Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region,which mounts the need for precise spatial water management.In this study,we investigated the irrigation water demand fo...Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region,which mounts the need for precise spatial water management.In this study,we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019.Using Logarithmic Mean Divisia Index(LMDI)decomposition and k-means clustering,we quantified how yield,area,water use efficiency,and cropping patterns affect water demand and identified five irrigation development clusters.Key water-saving areas were identified by tracking transitions among clusters,and NSGA-II was applied to optimize crop structure.The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m3/year,with wheat accounting for 54.7%.The increase in yield and area increased demand by 15.2 and 5.5 billion m3,respectively,which was partly offset by changes in water use efficiency and cropping pattern(−7.0 and−1.8 billion m^(3),respectively).Regions in the upper reaches,particularly within the Lanzhou-Toudaoguai section,were identified as critical for water conservation.Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m3,which accounts for 4.9%of the total demand in these areas,with minimal impact on crop production.This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.展开更多
Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled t...Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.展开更多
The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehen...The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements.First,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP branch.Then,a feature fusion module was designed to fuse the features of the roofs and walls.The precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or BFPs.The same three indexes of the proposed model were improved by more than 2%compared with other deep learning models.The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.展开更多
The productivity of vegetation is influenced by both climate change and human activities.Understanding the specific contributions of these influencing factors is crucial for ecological conservation and regional sustai...The productivity of vegetation is influenced by both climate change and human activities.Understanding the specific contributions of these influencing factors is crucial for ecological conservation and regional sustainability.This study utilized a combination of multi-source data to examine the spatiotemporal patterns of Net Primary Productivity(NPP)in the Yellow River Basin(YRB),China from 1982 to 2020.Additionally,a scenario-based approach was employed to compare Potential NPP(PNPP)with Actual NPP(ANPP)to determine the relative roles of climatic and human factors in NPP changes.The PNPP was estimated using the Lund-Potsdam-Jena General Ecosystem Simulator(LPJ-GUESS)model,while ANPP was evaluated by the Carnegie-Ames-Stanford Approach(CASA)model using different NDVI data sources.Both model simulations revealed that significant greening occurring in the YRB,with a gradual decrease observed from southeast to northwest.According to the LPJ_GUESS model simulations,areas experiencing an increasing trend in NPP accounted for 86.82% of the YRB.When using GIMMS and MODIS NDVI data with CASA model simulations,areas showing an increasing trend in NPP accounted for 71.42% and 97.02%,respectively.Furthermore,both climatic conditions and human factors had positive effects on vegetation restoration;approximated 41.15% of restored vegetation areas were influenced by both climate variation and human activities,while around 31.93% were solely affected by climate variation.However,it was found that human activities served as the principal driving force of vegetation degradation within the YRB,impacting 26.35% of degraded areas solely due to human activities.Therefore,effective management strategies encompassing both human activities and climate change adaptation are imperative for facilitating vegetation restoration within this region.These findings will valuable for enhancing our understanding in NPP changes and its underlying factors,thereby contributing to improved ecological management and the pursuit of regional carbon neutrality in China.展开更多
Climate warming and humidification trends have significantly influenced vegetation growth patterns in Chinese semi-arid areas.Exploring vegetation dynamics is crucial for understanding regional ecosystem structure and...Climate warming and humidification trends have significantly influenced vegetation growth patterns in Chinese semi-arid areas.Exploring vegetation dynamics is crucial for understanding regional ecosystem structure and improving the efforts of ecosystem restoration.However,the applicability of various vegetation indices(VIs)in these arid areas remains uncertain.Evaluating the applicability of multiple VIs for vegetation monitoring can elucidate the variability of VIs performance at regional scale.Therefore,this study selected the Zuli River Basin(ZLRB),a typical loess hilly watershed in the semi-arid areas of China.Using Landsat data,we calculated the Normalized Difference Vegetation Index(NDVI),Enhanced Vegetation Index(EVI),and kernel NDVI(kNDVI)for the ZLRB from 1990 to 2020.We analyzed the spatiotemporal variations of these VIs using trend analysis and the Mann-Kendall test,and quantified the contributions of climate change(considering time-lag effects)and human activities to VIs changes through wavelet and residual analyses.Results indicated that VIs generally exhibited an upward trend in the ZLRB,with significant improvements observed in 54.91% of the area for NDVI,31.69% for EVI,and 33.71% for kNDVI.Among them,NDVI outperformed EVI and kNDVI in capturing vegetation changes in the semi-arid area.VIs responded to precipitation with 1-month time lag and no time lag to temperature during growing season.Moreover,precipitation had a stronger positive correlation with VIs than temperature.Climate change was identified as the dominant driver of vegetation dynamics in the ZLRB,accounting for 93.12% of NDVI variation,while human activities contributed only 6.88%.Comparative analysis of VIs suggests that NDVI was more suitable for describing vegetation changes in the typical arid area of the ZLRB.Our findings underscore the importance of selecting appropriate VIs for targeted ecological restoration and sustainable land management.展开更多
A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However...A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.展开更多
The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DT...The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DTVGM） to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year （2001-2010） daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R2 varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years （2001-2007） of data on daily hydro- logical processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamfiow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.展开更多
Water retention is important in forest ecosystem services. The heterogeneity analysis of water-retention capacity and its influencing factors is of great significance for the construction of water-retention functional...Water retention is important in forest ecosystem services. The heterogeneity analysis of water-retention capacity and its influencing factors is of great significance for the construction of water-retention functional areas, restoration of vegetation, and the protection of forest ecosystems in the Beijing-Tianjin-Hebei region. A total of 1366 records concerning water-retention capacity in the canopy layer, litter layer, and soil layer of forest ecosystem in this region were obtained from 193 literature published from 1980 to 2017. The influencing factors of water-retention capacity in each layer were analyzed, and path analysis was used to investigate the contribution of the factors to the water-retention capacity of the three layers. The results showed that mixed forests had the highest water-retention capacity, followed by broad-leaved forests, coniferous forests, and shrub forests. In addition, no matter the forest type, the ranking of the water-retention capacity was soil layer, canopy layer, and litter layer from high to low. The main influencing factors of water-retention capacity in forest canopy were leaf area index and maximum daily precipitation(R2=0.49), and the influencing coefficients were 0.34 and 0.30, respectively. The main influencing factors of water-retention capacity in the litter layer were semi-decomposed litter(R2=0.51), and the influencing coefficient was 0.51. The main influencing factors of water-retention capacity in the soil layer were non-capillary porosity and soil depth(R2=0.61), the influencing coefficients were 0.60 and 0.38, respectively. This study verifies the simulation of the water balance model or inversion of remote sensing of the water-retention capacity at the site scale, and provides scientific basis for further study of the impact of global change on water retention.展开更多
Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population.We took Shijiazhuang as the research area,with comprehensive zoning ba...Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population.We took Shijiazhuang as the research area,with comprehensive zoning based on endowments as the modeling unit,conducted stratified sampling on a hectare grid cell,and systematically carried out incremental selection experiments of population density impact factors,optimizing the population density random forest model throughout the process(zonal modeling,stratified sampling,factor selection,weighted output).The results are as follows:(1)Zonal modeling addresses the issue of confusion in population distribution laws caused by a single model.Sampling on a grid cell not only ensures the quality of training data by avoiding the modifiable areal unit problem(MAUP)but also attempts to mitigate the adverse effects of the ecological fallacy.Stratified sampling ensures the stability of population density label values(target variable)in the training sample.(2)Zonal selection experiments on population density impact factors help identify suitable combinations of factors,leading to a significant improvement in the goodness of fit(R^(2))of the zonal models.(3)Weighted combination output of the population density prediction dataset substantially enhances the model's robustness.(4)The population density dataset exhibits multi-scale superposition characteristics.On a large scale,the population density in plains is higher than that in mountainous areas,while on a small scale,urban areas have higher density compared to rural areas.The optimization scheme for the population density random forest model that we propose offers a unified technical framework for uncovering local population distribution law and the impact mechanisms.展开更多
Desertification caused by land degradation and overexploitation of natural resources is threatening large parts of eastern and southern Mediterranean. The actual state of desertification sensitivity in Lebanon was spa...Desertification caused by land degradation and overexploitation of natural resources is threatening large parts of eastern and southern Mediterranean. The actual state of desertification sensitivity in Lebanon was spatially assessed using site specific environmental bio-physical indicators, demographic pressure and socioeconomic conditions. Bio-physical assessment included the aridity index derived from integrated assessment of the historical data for 48 climatic stations spread throughout the country, the new detailed soil map at 1：50,000 scale, and the updated land cover/use map at 1：20,000 derived from IKONOS 2005. The methodology also included livelihood conditions and poverty at local administrative ＂Caza＂ level. Results showed the integrated impact of local climate, soil and vegetation quality and socioeconomic conditions on sensitivity to desertification. A total of 78% of the territories have low and very low climate quality index preconditioning the sensitivity to desertification. Fourteen Cazas out of 26 in total, representing more than 66% of the country, have low socioeconomic satisfaction index. Furthermore, negative trends are alleviated by good quality relict soils and vegetation cover. The actual extent of desertification covers 40.48% of the national territory, much of which occurs under semi-arid climate, moderate or low soil and vegetation quality and poor living conditions. The outcome of this research adjusted the previous coarse estimates of desertification prone areas at the national level. Results allow for realistic, policy oriented local assessment for responsive land use planning and proactive sustainable, national and local land management in the context of the national action plan to combat desertification.展开更多
Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Curre...Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Currently,in-situ drought monitoring based on weather stations and based on remote sensing data has limitations,including infrequent updates,limited coverage,and low accuracy.This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province,China.We developed multi-source composite drought indices(MCDIs)at various timescales(3,6,9,and 12 months)by integrating precipitation,land surface temperature,soil moisture,and vegetation indices.Utilizing remote sensing data from various sources,we calculated a series of single drought indices,which are the precipitation condition index,soil moisture condition index,vegetation condition index,and temperature condition index.These are then integrated into MCDIs using a multivariable linear regression approach.The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index(SPEI)than single drought indices.When examining the correlation between different MCDIs and the affected area of crops and major grain production,MCDI-9 showed the highest correlation with the affected area of crops,while MCDI-12 showed the highest correlation with grain production.This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring,gradually spreading from the Greater Khingan Mountains region to the southeastern plains.The drought gradually alleviates during the summer,ending by the autumn harvest period.Therefore,the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions.展开更多
In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data,it is necessary to understand what the information flow in quantit...In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data,it is necessary to understand what the information flow in quantitative remote sensing model inversion is,thus control the information flow.Aiming at this,the paper takes the linear kernel-driven model inversion as an example.At first,the information flow in different inversion methods is calculated and analyzed,then the effect of information flow controlled by multi-stage inversion strategy is studied,finally,an information matrix based on USM is defined to control information flow in inversion.It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly.Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow.In regularization inversion of remote sensing,information matrix based on USM may be a better tool for quantitatively controlling information flow.展开更多
The well-facilitated farmland projects(WFFPs)involve the typical sustainable intensification of farmland use and play a key role in raising food production in China.However,whether such WFFPs can enhance the nitrogen(...The well-facilitated farmland projects(WFFPs)involve the typical sustainable intensification of farmland use and play a key role in raising food production in China.However,whether such WFFPs can enhance the nitrogen(N)use efficiency and reduce environmental impacts is still unclear.Here,we examined the data from 502 valid questionnaires collected from WFFPs in the major grain-producing area,the Huang-Huai-Hai Region(HHHR)in China,with 429 samples for wheat,328 for maize,and 122 for rice.We identified gaps in N use efficiency(NUE)and N losses from the production of the three crops between the sampled WFFPs and counties based on the statistical data.The results showed that compared to the county-level(wheat,39.1%;maize,33.8%;rice,35.1%),the NUEs for wheat(55.2%),maize(52.1%),and rice(50.2%)in the WFFPs were significantly improved(P<0.05).In addition,the intensities of ammonia(NH3)volatilization(9.9-12.2 kg N ha–1),N leaching(6.5-16.9 kg N ha–1),and nitrous oxide(N2O)emissions(1.2-1.6 kg N ha–1)from crop production in the sampled WFFPs were significantly lower than the county averages(P<0.05).Simulations showed that if the N rates are reduced by 10.0,15.0,and 20.0%for the counties,the NUEs of wheat,maize,and rice in the HHHR will increase by 2.9-6.3,2.4-5.2,and 2.6-5.7%,respectively.If the N rate is reduced to the WFFP level in each county,the NUEs of the three crops will increase by 12.9-19.5%,and the N leaching,NH3,and N2O emissions will be reduced by 48.9-56.2,37.4-42.9,and 46.0-66.5%,respectively.Our findings highlight that efficient N management practices in sustainable intensive farmland have considerable potential for reducing environmental impacts.展开更多
MODerate resolution atmospheric TRANsmission(MODTRAN)is a commercial remote sensing(RS)software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth’s atmosp...MODerate resolution atmospheric TRANsmission(MODTRAN)is a commercial remote sensing(RS)software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth’s atmosphere and the radiation observed by a remote sensor.However,when very large RS datasets must be processed in simulation applications at a global scale,it is extremely time-consuming to operate MODTRAN on a modern workstation.Under this circumstance,the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task.This paper presents PMODTRAN,an implementation of a parallel task-scheduling algorithm based on MODTRAN.PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster.In addition,PMODTRAN can distribute tasks with different levels of granularity and has some extra features,such as dynamic load balancing and parameter checking.展开更多
Soil organic carbon(SOC)and total nitrogen(TN)play an important role in the global carbon and nitrogen cycles.Soil aggregates are critical reservoir of SOC and TN.Therefore,in areas with severe wind erosion,the change...Soil organic carbon(SOC)and total nitrogen(TN)play an important role in the global carbon and nitrogen cycles.Soil aggregates are critical reservoir of SOC and TN.Therefore,in areas with severe wind erosion,the changes in the accumulation of SOC,TN,clay,silt,and sand contents within different dry aggregate size fractions can offer crucial insights into soil conservation by the control of wind erosion.In this study,surface soil samples(0–5 cm depth)were collected from farmland and grassland in the Bashang region of northern China in 2020.The bulk soil and aggregate size fractions were used to determine the concentrations of SOC,TN,clay,silt,and sand.The results showed that:(1)farmland had lower SOC and higher TN than grassland;(2)SOC in the aggregates of farmland decreased with increasing aggregate size(P<0.010),while SOC in the aggregates of grassland increased with increasing aggregate size(P<0.010),and nonsignificant variation of TN and clay was observed among different aggregate sizes;(3)the mean of aggregate silt significantly decreased with increasing aggregate size and the mean of aggregate sand increased with increasing aggregate size(P<0.001);(4)no correlations between sand or silt of aggregate and TN or texture of bulk soil was found;and(5)SOC in bulk soil was correlated with those in different aggregate sizes,and was also affected by the texture of bulk soil(P<0.010).This study highlights the role of dry soil aggregate size in the redistribution of SOC,TN,clay,silt,and sand contents under different land uses,thereby facilitating the understanding of the process of wind erosion induced SOC,TN,and mineral dust emission.展开更多
Carbon Monoxide(CO)is an important urban pollutant with a relation to 5,transition economies based on emission intensities.In this study,Sentinel-MODerate resolution Imaging Spectroradiometer(MODIS),and Landsat-8 imag...Carbon Monoxide(CO)is an important urban pollutant with a relation to 5,transition economies based on emission intensities.In this study,Sentinel-MODerate resolution Imaging Spectroradiometer(MODIS),and Landsat-8 images were used to investigate the variations of CO and urban environmental indices and the correlations between them.From the assessed correlations for 932 Iranian cities,it occurred that the assessed indices were all correlated.The highest CO levels were 0.031 in the spring of 2019 and 2020,whereas in 2021 it was equal to 0.030 in both the spring and winter,respectively.In 2019 and 2020 the maximum values of the Enhanced Vegetation Index(EVI)in the spring were 0.181 and 0.183.Exceptionally high Absorbing Aerosol Index(AAI)values of–0.834 and–1.0,along with Urban Index(UI)of 0.102 and 0.092,were correlated with recorded spikes in CO level,despite that these seasons’EVI values were not so abnormal.It was forecasted that in 2030 rises in the CO level by 13.2%in the winter and by 17.5%in the fall are expected,with the simultaneous increase of AAI by 204.5%and 980.2%,and Aerosol Optical Depth(AOD)by 27%and 5%in the winter and spring,respectively.展开更多
Biomass burning is a major source of carbonaceous aerosols that significantly influences the Earth's radiation balance.However,the spectral light absorption properties of biomass burning aerosols(BBAs),particularl...Biomass burning is a major source of carbonaceous aerosols that significantly influences the Earth's radiation balance.However,the spectral light absorption properties of biomass burning aerosols(BBAs),particularly the contribution of brown carbon(BrC),remain poorly constrained due to reliance on laboratory measurements that may not accurately represent real-world atmospheric conditions.To address this limitation,we developed an unmanned aerial vehicle(UAV)based-platform for direct in-situ measurements of BBAs in the ambient atmosphere over the rural North China Plain.This approach reduces biases inherent to laboratory chamber experiments and enables a more realistic characterization of BBAs absorption properties.Our measurements revealed that the absorption?ngstr?m exponent(AAE)for typical residential biomass burning was 3.70±0.04 under smoldering conditions and 1.50±0.08 under flaming conditions.Variations in AAE were driven primarily by combustion conditions and smoke humidity rather than fuel type.Additionally,field-observed OC/EC ratios were up to ten times higher than those reported in laboratory chamber studies,resulting in systematically lower mass absorption cross-sections.This finding suggests that the BBAs light absorption and radiative forcing estimates in the North China Plain may be systematically overestimated by chamber-based studies.Notably,under smoldering conditions,BrC absorption at 375 nm was up to 6.6 times greater than that of black carbon(BC)once mass emissions are considered,emphasizing that strategies aiming at reducing smoldering combustion could be particularly effective in mitigating the ultraviolet radiative effects of BBAs.Our results demonstrate that ambient atmospheric measurements are essential for accurately constraining BBAs absorption properties and their climate impacts.展开更多
基金National Natural Science Foundation of China,No.42041007。
文摘Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region,which mounts the need for precise spatial water management.In this study,we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019.Using Logarithmic Mean Divisia Index(LMDI)decomposition and k-means clustering,we quantified how yield,area,water use efficiency,and cropping patterns affect water demand and identified five irrigation development clusters.Key water-saving areas were identified by tracking transitions among clusters,and NSGA-II was applied to optimize crop structure.The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m3/year,with wheat accounting for 54.7%.The increase in yield and area increased demand by 15.2 and 5.5 billion m3,respectively,which was partly offset by changes in water use efficiency and cropping pattern(−7.0 and−1.8 billion m^(3),respectively).Regions in the upper reaches,particularly within the Lanzhou-Toudaoguai section,were identified as critical for water conservation.Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m3,which accounts for 4.9%of the total demand in these areas,with minimal impact on crop production.This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.
基金the Shandong Key Research and Development Project,China(2018GNC110025)the National Natural Science Foundation of China(41871253)+2 种基金the Central Guiding Local Science and Technology Development Fund of Shandong—Yellow River Basin Collaborative Science and Technology Innovation Special Project,China(YDZX2023019)the Natural Science Foundation of Shandong Province,China(ZR2020QD016)the“Taishan Scholar”Project of Shandong Province,China(TSXZ201712)。
文摘Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.
基金The Science and Technology Project of Hebei Education Department,No.BJK2022031The Open Fund of Hebei Key Laboratory of Geological Resources and Environmental Monitoring and Protection,No.JCYKT202310。
文摘The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements.First,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP branch.Then,a feature fusion module was designed to fuse the features of the roofs and walls.The precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or BFPs.The same three indexes of the proposed model were improved by more than 2%compared with other deep learning models.The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.
基金Under the auspices of National Natural Science Foundation of China(No.41991231,U21A2011)。
文摘The productivity of vegetation is influenced by both climate change and human activities.Understanding the specific contributions of these influencing factors is crucial for ecological conservation and regional sustainability.This study utilized a combination of multi-source data to examine the spatiotemporal patterns of Net Primary Productivity(NPP)in the Yellow River Basin(YRB),China from 1982 to 2020.Additionally,a scenario-based approach was employed to compare Potential NPP(PNPP)with Actual NPP(ANPP)to determine the relative roles of climatic and human factors in NPP changes.The PNPP was estimated using the Lund-Potsdam-Jena General Ecosystem Simulator(LPJ-GUESS)model,while ANPP was evaluated by the Carnegie-Ames-Stanford Approach(CASA)model using different NDVI data sources.Both model simulations revealed that significant greening occurring in the YRB,with a gradual decrease observed from southeast to northwest.According to the LPJ_GUESS model simulations,areas experiencing an increasing trend in NPP accounted for 86.82% of the YRB.When using GIMMS and MODIS NDVI data with CASA model simulations,areas showing an increasing trend in NPP accounted for 71.42% and 97.02%,respectively.Furthermore,both climatic conditions and human factors had positive effects on vegetation restoration;approximated 41.15% of restored vegetation areas were influenced by both climate variation and human activities,while around 31.93% were solely affected by climate variation.However,it was found that human activities served as the principal driving force of vegetation degradation within the YRB,impacting 26.35% of degraded areas solely due to human activities.Therefore,effective management strategies encompassing both human activities and climate change adaptation are imperative for facilitating vegetation restoration within this region.These findings will valuable for enhancing our understanding in NPP changes and its underlying factors,thereby contributing to improved ecological management and the pursuit of regional carbon neutrality in China.
基金funded by the National Natural Science Foundation of China(U21A2011).
文摘Climate warming and humidification trends have significantly influenced vegetation growth patterns in Chinese semi-arid areas.Exploring vegetation dynamics is crucial for understanding regional ecosystem structure and improving the efforts of ecosystem restoration.However,the applicability of various vegetation indices(VIs)in these arid areas remains uncertain.Evaluating the applicability of multiple VIs for vegetation monitoring can elucidate the variability of VIs performance at regional scale.Therefore,this study selected the Zuli River Basin(ZLRB),a typical loess hilly watershed in the semi-arid areas of China.Using Landsat data,we calculated the Normalized Difference Vegetation Index(NDVI),Enhanced Vegetation Index(EVI),and kernel NDVI(kNDVI)for the ZLRB from 1990 to 2020.We analyzed the spatiotemporal variations of these VIs using trend analysis and the Mann-Kendall test,and quantified the contributions of climate change(considering time-lag effects)and human activities to VIs changes through wavelet and residual analyses.Results indicated that VIs generally exhibited an upward trend in the ZLRB,with significant improvements observed in 54.91% of the area for NDVI,31.69% for EVI,and 33.71% for kNDVI.Among them,NDVI outperformed EVI and kNDVI in capturing vegetation changes in the semi-arid area.VIs responded to precipitation with 1-month time lag and no time lag to temperature during growing season.Moreover,precipitation had a stronger positive correlation with VIs than temperature.Climate change was identified as the dominant driver of vegetation dynamics in the ZLRB,accounting for 93.12% of NDVI variation,while human activities contributed only 6.88%.Comparative analysis of VIs suggests that NDVI was more suitable for describing vegetation changes in the typical arid area of the ZLRB.Our findings underscore the importance of selecting appropriate VIs for targeted ecological restoration and sustainable land management.
基金supported by the National Natural Science Foundation of China(42471336,52379021 and 42201278)the Hebei Province Backbone Talent Program,China(Returnee Platform for Overseas Study)(A20240028)+2 种基金the Hebei Province Statistical Science Research Project,China(2024HZ04)the Hebei Province Graduate Education and Teaching Reform Research Project,China(YJG2024046)the Innovation Ability Training Program for Postgraduate Students of Hebei Provincial Department of Education,China(CXZZSS2025048)。
文摘A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.
基金National Key Technology P&D Program,No.2012BAB02B00The Fundamental Research Funds for the Central Universities
文摘The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DTVGM） to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year （2001-2010） daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R2 varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years （2001-2007） of data on daily hydro- logical processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamfiow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.
基金National Key R&D Program of China,No.2017YFA0604703National Natural Science Foundation of China,No.41771111+4 种基金Hebei Natural Science Foundation,No.D2019205123Youth Innovation Promotion Association,No.2018071Research Fund Project of Hebei Normal University,No.L052018Z09Key Subject of Physical Geography of Hebei ProvinceInvestigation and Monitoring Project of Ministry of Natural Resources,No.JCQQ191504-06。
文摘Water retention is important in forest ecosystem services. The heterogeneity analysis of water-retention capacity and its influencing factors is of great significance for the construction of water-retention functional areas, restoration of vegetation, and the protection of forest ecosystems in the Beijing-Tianjin-Hebei region. A total of 1366 records concerning water-retention capacity in the canopy layer, litter layer, and soil layer of forest ecosystem in this region were obtained from 193 literature published from 1980 to 2017. The influencing factors of water-retention capacity in each layer were analyzed, and path analysis was used to investigate the contribution of the factors to the water-retention capacity of the three layers. The results showed that mixed forests had the highest water-retention capacity, followed by broad-leaved forests, coniferous forests, and shrub forests. In addition, no matter the forest type, the ranking of the water-retention capacity was soil layer, canopy layer, and litter layer from high to low. The main influencing factors of water-retention capacity in forest canopy were leaf area index and maximum daily precipitation(R2=0.49), and the influencing coefficients were 0.34 and 0.30, respectively. The main influencing factors of water-retention capacity in the litter layer were semi-decomposed litter(R2=0.51), and the influencing coefficient was 0.51. The main influencing factors of water-retention capacity in the soil layer were non-capillary porosity and soil depth(R2=0.61), the influencing coefficients were 0.60 and 0.38, respectively. This study verifies the simulation of the water balance model or inversion of remote sensing of the water-retention capacity at the site scale, and provides scientific basis for further study of the impact of global change on water retention.
基金National Natural Science Foundation of China,No.42071167,No.42201197,No.40871073The Second Tibetan Plateau Scientific Expedition and Research Program,No.2019QZKK0406Natural Science Foundation of Hebei Province,No.D2007000272。
文摘Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population.We took Shijiazhuang as the research area,with comprehensive zoning based on endowments as the modeling unit,conducted stratified sampling on a hectare grid cell,and systematically carried out incremental selection experiments of population density impact factors,optimizing the population density random forest model throughout the process(zonal modeling,stratified sampling,factor selection,weighted output).The results are as follows:(1)Zonal modeling addresses the issue of confusion in population distribution laws caused by a single model.Sampling on a grid cell not only ensures the quality of training data by avoiding the modifiable areal unit problem(MAUP)but also attempts to mitigate the adverse effects of the ecological fallacy.Stratified sampling ensures the stability of population density label values(target variable)in the training sample.(2)Zonal selection experiments on population density impact factors help identify suitable combinations of factors,leading to a significant improvement in the goodness of fit(R^(2))of the zonal models.(3)Weighted combination output of the population density prediction dataset substantially enhances the model's robustness.(4)The population density dataset exhibits multi-scale superposition characteristics.On a large scale,the population density in plains is higher than that in mountainous areas,while on a small scale,urban areas have higher density compared to rural areas.The optimization scheme for the population density random forest model that we propose offers a unified technical framework for uncovering local population distribution law and the impact mechanisms.
文摘Desertification caused by land degradation and overexploitation of natural resources is threatening large parts of eastern and southern Mediterranean. The actual state of desertification sensitivity in Lebanon was spatially assessed using site specific environmental bio-physical indicators, demographic pressure and socioeconomic conditions. Bio-physical assessment included the aridity index derived from integrated assessment of the historical data for 48 climatic stations spread throughout the country, the new detailed soil map at 1：50,000 scale, and the updated land cover/use map at 1：20,000 derived from IKONOS 2005. The methodology also included livelihood conditions and poverty at local administrative ＂Caza＂ level. Results showed the integrated impact of local climate, soil and vegetation quality and socioeconomic conditions on sensitivity to desertification. A total of 78% of the territories have low and very low climate quality index preconditioning the sensitivity to desertification. Fourteen Cazas out of 26 in total, representing more than 66% of the country, have low socioeconomic satisfaction index. Furthermore, negative trends are alleviated by good quality relict soils and vegetation cover. The actual extent of desertification covers 40.48% of the national territory, much of which occurs under semi-arid climate, moderate or low soil and vegetation quality and poor living conditions. The outcome of this research adjusted the previous coarse estimates of desertification prone areas at the national level. Results allow for realistic, policy oriented local assessment for responsive land use planning and proactive sustainable, national and local land management in the context of the national action plan to combat desertification.
基金supported by the National Key Research and Development Program of China(2022YFD2001105)。
文摘Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Currently,in-situ drought monitoring based on weather stations and based on remote sensing data has limitations,including infrequent updates,limited coverage,and low accuracy.This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province,China.We developed multi-source composite drought indices(MCDIs)at various timescales(3,6,9,and 12 months)by integrating precipitation,land surface temperature,soil moisture,and vegetation indices.Utilizing remote sensing data from various sources,we calculated a series of single drought indices,which are the precipitation condition index,soil moisture condition index,vegetation condition index,and temperature condition index.These are then integrated into MCDIs using a multivariable linear regression approach.The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index(SPEI)than single drought indices.When examining the correlation between different MCDIs and the affected area of crops and major grain production,MCDI-9 showed the highest correlation with the affected area of crops,while MCDI-12 showed the highest correlation with grain production.This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring,gradually spreading from the Greater Khingan Mountains region to the southeastern plains.The drought gradually alleviates during the summer,ending by the autumn harvest period.Therefore,the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions.
基金This work was supported by the Special Funds for the Major State Basic Research Project(Grant No.G2000077903)the National Natural Science Foundation of China(Grant No.40171068).
文摘In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data,it is necessary to understand what the information flow in quantitative remote sensing model inversion is,thus control the information flow.Aiming at this,the paper takes the linear kernel-driven model inversion as an example.At first,the information flow in different inversion methods is calculated and analyzed,then the effect of information flow controlled by multi-stage inversion strategy is studied,finally,an information matrix based on USM is defined to control information flow in inversion.It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly.Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow.In regularization inversion of remote sensing,information matrix based on USM may be a better tool for quantitatively controlling information flow.
基金supported by the National Key Research and Development Program of China(2022YFB3903505)the National Natural Science Foundation of China(72221002)。
文摘The well-facilitated farmland projects(WFFPs)involve the typical sustainable intensification of farmland use and play a key role in raising food production in China.However,whether such WFFPs can enhance the nitrogen(N)use efficiency and reduce environmental impacts is still unclear.Here,we examined the data from 502 valid questionnaires collected from WFFPs in the major grain-producing area,the Huang-Huai-Hai Region(HHHR)in China,with 429 samples for wheat,328 for maize,and 122 for rice.We identified gaps in N use efficiency(NUE)and N losses from the production of the three crops between the sampled WFFPs and counties based on the statistical data.The results showed that compared to the county-level(wheat,39.1%;maize,33.8%;rice,35.1%),the NUEs for wheat(55.2%),maize(52.1%),and rice(50.2%)in the WFFPs were significantly improved(P<0.05).In addition,the intensities of ammonia(NH3)volatilization(9.9-12.2 kg N ha–1),N leaching(6.5-16.9 kg N ha–1),and nitrous oxide(N2O)emissions(1.2-1.6 kg N ha–1)from crop production in the sampled WFFPs were significantly lower than the county averages(P<0.05).Simulations showed that if the N rates are reduced by 10.0,15.0,and 20.0%for the counties,the NUEs of wheat,maize,and rice in the HHHR will increase by 2.9-6.3,2.4-5.2,and 2.6-5.7%,respectively.If the N rate is reduced to the WFFP level in each county,the NUEs of the three crops will increase by 12.9-19.5%,and the N leaching,NH3,and N2O emissions will be reduced by 48.9-56.2,37.4-42.9,and 46.0-66.5%,respectively.Our findings highlight that efficient N management practices in sustainable intensive farmland have considerable potential for reducing environmental impacts.
基金This work was mainly supported by the National High-Technology Research and Development Program(863)[grant number 2013AA122801]the National Science Foundation of the United States[Award No.1251095]+3 种基金Also it was partially supported by the Fundamental Research Funds for the Central Universities[grant number ZYGX2015J111]the project entitled‘Design and development of the parallelism for typical remote sensing image algorithm based on heterogeneous computing’from the Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciencesthe project entitled‘CAST Innovation Fund:the Study of Agent and Cloud Based Spatial Big Data Service Chain’also the National Natural Science Foundation of China[grant number 51277167].
文摘MODerate resolution atmospheric TRANsmission(MODTRAN)is a commercial remote sensing(RS)software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth’s atmosphere and the radiation observed by a remote sensor.However,when very large RS datasets must be processed in simulation applications at a global scale,it is extremely time-consuming to operate MODTRAN on a modern workstation.Under this circumstance,the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task.This paper presents PMODTRAN,an implementation of a parallel task-scheduling algorithm based on MODTRAN.PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster.In addition,PMODTRAN can distribute tasks with different levels of granularity and has some extra features,such as dynamic load balancing and parameter checking.
基金supported by the National Natural Science Foundation of China(4227100242201002)+2 种基金the Foundation of Central Guidance for Local Scientific and Technological Development(246Z3705G)the Water Conservancy Science and Technology Plan Project of Hebei Province(2023-64)the Hebei Natural Science Foundation(D2021205013).
文摘Soil organic carbon(SOC)and total nitrogen(TN)play an important role in the global carbon and nitrogen cycles.Soil aggregates are critical reservoir of SOC and TN.Therefore,in areas with severe wind erosion,the changes in the accumulation of SOC,TN,clay,silt,and sand contents within different dry aggregate size fractions can offer crucial insights into soil conservation by the control of wind erosion.In this study,surface soil samples(0–5 cm depth)were collected from farmland and grassland in the Bashang region of northern China in 2020.The bulk soil and aggregate size fractions were used to determine the concentrations of SOC,TN,clay,silt,and sand.The results showed that:(1)farmland had lower SOC and higher TN than grassland;(2)SOC in the aggregates of farmland decreased with increasing aggregate size(P<0.010),while SOC in the aggregates of grassland increased with increasing aggregate size(P<0.010),and nonsignificant variation of TN and clay was observed among different aggregate sizes;(3)the mean of aggregate silt significantly decreased with increasing aggregate size and the mean of aggregate sand increased with increasing aggregate size(P<0.001);(4)no correlations between sand or silt of aggregate and TN or texture of bulk soil was found;and(5)SOC in bulk soil was correlated with those in different aggregate sizes,and was also affected by the texture of bulk soil(P<0.010).This study highlights the role of dry soil aggregate size in the redistribution of SOC,TN,clay,silt,and sand contents under different land uses,thereby facilitating the understanding of the process of wind erosion induced SOC,TN,and mineral dust emission.
文摘Carbon Monoxide(CO)is an important urban pollutant with a relation to 5,transition economies based on emission intensities.In this study,Sentinel-MODerate resolution Imaging Spectroradiometer(MODIS),and Landsat-8 images were used to investigate the variations of CO and urban environmental indices and the correlations between them.From the assessed correlations for 932 Iranian cities,it occurred that the assessed indices were all correlated.The highest CO levels were 0.031 in the spring of 2019 and 2020,whereas in 2021 it was equal to 0.030 in both the spring and winter,respectively.In 2019 and 2020 the maximum values of the Enhanced Vegetation Index(EVI)in the spring were 0.181 and 0.183.Exceptionally high Absorbing Aerosol Index(AAI)values of–0.834 and–1.0,along with Urban Index(UI)of 0.102 and 0.092,were correlated with recorded spikes in CO level,despite that these seasons’EVI values were not so abnormal.It was forecasted that in 2030 rises in the CO level by 13.2%in the winter and by 17.5%in the fall are expected,with the simultaneous increase of AAI by 204.5%and 980.2%,and Aerosol Optical Depth(AOD)by 27%and 5%in the winter and spring,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.42205131)the China Scholarship Council。
文摘Biomass burning is a major source of carbonaceous aerosols that significantly influences the Earth's radiation balance.However,the spectral light absorption properties of biomass burning aerosols(BBAs),particularly the contribution of brown carbon(BrC),remain poorly constrained due to reliance on laboratory measurements that may not accurately represent real-world atmospheric conditions.To address this limitation,we developed an unmanned aerial vehicle(UAV)based-platform for direct in-situ measurements of BBAs in the ambient atmosphere over the rural North China Plain.This approach reduces biases inherent to laboratory chamber experiments and enables a more realistic characterization of BBAs absorption properties.Our measurements revealed that the absorption?ngstr?m exponent(AAE)for typical residential biomass burning was 3.70±0.04 under smoldering conditions and 1.50±0.08 under flaming conditions.Variations in AAE were driven primarily by combustion conditions and smoke humidity rather than fuel type.Additionally,field-observed OC/EC ratios were up to ten times higher than those reported in laboratory chamber studies,resulting in systematically lower mass absorption cross-sections.This finding suggests that the BBAs light absorption and radiative forcing estimates in the North China Plain may be systematically overestimated by chamber-based studies.Notably,under smoldering conditions,BrC absorption at 375 nm was up to 6.6 times greater than that of black carbon(BC)once mass emissions are considered,emphasizing that strategies aiming at reducing smoldering combustion could be particularly effective in mitigating the ultraviolet radiative effects of BBAs.Our results demonstrate that ambient atmospheric measurements are essential for accurately constraining BBAs absorption properties and their climate impacts.
