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The process of urbanization alters the distribution of land use and gives rise to certain climatic modifications that have a direct influence on vegetation phenology. Mountainous areas have a fragile biological environment, and vegetation phenology is relatively sensitive to urbanization. However, there is a paucity of research on the impact of urbanization in mountainous areas on vegetation phenology. The Urban Agglomeration of Central Yunnan (UACY) is located on the Yunnan–Guizhou Plateau in China. This study explored the vegetation phenological changes in different terrains from 2001 to 2020 in the UACY based on remote sensing data. Using the dynamic gradient method, we examined the response of vegetation phenology to urbanization from three aspects: urban–rural gradient, urbanization intensity (UI), and population density. The results showed that landform has a significant impact on SOS (start of growing season), with a topographic relief difference of 200 m acting as the dividing line. The findings derived from the dynamic gradient analysis indicate that UI has the most significant effect. SOS advances by 5.77 days (R2 = 0.96), EOS (end of growing season) advances by 2.30 days (R2 = 0.83), and LOS (length of growing season) lengthens by 2.59 days (R2 = 0.87) for every 10% increase in UI. This study has the potential to serve as a valuable resource for future urban planning and administration in the UACY. Additionally, it could provide decision-making support for the development of mountainous urban agglomerations in ecological environments. [ABSTRACT FROM AUTHOR]

Secchi depth (SD) is a valuable and feasible water quality indicator of lake eutrophication. The establishment of an automated system with efficient image processing and an algorithm suitable for the inversion of transparency in lake-rich regions could provide sufficient temporal and spatial information for lake management. These are especially critical for lake-rich regions where in situ monitoring data are scarce. This study demonstrated the implementation of an atmospheric correction algorithm (ACOLITE algorithm) in conjunction with the Google Earth Engine platform to generate remote-sensing reflectance products of specific points efficiently. The study also evaluated the performance of an algorithm for inverting lake SDs in Yunnan Plateau lakes, which is one of the five lake districts in China, since there is a lack of in situ data for most of the lakes in the region. The in situ data from four lakes with large SD ranges and imagery from Landsat Operational Land Imager were used to train and evaluate the performance of two algorithms: an empirical algorithm (stepwise regression) and machine learning (support vector machines and multi-layer perception). The results revealed that the retrieval accuracy of models with bands and band ratio combinations could be substantially improved compared with models with a single band or band combinations. A negative correlation was also observed between the temporal match between observations and the model accuracy. This study found that the MLP model with sufficient training data was more suitable for transparency estimation of lakes belonging to the dataset; the SVM model was more suitable for transparency prediction outside the training set, regardless of the adequacy of the training data. This study provides a reference for monitoring lakes within the Yunnan region using remote sensing. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the main climatic factors influencing nine plateau lakes in Yunnan Province over the past 40 years (from 1980 to 2019) based on trend and mutation analyses. The results revealed that the air temperatures of these plateau lakes have increased, with an annual average air temperature increase of 0.18 °C per decade, during this 40-year period. From 2000 to 2005, there was an abrupt change in the air temperature increase, the rate of which was 0.20 °C per decade, and there were reductions in long-wave radiation, relative humidity, wind speed, precipitation, and snowfall. In addition, the rising trend in the air temperature of these lakes located in northwest Yunnan (temperate climate, higher elevation) was found to be significantly higher than that of the lakes in central Yunnan (subtropical climate, lower elevation), whereas in contrast, the declining trends in long-wave radiation, relative humidity, wind speed, and precipitation were more pronounced in central than in northwestern Yunnan. [ABSTRACT FROM AUTHOR]

Human urbanization has a great impact on the surface ecological environment, and few existing studies have explored the impact of urbanization on regional comfort on a long time scale. This study took Chenggong District, Kunming City, Yunnan Province, China, where urbanization was obvious, as the study area, and used the comfort evaluation model to evaluate the annual summer Discomfort Index (DI) in different periods of urbanization. Meanwhile, the impact strength of each factor characterizing human activities (Impervious surface, Gross National Product, and Total Population) on DI changes was analyzed, and the contribution rate of the main factors was quantified. The experimental results show that (1) over the past 20 years, under the background of the rapid economic development of Chenggong District, the annual average DI in summer showed an upward trend. The growth rate after the completion of University Town (2010–2020, Post-UT) was higher than that before the completion of University Town (2001–2005, Pre-UT). University Town was growing much faster than other regions. The monthly average DI changes were similar to the annual average changes. However, due to the movement of students in University Town during the summer vacation, the growth rate of DI in June was significantly higher than that in other months. (2) In terms of spatial changes, DI in the central and northwestern parts of Chenggong District increased significantly from 2001 to 2020. There were differences in the change rate before and after the completion of University Town. The area occupied by significant growth areas in June was much higher than in other months. It is proved that the economic and social development of Chenggong District would impact the regional human comfort, and the construction of University Town has aggravated the intensity of this impact. (3) In the during-UT, affected by the complex changes in land use types, the DI in Chenggong District showed fluctuations in time, but there was no obvious change in space. (4) The correlation and contribution analysis showed that the annual average DI in summer was closely related to human activities, especially the impervious surface had a strong contribution rate of 52.7%. The research shows that the development of new cities would have a strong impact on regional DI changes. And the obtained results will provide theoretical support for rational planning and management in the process of urban development in the future, thereby promoting the sustainable development of the region. [ABSTRACT FROM AUTHOR]

[Display omitted] • Anthropogenic impact has warming effect on the LSWT. • When human activities cause a 1℃ increase in LSWT, the overall average increase in LSWT is 0.24℃. • The impact of human social policies on the surface water temperature of lakes will continue. In the past 40 years, the surface water temperature of lakes worldwide has generally shown an upward trend, with a significant spatial heterogeneity. Previous studies generally attributed the change in lake surface water temperature to direct impact of climate change. However, few studies have explored the potential impact of additional anthropogenic factors, resulting in an incomplete understanding of the anthropogenic influence on lake surface water temperatures. We thus propose a new method to quantify the anthropogenic impact on the surface water temperature of lakes. We selected 11 lakes characterized by significant variations in the intensity of watershed urbanization development on the Yunnan-Guizhou Plateau in China for an empirical study, and discussed the trend of LSWT (lake surface water temperature) changes under the anthropogenic impact, as well as the potential link between these changes and human social policies. The research results show that (1) The mean annual rate of change in LSWT due to anthropogenic impact fluctuates of 0.06℃ per year. (2) LSWT is sensitive to changes in anthropogenic activities; a 1℃ increase in LSWT due to anthropogenic factors typically results in a mean temperature variation of 0.24℃. (3) During the three years of the COVID-19 pandemic, LSWT on the Yunnan-Guizhou Plateau was significantly affected by anthropogenic activities. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is an important factor in lake ecological environments. It has been observed that LSWT have followed an upward trend in the last half century, which has had serious impacts on regional biodiversity and climate. It is important to understand the main reason for this phenomenon in order to have a basis for controlling and improving the regional ecological environment. In this study, the contribution rates of near surface air temperature (NSAT), surface pressure (SP), surface solar radiation (SSR), total cloud cover (TCC), wind speed (WS) and Secchi depth (SD) to LSWT of 11 naturally formed lakes in the Yunnan-Guizhou Plateau are quantified. The characteristics of and relationships between the various factors and LSWT in lakes of different types and attributes are revealed. The results show that: (1) from 2001 to 2018, most lakes were warming; the change rate of LSWT-day was higher than that of LSWT-night. The mean comprehensive warming rate (MCWR) of LSWT-day was 0.42 °C/decade, and the mean comprehensive change rate (MCCR) was 0.31 °C/decade; the MCWR of LSWT-night was 0.19 °C/decade, and the MCCR was 0.01 °C/decade. NSAT and SSR were most strongly correlated with LSWT-day/night. There were no large seasonal differences in the correlation between NSAT and LSWT-day, while seasonal differences in the correlations between NSAT with LSWT-night and SSR with LSWT-day/night were observed. (2) NSAT and SSR were the most important factors affecting LSWT-day/night changes, with contribution rates of 30.24% and 44.34%, respectively. LSWT-day was more affected by SP and SSR in small, shallow, and low-storage lakes. For larger lakes, LSWT-day was more affected by WS, while LSWT-night was greatly affected by TCC. Urban and semi-urban lakes were more affected by SSR and NSAT; for natural lakes, the decreasing SD affected the increases in LSWT, which indirectly reflects the impact of human activities. LSWT-day/night responded differently to different morphological characteristics of the lakes and different intensities of human activity. Image 1 • The heterogeneity of LSWT warming of 11 major plateau lakes were discussed. • 6 main factors impact on LSWT were analyzed quantitively. • LSWT changing heterogeneity was revealed from lake type and morphometry perspective. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5) concentrations exhibit distinct spatiotemporal heterogeneity, mainly due to the natural environment and human activities. Yunnan Province of China was selected as the research area, and a real-time measured PM 2.5 concentration dataset was acquired from 41 monitoring stations in 16 major cities from February 2013 to December 2018. Aerosol optical depth (AOD) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and data on four meteorological variables from 2000 to 2018 were employed. A novel hybrid model was constructed to estimate the historical missing PM 2.5 values from 2000 to 2012, calculate the missing PM 2.5 concentrations from 2012 to 2014 in some major cities, and analyze the driving factors of the PM 2.5 concentration changes and causes of key pollution events in Yunnan Province over the past 19 years. The temporal analysis results indicate that the annual mean PM 2.5 concentration in Yunnan Province exhibited three stages: continuous stability, a rapid increase and a rapid decrease. The year 2013 was an important breakpoint in the trend of the concentration change. The spatial analysis results reveal that the annual mean PM 2.5 concentration in the north was lower than that in the south, and there was a significant difference between the east and the west. In addition, springtime biomass burning in Southeast Asia was found to be the main cause of PM 2.5 pollution in Yunnan Province in spring. Image 1 • The PM 2.5 concentration exhibits heterogeneity due to human activities and the natural environment. • Springtime biomass burning has a significant influence on the surrounding air quality. • The air quality of Yunnan Province improved after 2013. [ABSTRACT FROM AUTHOR]

Sugarcane is a high biomass crop grown mostly in rain-fed environments. Water is a key determinant of sugarcane productivity, and in most production regions, varying and sometimes severe water stress occurs. Understanding the extent of genetic variation in response to water stress may allow for better targeted breeding programs. In this study a set of field experiments evaluating 31 sugarcane clones under rain-fed and irrigated conditions was conducted in two sugarcane production areas in Yunnan province, South China. While the water treatments had a large impact on mean cane yield there were little or no genotype × water treatment interactions for cane yield or sugar content at harvest, or for dry biomass sampled about five months (early biomass) after the start of crop growth. The high genetic correlation between well-watered and water stress treatments is consistent with the hypothesis that clone performance under water stress is mostly associated with general vigour rather than traits acting specifically under water stress. From a practical breeding program perspective the results suggest that choice or management of water stress level in selection trials is not a critical consideration for sugarcane breeding programs, and that selection under reasonably well watered conditions will be effective for attaining satisfactory selection gains under at least moderate levels of water stress. Early biomass was positively correlated with yield at final harvest for all water treatments. Resolution of the physiological basis of the high genetic correlations between growth under well-watered and water limited conditions may assist in future breeding efforts. [ABSTRACT FROM AUTHOR]

Abstract: A new species of Ophiocordyceps, Ophiocordyceps lanpingensis collected from Lanping County, Yunnan Province, southwestern China, was described based on morphological characteristic, ITS1-5.8S-ITS2 rDNA sequences analyses, 5-gene (nrSSU, nrLSU, tef-1α, rpb1 and rpb2) sequences analyses and MAT1-2-1 gene sequences analyses. This species was characterized by thinner stroma, smaller perithecium, thinner ascospore (multiseptate with short septation). The phylogenetic analyses based on the ITS, the 5-gene and MAT1-2-1 gene dataset showed O. lanpingensis had the closest evolution relationship with O. robertsii and O. sinensis, but still had obvious distances to them. Both morphological character and systematic analyses supported that O. lanpingensis was a new species of Ophiocordyceps. [Copyright &y& Elsevier]

[Display omitted] • Proteomics was used to explore the bio-functions of D. sinensis stems proteins. • Proteomics can provide insights in the purification of milk-clotting enzymes. • A cysteine protease with MW of 25.8 kDa and stable properties was identified. Dregea sinensis (D. sinensis) stems have traditionally been used as milk coagulant in Dali of Yunnan Province, China. In this study, proteomics was used to investigate the bio-functions of D. sinensis stem proteins, leading to the purification and identification of the milk-clotting enzyme. A total of 205 proteins mainly involved in the catalytic and metabolic processes were identified, of which 28 proteins exhibited hydrolase activity. Among the 28 proteins, we focused on two enzymes (M9QMC9 and B7VF65). Based on proteomics, a cysteine protease (M9QMC9) with a molecular weight of 25.8 kDa and milk-clotting activity was purified from D. sinensis stems using double ammonium sulfate precipitation and was confirmed using liquid chromatography-mass spectrometry (LC-MS/MS). The milk-clotting temperature using the purified enzyme was around 80 °C (specific activity at 314.38 U/mg), and it was found to be stable in the pH range of 6–9 in NaCl concentration of <0.8 mol/L. These findings indicated that the enzyme isolated from D. sinensis stems has potential in the dairy and food sectors, especially in the cheese-making industry. [ABSTRACT FROM AUTHOR]