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Seasonal variation and human exposure characteristics of PBDEs in indoor dust, airborne particles and vapor phase in office environments

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Environmental pollution (Barking, Essex : 1987) 2025年 381卷 126581页

作者： Bayier Wulijia You Li Huan Tao Liang Wang Xiaoyong Liao Hongying Cao Dan Zhao Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences (CAS) Beijing 100101 China Beijing Key Laboratory of Environmental Damage Assessment and Remediation Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China. University of Chinese Academy of Sciences Beijing 100049 China. Electronic address: caohy@***. State Environmental Protection Key Laboratory of Environmental Damage Identification and Restoration Beijing 100041 China Center for Environmental Risk and Damage Assessment Chinese Academy of Environmental Planning Beijing 100041 China. Electronic address: zhaodan@***.

Polybrominated diphenyl ethers (PBDEs) are widely used as flame retardants and are prevalent indoors, yet long-term monitoring of indoor PBDEs remains rare. This study systematically investigated the seasonal variations, compositional profiles, and exposure risks of PBDEs across three indoor media-airborne particles (APs, n = 28), vapor phase (n = 28), and dust (n = 47)-collected from four offices. Seasonal trends of PBDEs showed the highest concentrations in autumn and the lowest in winter in both the indoor vapor phase and dust. Correlation analysis revealed a significant negative correlation (p < 0.001) between indoor vapor BDE-17 and BDE-209, suggesting photodegradation of high-weight to low-weight BDEs. In APs, twelve PBDE congeners-excluding BDE-17 and 85-exhibited significant positive correlations (p < 0.05) with PM concentrations. Dust on glass surfaces showed strong correlations between penta-BDEs and octa-/deca-BDEs (r = 0.79). Risk assessments found that dust ingestion was the primary exposure pathway, with students exhibiting the highest total exposure. Dermal contact exposure reached its highest levels in summer, while inhalation, dermal absorption, and dust ingestion were highest in autumn. This study reveals significant seasonal variations in office PBDE concentrations and reinforces the importance of dust as a critical exposure pathway, providing a scientific basis for PBDEs pollution control and health protection strategies.

关键词： Daily intake Office environment Polybrominated diphenyl ethers Risk assessment Seasonal variation

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Calibration and validation of SiBcrop Model for simulating LAI and surface heat fluxes of winter wheat in the North China Plain

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Journal of Integrative Agriculture 2020年第9期19卷 2206-2215页

作者： CHEN Ying LIU Feng-shan TAO Fu-lu GE Quan-sheng JIANG Min WANG Meng ZHAO Feng-hua Forestry College Fujian Agriculture and Forestry UniversityFuzhou 350002P.R.China China National Engineering Research Center of JUNCAO Technology Fujian Agriculture and Forestry UniversityFuzhou 350002P.R.China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101P.R.China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049P.R.China College of Crop Science Fujian Agriculture and Forestry UniversityFuzhou 350002P.R.China School of Geography and Tourism Qufu Normal UniversityRizhao 276800P.R.China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101P.R.China

The accurate representation of surface characteristic is an important process to simulate surface energy and water flux in land-atmosphere boundary *** crop growth model in land surface model is an important method to accurately express the surface characteristics and biophysical processes in ***,the previous work mainly focused on crops in single cropping system,less work was done in multiple cropping *** article described how to modify the sub-model in the SiBcrop to realize the accuracy simulation of leaf area index(LAI),latent heat flux(LHF)and sensible heat flux(SHF)of winter wheat growing in double cropping system in the North China Plain(NCP).The seeding date of winter wheat was firstly reset according to the actual growing environment in the *** phenophases,LAI and heat fluxes in 2004–2006 at Yucheng Station,Shandong Province,China were used to calibrate the *** validations of LHF and SHF were based on the measurements at Yucheng Station in 2007–2010 and at Guantao Station,Hebei Province,China in 2009–*** results showed the significant accuracy of the calibrated model in simulating these variables,with which the R2,root mean square error(RMSE)and index of agreement(IOA)between simulated and observed variables were obviously improved than the original *** sensitivities of the above variables to seeding date were also displayed to further explain the simulation error of the SiBcrop ***,the research results indicated the modified SiBcrop Model can be applied to simulate the growth and flux process of winter wheat growing in double cropping system in the NCP.

关键词： winter wheat LAI crop growth model SiBcrop North China Plain latent heat flux sensible heat flux

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Mapping soil organic carbon in fragmented agricultural landscapes: The efficacy and interpretability of multi-category remote sensing variables

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Journal of Integrative Agriculture 2025年

作者： Yujiao Wei Yiyun Chen Jiaxue Wang Peiheng Yu Lu Xu Chi Zhang Huanfeng Shen Yaolin Liu GanLin Zhang School of Resource and Environmental Sciences Wuhan University Wuhan 430079 China Key Laboratory of Digital Cartography and Land Information Application Engineering Ministry of Natural Resources Wuhan 430079 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China School of Geology and Geomatics Tianjin Chengjian University Tianjin 300384 China Duke Kunshan University Kunshan 215316 China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing 210008 China University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Watershed Geographic Science Nanjing Institute of Geography and Limnology Chinese Academy of Sciences Nanjing 210008 China

Accurately mapping the spatial distribution of soil organic carbon (SOC) is crucial for guiding agricultural management and improving soil carbon sequestration, especially in fragmented agricultural landscapes. Although remote sensing provides spatially continuous environmental information about heterogeneous agricultural landscapes, its relationship with SOC remains unclear. In this study, we hypothesized that multi-category remote sensing-derived variables can enhance our understanding of SOC variation within complex landscape conditions. Taking the Qilu Lake watershed in Yunnan, China, as a case study area and based on 216 topsoil samples collected from irrigation areas, we applied the extreme gradient boosting (XGBoost) model to investigate the contributions of vegetation indices (VI), brightness indices (BI), moisture indices (MI), and spectral transformations (ST, principal component analysis and tasseled cap transformation) to SOC mapping. The results showed that ST contributed the most to SOC prediction accuracy, followed by MI, VI, and BI, with improvements in R 2 of 29.27, 26.83, 19.51, and 14.43%, respectively. The dominance of ST can be attributed to the fact that it contains richer remote sensing spectral information. The optimal SOC prediction model integrated soil properties, topographic factors, location factors, and landscape metrics, as well as remote sensing-derived variables, and achieved RMSE and MAE of 15.05 and 11.42 g kg -1 , and R 2 and CCC of 0.57 and 0.72, respectively. The Shapley additive explanations deciphered the nonlinear and threshold effects that exist between soil moisture, vegetation status, soil brightness and SOC. Compared with traditional linear regression models, interpretable machine learning has advantages in prediction accuracy and revealing the influences of variables that reflect landscape characteristics on SOC. Overall, this study not only reveals how remote sensing-derived variables contribute to our understanding of

关键词： soil organic carbon remote sensing-derived variables Shapley additive explanations efficacy and interpretability fragmented agricultural landscapes

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Investigating the spatially heterogeneous relationships between climate factors and NDVI in China during 1982 to 2013

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Journal of Geographical Sciences 2019年第10期29卷 1597-1609页

作者： GAO Jiangbo JIAO Kewei WU Shaohong Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research CASBeijing 100101 China Key Laboratory of Forest Ecology and Management Institute of Applied Ecology CASShenyang 110016China University of Chinese Academy of Sciences Beijing 100049 China

Climate change is a major driver of vegetation activity, and thus their complex processes become a frontier and difficulty in global change research. To understand this relationship between climate change and vegetation activity, the spatial distribution and dynamic characteristics of the response of NDVI to climate change were investigated by the geographically weighted regression(GWR) model during 1982 to 2013 in China. This model was run based on the combined datasets of satellite vegetation index(NDVI) and climate observation(temperature and moisture) from meteorological stations nationwide. The results showed that the spatial non-stationary relationship between NDVI and surface temperature has appeared in China: the significant negative temperature-vegetation relationship was located in Northeast, Northwest and Southeast China, while the positive correlation was more concentrated from southwest to northeast. By comparing the normalized regression coefficients from GWR model for different climate factors, it presented the regions with moisture dominants for NDVI were in North China and the Tibetan Plateau, and the areas of temperature dominants were distributed in East, Central and Southwest China, where the annual mean maximum temperature accounted for the largest areas. In addition, regression coefficients from GWR model between NDVI dynamics and climate variability indicated that the higher warming rate could result in the weakened vegetation activity through some mechanisms such as enhanced drought, while the moisture variability could mediate the hydrothermal conditions for the variation of vegetation activity. When the increasing rate of photosynthesis exceeded that of respiration, the positive correlation between vegetation dynamics and climate variability was reflected. However, the continuous and dynamic process of vegetation activity response to climate change will be determined by spatially heterogeneous conditions in climate change and vegetation cov

关键词： NDVI climate change spatial heterogeneity GWR China

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Early- And in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach

arXiv

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arXiv 2021年

作者： Lin, Chenxi Zhong, Liheng Song, Xiao-Peng Dong, Jinwei Lobell, David B. Jin, Zhenong Department of Bioproducts and Biosystems Engineering University of Minnesota Twin Cities St PaulMN United States Ant Group Beijing China Department of Geosciences Texas Tech University LubbockTX United States Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China Department of Earth System Science Center on Food Security and the Environment Stanford University StanfordCA United States

Land cover classification in remote sensing is often faced with the challenge of limited ground truth labels. Incorporating historical ground information has the potential to significantly lower the expensive cost associated with collecting ground truth and, more importantly, enable early- and in-season mapping that is helpful to many pre-harvest decisions. In this study, we propose a new approach that can effectively transfer knowledge about the topology (i.e. relative position) of different crop types in the spectral feature space (e.g. the histogram of SWIR1 vs RDEG1 bands) to generate labels, thereby support crop classification in a different year. Importantly, our approach does not attempt to transfer classification decision boundaries that are susceptible to inter-annual variations of weather and management, but relies on the more robust and shift-invariant topology information. We tested this approach for mapping corn/soybeans in the US Midwest and paddy rice/corn/soybeans in Northeast China using Landsat-8 and Sentinel-2 data. Results show that our approach automatically generates high-quality labels for crops in the target year immediately after each image becomes available. Based on these generated labels from our approach, the subsequent crop type mapping using a random forest classifier can reach the F1 score as high as 0.887 for corn as early as the silking stage and 0.851 for soybean as early as the flowering stage and the overall accuracy of 0.873 in the test state of Iowa. In Northeast China, F1 scores of paddy rice, corn and soybeans and the overall accuracy can exceed 0.85 two and half months ahead of harvest. Overall, these results highlight unique advantages of our approach in transferring historical knowledge and maximizing the timeliness of crop maps. Our approach supports a general paradigm shift towards learning transferrable and generalizable knowledge to facilitate land cover classification. © 2021, CC BY-NC-ND.

关键词： Remote sensing

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The 2012 Flash Drought Threatened US Midwest Agroecosystems

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Chinese Geographical Science 2019年第5期29卷 768-783页

作者： JIN Cui LUO Xue XIAO Xiangming DONG Jinwei LI Xueming YANG Jun ZHAO Deyu College of Urban and Environment Liaoning Normal UniversityDalian116029China Department of Microbiology and PlantBiology Center for Spatial AnalysisUniversity of OklahomaNormanOK73019USA Ministry of Education Key Laboratory ofBiodiversity Science and Ecological Engineering Institute of Biodiversity ScienceFudan UniversityShanghai200433China KeyLaboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academyof SciencesBeijing100101China

In the summer of 2012, the US Midwest, the most productive agricultural region in the world, experienced the most intense and widespread drought on record for the past hundred years. The 2012 drought, characterized as ‘flash drought’, developed in May with a rapid intensification afterwards, and peaked in mid-July. ~76% of crop region and 60% of grassland and pasture regions have been under moderate to severe dry conditions. This study used multiple lines of evidences, i.e., in-situ AmeriFlux measurements, spatial satellite observations, and scaled ecosystem modeling, to provide independent and complementary analysis on the impact of 2012 flash drought on the US Midwest vegetation greenness and photosynthesis carbon uptake. Three datasets consistently showed that 1) phenological activities of all biomes advanced 1–2 weeks earlier in 2012 compared to the other years of 2010–2014;2) the drought had a more severe impact on agroecosystems(crop and grassland) than on forests;3) the growth of crop and grassland was suppressed from June with significant reduction of vegetation index, sun-induced fluorescence(SIF) and gross primary production(GPP), and did not recover until the end of growing season. The modeling results showed that regional total GPP in 2012 was the lowest(1.76 Pg C/yr) during 2010–2014, and decreased by 63 Tg C compared with the other-year mean. Agroecosystems, accounting for 84% of regional GPP assimilation, were the most impacted by 2012 drought with total GPP reduction of 9%, 7%, 6%, and 29% for maize, soybean, cropland, and grassland, respectively. The frequency and severity of droughts have been predicted to increase in future. The results imply the importance to investigate the influences of flash droughts on vegetation productivity and terrestrial carbon cycling.

关键词： food security terrestrial carbon cycling eddy covariance Vegetation Photosynthesis Model sun-induced fluorescence(SIF)

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Analysis of differentiation effect of surface thermal environment in intensive mining areas based on NDVI-DFI model

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 2020年第3期36卷 160-168页

作者： Hou, Chunhua Li, Fuping Feng, Yifan Gu, Haihong Yuan, Xuetao Song, Wen Liu, Huixin College of Mining Engineering North China University of Science and Technology Tangshan063210 China Hebei Key Laboratory of Mining Developmeng and Security Technology Tangshan063210 China Hebei Industrial Technology Institute of Mine Ecological Remediation Tangshan063210 China Co. Ltd. Qingdao266109 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China North China University of Science and Technology Library Tangshan063210 China

Currently, the dimidiate pixel model is used in most studies to discuss vegetation fractional coverage (VFC) and explore its relationship with urban heat islands. Few studies have used the three-component pixel model to invert vegetation fractional coverage (VFC) and discuss the relationship between VFC and the differentiation effect of the surface thermal environment in mining-intensive areas. In this study, VFC was inverted based on the normalized difference vegetation index (NDVI)-dry fuel index (DFI) three-component pixel model using Landsat satellite remote sensing images from 2000 to 2018. Mixed pixels were decomposed into photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil (BS). The radiative transfer equation (RTE) was used to invert the surface temperature. The basic principle of this method is to remove the atmospheric influence and use the atmospheric radiation transfer equation;surface heat radiation intensity observed by the satellite is converted to the corresponding surface temperature. The parameters involved are mainly atmospheric profile parameters and surface specific emissivity. Heat-island index (HI) was used to grade the mean LST of the 2000-2018 study area. The relationship between the spatial-temporal changes in VFC and the differentiation effect of the surface thermal environment in the study area was analyzed using hierarchical statistics, a red-blue difference image method, correlation analysis, and regression analysis. On this basis, the influence mechanism of spatial-temporal variation in the VFC on the differentiation effect of surface thermal environment was studied. The results showed that the NDVI-DFI feature space of the study area conformed to the basic assumption of the three-component pixel model. The correlation matrix showed that there was a strong correlation between the changes in land surface temperature (LST) and VFC (the fractional cover of PV, fractional cover of NPV, and fractional cover of

关键词： Remote sensing

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Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing

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Chinese Geographical Science 2019年第6期29卷 905-916页

作者： LI Yuanzheng WANG Lan ZHANG Liping LIU Min ZHAO Guosong School of Resources and Environment Henan University of Economics and LawZhengzhou 450046China Academician Laboratory for Urban and Rural Spatial Data Mining of Henan Province Henan University of Economics and LawZhengzhou 450046China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Key Laboratory of Urban Environment and Health Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China Center for Environmental Zoning Chinese Academy for Environmental PlanningMinistry of Environmental Protection of ChinaBeijing 100012China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101

This study aimed to accurately study the intra-annual spatiotemporal variation in the surface urban heat island intensities(SUHIIs) in 1449 cities in ***, China was divided into five environmental ***, the SUHIIs were accurately calculated based on the modified definitions of the city extents and their corresponding nearby rural ***, we explored the spatiotemporal variation of the mean, maximum, and minimum values, and ranges of SUHIIs from several *** results showed that larger annual mean daytime SUHIIs occurred in hot-humid South China and cold-humid northeastern China, and the smallest occurred in arid and semiarid west *** seasonal order of the SUHIIs was summer > spring > autumn > winter in all the temperate regions except west *** SUHIIs were obviously larger during the rainy season than the dry season in the tropical ***, significant differences were not observed between the two seasons within the rainy or dry *** the daytime, the maximum SUHIIs mostly occurred in summer in each region, while the minimum occurred in winter.A few cold island phenomena existed during the *** maximum SUHIIs were generally significantly positively correlated with the minimum SUHIIs during the daytime, nighttime and all-day in all environmental regions throughout the year and the four ***, significant correlation scarcely existed between the daytime and nighttime ranges of the *** addition, the daytime SUHIIs were also insignificantly correlated with the nighttime SUHIIs in half of the cases.

关键词： surface urban heat island intensities(SUHIIs) land surface temperature(LST) seasonal changes maximum and minimum SUHII cold island China

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Farmland Abandonment Research Progress: Influencing Factors and simulation Model

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Journal of Resources and Ecology 2019年第4期10卷 345-352页

作者： SONG Wei ZHANG Ying Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China Chinese Academy of Surveying & Mapping Beijing 100830 China

Farmland abandonment is a global problem and considered one of the most important areas in land use change research. Farmland abandonment research currently focuses on understanding the factors that affect farmland abandonment and developing scientific models to simulate farmland abandonment. The study reviewed the natural and political factors driving farmland abandonment and summarized the main models for farmland abandonment simulation together with their advantages and disadvantages. We discuss the main ecological effects of farmland abandonment and propose farmland abandonment research directions. The study found that:(1) the influence of labor cost change and ageing labor force on farmland abandonment needs further investigation,(2) simulation models for farmland abandonment must include the decision-making mechanism of individual farmers and focus on macro large-scale abandonment prediction models, and(3) the influence of farmland abandonment on landscape culture must be investigated in detail.

关键词： farmland abandonment driving factors model simulation eco-environmental effects research progress

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The mechanism of farmland marginalization in Chinese mountainous areas:Evidence from cost and return changes

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Journal of Geographical Sciences 2019年第4期29卷 531-548页

作者： LI Shengfa LI Xiubin Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of GeographyGuangzhou 510070China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Farmland marginalization has become the main trend of land-use change in the mountainous areas of China. Using annual survey data of major agricultural production costs and earnings at national and provincial levels in China, this study aims to analyze the reasons and mechanism behind farmland marginalization in mountainous areas. We find that farmers on plains are able to reduce their per mu labor input effectively through intensive use of agricultural machinery, which has minimized the impact of the increase in labor price. However, it is extremely challenging for farmers in mountainous areas to use the same method owing to the rough terrain. Thus, per laborer farming area in these areas has increased relatively slowly, causing a widening gap in agricultural labor productivity between the two regions. With the rapid rise in labor costs since 2003, the marginalization of cultivated land in mountainous areas is evident. In 2013, the net profit of agricultural production in mountainous China fell below zero. Since 2000, the land-use and land-cover change in these areas was characterized by the reduction of farmland area, reforestation, and the enhancement of the NDVI value. The high correlation between the NDVI change rate and the ratio of change in farmland(r = –0.70) and forest(r = 0.91) in mountainous areas at provincial level further attests to the trend of farmland marginalization there. Finally, we summarize the mechanism of such marginalization against the backdrop of the rapid increase in the opportunity cost of farming and the rapid fall of agricultural labor forces in mountainous areas. This study contributes to a deep understanding of the development process of farmland marginalization and abandonment as well as forest transition in Chinese mountainous areas.

关键词： farmland marginalization farmland abandonment labor costs returns agricultural labor productivity mountainous areas China

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