total SIC stocks have decreased by approximately 8.99 ± 2.24% (1.37 ± 0.37 Pg C). The average SIC losses across China (0.046 Pg C yr–1) and in cropland (0.016 Pg C yr–1) account for approximately 17.6–24.0% of the terrestrial C sink and 57.1% of the soil organic carbon sink in cropland, respectively. Nitrogen deposition and climate change have profound influences on SIC cycling. We estimate that approximately 19.12–19.47% of the SIC stocks will be further lost by 2100. The consumption of SIC may offset a large portion of the global efforts aimed at ecosystem carbon sequestration, which emphasizes the importance of better understanding the indirect coupling mechanisms of nitrogen and carbon cycling and of effective countermeasures to minimize SIC loss.

Data placement considerably affects the I/O performance of distributed storage systems such as HDFS. An ideal placement algorithm should keep the I/O load evenly distributed among different storage nodes. Most of the existing placement algorithms with I/O load balance guarantee depend on the information of data popularity to make the placement decisions. However, the popularity information is typically not available in the data placement phase. Furthermore, it usually varies during the data lifecycle. In this paper, we propose a new placement algorithm called Balanced Distribution for Each Age Group (BEAG), which makes data placement decisions in the absence of the popularity information. This algorithm maintains multiple counters for each storage node, with each counter representing the amount of data belonging to a certain age group. It ensures that the data in each age group are equally scattered among the different storage nodes. As the popularity variance of the data belonging to the same age group is considerably smaller than that of the entire data, BEAG significantly improves the I/O load balance. Experimental results show that compared to other popularity independent algorithms, BEAG decreases the I/O load standard deviation by 11.6% to 30.4%.

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