Uncertainties of soil organic carbon stock estimation caused by paleoclimate and human footprint on the Qinghai Plateau

Crucial roles of the optimal time-scale of water condition on grassland biomass estimation on Qinghai-Tibet Plateau

The effect of the time-scale of water conditions on vegetation productivity has been widely studied by the academic community. However, the relationship between the time-scale of water conditions and the vegetation growth rhythm and the effect of this relationship on vegetation biomass estimation have rarely been discussed. Here, we analyzed the occurrence times of major phenological events on alpine grasslands using the widely distributed "site-dominant species" dataset and set a series of time-scales for accumulated precipitation and standardized precipitation evapotranspiration index based on phenological information. Then, we combined large-scale aboveground/belowground biomass datasets to evaluate the role of the optimal time-scale for water conditions in aboveground/belowground biomass estimation. The results showed that (1) the optimal time-scale for water conditions with the greatest effects on aboveground biomass was on the month before the end of flowering or the onset of fruit maturity. The optimal time-scale for water condition effects on belowground biomass was earlier and longer than that for the aboveground biomass. The optimal time-scales for accumulated precipitation and standardized precipitation evapotranspiration index effects on belowground biomass were at five months before the end of flowering or the beginning of fruit ripening and the three months before the first flowering, respectively. (2) The aboveground and belowground biomass were underestimated by 11 % and 9 %, respectively, when the water conditions at the optimal time-scales were ignored. (3) The interannual variability in aboveground/belowground biomass was more effectively captured by considering the optimal time-scales of water conditions, especially in water-restricted areas. Overall, this study indicated that terrestrial carbon cycle models should incorporate information on the lag-effects of the water conditions in previous periods. In the future, increasing the number of belowground biomass observations and conducting monthly belowground biomass monitoring sooner will be key to revealing the mechanisms of the belowground biomass response to climate change.

The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial

BACKGROUND

The Qinghai-Tibet Plateau is the "sensitive area" of climate change, and also the "driver" and "amplifier" of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the unique geographical environment characteristics of the Qinghai-Tibet Plateau, there is still much controversy about its carbon source and sink estimation results. This study designed a new algorithm based on machine learning to improve the accuracy of carbon source and sink estimation by integrating multiple scale carbon input (net primary productivity, NPP) and output (soil heterotrophic respiration, Rh) information from remote sensing and ground observations. Then, we compared spatial patterns of NPP and Rh derived from the fusion of multiple scale data with other widely used products and tried to quantify the differences and uncertainties of carbon sink simulation at a regional scale.

RESULTS

Our results indicate that although global warming has potentially increased the Rh of the Qinghai-Tibet Plateau, it will also increase its NPP, and its current performance is a net carbon sink area (carbon sink amount is 22.3 Tg C/year). Comparative analysis with other data products shows that CASA, GLOPEM, and MODIS products based on remote sensing underestimate the carbon input of the Qinghai-Tibet Plateau (30-70%), which is the main reason for the severe underestimation of the carbon sink level of the Qinghai-Tibet Plateau (even considered as a carbon source).

CONCLUSIONS

The estimation of the carbon sink in the Qinghai-Tibet Plateau is of great significance for ensuring its ecological barrier function. It can deepen the community's understanding of the response to climate change in sensitive areas of the plateau. This study can provide an essential basis for assessing the uncertainty of carbon sources and sinks in the Qinghai-Tibet Plateau, and also provide a scientific reference for helping China achieve "carbon neutrality" by 2060.

Advanced bioremediation by an amalgamation of nanotechnology and modern artificial intelligence for efficient restoration of crude petroleum oil-contaminated sites: a prospective study

Crude petroleum oil spillage is becoming a global concern for environmental pollution and poses a severe threat to flora and fauna. Bioremediation is considered a clean, eco-friendly, and cost-effective process to achieve success among the several technologies adopted to mitigate fossil fuel pollution. However, due to the hydrophobic and recalcitrant nature of the oily components, they are not readily bioavailable to the biological components for the remediation process. In the last decade, nanoparticle-based restoration of oil-contaminated, owing to several attractive properties, has gained significant momentum. Thus, intertwining nano- and bioremediation can lead to a suitable technology termed 'nanobioremediation' expected to nullify bioremediation's drawbacks. Furthermore, artificial intelligence (AI), an advanced and sophisticated technique that utilizes digital brains or software to perform different tasks, may radically transfer the bioremediation process to develop an efficient, faster, robust, and more accurate method for rehabilitating oil-contaminated systems. The present review outlines the critical issues associated with the conventional bioremediation process. It analyses the significance of the nanobioremediation process in combination with AI to overcome such drawbacks of a traditional approach for efficiently remedying crude petroleum oil-contaminated sites.