Most Parts Of China Research Articles

Sympatric diversity pattern driven by the secondary contact of two deeply divergent lineages of the soybean pod borer Leguminivora glycinivorella.

The soybean pod borer, Leguminivora glycinivorella (Matsumura), is an important tortricid pest species widely distributed in most parts of China and its adjacent regions. Here, we analyzed the genetic diversity and population differentiation of L. glycinivorella using diverse genetic information including the standard cox1 barcode sequences, mitochondrial genomes (mitogenomes), and single-nucleotide polymorphisms (SNPs) from genotyping-by-sequencing. Based on a comprehensive sampling (including adults or larvae of L. glycinivorella newly collected at 22 of the total 30 localities examined) that covers most of the known distribution range of this pest, analyses of 543 cox1 barcode sequences and 60 mitogenomes revealed that the traditionally recognized and widely distributed L. glycinivorella contains two sympatric and widely distributed genetic lineages (A and B) that were estimated to have diverged ∼1.14 million years ago during the middle Pleistocene. Moreover, low but statistically significant correlations were recognized between genetic differentiation and geographic or environmental distances, indicating the existence of local adaptation to some extent. Based on SNPs, phylogenetic inference, principal component analysis, fixation index, and admixture analysis all confirm the two divergent sympatric lineages. Compared with the stable demographic history of Lineage B, the expansion of Lineage A had possibly made the secondary contact of the two lineages probable, and this process may be driven by the climate fluctuation during the late Pleistocene as revealed by ecological niche modeling.

Spatialization and Analysis of China’s GDP Based on NPP/VIIRS Data from 2013 to 2023

The quality of nighttime light (NTL) data is an important factor affecting the estimation of gross domestic product (GDP), but most studies do not use the latest NPP/VIIRS V2 annual composite product, and there is a lack of China’s GDP estimation products in recent years. To address this problem, this paper studies the NPP/VIIRS remote sensing estimation method for the GDP in mainland China from 2013 to 2023. First, the remote sensing data are preprocessed, and the noise masking method is used to remove outliers. The total amount of NTL, average NTL value, and comprehensive NTL index data are extracted. Combined with the GDP data from the Statistical Yearbook, a fitting model of the GDP and NTL index is constructed. The differences between different GDP estimation models are compared and analyzed, and the optimal model is selected as the estimation model. In addition, through the optimal fitting model, GDP spatial estimation products from 2013 to 2023 are produced. Moreover, the spatiotemporal variation characteristics of the GDP in mainland China are analyzed, with a focus on the spatiotemporal variation of GDP decline regions and the changes in the GDP rankings of provinces and cities. The main conclusions include the following: (1) In the time regression analysis, the linear model MNL has a strong correlation with the GDP, with an R2 of 0.972. This model is selected as the optimal fitting model to calculate the spatial data of the GDP. (2) The spatial distribution of the GDP in mainland China is high in the east and low in the west, and it shows a characteristic of extending from the provincial capital to the surrounding cities. The connectivity between adjacent high-GDP areas continues to increase. (3) From 2013 to 2023, the GDP in most parts of China showed an upward trend, with 98.56% of pixels growing and only 0.99% of pixels declining. The declining pixels are mainly distributed in heavy industrial cities supported by fossil fuel resources, such as Ordos, Daqing, Aksu, etc. (4) Compared with statistical data, the overall difference of the GDP estimated by NTL data is not large, and the relative error is between 0.04% and 1.95%. From the perspective of the GDP ranking of each province, the ranking of most provinces is not much different, fluctuating between ±2. A small number of provinces have large ranking differences due to reasons such as dominant industries and power supply. By spatializing the GDP data of mainland China in the past 11 years, the spatiotemporal changes of the GDP within mainland China were analyzed. The research results can provide support for government economic decisions such as urban development.

Features of subseasonal concurrent variations between subtropical and polar front jets and their association with temperature anomalies in China

Subseasonal variability of atmospheric circulation in mid-high latitudes is highly correlated to the upper-level jet streams, acting as a major contributor to the global or regional persistent climate anomalies. In this study, the features of subseasonal concurrent variations (SCVs) between East Asian Polar Front Jet (EAPJ) and Subtropical Jet (EASJ), and corresponding atmospheric circulation and temperature anomalies in China are investigated by using the observational data and NCEP/NCAR reanalysis datasets. Results show that the main variability modes of the winter upper-level wind fields on subseasonal time scales are characterized by the meridional shift in opposite directions and out-of-phase variations in the intensity of the EASJ and EAPJ. These concurrent variations between the two jets have a significant oscillation cycle with a period of 10–25 days. Accompanied by the location (intensity) SCV of the two jets, the mid-latitude circulation systems show significant zonal shifts (local intensity changes), resulting in alternations of the atmospheric circulation between zonal and meridional types. The typical meridional circulation is characterized by a strong East Asian trough and its upstream strong high-pressure ridges. Meanwhile, gradient force between Siberian high and the Aleutian low is anomalously strong. As a result, the SCVs between two jets can cause widespread and persistent low temperature anomalies over China, usually lasting more than three days. In the case of the location SCV of the two jets, the low temperature anomalies concentrate in northern China, particularly in north of 40°N. In the case of the intensity SCV of the two jets, the low temperature anomalies occur in most parts of China, notably in south of 40°N. These results are helpful to improve the prediction of atmospheric circulation and temperature anomalies in East Asia during winter.

Machine-learning-based corrections of CMIP6 historical surface ozone in China during 1950–2014

Due to a lack of long-term observations in China, reports on historical ozone concentration are severely limited. In this study, by combining observation, reanalysis and model simulation data, XGBoost machine learning algorithm is used to correct the surface ozone concentration from CMIP6 climate model, and the long-term and large-scale surface ozone concentration of China during 1950–2014 is obtained. The long-term evolutions and trends of ozone and meteorological effects on interannual ozone variations are further analyzed. The results reveal that CMIP6 historical simulations have a large underestimation in ozone concentrations and their trends. The XGB-derived ozone are closer to observations, with R2 value of 0.66 and 0.74 for daily and monthly retrievals, respectively. Both the concentrations and exceedances of ozone in most parts of China have shown increasing trends from 1950 to 2014. The daily mean ozone concentration without climate change effects is estimated to be 117 ppb in the year 1950 averaged over China. It indicates that the increase in anthropogenic emissions of China has a significant contribution to ozone enhancement between 1950 and 2014. The higher ozone growth rates of XGB retrievals than those from the model indicate a regional surface ozone penalty due to the warming climate. The relatively significant increment in ozone are estimated in the Central and Western China. Seasonally, the ozone enhancement is largest in spring, indicating a shift in seasonal variation of ozone. Given the uncertainty in simulating historical ozone by climate model, we show that machine learning approaches can provide improved assessment of evolution in surface ozone, along with valuable information to guide future model development and formulate future ozone pollution prevention and control policies.

First Report of Alternaria alternata Causing Leaf Blight on Elsholtzia ciliata in China.

Elsholtzia ciliata is an annual medicinal plant characterized to the family Lamiaceae Martinov. It is grown in most parts of China and has high economic value as a traditional Chinese medicine. In September of 2022, E. ciliata plants located at the planting base of traditional Chinses medicine in Daying county (30°35'40″N, 105°14 12″E), Sichuan Province, China, were recorded with leaf blight. The incidence of symptomatic plants was 15% (30 infected plants out of 200 surveyed). The symptoms included an irregular necrotic lesion at the tip of the leaf, which gradually expanded across the entire leaf. To elucidate the cause of the symptoms, 12 symptomatic leaves were sampled from four different plants and 5×5 mm section, including symptomatic and non-symptomatic tissue was excised. Tissue samples were disinfected in 75% ethanol for 30s, and 7% sodium hypochlorite for 1 min, and then rinsed three times with sterile distilled water (Sun et al. 2022). The sampled tissues were placed onto potato dextrose agar (PDA) and incubated at 25℃ in the dark. Seven days later, single spores were recovered onto fresh PDA (Zhu et al. 1992). Colonies on PDA initially appeared white, developing grayish-green conidia with white margins. Conidia (n=150) were collected and observed under the microscope. The conidia were smooth walled and dark brown, with pear-shaped, 12.1-31.4 × 5.0-9.4μm, with 3-5 transverse septa, 1-3 longitudinal or oblique septa. Conidiophores were thick, dark brown, simple with multiple conidial scars, 5.0-75.5 × 2.5.0-5.0μm. Based on morphological observations the 12 isolates were most similar to Alternaria alternata (Simmons 2007). The internal transcribed spacer (ITS) rDNA regions, glyceraldehyde-3-phosphate dehydrogenase (gpd), Alternaria major allergen (Alt a 1), RNA polymerase second largest subunit gene (RPB2) and translation elongation factor 1-alpha (TEF 1) were amplified and sequenced using the primers ITS4/ITS5, RPB2-5F/RPB2-7CR, gpd1/gpd2, EF1-728F/EF1-986R, and Alt-for/Alt-rev respectively (Woudenberg et al. 2015). The sequences of representative isolate (XR) were uploaded in GenBank (ITS: OM319521, RPB2: OM849248, gpd: OM296240, TEF1: OM238122, and Alt a 1: OM649814). The bootstrap value of the isolate and the type strain CBS 595.93 (ITS: KP124320, RPB2: KP124788, gpd: KP124175, TEF1: KP125096, and Alt a 1: JQ646399) on the phylogenetic tree was 99%. Therefore, based on morphology and phylogenetic analysis the fungus was identified as A. alternata. To verify pathogenicity, a spore suspension (1 × 106 conidia/ml) of the representative isolate XR was misted onto the foliage of six twenty-day-old non-symptomatic plants. Six additional plants were sprayed with distilled water and used as controls. The plants were covered with plastic bags for 48 h and incubated at a temperature of 28℃ in the dark. Eight days later, all inoculated plants demonstrated similar symptoms as recorded on the original source, while the control plants were symptomless. The experiment was repeated three times with similar results. A. alternata was re-isolated from the artificially inoculated plants, hence fulfilling Koch's postulates. To our best knowledge this is the first report of leaf blight caused by A. alternata in China on E. ciliate. The disease may be an economic threat and should be further monitored and studied.

Impacts of aerosol meteorological feedback on China's yield potential of soybean

AbstractChina's severe particle pollution could affect the regional climate and weather conditions that consequently threaten local to global food security. Yet, the underlying mechanisms and quantitative assessment of aerosols on crop yields remain unknown. Here, by integrating a meteorology–chemistry model and a crop model, we show the impacts of atmospheric aerosols on China's meteorology and soybean yields. We find that the potential yields of soybean would decrease in most parts of China due to direct aerosol radiation effects, while showing diverse responses in parts of the Northeast and North China Plain. Moreover, because of the high sensitivity of soybean growth to water, potential yield fluctuations are closely related to aerosol‐induced precipitation changes in most soybean‐growing regions of China. In particular, aerosols play the most important role during soybean's pod‐filling stage, in which the influence of both precipitation perturbations and negative solar radiative forcing is about 5–10 times that of air temperature on crop yield. Our study thereby identifies aerosol mitigation can bring a notable increase in crop yields, highlighting the potential for important co‐benefits in food security across polluting developing countries.

Evaluation of food safety on traditional Chinese snacks based on system safety theory: The case study of Wuhan hot-dry noodles

Wuhan hot-dry noodles, as one of the traditional Chinese snacks, have been famous in most parts of China for nearly 100 years. Its characteristic, taste, and quick cooking method cater to the needs of many people. However, food safety has become an essential obstacle in developing Wuhan hot-dry noodles. The present work developed evaluation indicators, and selected an entropy method to analyse the food safety problems of Wuhan hot-dry noodles based on the system safety theory. Results showed that the score was 2.59 over 5.00, which meant the performance was below average. This could have been due to poor perception of employees on food safety, primary enterprise management, and less effective and authoritative standards within the industry. Based on the system theory, the present work suggested engineering, education, and enforcement, namely the "3E principle”, to solve the above problems. The key to the "3E principle” is to focus on identifying, controlling, and evaluating hazards around the most critical issues, and solving them in order of importance and urgency. The "3E principle” also emphasises circular processing for better food safety.

Does climate policy uncertainty affect carbon emissions in China? A novel dynamic ARDL simulation perspective

This study provides new insights into the impact of climate policy uncertainty, energy consumption, and economic development on China’s carbon emissions. In doing so, we develop a novel index of China’s climate policy uncertainty (CCPU). We then use the newly constructed dynamic autoregressive distributed lag (ARDL) simulation model, the frequency-domain causality (FDC) test, and the fully modified OLS (FMOLS) estimation to investigate these potential relationships from 2005 to 2021. The empirical results suggest that increasing CCPU reduces carbon emissions in most parts of China, which improves environmental degradation. Furthermore, the effects of energy consumption and economic growth on carbon emissions are confirmed to be positive in each location. Finally, the results of the FDC and FMOLS confirm the robustness of the model. Our findings suggest that information from the CCPU can be used to forecast CO2 emissions in China. Furthermore, the government should strike a balance between economic growth and environmental regulation and promote the use of renewable energy to reduce carbon emissions. Proactively developing climate policy is important to achieve the goal of carbon neutrality.

More extreme-heat occurrences related to humidity in China

The co-occurrence of day and night compound heat extremes has attracted much attention because of the amplified socioeconomic and human health impacts. Based on ERA5 hourly reanalysis data, this study characterized and compared extreme day–night compound humid-heat/high-temperature events (CHHEs/CHTEs) in China as well as the associated impacts. Results indicated that the spatial patterns of summer mean extreme CHHEs are consistent with those of extreme CHTEs, except in northwestern China. A greater magnitude of these two types of events dominates over southern China, but the high-frequency centers are mainly observed over northern China. Significant increasing trends in frequency are captured nationwide, but with much stronger trends detected in northern and western China. Further analysis shows that the anomalies of humidity play a more important role than those of temperature in the occurrence of extreme CHHEs in most parts of China, but particularly in eastern regions. Since 1961, the human population and land areas of China have experienced strongly increasing compound heat extremes, with a faster rate of exposure to extreme CHHEs than to extreme CHTEs. This study highlights the importance of understanding regional changes in humidity when considering heat stress in the future.

Large Modeling Uncertainty in Projecting Decadal Surface Ozone Changes Over City Clusters of China

AbstractClimate policies will affect future surface ozone pollution in China. Here, we simulate changes in summertime ozone across China by 2030 under four emission scenarios reflecting different levels of climate action. We also contrast results obtained with two different chemical mechanisms employed in the chemical transport model (WRF‐Chem). With emission reductions in ozone precursors introduced by climate policies, both mechanisms show promising ozone mitigation for most parts of China. However, they disagree starkly in China's three main city clusters, where one mechanism projects worsening ozone pollution by 2030 despite the emission reductions. We analyze possible drivers of this important discrepancy, in particular the role of varying ozone chemical regimes affecting its sensitivity to emission changes. We recommend an intercomparison project to examine this critical modeling uncertainty among other models/mechanisms, which would be invaluable for informing local and regional emission control strategies that are based on single‐model results.

Deciphering the effects of genotype and climatic factors on the performance, active ingredients and rhizosphere soil properties of Salvia miltiorrhiza.

Salvia miltiorrhiza Bunge is an important medicinal herb, which is widely cultivated in most parts of China. It has attracted considerable attention because of its pharmacological properties and potential health benefits. We used a field experiment to determine the effects of different genotypes and climatic factors on the performance (plant biomass, morphological parameters), active ingredients, rhizosphere soil physicochemical properties and microbial composition of S. miltiorrhiza at five cultivation locations. The results showed that these parameters were significantly different in the six different genotypes of S. miltiorrhiza from five producing areas. Genotype and soil physicochemical properties were the main factors affecting the growth traits of S. miltiorrhiza, while genotype, climate and soil physicochemical properties were the main factors affecting the content of active components of S. miltiorrhiza. Microbial phospholipid fatty acid analysis showed that the biomass of Gram-positive and Gram-negative bacteria was affected by the genotypes of S. miltiorrhiza plants, while the biomass of arbuscular mycorrhizal fungi, fungi, Gram-positive and Gram-negative bacteria was affected by climate factors. Based on the main results, DS993 was the most suitable genotype for S. miltiorrhiza in the five producing areas from the perspective of comprehensive growth traits and medicinal components, while DS993 and DS2000 were suitable for planting in Shandong province from the perspective of origin. DS996 is not suitable for all of the above production areas. These results are helpful to understand the ecological adaptability of different genotypes of S. miltiorrhiza resources, and to select appropriate S. miltiorrhiza genotypes for specific planting areas, so as to maximize yield and quality.

Drivers of spatiotemporal variations in traditional heating under carbon neutrality commitment: Evidence from China at city-level

Coal-fired winter heating (CFH), one of the humankind's most carbon-intensive activities, imposes a considerable burden on the environment, society, and economy. To support the sustainable development of cities, this study constructed a CFH inventory characterized by long time series and high spatial resolution, including 187 prefecture-level cities in China. Based on the inventory, the life-cycle environmental and social economic impacts of CFH since 2011 were analyzed. Temporospatial variations, driving factors, and optimal heat combinations were further explored. In accordance with the results, the carbon footprint of CFH in China was 1.14 billion tons CO2-eq in 2020. The social cost in 2020 decreased to $ 139.7 billion in 2020 compared with $ 149.9 billion in 2017. Scattered heating activities only comprised only around 27% of heating fuel coal consumption but accounted for 60% of the total social costs. Region-specific heating policies are necessary because driving factors significantly vary among cities. Promoting central heating systems in most parts of China has mitigated the conflict between social costs and heating demand. However, the scattered heating systems still need to improve the development of sustainable winter heating in northwestern China. The scientific and reasonable top-level design of heating methods is highly required because clean heating methods may take more than 7 years to realize their environmental–economic co–benefits compared with CFH.

Influence of spatial resolution of PM2.5 concentrations and population on health impact assessment from 2010 to 2020 in China

Ambient PM2.5 pollution is a leading environmental health risk factor worldwide. The spatial resolution of PM2.5 concentrations and population strongly impacts PM2.5-related health impact estimates. However, long-term variations and regional differences in this impact have rarely been explored, particularly in China. Here, by aggregating satellite-derived PM2.5 concentration and population datasets at 1-km resolution in China to coarser resolutions (10, 50, and 100 km), we evaluated decadal changes in the impact of resolution on health assessments at national and local scales. For the sensitivity of population-weighted mean (PWM) PM2.5 concentrations to resolution, we found that the national PWM PM2.5 concentration decreased with coarser resolutions; this pattern was widely observed and was more obvious in southern and central China and the Sichuan Basin. The results showed that the sensitivity of national PWM PM2.5 concentrations to resolution continuously weakened from 2010 to 2020, likely due to a reduction in the spatial heterogeneity of PM2.5 concentrations in regions with high sensitivity. This weakness caused a large underestimation of the long-term trend of national PWM PM2.5 using a 100-km resolution, which was 7% lower than the trend at 1 km. Regarding the sensitivity of PM2.5-attributable mortality to resolution, most of China exhibited a pattern in which attributable mortality decreased with coarser resolution. The sensitivity of the estimated PM2.5-attributable mortality to resolution also weakened over time on a national scale and in most parts of China. Nevertheless, the weakness for mortality sensitivity was not as apparent as for PWM PM2.5 sensitivity. This was likely because different drivers played distinct roles in the temporal variation of the mortality sensitivity: population aging enhanced the sensitivity, and variations in PM2.5 concentrations and population distribution both weakened the sensitivity. However, the national attributable mortality trend at a 100-km resolution was still underestimated by 1.75% relative to the 1-km resolution.

Long-term trends and sensitivities of PM2.5 pH and aerosol liquid water to chemical composition changes and meteorological parameters in Hong Kong, South China: Insights from 10-year records from three urban sites

Aerosol pH and aerosol liquid water (ALW) play key roles in regulating many atmospheric processes. In many countries, large reductions in SO2 and NOx emissions as a result of air pollution regulatory policies have led to improved air quality and significant changes in aerosol mass and chemical composition. Understanding the long-term trends and sensitivities of aerosol pH and ALW to changes in meteorological conditions and chemical composition is useful for predicting how atmospheric processes that depend on aerosol acidity and/or ALW will evolve as air pollutant emissions change over time. At present, long-term monitoring records of aerosol pH and ALW are limited in most parts of China and elsewhere in Asia due to the scarcity of long-term aerosol chemical speciation measurements acquired through regular sampling schedules and consistent analysis protocols in this region. We used a 10-year dataset (2011–2020) from three long-term monitoring sites to investigate the long-term trends of PM2.5 pH and ALW concentrations, identify key factors that drive the variations of PM2.5 pH and ALW concentrations, and investigate the sensitivities of PM2.5 pH and ALW concentrations to chemical composition changes during different seasons in Hong Kong, a highly populated urbanized city located in South China that has experienced improved air quality as a result of various effective air pollution regulatory policies. Despite the substantial decreases in PM2.5 concentrations and changes in chemical composition in this 10-year period, PM2.5 pH showed little change. The rates at which PM2.5 pH and ALW concentration changed ranged from −0.007 to −0.001 pH units/year and from −1.45 to −0.53 μg m−3/year, respectively. Clear and consistent seasonal oscillatory cycles were observed for both PM2.5 pH (∼2–∼4) and ALW concentrations (∼5–∼40 μg m−3) at the three sites during this 10-year period. PM2.5 was the most acidic in the fall, and was the least acidic in winter. ALW concentrations were the lowest in summer, and the highest in winter/spring. Sensitivity tests revealed that chemical composition, especially particulate sulfate concentrations, plays a major role in driving the variations in aerosol pH and ALW concentration during each season. However, the magnitudes to which aerosol pH and ALW concentration respond to changes in chemical composition depend strongly on the meteorological conditions in each season. Given that many urbanized cities in South China share similar air quality and meteorological characteristics as Hong Kong, the results presented here are useful for advancing our understanding of how air pollution regulatory policies can alter aerosol pH and ALW concentrations, and consequently aqueous aerosol chemistry, in other South China cities.

Historical and Projected Changes in Temperature Extremes Over China and the Inconsistency Between Multimodel Ensembles and Individual Models From CMIP5 and CMIP6

AbstractHistorical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models.

HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020

Abstract. Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).

Morphological, phytochemical and ethnopharmacological attributes of Xylosma longifolia Clos: A review

Xylosma longifolia Clos. (Salicaceae), a medium-sized tree is found mainly in India and China. It is known by different names such as Kattari, Kata-koli, Mota-koli, Kataponial, Nongleishang and Dieng-kani in different parts of India. It is widely spread in northeast India and also in most parts of China. In India, tribal communities such as ‘‘Miri’’ of Assam, ‘‘Kom’’ and “Tangkhul” of Manipur, “Lois’’ of Imphal, use it to treat cough, spasms, dysentery, insomnia, restlessness, pustules and jaundice. This plant is also enormously used in Ayurveda, Chinese and Vietnamese traditional medicinal systems. The principle compounds reported are xylongoside A, xylongoside B and xylosmaloside. The presence of secondary metabolites imparts its bioactivities including antimicrobial, analgesic, anti-dermatophytic, anti-tubercular, antioxidant and insecticidal activities. To date, not even a single review has been published on X. longifolia. Due to the broad continued interest in the efficacy of X. longifolia and extensive practice in traditional medicines, this review intends to recapitulate detailed and up-to-date information on this plant. The current review assessed and summarized the knowledge on geographic distribution, morphological features, ethnobotany, phytochemistry and pharmacological activities of X. longifolia.

Synergistic Effects of Graphene Oxide and Pesticides on Fall Armyworm, Spodoptera frugiperda.

Fall armyworm Spodoptera frugiperda, a native insect pest in tropical and subtropical America, has rapidly spread to most parts of China and become a major pest of corn and other crops since invading in early January 2019. As an emergency and important control measure, chemical control of S. frugiperda has the advantages of quick effect and low cost. However, long-term and large-scale use of pesticides might pollute the environment and increase pest resistance. By improving the control effect and reducing the dosage of chemical pesticides, graphene oxide (GO) is used synergistically with insecticides to increase control efficacy to achieve low-cost and sustainable management of insect pests as a new type of synergist. In this study, graphene oxide was compounded with insecticides to form nanocomposites. To clarify pest physiological responses, the laboratory toxicity of graphene oxide-insecticide nanocomposites was measured on the larvae of S. frugiperda. The results demonstrated that GO could enhance the activity of four selected pesticides: chlorantraniliprole (Chl), beta cypermethrin (Bet), methoxyhydrazide (Met) and spinetoram (Spi). Compared with pesticides alone, the toxicity of Chl-GO, Bet-GO, Met-GO and Spi-GO mixtures to the third instar larvae of S. frugiperda increased by 1.56, 1.54, 2.53 and 1.74 times, respectively. The easy preparation and higher bioactivity of GO-pesticide nanocomposites indicated their promising application potential in pest control.

Spatialization and Spatio-temporal Dynamics of Energy Consumption Carbon Emissions in China

The adverse effects of global climate change on human production and life are becoming increasingly prominent. Responding to climate change has become a severe challenge faced by human society, and the reduction in greenhouse gas emissions has gradually become a common action by all countries. Therefore, analyzing carbon emissions through scientific methods has become an important foundation for responding to the national "dual carbon" strategy. This study used provincial-level carbon emission statistics, combined with nighttime light data and population data, and assigned carbon emissions to the grid scale. It also analyzed the temporal and spatial characteristics and evolution characteristics of carbon emissions in China in 2000, 2005, 2010, 2015, and 2018, as well as the correlation between carbon emissions and the economy. The results showed that:① from 2000 to 2018, the total CO2 emissions in China continued to grow, but the growth rate slowed over time. The average annual growth rate of carbon emissions dropped from 9.9% in 2000-2010 to 7.4% in 2010-2018. From the perspective of spatial distribution, carbon-free areas were mainly distributed in the northwest uninhabited area and northeast forest and mountainous areas, low-carbon emissions were mainly distributed in the vast small and medium-sized cities and towns, and high-carbon emissions were concentrated in northern, central, eastern coastal, and western provincial capitals and urban agglomerations. ② Carbon emissions had high-value or low-value agglomerations at prefecture-level cities; this agglomeration tended to stabilize as a whole and had strengthened after 2005. Low-low agglomeration areas were mainly distributed in the western contiguous areas and Hainan Island. With economic and social development, low-low agglomeration areas began to fragment and reduce in size; high-high agglomeration areas were mainly distributed in the Beijing-Tianjin-Hebei urban agglomeration, Taiyuan urban agglomeration, Yangtze River Delta urban agglomerations, and Pearl River Delta urban agglomerations, and the scale was gradually strengthened and consolidated; high-low and low-high agglomeration areas mainly appeared in neighboring cities with large differences in economic development levels. ③ Carbon emissions in most parts of China were relatively stable. The areas where carbon emissions had changed were mainly distributed in the peripheral areas of provincial capitals and key cities, and there was a circle structure with no changes in the central urban area and changes in carbon emissions in the peripheral areas. ④ The overall process of urban development in China from 2000 to 2018 followed a shift from "low emission-low income" to "high emission-low income" to "high emission-high income" and finally to "low emission-high income." The growth rate of carbon emissions in China is slowing down. Under the background of the "dual carbon" strategy, different regions face different carbon emission reduction tasks and pressures due to different carbon emission situations. Therefore, the differentiated carbon emissions policy should be implemented by regions and industries.

Realistic ocean initial condition for stimulating the successful prediction of extreme cold events in the 2020/2021 winter

In the first half of winter 2020/2021, several unprecedented cold events occurred in most parts of China and caused record-breaking low temperatures in many cities. However, seasonal predictions related to the onset and evolution of extreme cold events are still challenging. In this study, we first evaluated the short-term climate prediction skills in winter of the CAS-ESM-c global coupled model and revealed the key dynamic processes of the onset and development of 2020/2021 extreme cold events based on the ensemble forecasts starting on October 1st, 2020. Under the background provided by the synergistic effect of the warm Arctic and the cold tropical Pacific (La Niña), the model captured the abnormal meridional atmospheric pattern well, including the intensification of the Ural Blocking High and the negative phase of the Arctic Oscillation, forecasted the negative geopotential height anomalies over the Eastern Asian region, and finally, successfully predicted the outbreak of cold waves in December 2020 two months in advance. Moreover, the dominant differences in the initial ocean fields between the best and the worst forecast members were further compared to isolate the triggering factor for predicting cold events by CAS-ESM-c. The initial warm sea surface temperature over the Barents Sea can gradually form a meridional pattern between the warm Arctic and the cold Asian continent, which could lead to a reduction in the large-scale meridional temperature gradient at mid-high latitudes and weaken the atmospheric baroclinicity with more conducive to the southward outbreak of cold waves, highlighting that realistic Arctic ocean conditions in autumn can be reasonably assimilated into coupled models to stimulate the successful prediction of extreme cold events in the 2020/2021 winter.