Spatiotemporal distribution of regional landslides and erosion rates in Southeastern Tibet

The difficulty in estimating ages of regional landslides hampers to assess frequency of landslides and hence to quantitatively assess regional landslide hazard.In this study,we used radiocarbon dating of organic sediment on boulder rock varnish to estimate landslide ages in Langxian(LX)arid region in southeastern Tibet.Samples of rock varnish with organic sediment were collected on site for radiocarbon dating,leading to landslide ages from 1880±30 to 18,430±30 yr B.P.To measure surface roughness characteristics of 109 remotely-mapped large bedrock landslide deposits,we estimated average standard deviation of slope(SDS)over an area of~640 km^(2) by calculating the slope gradient of each raster cell and using a rectangular moving window method in Arc Map from a 5 m-resolution Digital Elevation Model generated from helicopterobtained photographs.Combing estimated landslide ages(t)with average surface roughness of mapped landslide deposits(R)quantified by SDS,we fit an exponential landslide deposits surface roughness-age function(t=1.47×10^(6)×e^(-1).46R,r2=0.63)that was used to estimate regional landslide ages in LX.We conclude that three periods with clusters of regional landslides in LX were revealed by different surface roughness of landslide deposits combing roughness-age function,with the values of 5563-7455 yr B.P.,1724-4151 yr B.P.,and 960-1287 yr B.P..Furthermore,we used our estimates of landslide ages to quantify landslide erosion rates of three corresponding hillslopes in LX ranging from 0.50 to 2.42 mm yr-1.Although rock varnish radiocarbon dating provides us a feasible option for timing regional landslides of arid regions,the epistemic uncertainty in the dating method should arouse our attention,which could be reduced by increasing the number of samples.

Spatiotemporal distribution and influencing factors of Ulva prolifera and Sargassum and their coexistence in the South Yellow Sea,China

Marine ecological disasters occurred frequently in recent years and raised widespread concerns about the ecological health of the ocean.We analyzed the spatiotemporal distributions of Ulva prolifera and Sargassum from April to July each year between 2016 and 2020 in the South Yellow Sea using multisource(GF-1 and HJ-1A/1B)remote sensing images,combined with the MODIS sea surface temperature(SST)data,photosynthetically active radiation(PAR)data,and Quick SCAT sea surface wind(SSW)data,to explore the potential influencing factors.The results show that(1)U.prolifera and Sargassum appeared mainly from May to July and April to June,respectively;(2)U.prolifera showed an impact in larger spatial scope than that of Sargassum.U.prolifera originated in the shoal area of northern Jiangsu and finally disappeared in the sea near Haiyang-Rongcheng area.The spatial scope of the impact of Sargassum tended to expand.Sargassum was first detected in the ocean northeast of the Changjiang(Yangtze)River estuary and disappeared near 35°N;and(3)correlation analysis showed that the SST influenced the growth rate of U.prolifera and Sargassum.PAR had varied eff ects on U.prolifera and Sargassum at different times.A moderate light conditions could accelerate the growth and reproduction of U.prolifera and Sargassum.High irradiance levels of ultraviolet radiation may cause photoinhibition and damage on U.prolifera and Sargassum.The southeast monsoon and surface currents promoted the drift of U.prolifera and Sargassum from the southeast to the northwest and north.Therefore,the spatial and temporal similarities and differences between U.prolifera and Sargassum were influenced by a combination of factors during their growth processes.

Remote Sensing-based Spatiotemporal Distribution of Grassland Aboveground Biomass and Its Response to Climate Change in the Hindu Kush Himalayan Region

The grassland in the Hindu Kush Himalayan(HKH) region is one of the large st and most biodiverse mountain grassland types in the world,and its ecosystem service functions have profound impacts on the sustainable development of the HKH region.Monitoring the spatiotemporal distribution of grassland aboveground biomass(AGB) accurately and quantifying its response to climate change are indispensable sources of information for sustainably managing grassland ecosystems in the HKH region.In this study,a pure vegetation index model(PVIM) was applied to estimate the long-term dynamics of grassland AGB in the HKH region during 2000-2018.We further quantified the response of grassland AGB to climate change(temperature and precipitation) by partial correlation and variance partitioning analyses and then compared their differences with elevation.Our results demonstrated that the grassland AGB predicted by the PVIM had a good linear relationship with the ground sampling data.The grassland AGB distribution pattern showed a decreasing trend from east to west across the HKH region except in the southern Himalayas.From 2000 to 2018,the mean AGB of the HKH region increased at a rate of 1.57 g/(m~2·yr) and ranged from 252.9(2000) to 307.8 g/m~2(2018).AGB had a positive correlation with precipitation in more than 80% of the grassland,and temperature was positively correlated with AGB in approximately half of the region.The change in grassland AGB was more responsive to the cumulative effect of annual precipitation,while it was more sensitive to the change in temperature in the growing season;in addition,the influence of climate varied at different elevations.Moreover,compared with that of temperature,the contribution of precipitation to grassland AGB change was greater in approximately 60% of the grassland,but the differences in the contribution for each climate factor were small between the two temporal scales at elevations over 2000 m.An accurate assessment of the temporal and spatial distributions of grassland AGB and the quantification of its response to climate change are of great significance for grassland management and sustainable development in the HKH region.

Validation and Spatiotemporal Distribution of GEOS-5-Based Planetary Boundary Layer Height and Relative Humidity in China

Few studies have specifically focused on the validation and spatiotemporal distribution of planetary boundary layer height （PBLH） and relative humidity （RH） data in China. In this analysis, continuous PBLH and surface-level RH data simulated from GEOS-5 between 2004 and 2012, were validated against ground-based observations. Overall, the simulated RH was consistent with the statistical data from meteorological stations, with a correlation coefficient of 0.78 and a slope of 0.9. However, the simulated PBLH was underestimated compared to LIDAR data by a factor of approximately two, which was primarily because of poor simulation in late summer and early autumn. We further examined the spatiotemporal distribution characteristics of two factors in four regions--North China, South China, Northwest China, and the Tibetan Plateau. The results showed that the annual PBLH trends in all regions were fairly moderate but sensitive to solar radiation and precipitation, which explains why the PBLH values were ranked in order from largest to smallest as follows： Tibetan Plateau, Northwest China, North China, and South China. Strong seasonal variation of the PBLH exhibited high values in summer and low values in winter, which was also consistent with the turbulent vertical exchange. Not surprisingly, the highest RH in South China and the lowest RH in desert areas of Northwest China （less than 30%）. Seasonally, South China exhibited little variation, whereas Northwest China exhibited its highest humidity in winter and lowest humidity in spring, the maximum values in the other regions were obtained from July to September.

Cardiac resident macrophages: Spatiotemporal distribution, development, physiological functions, and their translational potential on cardiac diseases

Cardiac resident macrophages (CRMs) are the main population of cardiac immune cells. The role of these cells in regeneration, functional remodeling, and repair after cardiac injury is always the focus of research. However, in recent years, their dynamic changes and contributions in physiological states have a significant attention. CRMs have specific phenotypes and functions in different cardiac chambers or locations of the heart and at different stages. They further show specific differentiation and development processes. The present review will summarize the new progress about the spatiotemporal distribution, potential developmental regulation, and their roles in cardiac development and aging as well as the translational potential of CRMs on cardiac diseases. Of course, the research tools for CRMs, their respective advantages and disadvantages, and key issues on CRMs will further be discussed.

Low-Carbon Dispatching for Virtual Power Plant with Aggregated Distributed Energy Storage Considering Spatiotemporal Distribution of Cleanness Value

The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness.To address this issue,the concept of cleanness value of distributed energy storage(DES)is proposed,and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness.Based on this,an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator.Then,an optimal low-carbon dispatching for a virtual power plant(VPP)with aggregated DES is constructed,where-in energy value and cleanness value are both considered.To achieve the goal,a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network(DN)and DES behavior,but as a cost,it brings multiple nonlinear relationships.Subsequently,a solution method based on the convex envelope(CE)linear re-construction method is proposed for the multivariate nonlinear programming problem,thereby improving solution efficiency and feasibility.Finally,the simulation verification based on the IEEE 33-bus DN is conducted.The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond.Meanwhile,resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.

Distribution and Formation Causes of PM_(2.5) and O_(3) Double High Pollution Events in China during 2013–20

Fine particulate matter(PM_(2.5))and ozone(O_(3))double high pollution(DHP)events have occurred frequently over China in recent years,but their causes are not completely clear.In this study,the spatiotemporal distribution of DHP events in China during 2013–20 is analyzed.The synoptic types affecting DHP events are identified with the Lamb–Jenkinson circulation classification method.The meteorological and chemical causes of DHP events controlled by the main synoptic types are further investigated.Results show that DHP events(1655 in total for China during 2013–20)mainly occur over the North China Plain,Yangtze River Delta,Pearl River Delta,Sichuan Basin,and Central China.The occurrence frequency increases by 5.1%during 2013–15,and then decreases by 56.1%during 2015–20.The main circulation types of DHP events are“cyclone”and“anticyclone”,accounting for over 40%of all DHP events over five main polluted regions in China,followed by southerly or easterly flat airflow types,like“southeast”,“southwest”,and“east”.Compared with non-DHP events,DHP events are characterized by static or weak wind,high temperature(20.9℃ versus 23.1℃)and low humidity(70.0%versus 64.9%).The diurnal cycles of meteorological conditions cause PM_(2.5)(0300–1200 LST,Local Standard Time=UTC+8 hours)and O_(3)(1500–2100 LST)to exceed the national standards at different periods of the DHP day.Three pollutant conversion indices further indicate the rapid secondary conversions during DHP events,and thus the concentrations of NO_(2),SO_(2) and volatile organic compounds decrease by 13.1%,4.7%and 4.4%,respectively.The results of this study can be informative for future decisions on the management of DHP events.

Spatiotemporal mapping of(ultra‐)mafic magmatic mine areas:Implications of economic and political realities in China

The spatiotemporal extension/expansion of mine areas is affected by multiple factors.So far,very little has been done to examine the interaction between mine areas and political or economic realities.The(ultra‐)mafic magmatic mines in China played a specific role in supporting national development and providing an ideal research subject for monitoring their interrelationship.In this study,remote sensing and mining‐related GIS data were used to identify and analyze 1233(ultra‐)mafic magmatic mine area polygons in China,which covered approximately 322.96 km2 of land and included a V–Ti–Fe mine,a copper–nickel mine,a chromite mine,an asbestos mine,and a diamond mine.It was found that(1)the areal expansion of mines is significantly related to the mine types,perimeter,topography,and population density.(2)The mine area variation also reflects market and policy realities.The temporal expansion of the mine area from 2010 to 2020 followed an S‐shaped pattern(with the turning point occurring in 2014),closely related to iron overcapacity and tightened mining policies.(3)The complexity(D)of the mine area may reflect mine design and excavation practices.To be specific,lower D indicates early‐stage or artisanal/small‐scale mining,whereas higher D represents large‐scale mining.This study demonstrates that the detailed mapping of mine land can serve as an indicator to implement miningrelated market and policy changes.The(ultra‐)mafic mines area data set can be accessed at https://zenodo.org/record/7636616#.Y-p0uXaZOa0.

Spatial and Temporal Distribution Characteristics of Solar Energy Resources in Tibet

The Tibet Plateau is one of the regions with the richest solar energy resources in the world.In the process of achieving carbon neutrality in China,the development and utilization of solar energy resources in the region will play an important role.In this study,the gridded solar resource data with 1km resolution in Tibet were obtained by spatial correction and downscaling of SMARTS model.On this basis,the spatial and temporal distribution characteristics of solar energy resources in the region in the past 30 years(1991–2020)are finely evaluated,and the annual global horizontal radiation resource is calculated.The results show that:1)The average annual global horizontal radiation amount in Tibet is 1816 kWh/m^(2).More than 60%of the area belongs to the“Most abundant”(GHI≥1750 kWh/m^(2))area of China’s solar energy resources category A,and nearly 40%belongs to the“Quite abundant”(1400≤GHI<1750)area of China’s solar energy resource category B.2)In space,the solar energy resources in Tibet increased gradually from north to south and from east to west.Lhasa,Central and Eastern Shigatse,Shannan,and Southwestern Ali are the most abundant cities,with a maximum annual radiation level of 2189 kWh/m2.3)In terms of time,the total horizontal radiation in Tibet was the highest in May and the lowest in December.74%of the total area belongs to the“Very stable”(R_(w)≥0.47)area of solar resource stability category A,and 26%belongs to the“stable”(0.36≤R_(w)<0.47)area of solar resource stability category B.Solar energy resources in the region show the characteristics of both strong and stable.Average solar energy resources in the region have shown a fluctuating downward trend over the past 30 years,with an average decline of about 12.86(kWh/m2)per decade.4)In terms of solar radiation resources reaching the earth’s surface,the theoretical total amount of annual horizontal radiation in Tibet is about 240.07 billion tons of standard coal or 222.91 billion kilowatts on average.

Spatiotemporal distribution of protists in the Yarlung Zangbo River,Tibetan Plateau

Protists,which include eukaryotic algae and protozoa,are crucially important for biogeochemical cycling and the aquatic food web but are sensitive to environmental fluctuations.Despite investigations in the 1970s,protist diversity and biogeographic characteristics in the Tibetan Plateau are poorly understood.Within the framework of a regional survey of protist diversity,we used high-throughput amplicon sequencing to investigate the composition and spatiotemporal turnover of protist communities along the Yarlung Zangbo River,a representative aquatic habitat of the Tibetan Plateau.In total,we detected 9,959 amplicon sequence variants in protist communities along the river and associated lakes and wetlands.The alpha diversity of protists was highest in spring and declined in the autumn.Moreover,alpha diversity was much higher in the middle and upstream reaches and tributaries than in the connected lakes and wetlands.We discovered spatiotemporal heterogeneity in the composition of protist communities across the seasons.Bacillariophyta predominated in protist communities in cold seasons(spring and autumn)and colder(high-altitude)areas,whereas Ciliophora and Cercozoa species predominated in the warm season(summer)and warmer(low-altitude)areas.Of the environmental factors,altitude,turbidity and salinity correlated with spatiotemporal dissimilarities among protist communities.The breadth of the habitat niche was restricted by turbidity and altitude in river areas and by salinity in lake and wetland areas.Co-occurrence network analysis suggested that protist communities in the Yarlung Zangbo River were substantially more complex in the warm season than in the cold seasons,and that amplicon sequence variants belonging to Ochrophyta,Chlorophyta,and Ciliophora contributed greatly to the stability of the cooccurrence network.In summary,this study provides a comprehensive overview of the spatiotemporal dynamics of protist diversity in the Yarlung Zangbo River and improves our understanding of the environmental adaptation of protists living in aquatic habitats of the Tibetan Plateau.

Landscape ecological risk assessment and its driving factors in the Weihe River basin,China

Weihe River basin is of great significance to analyze the changes of land use pattern and landscape ecological risk and to improve the ecological basis of regional development.Based on land use data of the Weihe River basin in 2000,2010,and 2020,with the support of Aeronautical Reconnaissance Coverage Geographic Information System(ArcGIS),GeoDa,and other technologies,this study analyzed the spatial-temporal characteristics and driving factors of land use pattern and landscape ecological risk.Results showed that land use structure of the Weihe River basin has changed significantly,with the decrease of cropland and the increase of forest land and construction land.In the past 20 a,cropland has decreased by 7347.70 km2,and cropland was mainly converted into forest land,grassland,and construction land.The fragmentation and dispersion of ecological landscape pattern in the Weihe River basin were improved,and land use pattern became more concentrated.Meanwhile,landscape ecological risk of the Weihe River basin has been improved.Severe landscape ecological risk area decreased by 19,177.87 km2,high landscape ecological risk area decreased by 3904.35 km2,and moderate and low landscape ecological risk areas continued to increase.It is worth noting that landscape ecological risks in the upper reaches of the Weihe River basin are still relatively serious,especially in the contiguous areas of high ecological risk,such as Tianshui,Pingliang,Dingxi areas and some areas of Ningxia Hui Autonomous Region.Landscape ecological risk showed obvious spatial dependence,and high ecological risk area was concentrated.Among the driving factors,population density,precipitation,normalized difference vegetation index(NDVI),and their interactions are the most important factors affecting the landscape ecological risk of the Weihe River basin.The findings significantly contribute to our understanding of the ecological dynamics in the Weihe River basin,providing crucial insights for sustainable management in the region.

GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems

Owing to the expansion of the grid interconnection scale,the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important.These characteristics can provide effective support in coordinated security control.However,traditional model-based frequencyprediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models.Therefore,this study presents a rolling frequency-prediction model based on a graph convolutional network(GCN)and a long short-term memory(LSTM)spatiotemporal network and named as STGCN-LSTM.In the proposed method,the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input.An improved GCN embedded with topology information is used to extract the spatial features,while the LSTM network is used to extract the temporal features.The spatiotemporal-network-regression model is further trained,and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information.The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response.The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems.

Abrupt change of winter temperature over the Mongolian Plateau during 1961-2017

Studying the abrupt change of winter temperature(ACWT)over the Mongolian Plateau(MP,including Inner Mongolia Autonomous Region and State of Mongolia)is of great significance for understanding the spatiotemporal distribution of temperature and the mechanism of global climate change.Monthly temperature data during 1961–2017was collected,and the abrupt change point was determined by the Mann–Kendall test and sliding ttest,to analyze the characteristics and causes of ACWT.The results showed that(a)The winter temperature has rapidly increased with a trend of 0.41℃/10a,which was significantly higher than that of the rest area of Chinese mainland,indicating that climate change in the MP was more sensitive to global warming.(b)The abrupt change point occurred in 1988,with temperature of-15.5℃and-14.1℃before and after abrupt change,respectively.The ACWT in 50°N was 1–3 years later than that in 40°N,and the isotherms of different temperatures moved northward by 10–200 km,especially-16℃isotherms moved approximately 200 km northward after 1988.(c)The Arctic Oscillation(AO)and Mongolian High(MH)anomaly affects winter temperature over the MP.When the AO is unusually strong,the MH and East Asian winter monsoon are weak,and southerly winds prevail in most regions,which is not conducive to the cold air developing southward,leading to higher winter temperature in the MP.Overwise,abnormally northerly winds prevail and temperature is low.Meanwhile,the abrupt change time of AO occurred in 1987 before winter temperature.It shows that the AO indirectly causes winter temperatures to rise by influencing the MH and is also the main driving factor of ACWT.

Dynamic Assessment of Spatiotemporal Population Distribution Based on Mobile Phone Data: A Case Study in Xining City,China

High-resolution,dynamic assessments of the spatiotemporal distributions of populations are critical for urban planning and disaster management.Mobile phone big data have real-time collection,wide coverage,and high resolution advantages and can thus be used to characterize human activities and population distributions at fne spatiotemporal scales.Based on six days of mobile phone user-location signal(MPLS)data,we assessed the dynamic spatiotemporal distribution of the population of Xining City,Qinghai Province,China.The results show that strong temporal regularity exists in the daily activities of local residents.The spatiotemporal distribution of the local population showed a signifcant downtown-suburban attenuation pattern.Factors such as land use types,holidays,and seasons signifcantly afect the spatiotemporal patterns of the local population.By combining other spatiotemporal trajectory data,high-resolution and dynamic real-time population distribution evaluations based on mobile phone location signals could be better developed and improved for use in urban management and disaster assessment research.

A High Resolution Emission Inventory of Domestic Burning in Rural Region of Northeast China Based on Household Consumption

Domestic burning emits large amounts of pollutants into the ambient air due to incomplete and inefficient combustion, with significant impacts on indoor air quality and human health. Northeast China is one of the major contributors to domestic burning emissions in China;however, the high-resolution emissions inventories of domestic biomass and coal burning in Northeast China are lacked, which are needed to estimate the extent of its impact. In this study, we established a town-level emissions inventory of gaseous pollutants and particulate matter(PM) from domestic biomass and coal burning, based on per household consumption in each town in rural region of Northeast China. The results revealed that biomass burning was the major domestic burning source over the region in 2016. Domestic biomass burning is the major contributor to PM and volatile organic compounds(VOCs) emissions, while coal burning is the major contributor to SO2 emissions. High emissions intensities were concentrated around the cities of Harbin, Suihua, Changchun, Qiqihar, and Chifeng, each with emissions intensity for PM2.5 and VOCs of more than 2000 Mg per 27 km × 27 km grid cell. Additionally, there are three burning peaks(6-7 am, 12 pm and 4-7 pm) during both the heating(from October to April) and non-heating seasons(from May to September), due to cooking and heating. The burning peaks in the non-heating season were more notable than those in the heating season. These results suggest that the government should pay more attention to domestic biomass and coal burning in rural areas, in order to reduce pollutant emissions and control regional haze during the heating season.

Sterilization mechanism of helium/helium-oxygen atmospheric-pressure pulsed dielectric barrier discharge on membrane surface

Pulsed dielectric barrier discharge(PDBD) exhibits several applications in different fields;however,the interaction of its components with substances remains a key issue.In this study,we employed experimental and numerical modeling to investigate the interactions between different PDBD components and substances in pure helium and a helium-oxygen mixture.A membrane comprising a Staphylococcus aureus strain was utilized as the treatment object to demonstrate the trace actions of the evolutions and distributions of certain components on the surface of the substance.The results revealed that the shapes and sizes of the discharging area and inhibition zone differed between groups.Under a pure helium condition,a discharge layer existed along the membrane surface,lying beside the main discharging channel within the electrode area.Further,an annulus inhibition zone was formed at the outer edge of the electrode in the pure helium group at 30 s and 1 min,and this zone extended to a solid circle at 2 min with a radius that was~50% larger than that of the electrode radius.Nevertheless,the discharging channel and inhibition zone in the helium-oxygen mixture were constrained inside the electrode area without forming any annulus.A 2D symmetrical model was developed with COMSOL to simulate the spatiotemporal distributions of different particles over the membrane surface,and the result demonstrated that the main components,which formed the annulus inhibition zone under the pure helium condition,contributed to the high concentration of the He^(+)annulus that was formed at the outer edge of the electrode.Moreover,O^(+)and O_(2)^(+)were the main components that killed the bacteria under the helium-oxygen mixture conditions.These results reveal that the homogenization treatment on a material surface via PDBD is closely related to the treatment time and working gas.

Intensity of Level Ice Simulated with the CICE Model for Oil-Gas Exploitation in the Southern Kara Sea, Arctic

Sea ice is the predominant natural threat to marine structures and oil-gas exploitation in the Arctic.However,for ice-resistant structural design,long-term successive level ice thickness measurements are still lacking.To fill this gap in the southern Kara Sea,the Los Alamos Sea Ice Model(CICE)is applied to achieve better simulation at the local and regional scales.Based on the validation against ice thickness observations in March and April in 1980-1986,the statistical root-mean-square error is determined to be less than 0.2 m.Then,based on the hindcast data,the spatiotemporal distributions of level ice thickness are analyzed annually,seasonally,and monthly,with thicker level ice of 1.2-1.5 m in spring and large ice-free zones in September and October.For floating platforms,a novel ice grade criterion with five classifications,namely,excellent,good,moderate,severe,and catastrophic,is pro-posed.The first two grades are most suitable for offshore activities,particularly from August to October,and the moderate grade is acceptable if with ice-resistant protections.Furthermore,hostile ice conditions are discussed in terms of the generalized extreme value distribution.The statistics reveal that at a return period of 100 yr,extreme level ice is primarily between 0.6 m and 1.0 m in December.The present investigation could be a useful reference for a feasibility study of the potential risk analysis and ice-resistant operation of oil-gas exploitation in the Arctic.

Characteristics of Pre-summer Daytime Cloud Regimes over Coastal South China from the Himawari-8 Satellite

Using the high spatiotemporal resolution(2 km-and-10 min)data from the Advanced Himawari Imager onboard the Himawari-8 satellite,this study documents the fine-scale characteristics of daytime cloud regimes(CRs)over coastal South China during the pre-summer rainy season(April–June).Six CRs(CR1–CR6)are identified based on the joint frequency distribution of cloud top brightness temperature and cloud optical thickness,namely,the optically thin-to-moderate cloud mixture,optically thin warm clouds with cirrus,optically thick warm clouds,weak convective cloud mixture,strong convective clouds,and extreme,deep convective clouds.The optically thick warm clouds are the major CR during April and May,with higher frequencies over land,especially along the urban agglomeration,rather than the offshore which may be an indicator of the higher aerosol concentrations being a contributing factor over the cities.The CRs with weak convective cloud mixtures and strong convective clouds appear more frequently over the land,while the two CRs with optically thinner clouds occur mainly offshore.Synoptic flow patterns(SPs)are objectively identified and examined focusing on those favoring the two major rainproducing CRs(CR5 and CR6)and the highly reflective CR with optically thick warm clouds(CR3).The two SPs favoring CR5 and CR6 are characterized by abundant moisture with low-level jets after monsoon onset,and a northwest highsoutheast low pattern with strong dynamic convergence along the coastline,respectively.The non-convective CR3 with high reflectance is related to a SP that features the western North Pacific subtropical high extending more westward,leading to a moderate moisture supply and a wide range of convective available potential energy,but also,large convective inhibition.

Potential responses of vegetation to atmospheric aerosols in arid and semi-arid regions of Asia

Changes in atmospheric aerosols have profound effects on ecosystem productivity,vegetation growth and activity by directly and indirectly influencing climate and environment conditions.However,few studies have focused on the effects of atmospheric aerosols on vegetation growth and activity in the vulnerable arid and semi-arid regions,which are also the source areas of aerosols.Using the datasets of aerosol optical depth(AOD),normalized difference vegetation index(NDVI)and multiple climatic variables including photosynthetically active radiation(PAR),surface solar radiation(SSR),surface air temperature(TEM)and total precipitation(PRE),we analyzed the potential responses of vegetation activity to atmospheric aerosols and their associated climatic factors in arid and semi-arid regions of Asia from 2005 to 2015.Our results suggested that areas with decreasing growing-season NDVI were mainly observed in regions with relatively sparse vegetation coverage,while AOD tended to increase as NDVI decreased in these regions.Upon further analysis,we found that aerosols might exert a negative influence on vegetation activity by reducing SSR,PAR and TEM,as well as suppressing PRE in most arid and semi-arid regions of Asia.Moreover,the responses of atmospheric aerosols on vegetation activity varied among different growing stages.At the early growing stage,higher concentration of aerosol was accompanied with suppressed vegetation growth by enhancing cooling effects and reducing SSR and PAR.At the middle growing stage,aerosols tended to alter microphysical properties of clouds with suppressed PRE,thereby restricting vegetation growth.At the late growing stage,aerosols exerted significantly positive influences on vegetation activity by increasing SSR,PAR and TEM in regions with high anthropogenic aerosols.Overall,at different growing stages,aerosols could influence vegetation activity by changing different climatic factors including SSR,PAR,TEM and PRE in arid and semi-arid regions of Asia.This study not only clarifies the impacts of aerosols on vegetation activity in source areas,but also explains the roles of aerosols in climate.

Data augmentation for bias correction in mapping PM_(2.5) based on satellite retrievals and ground observations

As most air quality monitoring sites are in urban areas worldwide,machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants.The bias stems from the issue of dataset shift,as the density distributions of predictor variables differ greatly between urban and rural areas.We propose a data-augmentation approach based on the multiple imputation by chained equations(MICE-DA)to remedy the dataset shift problem.Compared with the benchmark models,MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM2.5 in the megacity(Chengdu)at the foot of the Tibetan Plateau,especially for correcting the estimation bias,with the mean bias decreasing from-3.4µg/m3 to-1.6µg/m3.As a complement to the holdout validation,the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM2.5 over the study area.The essence of MICE-DA is strengthening the correlation between PM2.5 and aerosol optical depth(AOD)during the data augmentation.Consequently,the importance of AOD is largely enhanced for predicting PM2.5,and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5%to 18.4%.This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies.The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.