Temporal Variation Research Articles

Coherent long-term body-size responses across all Northwest Atlantic herring populations to warming and environmental change despite contrasting harvest and ecological factors.

Body size is a key component of individual fitness and an important factor in the structure and functioning of populations and ecosystems. Disentangling the effects of environmental change, harvest and intra- and inter-specific trophic effects on body size remains challenging for populations in the wild. Herring in the Northwest Atlantic provide a strong basis for evaluating hypotheses related to these drivers given that they have experienced significant warming and harvest over the past century, while also having been exposed to a wide range of other selective constraints across their range. Using data on mean length-at-age 4 for the sixteen principal populations over a period of 53 cohorts (1962-2014), we fitted a series of empirical models for temporal and between-population variation in the response to changes in sea surface temperature. We find evidence for a unified cross-population response in the form of a parabolic function according to which populations in naturally warmer environments have responded more negatively to increasing temperature compared with those in colder locations. Temporal variation in residuals from this function was highly coherent among populations, further suggesting a common response to a large-scale environmental driver. The synchrony observed in this study system, despite strong differences in harvest and ecological histories among populations and over time, clearly indicates a dominant role of environmental change on size-at-age in wild populations, in contrast to commonly reported effects of fishing. This finding has important implications for the management of fisheries as it indicates that a key trait associated with population productivity may be under considerably less short-term management control than currently assumed. Our study, overall, illustrates the need for a comparative approach within species for inferences concerning the many possible effects on body size of natural and anthropogenic drivers in the wild.

Modeling temporal dual variations for return air temperature prediction of mK-level temperature-controlled clean chamber

Clean chamber with mK-level temperature stability is required for precision manufacturing and optical equipment to achieve nanoscale manufacturing tolerances and measurement accuracy, however, the equipped precision air conditioning system cannot respond immediately to temperature fluctuation in the chamber due to large time delay. Predicting the future return air temperature gives the control module a prescient ability to act in advance, thus aiding in temperature regulation. To this end, this paper deals with the mK-level temperature prediction problem under a high-precision temperature control situation, and a novel prediction network model dubbed TDVNet is designed based on convolutional neural network (CNN) and gated recurrent unit (GRU), where Temporal Dual Variations of time series data, namely variations-between-phases and variations-across-periods, are modeled to achieve a higher accuracy of return air temperature prediction. Utilizing chamber temperature data collected on-site for model training and evaluation, the experimental results show that capturing data change patterns from dual perspectives has reduced the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the TDVNet model by 21.4% and 24.3%, respectively, compared to the model only capturing the variations-between-phases. Under a signal sampling interval of 10 s and a temperature fluctuation of (66.9 ± 25.8) mK@10min, the TDVNet model can accurately predict the return air temperature change in the next 5 min, and the proportion of prediction errors within 15 mK in the test set can reach over 90.1%.

Geospatial analysis of shoreline changes in the Oman coastal region (2000-2022) using GIS and remote sensing techniques

This research introduces an innovative method employing the Canny edge detector for automatic and precise coastline extraction, aiming to analyze spatial and temporal variations in the Oman coastline from 2000 to 2022 using GIS and remote sensing (RS) techniques. Focusing on both multi-decadal and short-term periods, the study aims to detect accretion and erosion rates through the observation and interpretation of coastal changes. Utilizing the Digital Shoreline Analysis System and LANDSAT imageries, Shoreline changes have been quantitatively evaluated using three distinct approaches: Linear Regression Rate (LRR), End Point Rate (EPR), and Net Shoreline Movement (NSM). The dynamic nature of the Oman coastal region necessitates a comprehensive understanding of its evolving coastline. Our investigation applies digital shoreline analysis to discern shifts in the coastline’s position, employing a multiple regression approach for quantifying the rate of coastal change. To facilitate automatic shoreline extraction, various methods were experimented with, ultimately determining the Canny Edge algorithm’s superiority in yielding precise results. The paper outlines the monitoring procedures for the coastal area and analyzes coastline changes using geospatial techniques. This analysis provides valuable insights for the planning and management of the Oman shore. Furthermore, the proposed model’s applicability is rigorously tested against other generic edge detection algorithms, including Sobel, Prewitt, and Robert’s techniques. The conclusive findings demonstrate that our model outperforms these alternatives, particularly excelling in the accurate detection of the coastline. This research contributes to a deeper understanding of coastal dynamics and offers a robust methodology for coastal monitoring, with implications for effective planning and management strategies in the Oman shore region.

Small but mighty: High-resolution spectroscopy of ultra-hot Jupiter atmospheres with compact telescopes

When observing transmission spectra produced by the atmospheres of ultra-hot Jupiters (UHJs), large telescopes are typically the instrument of choice given the very weak signal of the planet’s atmopshere. The aim of the present study is to demonstrate that, for favourable targets, smaller telescopes are fully capable of conducting high-resolution cross-correlation spectroscopy. We apply the cross-correlation technique to data from the 2.1 m telescope at the Wendelstein Observatory, using its high-resolution spectrograph FOCES, in order to demonstrate its efficacy in resolving the atmosphere of the UHJ KELT-9 b. Using three nights of observations with the FOCES spectrograph and one with the HARPS-N spectrograph, we conduct a performance comparison between FOCES and HARPS-N. This comparison considers both single-transit and combined observations over the three nights. We then consider the potential of 2 m class telescopes by generalising our results to create a transit emulator capable of evaluating the potential of telescopes of this size. With FOCES, we detected seven species in the atmosphere of KELT-9b: Ti II, Fe I, Fe II, Na I, Mg I, Na II, Cr II, and Sc II. Although HARPS-N surpasses FOCES in performance thanks to the mirror of the TNG, our results reveal that smaller telescope classes are capable of resolving the atmospheres of UHJs given sufficient observing time. This broadens the potential scope of such studies, demonstrating that smaller telescopes can be used to investigate phenomena such as temporal variations in atmospheric signals and the atmospheric loss characteristics of these close-in planets.

Research Themes, Evolution Trends, and Future Challenges in China’s Carbon Emission Studies

A profound analysis of China’s research achievements in the realm of carbon emissions holds the potential to furnish insightful references for analogous endeavors and inquiries in other nations. Employing the CiteSpace tool, this paper identifies five major focal points in Chinese scholars’ research on carbon emissions: carbon emission computation and prediction, influencing factors of carbon emissions, carbon footprint, carbon emission efficiency, and differential analysis of carbon emissions. Subsequently, this article systematically scrutinizes and dissects the outcomes of Chinese scholars’ endeavors in the aforementioned five focal points, culminating in recommending China’s forthcoming research on carbon emissions. (1) The research findings reveal a diversified evolution in the methods employed for calculating and predicting carbon emissions in China. However, due to the limited exploration of delineating carbon emission boundaries, instances of overlap and deviation in carbon emission quantification have emerged. (2) Factors influencing carbon emissions can be categorized into five major classes: economic, demographic, energy-related, policy-driven, and others. Yet, studies investigating industry-specific influencing factors remain relatively scarce. (3) Overcoming challenges associated with cross-boundary measurements, comprehensive effects, and policy applications is imperative in carbon footprint research. (4) Significantly disparate levels of carbon emission efficiency prevail across distinct regions or industries, with intricacies characterizing the influencing factors and a notable dearth of micro-level investigations. (5) The analysis of carbon emission differentials primarily encompasses regional disparities, industrial differentials, and temporal variations, lacking sustained tracking studies on the nuances of carbon emission disparities.

Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.

Psychiatric evaluation suffers from subjectivity and bias, and is hard to scale due to intensive professional training requirements. In this work, we investigated whether behavioral and physiological signals, extracted from tele-video interviews, differ in individuals with psychiatric disorders. Temporal variations in facial expression, vocal expression, linguistic expression, and cardiovascular modulation were extracted from simultaneously recorded audio and video of remote interviews. Averages, standard deviations, and Markovian process-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination. Statistically significant feature differences were found between psychiatric and control subjects. Correlations were found between features and self-rated depression and anxiety scores. Heart rate dynamics provided the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities provided AUROCs of 0.72-0.82. Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status. The proposed multimodal approach has the potential to facilitate scalable, remote, and low-cost assessment for low-burden automated mental health services.

Understanding the Spatiotemporal Variability of Tropical Orographic Rainfall Using Convective Plume Buoyancy

Abstract Mechanical forcing by orography affects precipitating convection across many tropical regions, but controls on the intensity and horizontal extent of the orographic precipitation peak and rain shadow remain poorly understood. A recent theory explains this control of precipitation as arising from modulation of lower-tropospheric temperature and moisture by orographic mechanical forcing, setting the distribution of convective rainfall by controlling parcel buoyancy. Using satellite and reanalysis data, we evaluate this theory by investigating spatiotemporal precipitation variations in six mountainous tropical regions spanning South and Southeast Asia, and the Maritime Continent. We show that a strong relationship holds in these regions between daily precipitation and a measure of convective plume buoyancy. This measure depends on boundary layer thermodynamic properties and lower-free-tropospheric moisture and temperature. Consistent with the theory, temporal variations in lower-free-tropospheric temperature are primarily modulated by orographic mechanical lifting through changes in cross-slope wind speed. However, winds directed along background horizontal moisture gradients also influence lower-tropospheric moisture variations in some regions. The buoyancy measure is also shown to explain many aspects of the spatial patterns of precipitation. Finally, we present a linear model with two horizontal dimensions that combines mountain wave dynamics with a linearized closure exploiting the relationship between precipitation and plume buoyancy. In some regions, this model skillfully captures the spatial structure and intensity of rainfall; it underestimates rainfall in regions where time-mean ascent in large-scale convergence zones shapes lower-tropospheric humidity. Overall, these results provide new understanding of fundamental processes controlling subseasonal and spatial variations in tropical orographic precipitation.

Statistical Characteristics and Synoptic Patterns of Convection Initiation over the Middle Reaches of the Yangtze River Basin as Observed Using the Fengyun-4A Satellite

Abstract Convection initiations (CIs) observed using the advanced geosynchronous radiation imager on the Chinese Fengyun-4A satellite were identified over the middle reaches of the Yangtze River basin during warm season (May–September) of 2018–21. A hybrid objective tracking algorithm combining the conventional area overlapping with the Kalman filter method was applied. Subsequently, spatial and temporal variations in the identified CIs and their synoptic circulation patterns were analyzed. The frequency of CIs was highest in August and lowest in May. Nearly 81% of CIs occurred during noon–afternoon (1100–1859 LST), with the highest frequency in the southern mountains of the study region, whereas the CIs with relatively low frequency moved to the plains from afternoon to morning (1700–1059 LST). The diurnal variation of CIs throughout the study region exhibited a unimodal structure, with a peak appearing at noon (1200–1259 LST). CIs during noon–afternoon in July and August had faster cloud-top cooling rates. The synoptic circulations without tropical cyclones during noon–afternoon hours were classified into four patterns by hierarchical clustering; two dominant patterns (i.e., SW-Flows and S-Flows) had broader areas of higher most unstable convective available potential energy (MUCAPE), whereas the 0–3-km shear (SHR3) was the weakest in the S-Flows pattern. It was clear that the high-frequency areas of CIs were most likely to occur in stronger MUCAPE and weaker SHR3 environments, and CIs were more controlled by thermally unstable environments. We further illustrated that CIs tend to concentrate in unstable and moisture flux convergence areas affected by mountains.

Bacterial rarity in a subarctic stream network: Biodiversity patterns, assembly mechanisms and types of rarity.

Dendritic stream networks are an intriguing subject for exploring the spatial and temporal variability of the rare and common bacterial biosphere, yet very few such studies have been conducted. We sampled riverine bacterioplankton at 13 sites in a subarctic riverine network across 3 years, with five sampling times each year. Ordinations showed a consistent pattern of downstream shift for both rare and abundant subcommunities. We also detected a temporal signal, with seasonal community shifts reflecting changes in water temperature and groundwater contribution, and an inter-annual pattern where the year 2018 differed from other years. Phylogenetic turnover of the rare subcommunity indicated homogeneous selection, whereas the abundant subcommunity was mainly stochastically structured. Transiently rare taxa were the dominant type of rarity with the highest proportion at the headwater regions. The bacterioplankton community was characterized by a small group of core taxa that occurred at most sites with little temporal variation, a very large number of permanently or transiently rare taxa, and taxa shifting through time between the rare and abundant biosphere. While this basic structure could have been detected with less extensive temporal replication, a comprehensive understanding of the rare biosphere in riverine bacterioplankton can only be achieved via inter-annual, spatially replicated sampling that covers the whole stream network.

Can the aerosol pollution extreme events be revealed by global reanalysis products?

Extreme aerosol pollution poses significant risks to the climate, environment, and human health. To investigate the formation and impacts of aerosol pollution extreme events (APEE), the reanalysis product presents meticulous spatiotemporal information on the three-dimensional distribution of aerosols. However, there is a lack of comprehensive evaluation and information regarding the data quality of reanalysis products employed in APEE research, as well as limited understanding of their spatial and temporal distribution, variation, and long-term trends. To address this scientific gap, we conducted a global study for distribution and variation patterns of APEE using two widely-used reanalysis products, MERRA-2 (Modern-Era Retrospective Analysis for Research-2) and CAMS (Copernicus Atmospheric Monitoring Service). The APEE was defined here as a day when the daily aerosol optical depth (AOD) exceeding its 90th percentile for a given station and month. Eleven distinct land regions worldwide were selected for evaluation by comparing both reanalysis products with MODIS satellite products and ground-based observations in terms of frequency, intensity, and temporal trends of APEE. The analysis indicates that MERRA-2 and CAMS exhibit high matching rates (70 % and 80 %, respectively) in terms of occurrence timeline for APEE at monthly and seasonal scales, while also exhibiting strong monthly correlation coefficients (>0.65) with ground-based observations over selected regions. The total AOD (−0.002 ∼ −0.123 decade−1), APEE AOD (−0.004 ∼ −0.293 decade−1), and APEE frequency (−0.264 ∼ −1.769 day month−1 decade−1) of both observations and reanalysis products in most regions showed a decreasing trend with various magnitude, except for some regions such as South Asia where the trend is increasing. Based on the aforementioned evaluation, it is evident that reanalysis products are effective and useful in identifying the temporal trends associated with APEE.

The spatial-temporal evolution and driving mechanism of Urban resilience in the Yellow River Basin cities

The rapid urbanization poses the potential risk of overload and collapse for cities. Addressing the problems brought about by rapid urbanization to ensure urban safety and sustainable development has become particularly urgent and crucial, especially for regions with high ecological vulnerability and weak economic foundations. This study utilizes panel data spanning from 2013 to 2020 and applies the entropy weight-TOPSIS model to assess the urban resilience of 54 cities within the Yellow River Basin. It explores the temporal evolution, spatial distribution, and dynamics of urban resilience. Further, through the application of the Geographically and Temporally Weighted Regression model, the research investigates the drivers of urban resilience, considering temporal and spatial variations. Findings indicate a consistent increase in urban resilience across the Yellow River Basin, albeit with widening regional disparities. The spatial pattern of urban resilience exhibits a gradient decline from downstream to midstream and then upstream. The impact of various factors on urban resilience differs by region, with technological innovation emerging as the most influential. To foster a sustainable future, the Yellow River Basin should prioritize technological innovation, particularly in industrial cities, and advocate for the adoption of green technologies to diminish resource consumption and mitigate environmental pollution.

ERA5 Precipitation over China: Better Relative Hourly and Daily Distribution than Absolute Values

Abstract The temporal distribution of precipitation is of great importance in water availability and the hydrological cycle. Existing studies, directly comparing observed and simulated precipitation amount and intensity over different temporal intervals, show that reanalyses overestimate precipitation frequency and underestimate precipitation intensity, and therefore cannot characterize extreme precipitations. In this study, relative values of precipitation (i.e., the cumulative fractions of precipitation on different percentiles of wettest hours or days to the annual total) were used to evaluate ERA5 over China during the warm seasons of from 1979 to 2015. We found that ERA5 well reproduced the relative values of daily precipitation to the annual total, although hourly results were less satisfied. The cumulative fractions at the hourly time scale at the 99th, 95th, and 90th percentiles were 15.2% ± 3.4%, 38.7% ± 6.6%, and 54.5% ± 7.4% for the gauge observations, and 9.0% ± 1.7%, 27.2% ± 4.0%, and 41.5% ± 5.0% for ERA5, respectively. ERA5 had excellent agreement with gauge observations at daily time scales: gauge observations were 13.0% ± 3.4%, 32.1% ± 5.1%, and 47.6% ± 5.7%, compared to 11.5% ± 3.7%, 30.7% ± 7.0%, and 46.0% ± 8.8% for ERA5, respectively. This difference was mainly due to the high frequency of hourly precipitation in the reanalyses, which were cancelled out at the daily scale. Gauge observations at the hourly scale showed an increase cumulative fraction at the 99th, 95th, and 90th percentiles (0.07% ± 0.03% decade−1, 0.15% ± 0.03% decade−1, and 0.17% ± 0.03% decade−1, respectively) from 1979 to 2015, but did not show an obvious trend at the daily scale. These tendencies were underestimated at the hourly scale but overestimated at the daily scale in ERA5. The results helped us to characterize the temporal distributions and variations of precipitation, and to deepen our understanding of hydrological processes and reanalyses performance.

Nodal modulation of M2 and N2 tides along the Norwegian coast

It is crucial to assess the nodal modulation for tides in high-latitude coast areas within the context of global warming. In this paper, five stations (Maloy, Rorvik, Andenes, Vardo, and Honningsvag) along the Norwegian coast are selected to analyze the nodal modulation using the S_TIDE toolbox, which is developed from the enhanced harmonic analysis method. Three criterions are proposed to determine the optimal number of independent points (IPs), a parameter in S_TIDE toolbox, and the decision steps are elaborated in detail. The optimal number of IPs is evaluated by comparing the primary and the hindcasts tidal amplitudes. The amplitudes of 18.61-year cycle and 4.42-year cycle show noticeable temporal and spatial variations, which can be attributed to the changes of sea levels, local topography, and the active and robust mesoscale activity in the Norwegian Sea. Moreover, the temporal and spatial variations in nodal modulation are quantitatively demonstrated at the Rorvik and Vardo stations, highlighting the importance of nodal modulation in assessing tides over interdecadal periods.

Research on the Agglomeration and Spatiotemporal Development of China's Green High-Tech Industries

Accurate understanding of the spatial and temporal development of the agglomeration of green high-tech industries holds significant importance for the scientific formulation of policies promoting industrial innovation and development. This has attracted increasing attention from scholars. Utilizing nearly a decade's worth of statistical data on China's green high-tech industries, this paper employs methods from spatial geography and other disciplines to analyze the temporal and spatial variations, as well as the agglomeration characteristics of these industries in China. A comprehensive analysis of the development levels of China's high-tech industries and their sub-sectors is conducted through calculations of spatial Gini coefficients, industrial concentration ratios, location quotients, and coefficients of variation.Results indicate that the four key indicators of the development of China's major high-tech industries, namely the number of enterprises, employment figures, operating income, and profits, exhibit linear growth trends. Overall, the agglomeration level of the industry shows a fluctuating downward trend. The regional agglomeration level follows a gradient distribution trend of "eastern region - central region - western region - northeastern region," with a decreasing concentration trend. Regional disparities in the agglomeration level of industries evolve over time, with an increasing concentration in the western and central regions, and a decreasing concentration in the eastern and northeastern regions. From a provincial perspective, Guangdong and Jiangsu provinces stand out with significantly higher levels of development in high-tech industries.Furthermore, distinct differences are observed in the development processes of four typical industries. The agglomeration levels, ranked from high to low, are as follows: computer and office equipment manufacturing, electronic information and communication equipment manufacturing, medical device manufacturing, and pharmaceutical manufacturing，the development of the four types of industries has undergone certain transfers and optimizations.

Deep-time temperature field simulation of hot dry rock: A deep learning method in both time and space dimensions

Hot dry rock has emerged as a crucial renewable resource with a potential to provide significant energy to China. Accurately simulating the temperature field of hot dry rocks in space is essential in estimating their reserves. Moreover, predicting the temperature change curve of hot dry rocks over time is crucial for research in the field. Although these tasks are interrelated and share a common ground of simulating the temperature of hot dry rocks, previous approaches have dealt with them separately, each being challenging. In this study, we propose a 3D U-shaped deep-time neural network that can extract the nonlinear relationship between the temperature field and rock strata parameters, thereby enabling the simulation of the temperature field of hot dry rocks in both space and time dimensions. The proposed neural network, which incorporates an attention mechanism and residual structure, can incorporate multiple rock parameters and geological structures, leading to more accurate simulation of the temperature field of hot dry rocks. To train the network, we constructed extensive datasets based on granite rock parameters and actual geological structures, with the temperature fields simulated by the finite element method serving as labels. The resulting trained network model can successfully emulate the complex hot dry rock model, reducing the workload by combining the two networks into one. Furthermore, the simulated temperature field allows the observation of the effect of different geological activities on hot dry rocks. Network simulation has shown notable advancements in efficiency compared to traditional finite element methods. Moreover, it offers the advantage of exploring the impact of distinct geological structures on geothermal reservoirs in a spatial context. Additionally, it allows for the observation of temporal variations in the temperature distribution across different geological strata during different time periods. Thereby laying the groundwork for further prediction and exploitation of hot dry rock geothermal reserves.

Measured leaf dark respiratory CO2 -release is not controlled by stomatal conductance.

Leaf dark respiratory CO2 -release (RD ) is, according to some literature, dependent on the rate of leaf transpiration. If this is true, then at a given vapor pressure deficit, the leaf stomatal conductance (gs ) will be expected to be a controlling factor of measured RD at any given time. We artificially lowered leaf gs by applying abscisic acid (ABA). Although leaf RD generally covaried temporally with gs , artificially lowering gs by applying ABA does not affect the measured leaf RD . These results indicate that observed diel fluctuations in gs are not directly influencing the measured leaf RD , thereby simplifying both future studies and the interpretation of past studies of the underlying environmental- and physiological drivers of temporal variation in leaf RD .

Evaluating dynamics in human‐bear interactions within social‐ecological system

Abstract Over the past few decades, social‐ecological transformations have contributed to the restoration of numerous carnivores, presenting new challenges for human‐wildlife coexistence. Evaluating and understanding human‐wildlife interaction dynamics, particularly for conflict‐prone species, is crucial for conservation and management strategies. Our study focuses on Asiatic Black Bear (Ursus thibetanus) in Western Yunnan, China, a region with favourable bear conditions and significant environmental changes due to conservation policies. This study employed a multifaceted approach to gather data on bear occurrences and human‐bear conflicts. Utilising species distribution models, we predicted the potential bear habitat in 2000 and 2020. We proposed a methodology integrating bear habitat with human footprint to delineate the human‐bear interface. Additionally, we evaluated the spatial–temporal variations in bear habitat and human‐bear interface between 2000 and 2020. Semi‐structured interviews were conducted to capture the perceptions of local communities regarding the bear population, human‐bear interactions and conservation policies. Our findings indicated that the potential habitats of bears were strongly influenced by forest cover. We noticed improvements in bear habitat from 2000 to 2020, notwithstanding localised losses in specific areas. Most respondents attributed the increasing bear population to forest restoration and reduced human impact during this period. Notably, a significant proportion of respondents supported the conservation policies, previously participating in their formulation and implementation. Our analysis further revealed increases in the human‐bear interface and human‐bear interactions from 2000 to 2020. Remarkably, these changes did not significantly diminish local communities' endorsement of conservation policies. This resilience can be attributed to the net positive impacts of conservation policies on the livelihoods of these communities. Our study highlights the complexity and dynamic nature of human‐wildlife interactions within the social‐ecological system and provides new insights to facilitate sustainable conservation and management measures. Read the free Plain Language Summary for this article on the Journal blog.

Urban Complexity and the Dynamic Evolution of Urban Land Functions in Yiwu City: A Micro-Analysis with Multi-Source Big Data

The diversification of business forms leads to functional and spatial complexity in cities. The efficient determination of the complexity of an urban system is the basis for the scientific monitoring of the multi-functional aggregation within cities. Previous studies on the urban spatial structure were limited by the difficulty of collecting micro-data and the high time cost, and they focused on the macro-spatial structure, lacking fine-grained investigations of the micro-spatial structure. Additionally, high-resolution remote sensing images, which mainly rely on the textural characteristics of the spectrum of ground objects, cannot detect the social and economic functions of ground objects. Thus, it is difficult to meet the actual needs of urban planning and management. The purpose of this paper is to automatically identify the spatial heterogeneity and temporal variation of urban land use functions in the context of complex urban systems. The TF-IDF (term frequency–inverse document frequency) algorithm, a machine learning classification algorithm, and other methods are applied to identify the urban functions and distribution characteristics of the main urban area based on the POI (point of interest) data and urban form data. The results show the following: (1) From 2012 to 2022, all types of land use in Yiwu city grew at different rates, with logistics and warehousing space growing the fastest, which is in line with Yiwu’s goal of building a national logistics center for trade and services. (2) The residential area has a spatial structure with a dense central circle and a scattered periphery extending from northeast to southwest and from east to west. (3) The commercial service sector shows clear spatial differentiation between the core and the periphery. The commercial functional areas of Niansanli, Houzhai, and Chengxi, where the number of commercial POIs is relatively small, are located at the intersection of the administrative subdistricts near the city center, indicating that the commercial economic activities of the downtown subdistrict have a certain spillover effect on adjacent subdistricts. (4) The public facilities of each subdistrict are generally located in the core of each subdistrict, which ensures better convenience and accessibility. (5) Industrial land with a large total area that is scattered and mixed with urban residential land gradually tends to be centralized, forming an industrial belt around the city. This study comprehensively considers the aggregation relationship between urban buildings and land use and improves the accuracy of land identification and functional zoning.

Resilience of Personalized Ventilation in Maintaining Acceptable Breathable Air Quality When Combined with Mixing Ventilation Subject to External Shocks

This work evaluates the ventilation resilience of the combined personalized ventilation (PV)-mixing ventilation (MV) system when implemented in a typical office space. This resilience is first evaluated by monitoring the ability of the PV devices when designed at different supply flow rates to maintain acceptable levels of CO2 at the occupant’s breathing zone when the MV system is subjected to a shock. The shock considers a malfunction of the MV system for periods of 3 h and 6 h, and at shutoff percentages of MV fan flow of 100% and 50%. This is followed by evaluating the resilience of the MV system when the PV air handling unit is shutoff for short periods. The following three aspects of resilience were calculated: the absorptivity, the recovery, and the resilience effectiveness. To monitor the CO2 temporal variation at the breathing zone, a computational fluid dynamic model was developed and validated experimentally. It was found that the resilience effectiveness varied between 0.61 (100% MV shutoff for 6 h and PV at 4 L/s) and 1 (50% MV shutoff for 3 h and PV at 13 L/s). Additionally, CO2 build-up and recovery took minutes during MV malfunctions and seconds during PV malfunctions.

Seasonality and longer-term development generate temporal dynamics in the Populus microbiome.

Temporal variation in community composition is central to our understanding of the assembly and functioning of microbial communities, yet the controls over temporal dynamics for microbiomes of long-lived plants, such as trees, remain unclear. Temporal variation in tree microbiomes could arise primarily from seasonal (i.e., intra-annual) fluctuations in community composition or from longer-term changes across years as host plants age. To test these alternatives, we experimentally isolated temporal variation in plant microbiome composition using a common garden and clonally propagated plants, and we used amplicon sequencing to characterize bacterial/archaeal and fungal communities in the leaf endosphere, root endosphere, and rhizosphere of two Populus spp. over four seasons across two consecutive years. Microbial community composition differed among seasons and years (which accounted for up to 21% of the variation in microbial community composition) and was correlated with seasonal dissimilarity in climatic conditions. However, microbial community dissimilarity was also positively correlated with time, reflecting longer-term compositional shifts as host trees aged. Together, our findings demonstrate that temporal patterns in tree microbiomes arise from both seasonal fluctuations and longer-term changes, which interact to generate unique seasonal patterns each year. In addition to shedding light on two important controls over the assembly of plant microbiomes, our results also suggest future studies of tree microbiomes should account for background temporal dynamics when testing the drivers of spatial patterns in microbial community composition and temporal responses of plant microbiomes to environmental change.IMPORTANCEMicrobiomes are integral to the health of host plants, but we have a limited understanding of the factors that control how the composition of plant microbiomes changes over time. Especially little is known about the microbiome of long-lived trees, relative to annual and non-woody plants. We tested how tree microbiomes changed between seasons and years in poplar (genus Populus), which are widespread and ecologically important tree species that also serve as important biofuel feedstocks. We found the composition of bacterial, archaeal, and fungal communities differed among seasons, but these seasonal differences depended on year. This dependence was driven by longer-term changes in microbial composition as host trees developed across consecutive years. Our findings suggest that temporal variation in tree microbiomes is driven by both seasonal fluctuations and longer-term (i.e., multiyear) development.