Multi-scale variations and impact factors of carbon emission intensity in China.

China's carbon emissions have developed swiftly in recent decades, which will not only affect the nation's own sustainable development, but have a potentially negative impact on global climate stability. Given that socioeconomic development is susceptible to regional heterogeneity and geographic scales, a systematic exploration of spatiotemporal variations of carbon emission intensity (CEI) and their drivers across different levels is conducive to enacting more reasonable and efficient measures for emission reduction. However, there is still a lack of comprehensive analysis of these issues. In this paper, we attempted to quantify and compare the spatiotemporal evolution and spatial spillover effects of impact factors on CEI from nighttime light imagery and socioeconomic data at two China's administrative levels by utilizing the variation coefficient, spatial autocorrelation model and spatial econometric methods. The results showed that the spatiotemporal variations of CEI were greater at the prefecture level compared to the provincial level during 2000-2017. There were significant positive spatial autocorrelation of CEI at two administrative levels, and self-reinforcing agglomeration was more substantial at the prefectural level than that provincial level. While the local spatial clustering of CEI of each administrative level altered with scale dependence, the binary spatial structure (High-High and Low-Low) of CEI remained relatively steady in China. Various driver factors not only had direct effects on local CEI, but had spatial spillover effects on neighboring areas. Our findings illustrate that China's CEI is sensitive to the space-time hierarchy of multi-mechanisms, and suggest that "proceed in the light of local conditions" strategies can assist the Chinese government for CEI mitigation.

[Geographical Detection of Spatial Heterogeneity and Drivers of PM2.5 in the Yangtze River Economic Belt].

Based on ground monitoring data, we explored the spatiotemporal characteristics and drivers of PM2.5 in the Yangtze River Economic Belt (YREB) in 2018 using spatial autocorrelation analysis and geodetector modeling methods. The results showed that:① the PM2.5 concentration in the YREB posed the obvious characteristics of low values in summer and high values in winter, seasonal variation in spring and autumn, monthly U-shaped variation, and daily pulse variation. The low value area was mainly concentrated in the south bank of the upper reaches, whereas the high value area was located in the north of the middle-lower reaches of the YREB. ② PM2.5 pollution in the YREB had a stable positive spatial correlation, and the local association pattern showed a significant HH and LL spatial convergence. ③ The spatial correlation of PM2.5 in the YREB decreased with the increase in geographical distance, and its spatial autocorrelation threshold was approximately 870 km, within which the spatial agglomeration of PM2.5 pollution was strong. ④ The influences of natural and anthropogenic factors on PM2.5 had significant spatial differences. Altitude, relief, and population density were the high impact factors of PM2.5 pollution in the YREB. The interaction of factors had a far greater explanatory power on PM2.5 pollution than that of single factors. The dominant interaction factor was industrial structure ∩ altitude, which reflected the complexity of the drivers of air pollution in the YREB.

Spatiotemporal dynamics and impacts of socioeconomic and natural conditions on PM2.5 in the Yangtze River Economic Belt.

The determination of the spatiotemporal patterns and driving factors of PM2.5 is of great interest to the atmospheric and climate science community, who aim to understand and better control the atmospheric linkage indicators. However, most previous studies have been conducted on pollution-sensitive cities, and there is a lack of large-scale and long-term systematic analyses. In this study, we investigated the spatiotemporal evolution of PM2.5 and its influencing factors by using an exploratory spatiotemporal data analysis (ESTDA) technique and spatial econometric model based on remote sensing imagery inversion data of the Yangtze River Economic Belt (YREB), China, between 2000 and 2016. The results showed that 1) the annual value of PM2.5 was in the range of 23.49-37.67 µg/m3 with an inverted U-shaped change trend, and the PM2.5 distribution presented distinct spatial heterogeneity; 2) there was a strong local spatial dependence and dynamic PM2.5 growth process, and the spatial agglomeration of PM2.5 exhibited higher path-dependence and spatial locking characteristics; and 3) the endogenous interaction effect of PM2.5 was significant, where each 1% increase in the neighbouring PM2.5 levels caused the local PM2.5 to increase by at least 0.4%. Natural and anthropogenic factors directly and indirectly influenced the PM2.5 levels. Our results provide spatial decision references for coordinated trans-regional air pollution governance as well as support for further studies which can inform sustainable development strategies in the YREB.

Preliminary report on Sichuan golden snub-nosed monkeys (Rhinopithecus roxellana roxellana) at Laohegou Nature Reserve, Sichuan, China.

Comparative studies of subspecies under different ecological environments offer insights into intraspecies evolutionary adaptive mechanisms. Golden snub-nosed monkeys (Rhinopithecus roxellana) include three subspecies in China classified mainly by their morphological variations: R. r. roxellana (Sichuan and Gansu province), R. r. qinlingensis (Shaanxi province) and R. r. hubeiensis (Hubei province). These three subspecies live in three isolated area with different environments. Past works focused on the last two subspecies, but little information of habitat and behaviors of the nominated subspecies (R. r. roxellana) is available to date. We conducted a two-year study on the diet, activity budget, home range and social organization of 4 herds of R. r. roxellana, based on a total of 106 days' observation in Laohegou (LHG) Nature Reserve, Sichuan province. By using scan sampling method, our results suggest that the R. r roxellana feeds predominantly on leaves (77.5%), and spends more time feeding (40.0%) and resting (27.0%) while compared to the other two subspecies. Kernel Density Estimation Method based on GPS technology confirms that R. r roxellana has relatively larger home ranges (49.1 km2). The unit size (8.3 ± 3.5 individuals) of R. r roxellana is also smaller. Therefore, it is possible that differences in food availability in relation to habitats have important impacts on the feeding strategy and social system of the golden snub-nosed monkey. These results provide data to further explore intraspecific adaptations of living primates.

Species classification and quality assessment of Chaihu (Radix Bupleuri) based on high-performance liquid chromatographic fingerprint and combined chemometrics methods.

A high-performance liquid chromatographic (HPLC) method was established to analyze 36 Chaihu (Radix Bupleuri) samples collected from three species (Bupleurum chinense DC., B. scorzonerifolium Willd. and B. smithii Wolff.). Addition of trifluoroacetic acid into the mobile phase resulted in fingerprint chromatograms with stable baselines. There were thirty-two characteristic peaks in the standard fingerprint of B. chinense DC. Different recognition pattern methods, including similarity analysis (SA), hierarchical cluster analysis (HCA), principal component analysis (PCA) and partial least squares-discrimination analysis (PLS-DA) were utilized to analyze the 36 samples based on the contents of chemical constituents. Consistent results from SA, HCA and PCA analysis illustrated the rationalisation for why B. smithii Wolff. was not quoted in the Chinese Pharmacopoeia and classified samples were in agreement with their species. PLS-DA loading plots showed the chemical markers which had the most influences on the separation among different species. However, SA, HCA and PCA could not differentiate between wild and cultivated B. chinense DC. as well as between samples from different provinces. HPLC fingerprint in combination with chemometric techniques provided a very flexible and reliable method for homogeneity evaluation and quality assessment of traditional Chinese medicine.