Climate change reduces elevational and latitudinal differences in spring phenology of pine caterpillar (Dendrolimus spectabilis Bulter).

The pine caterpillar (Dendrolimus spectabilis Bulter, Lepidoptera: Lasiocampidae), as an ectotherm, temperature plays a crucial role in its development. With climate change, earlier development of insect pests is expected to pose a more frequent threat to forest communities. Yet the quantitative research about the extent to which global warming affects pine caterpillar populations is rarely understood, particularly across various elevations and latitudes. Spring phenology of pine caterpillars showed an advancing trend with 0.8 d/10a, 2.2 d/10a, 2.2 d/10a, and 3.3 d/10a under the SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenario, respectively. There was a maximum advance of 20 d in spring phenology of pine caterpillars during the 2090s, from mid-March to early March, and even late February. This study highlighted the significant advance in spring phenology at elevations >1000 m and lower latitudes. Consequently, the differences in elevational and latitudinal gradients were relatively small as the increasing temperatures at the end of the 21st century. And the average temperature in February-March was effective in explaining theses variability. These findings are crucial for adapting and mitigating to climate change.

Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis-NIR Spectroscopy: A Case Study of Inner Mongolia, China.

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible-near-infrared (Vis-NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis-NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis-NIR spectral band. The R2 value of the Vis-NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07-0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.

Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models.

Severe natural disasters and related secondary disasters are a huge menace to society. Currently, it is difficult to identify risk formation mechanisms and quantitatively evaluate the risks associated with disaster chains; thus, there is a need to further develop relevant risk assessment methods. In this research, we propose an earthquake disaster chain risk evaluation method that couples Bayesian network and Newmark models that are based on natural hazard risk formation theory with the aim of identifying the influence of earthquake disaster chains. This new method effectively considers two risk elements: hazard and vulnerability, and hazard analysis, which includes chain probability analysis and hazard intensity analysis. The chain probability of adjacent disasters was obtained from the Bayesian network model, and the permanent displacement that was applied to represent the potential hazard intensity was calculated by the Newmark model. To validate the method, the Changbai Mountain volcano earthquake-collapse-landslide disaster chain was selected as a case study. The risk assessment results showed that the high-and medium-risk zones were predominantly located within a 10 km radius of Tianchi, and that other regions within the study area were mainly associated with very low-to low-risk values. The verified results of the reported method showed that the area of the receiver operating characteristic (ROC) curve was 0.817, which indicates that the method is very effective for earthquake disaster chain risk recognition and assessment.

Environmental Risk Assessment of Metals in the Volcanic Soil of Changbai Mountain.

Tianchi volcano is a dormant active volcano with a risk of re-eruption. Volcanic soil and volcanic ash samples were collected around the volcano and the concentrations of 21 metals (major and trace elements) were determined. The spatial distribution of the metals was obtained by inverse distance weight (IDW) interpolation. The metals' sources were identified and their pollution levels were assessed to determine their potential ecological and human health risks. The metal concentrations were higher around Tianchi and at the north to the west of the study area. According to the geo-accumulation index (Igeo), enrichment factor (EF) and contamination factor (CF) calculations, Zn pollution was high in the study area. Pearson's correlation analysis and principal component analysis showed that with the exception of Fe, Mn and As, the metals that were investigated (Al, K, Ca, Na, Mg, Ti, Cu, Pb, Zn, Cr, Ni, Ba, Ga, Li, Co, Cd, Sn, Sr) were mostly naturally derived. A small proportion of Li, Pb and Zn may have come from vehicle traffic. There is no potential ecological risk and non-carcinogenic risk because of the low concentrations of the metals; however, it is necessary to pay attention to the carcinogenic risk of Cr and As in children.

Spatial and temporal variability in extreme temperature and precipitation events in Inner Mongolia (China) during 1960-2017.

Due to global warming, extreme climate events have become an important issue, and different geographical regions have different sensitivities to climate change. Therefore, temporal and spatial variations in extreme temperature and precipitation events in Inner Mongolia were analyzed based on the daily maximum temperature, minimum temperature, and precipitation data during the period of 1960-2017. The results showed that warm extreme indices, such as SU25, TX90p, TN90p, and WSDI, significantly increased, whereas the cold extreme indices, such as FD0, TX10p, TN10p, and CSDI, significantly decreased; all indices have obvious abrupt changes based on the Mann-Kendall test; nighttime warming was higher than daytime warming. Extreme precipitation indices slightly decreased overall. All of the extreme temperature and precipitation indices had long-range correlations based on detrended fluctuation analysis (a > 0.5), thereby indicating that the extreme climate indices will maintain their current trend directions in the future. ENSO, AO, and IOD had a strong positive influence on warm extremes and a strong negative influence on cold extremes in Inner Mongolia. NCEP/NCAR and ERA-20CM reanalysis showed that strengthening anticyclone circulation, increasing geopotential height, decreasing daytime cloudiness and increasing nightime cloudiness contributed to changes in climate extremes in Inner Mongolia.

Himawari-8 Satellite Based Dynamic Monitoring of Grassland Fire in China-Mongolia Border Regions.

In this study, we used bands 7, 4, and 3 of the Advance Himawari Imager (AHI) data, combined with a Threshold Algorithm and a visual interpretation method to monitor the entire process of grassland fires that occurred on the China-Mongolia border regions, between 05:40 (UTC) on April 19th to 13:50 (UTC) on April 21st 2016. The results of the AHI data monitoring are evaluated by the fire point product data, the wind field data, and the environmental information data of the area in which the fire took place. The monitoring result shows that, the grassland fire burned for two days and eight hours with a total burned area of about 2708.29 km². It mainly spread from the northwest to the southeast, with a maximum burning speed of 20.9 m/s, a minimum speed of 2.52 m/s, and an average speed of about 12.07 m/s. Thus, using AHI data can not only quickly and accurately track the dynamic development of a grassland fire, but also estimate the spread speed and direction. The evaluation of fire monitoring results reveals that AHI data with high precision and timeliness can be highly consistent with the actual situation.