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Tree stems exchange greenhouse gases with the atmosphere but the magnitude, variability and drivers of these fluxes remain poorly understood. Here, we report stem fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) in a boreal riparian forest, and investigate their spatiotemporal variability and ecosystem level importance. For two years, we measured CO2 and CH4 fluxes on a monthly basis in 14 spruces (Picea abies) and 14 birches (Betula pendula) growing near a headwater stream affected by historic ditching. We also measured N2O fluxes on three occasions. All tree stems were net emitters of CO2 and CH4, while N2O fluxes were around zero. CO2 fluxes correlated strongly with air temperature and peaked in summer. CH4 fluxes correlated modestly with air temperature and solar radiation and peaked in late winter and summer. Trees with larger stem diameter emitted more CO2 and less CH4 and trees closer to the stream emitted more CO2 and CH4. The CO2 and CH4 fluxes did not differ between spruce and birch, but correlations of CO2 fluxes with stem diameter and distance to stream differed between the tree species. The absence of vertical trends in CO2 and CH4 fluxes along the stems and their low correlation with groundwater levels and soil CO2 and CH4 partial pressures suggest tree internal production as the primary source of stem emissions. At the ecosystem level, the stem CO2, CH4 and N2O emissions represented 52 ±â€¯16 % of the forest floor CO2 emissions and 3 ±â€¯1 % and 11 ±â€¯40 % of the forest floor CH4 and N2O uptake, respectively, during the snow-free period (median ±â€¯SE). The six month snow-cover period contributed 11 ±â€¯45 % and 40 ±â€¯29 % to annual stem CO2 and CH4 emissions, respectively. Overall, the stem gas fluxes were more typical for upland rather than wetland ecosystems likely due to historic ditching and subsequent groundwater level decrease.

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The study characterized the temporal and spatial variability in greenhouse gas (GHG) fluxes (CO2, CH4, and N2O) between December 2020 and November 2021 and their regulating drivers in the subtropical wetland of the Indian Himalayan foothill. Five distinct habitats (M1-sloppy surface at swamp forest, M2-plain surface at swamp forest, M3-swamp surface with small grasses, M4-marshy land with dense macrophytes, and M5-marshy land with sparse macrophytes) were studied. We conducted in situ measurements of GHG fluxes, microclimate (AT, ST, and SMC(v/v)), and soil properties (pH, EC, N, P, K, and SOC) in triplicates in all the habitat types. Across the habitats, CO2, CH4, and N2O fluxes ranged from 125 to 536 mg m-2 h-1, 0.32 to 28.4 mg m-2 h-1, and 0.16 to 3.14 mg m-2 h-1, respectively. The habitats (M3 and M5) exhibited higher GHG fluxes than the others. The CH4 flux followed the summer > autumn > spring > winter hierarchy. However, CO2 and N2O fluxes followed the summer > spring > autumn > winter. CO2 fluxes were primarily governed by ST and SOC. However, CH4 and N2O fluxes were mainly regulated by ST and SMC(v/v) across the habitats. In the case of N2O fluxes, soil P and EC also played a crucial role across the habitats. AT was a universal driver controlling all GHG fluxes across the habitats. The results emphasize that long-term GHG flux monitoring in sub-tropical Himalayan Wetlands has become imperative to accurately predict the near-future GHG fluxes and their changing nature with the ongoing climate change.

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Urbanization, agriculture, and climate change affect water quality and water hyacinth growth in lakes. This study examines the spatiotemporal variability of lake surface water temperature, turbidity, and chlorophyll-a (Chl-a) and their association with water hyacinth biomass in Lake Tana. MODIS Land/ Lake surface water temperature (LSWT), Sentinel 2 MSI Imagery, and in-situ water quality data were used. Validation results revealed strong positive correlations between MODIS LSWT and on-site measured water temperature (R = 0.90), in-situ turbidity and normalized difference turbidity index (NDTI) (R = 0.92), and in-situ Chl-a and normalized difference chlorophyll index (NDCI) (R = 0.84). LSWT trends varied across the lake, with increasing trends in the northeastern, northwestern, and southwestern regions and decreasing trends in the western, southern, and central areas (2001-2022). The spatial average LSWT trend decreased significantly in pre-rainy (0.01 ℃/year), rainy (0.02 ℃/year), and post-rainy seasons (0.01℃/year) but increased non-significantly in the dry season (0.00 ℃/year) (2001-2022, P < 0.05). Spatial average turbidity decreased significantly in all seasons, except in the pre-rainy season (2016-2022). Likewise, spatial average Chl-a decreased significantly in pre-rainy and rainy seasons, whereas it showed a non-significant increasing trend in the dry and post-rainy seasons (2016-2022). Water hyacinth biomass was positively correlated with LSWT (R = 0.18) but negatively with turbidity (R = -0.33) and Chl-a (R = -0.35). High spatiotemporal variability was observed in LSWT, turbidity, and Chl-a, along with overall decreasing trends. The findings suggest integrated management strategies to balance water hyacinth eradication and its role in water purification. The results will be vital in decision support systems and preparing strategic plans for sustainable water resource management, environmental protection, and pollution prevention.

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Although many studies have discussed the impact of Europe's air quality, very limited research focused on the detailed phenomenology of ambient trace elements (TEs) in PM10 in urban atmosphere. This study compiled long-term (2013-2022) measurements of speciation of ambient urban PM10 from 55 sites of 7 countries (Switzerland, Spain, France, Greece, Italy, Portugal, UK), aiming to elucidate the phenomenology of 20 TEs in PM10 in urban Europe. The monitoring sites comprised urban background (UB, n = 26), traffic (TR, n = 10), industrial (IN, n = 5), suburban background (SUB, n = 7), and rural background (RB, n = 7) types. The sampling campaigns were conducted using standardized protocols to ensure data comparability. In each country, PM10 samples were collected over a fixed period using high-volume air samplers. The analysis encompassed the spatio-temporal distribution of TEs, and relationships between TEs at each site. Results indicated an annual average for the sum of 20 TEs of 90 ± 65 ng/m3, with TR and IN sites exhibiting the highest concentrations (130 ± 66 and 131 ± 80 ng/m3, respectively). Seasonal variability in TEs concentrations, influenced by emission sources and meteorology, revealed significant differences (p < 0.05) across all monitoring sites. Estimation of TE concentrations highlighted distinct ratios between non-carcinogenic and carcinogenic metals, with Zn (40 ± 49 ng/m3), Ti (21 ± 29 ng/m3), and Cu (23 ± 35 ng/m3) dominating non-carcinogenic TEs, while Cr (5 ± 7 ng/m3), and Ni (2 ± 6 ng/m3) were prominent among carcinogenic ones. Correlations between TEs across diverse locations and seasons varied, in agreement with differences in emission sources and meteorological conditions. This study provides valuable insights into TEs in pan-European urban atmosphere, contributing to a comprehensive dataset for future environmental protection policies.

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Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed 116 TLE patients compared with 51 healthy controls. Employing microstate analysis, we assessed brain dynamic disparities between TLE patients and healthy controls, as well as between drug-resistant epilepsy (DRE) and drug-sensitive epilepsy (DSE) patients. We constructed dynamic functional connectivity networks based on microstates and quantified their spatial and temporal variability. Utilizing these brain network features, we developed machine learning models to discriminate between TLE patients and healthy controls, and between DRE and DSE patients. Temporal dynamics in TLE patients exhibited significant acceleration compared to healthy controls, along with heightened synchronization and instability in brain networks. Moreover, DRE patients displayed notably lower spatial variability in certain parts of microstate B, E and F dynamic functional connectivity networks, while temporal variability in certain parts of microstate E and G dynamic functional connectivity networks was markedly higher in DRE patients compared to DSE patients. The machine learning model based on these spatiotemporal metrics effectively differentiated TLE patients from healthy controls and discerned DRE from DSE patients. The accelerated microstate dynamics and disrupted microstate sequences observed in TLE patients mirror highly unstable intrinsic brain dynamics, potentially underlying abnormal discharges. Additionally, the presence of highly synchronized and unstable activities in brain networks of DRE patients signifies the establishment of stable epileptogenic networks, contributing to the poor responsiveness to antiseizure medications. The model based on spatiotemporal metrics demonstrated robust predictive performance, accurately distinguishing both TLE patients from healthy controls and DRE patients from DSE patients.

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The water quality evolution of surface and groundwater caused by mining activities and mine drainage is a grave public concern worldwide. To explore the effect of mine drainage on sulfate evolution, a multi-aquifer system in a typical coal mine in Northwest China was investigated using multi-isotopes (δ34SSO4, δ18OSO4, δD, and δ18Owater) and Positive Matrix Factorization (PMF) model. Before mining, the Jurassic aquifer was dominated by gypsum dissolution, accompanied by cation exchange and bacterial sulfate reduction, and the phreatic aquifers and surface water were dominated by carbonate dissolution. Significant increase in sulfate in phreatic aquifers due to mine drainage during the early stages of coal mining. However, in contrast to common mining activities that result in sulfate contamination from pyrite oxidation, mine drainage in this mining area resulted in accelerated groundwater flow and enhanced hydraulic connections between the phreatic and confined aquifers. Dilution caused by the altered groundwater flow system controlled the evolution of sulphate, leading to different degrees of sulfate decrease in all aquifers and surface water. As the hydrogeochemical characteristic of Jurassic aquifer evolved toward phreatic aquifer, this factor should be considered to avoid misjudgment in determining the source of mine water intrusion. The study reveals the hydrogeochemical evolution induced by mine drainage, which could benefit to the management of groundwater resources in mining areas.

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This study investigated the spatiotemporal distribution of the phytoplankton in the coral habitat of Dongshan Bay (China), along with critical factors affecting the distribution, during June, August, and December 2022. Phytoplankton abundance in Dongshan Bay exhibited considerably temporal variation, peaking in June 2022, gradually decreasing thereafter, and reaching its lowest point in December 2022. The abundance of bottom-layer phytoplankton consistently exceeded that of the surface layer throughout all seasons. The average phytoplankton abundance in the coral habitat of Dongshan Bay was lower than that in non-coral habitat areas. Fluctuations in the Zhangjiang River and coastal upwelling influenced the diversity and community structure of the phytoplankton. Critical factors causing spatiotemporal variability in phytoplankton community structure included nutrient concentrations and seawater temperature. Nutrients played key roles in influencing various phytoplankton groups. Dominant diatom species, such as Thalassionema nitzschioides and Thalassiosira diporocyclus, were positively correlated with ammonia nitrogen, seawater salinity, coral cover, and the number of coral species present. In winter, Calanus sinicus exhibited a negative correlation with harmful algal bloom species. Additionally, it was found that both in the coral habitat and surrounding open sea, currents, nutrients, and zooplankton may play crucial roles in determining the spatiotemporal variability in the phytoplankton community structure.

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Climate refugia are areas where species can persist through climate change with little to no movement. Among the factors associated with climate refugia are high spatial heterogeneity, such that there is only a short distance between current and future optimal climates, as well as biotic or abiotic environmental factors that buffer against variability in time. However, these types of climate refugia may be declining due to anthropogenic homogenization of environments and degradation of environmental buffers. To quantify the potential for restoration of refugia-like environmental conditions to increase population persistence under climate change, we simulated a population's capacity to track their temperature over space and time given different levels of spatial and temporal variability in temperature. To determine how species traits affected the efficacy of restoring heterogeneity, we explored an array of values for species' dispersal ability, thermal tolerance, and fecundity. We found that species were more likely to persist in environments with higher spatial heterogeneity and lower environmental stochasticity. When simulating a management action that increased the spatial heterogeneity of a previously homogenized environment, species were more likely to persist through climate change, and population sizes were generally higher, but there was little effect with mild temperature change. The benefits of heterogeneity restoration were greatest for species with limited dispersal ability. In contrast, species with longer dispersal but lower fecundity were more likely to benefit from a reduction in environmental stochasticity than an increase in spatial heterogeneity. Our results suggest that restoring environments to refugia-like conditions could promote species' persistence under the influence of climate change in addition to conservation strategies such as assisted migration, corridors, and increased protection.

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Asian over-land aerosols are complexities due to a mixture of anthropogenic air pollutants and natural dust. The accuracy of the aerosol optical thickness (AOT) retrieved from the satellite is crucial to their application in the aerosol data assimilation system. Fusion of AOTs with high spatiotemporal resolution from next-generation geostationary satellites such as Fengyun-4B (FY-4B) and Himawari-9, provides a new high-quality dataset capturing the aerosol spatiotemporal variability for data assimilation. This study develops a complete fusion algorithm to estimate the optimal AOT over-land in Asia from September 2022 to August 2023 at 10 km × 10 km resolution with high efficiency. The data fusion involves four steps: (1) investigating the spatiotemporal variability of FY-4B AOT within the past 1 h and 12 km radius calculation domain; (2) utilizing the aerosol spatiotemporal variability characteristics to estimate FY-4B pure and hourly merged AOTs; (3) performing bias corrections for FY-4B and Himwari-9 hourly merged AOT for different observation times and seasons considering pixel-level errors for each satellite; (4) fusing the bias-corrected FY-4B and Himawari-9 hourly merged AOT based on maximum-likelihood estimation (MLE) method. Compared to the original FY-4B AOT, validation with AERONET observation confirms that the root mean square error (RMSE) of hourly merged FY-4B AOT decreases by around 40.6 % and the correlation coefficient (CORR) increases by about 27.8 %. Compared to FY-4B and Himawari-9 merged AOT, the fused AOT significantly decreases (increases) RMSE (CORR) by around 24.7 % (7.3 %) and 20.2 % (5.6 %). In addition, fused AOT is double the number of single-sensor merged AOT. Fusion aerosol map accurately describes the spatial and temporal variations in Asian regions controlled by air pollution and dust storms. Further studies are required for other landscapes with different satellite combinations to promote the application in the data assimilation system.

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Uncovering the spatiotemporal features of ecosystem services (ESs) and their intricate interrelations in large lake basins can facilitate the development of scientific management measures for various ESs. Previous studies have focused less on watershed units and their historical dynamics, and the ecosystem service (ES) driving mechanisms remain unclear. Here, we focused on Hunan Province-the main coverage area of the Dongting Lake Basin (China's second largest freshwater lake), investigated the spatiotemporal characteristics of seven typical ESs and their interactions, identified the ecosystem service bundle (ESB) historical spatial patterns and revealed the socio-ecological driving mechanisms of these ES changes. Results showed that: (1) the spatial distribution of most ESs remained stable in the basin. Food production (FP), water yield (WY), soil conservation (SC) and net primary productivity (NPP) improved over time, whereas nitrogen retention (NR), habitat quality (HQ) and outdoor recreation (OR) declined; (2) tradeoffs were observed between food production and most ESs, whereas synergistic relationships between all ESs except food production. The tradeoff relationship between food production and water yield increased significantly over time, while the synergistic relationship between water yield and nitrogen retention decreased significantly; (3) five ecosystem service bundles were identified. And the transformation of soil conservation area into integrated ecological regulation area mainly occurred from 2000 to 2020, resulting in an increase in the function of ecological regulation services; (4) natural conditions such as precipitation, topography and vegetation, as well as socio-economic factors such as Gross Domestic Product (GDP) and population, were key factors affecting ESs. The interactions among most of these drivers can further elucidate the ES changes. Our results emphasize the need for a watershed-based assessment and a historical dynamic perspective in the sustainable management of ESs.

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Oil exploitation may pose adverse effects on marine ecosystems, but its impacts on surface carbonate dynamics remain unknown. In a carbonate system with low air-sea ∆pCO2, such as the South China Sea (SCS), human activities may affect the pCO2 distribution patterns and potentially alter CO2 sink or source at the surface. This study investigates the surface carbonate system in two oil fields, namely the Wenchang Oil Feld and Enping Oil Feld, located on the northwestern SCS (NWSCS) shelf. In Enping Oil Field, although there is a slight increase in surface pCO2 due to probable total alkalinity (TA) consumption from CaCO3 precipitation, strong biological production makes the plume water a strong CO2 sink. Similarly, the biological processes dominated the pCO2 variability in Wenchang Oil Feld, exhibiting high values in its central area. In NWSCS, the influence of shelf water was observed during both cruises. And the pCO2 drawdown caused by the decreased sea surface temperature (SST) and CO2 outgassing outweighed their increases via enhanced vertical mixing, leading to a pCO2 drawdown from September to October within this water mass. More importantly, there were no significant disparities observed in carbonate parameters at stations along transects with and without wells, and the observed parameter values in this study fell within the range reported previously on the nSCS shelf with similar controlling processes. Thus the impact of oil exploitation on carbonate dynamics is negligible, and the characteristics of the carbonate system in oil field are primarily governed by natural processes such as the mixing of plume water and basin water, CaCO3 precipitation and the changes in SST. The provided data establish a crucial baseline for detecting future alterations in carbonate chemistry within oil fields, and the rapid fluctuations in sea surface pCO2 highlight the need for higher spatiotemporal resolution observation.

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Agriculture is a main source of nitrous oxide (N2O) emissions. In agricultural systems, direct N2O emissions from nitrogen (N) addition to soils have been widely investigated, whereas indirect emissions from aquatic ecosystems such as ditches are poorly known, with insufficient data available to refine the IPCC emission factor. In this contribution, in situ N2O emissions from two ditch water‒air interfaces based on a diffusion model were investigated (almost once per month) from June 2021 to December 2022 in an intensive arable catchment with high N inputs and salt-affected conditions in the Qingtongxia Irrigation District, northwestern China. Our results implied that agricultural ditches (mean 148 µg N m-2 h-1) were significant sources for N2O emissions, and were approximately 2.1 times greater than those of the Yellow River directly connected to ditches. Agronomic management strategies increased N2O fluxes in summer, while precipitation events decreased N2O fluxes. Agronomic management strategies, including fertilization (294--540 kg N hm-2) and irrigation on farmland, resulted in enhanced diffuse N loads in drain water, whereas precipitation diluted the dissolved N2O concentration in ditches and accelerated the ditch flow rate, leading to changes in the residence time of N-containing substances in water. The spatial analysis showed that N2O fluxes (202-233 µg N m-2 h-1) in the headstream and upstream regions of ditches due to livestock and aquaculture pollution sources were relatively high compared to those in the midstream and downstream regions (100-114 µg N m-2 h-1). Furthermore, high available carbon (C) relative to N reduced N2O fluxes at low DOC:DIN ratio levels by inhibiting nitrification. Spatiotemporal variations in the N2O emission factor (EF5) across ditches with higher N resulted in lower EF5 and a large coefficient of variation (CV) range. EF5 was 0.0011 for the ditches in this region, while the EF5 (0.0025) currently adopted by the IPCC is relatively high. The EF5 variation was strongly controlled by the DOC:DIN ratio, TN, and NO3--N, while salinity was also a nonnegligible factor regulating the EF5 variation. The regression model incorporating NO3--N and the DOC:DIN ratio could greatly enhance the predictions of EF5 for agricultural ditches. Our study filled a key knowledge gap regarding EF5 from agricultural ditches in salt-affected farmland and offered a field investigation for refining the EF5 currently used by the IPCC.

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Understanding the long-term change trends of ozone-induced yield losses is crucial for formulating strategies to alleviate ozone damaging effects, aiming towards achieving the Zero Hunger Sustainable Development Goal. Despite a wealth of experimental research indicating that ozone's influence on agricultural production exhibits marked fluctuations and differs significantly across various geographical locations, previous studies using global statistical models often failed to capture this spatial-temporal variability, leading to uncertainties in ozone impact estimation. To address this issue, we conducted a comprehensive assessment of the spatial-temporal variability of ozone impacts on maize and soybean yields in the United States (1981-2021) using a geographically and temporally weighted regression (GTWR) model. Our results revealed that over the past four decades, ozone pollution has led to average yield losses of -3.5% for maize and -6.1% for soybean, translating into an annual economic loss of approximately $2.6 billion. Interestingly, despite an overall downward trend in ozone impacts on crop yields following the implementation of stringent ozone emission control measures in 1997, our study identified distinct peaks of abnormally high yield reduction rates in drought years. Significant spatial heterogeneity was detected in ozone impacts across the study area, with ozone damage hotspots located in the Southeast Region and the Mississippi River Basin for maize and soybean, respectively. Furthermore, we discovered that hydrothermal factors modulate crop responses to ozone, with maize showing an inverted U-shaped yield loss trend with temperature increases, while soybean demonstrated an upward trend. Both crops experienced amplified ozone-induced yield losses with rising precipitation. Overall, our study highlights the necessity of incorporating spatiotemporal variability into assessments of crop yield losses attributable to ozone pollution. The insights garnered from our findings can contribute to the formulation of region-specific pollutant emission policies, based on the distinct profiles of ozone-induced agricultural damage across different regions.

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Climate-sensitive ice-covered reservoirs are critical components of methane (CH4) release. However, the mechanisms that influence CH4 dynamics during ice-covered periods remain poorly studied. To investigate the effects of bubbles on CH4 dynamics, we conducted intensive field and incubation experiments in an ice-covered reservoir (ice growth, stability, and melt period) in Northeast China. We found that the mean dissolved CH4 concentrations in the ice (625.9 ± 2419.7 nmol L-1) and underlying water (1218.9 ± 2678.9 nmol L-1) were high, making them atmosphere CH4 sources. The visible bubble bands (bubble area) in the riverine zone and the vertical profile of the CH4 concentration in the ice reflect the distribution of trapped bubbles. The mean CH4 concentration in the ice of the bubble area (1674.8 ± 3926.8 nmol L-1) was 2 orders of magnitude higher than that of no-bubble area (53.7 ± 9.2 nmol L-1). Moreover, a large amount of CH4 accumulated under the ice in the bubble area. These findings suggest that bubbles determine the CH4 storage in ice and CH4 accumulation in the underlying water. Ice growth increases CH4 storage in ice and the underlying water because of the entrapment and re-dissolution of CH4 bubbles. However, ice melting releases the CH4 accumulated in the ice and underlying water. A comparison of the field and incubation experiments indicated that the deep-water environment of the reservoir had a CH4 burial effect. Stepwise regression analysis revealed that higher sediment organic matter content, median particle size, and porosity increased the production and release of CH4 bubbles, trapping more CH4 bubbles in ice. Overall, this study improves the mechanistic understanding of CH4 dynamics and predictability of CH4 emissions during ice-covered periods.

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Ice cover restructures the distribution of substances in ice and underlying water and poses non-negligible environmental effects. This study aimed to clarify the spatiotemporal variability and environmental effects of methane (CH4), nitrous oxide (N2O), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), and dissolved inorganic carbon (DIC) in ice and water columns during different ice-covered periods. We surveyed the ice-growth, ice-stability, and ice-melt periods in an ice-covered reservoir located in Northeast China. The results showed that underlying water (CH4: 1218.9 ± 2678.9 nmol L-1 and N2O: 19.3 ± 7.3 nmol L-1) and ice (CH4: 535.2 ± 2373.1 nmol L-1 and N2O: 9.9 ± 1.5 nmol L-1) were sources of atmospheric greenhouse gases. N2O concentrations were the highest in the bottom water of the reservoir while CH4 accumulated the most below the ice in the riverine zone. These can be attributed to differences in the solubilities and relative molecular masses of the two gases. Higher concentrations of N2O, TN, TP, DOC, and DIC were recorded in the underlying water than those in the ice due to the preferential redistribution of these substances in the aqueous phase during ice formation. Additionally, we distinguished between bubble and no-bubble areas in the riverine zone and found that the higher CH4 concentrations in the underlying water than those in the ice were due to CH4 bubbles. In addition, we reviewed various substances in ice-water systems and found that the substances in ice-water systems can be divided into solute exclusion and particle entrapment, which are attributed to differences between dissolved and particulate states. These findings are important for a comprehensive understanding of substances dynamics during ice-covered periods.

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Freshwater river systems are commonly defined as the main transport paths of microplastics (MP) from land into the seas. A shift in research interest from oceans to rivers can be observed, as a large number of i) case studies, ii) review papers and iii) experimental studies in this field have been published recently. Still, studies often lack an in-depth consideration of quantification, as units are mostly based on item numbers. Spatiotemporal aspects are often neglected. Transport paths linking MP sources and sinks in the environment are insufficiently understood and only recently the awareness increased that sustainable management of the MP pollution cannot be addressed without a sound knowledge of water- and sediment-driven MP transport. Within this review paper, we therefore i) reviewed 92 MP case-studies, with a special focus on spatiotemporal aspects and ii) gathered and compared global load-estimation data from these studies. We then outlined the key processes determining MP movement in rivers on the basis of existing laboratory experiments and theoretical approaches. A procedure to effectively compare units of MP in the water column and in riverine sediments was developed on the basis of i) an extensive MP-dataset in German waterways and ii) suspended sediment concentrations (SSC) of nearest monitoring stations of the German water and shipping authority. Our analysis indicates that relating MP in water samples to SSC reduces the often stated large difference between MP concentrations in the water column and bed sediments and therefore relativizes the importance of river beds as a major "MP sink". As for a quantification of MP fluxes, the use of MP masses as unit is crucial, we applied an approach to convert MP items to masses with the help of i) a power-law distribution of MP-particle size, triangular distributions of ii) form-ratios and iii) polymer densities. An evaluation with an own, extensive dataset of MP-particles showed reasonable results. Therefore, we translated global load data from item numbers to mass values for further analysis. Values were within a reasonable range, especially when considering the respective catchment size of each river at the sampling site.

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Spatiotemporal variability during gait is linked to fall risk and could be monitored using wearable sensors. Although many users prefer wrist-worn sensors, most applications position at other sites. We developed and evaluated an application using a consumer-grade smartwatch inertial measurement unit (IMU). Young adults (n = 41) completed seven-minute conditions of treadmill gait at three speeds. Single-stride outcomes (stride time, length, width, and speed) and spatiotemporal variability (coefficient of variation of each single-stride outcome) were recorded using an optoelectronic system, while 232 single- and multi-stride IMU metrics were recorded using an Apple Watch Series 5. These metrics were input to train linear, ridge, support vector machine (SVM), random forest, and extreme gradient boosting (xGB) models of each spatiotemporal outcome. We conducted Model × Condition ANOVAs to explore model sensitivity to speed-related responses. xGB models were best for single-stride outcomes [relative mean absolute error (% error): 7-11%; intraclass correlation coefficient (ICC2,1) 0.60-0.86], and SVM models were best for spatiotemporal variability (% error: 18-22%; ICC2,1 = 0.47-0.64). Spatiotemporal changes with speed were captured by these models (Condition: p < 0.00625). Results support the feasibility of monitoring single-stride and multi-stride spatiotemporal parameters using a smartwatch IMU and machine learning.

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Well-mixed zone models are often employed to compute indoor air quality and occupant exposures. While effective, a potential downside to assuming instantaneous, perfect mixing is underpredicting exposures to high intermittent concentrations within a room. When such cases are of concern, more spatially resolved models, like computational-fluid dynamics methods, are used for some or all of the zones. But, these models have higher computational costs and require more input information. A preferred compromise would be to continue with a multi-zone modeling approach for all rooms, but with a better assessment of the spatial variability within a room. To do so, we present a quantitative method for estimating a room's spatiotemporal variability, based on influential room parameters. Our proposed method disaggregates variability into the variability in a room's average concentration, and the spatial variability within the room relative to that average. This enables a detailed assessment of how variability in particular room parameters impacts the uncertain occupant exposures. To demonstrate the utility of this method, we simulate contaminant dispersion for a variety of possible source locations. We compute breathing-zone exposure during the releasing (source is active) and decaying (source is removed) periods. Using CFD methods, we found after a 30 minutes release the average standard deviation in the spatial distribution of exposure was approximately 28% of the source average exposure, whereas variability in the different average exposures was lower, only 10% of the total average. We also find that although uncertainty in the source location leads to variability in the average magnitude of transient exposure, it does not have a particularly large influence on the spatial distribution during the decaying period, or on the average contaminant removal rate. By systematically characterizing a room's average concentration, its variability, and the spatial variability within the room important insights can be gained as to how much uncertainty is introduced into occupant exposure predictions by assuming a uniform in-room contaminant concentration. We discuss how the results of these characterizations can improve our understanding of the uncertainty in occupant exposures relative to well-mixed models.

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Schizophrenia is a self-disorder characterized by disrupted brain dynamics and architectures of multiple molecules. This study aims to explore spatiotemporal dynamics and its association with psychiatric symptoms. Resting-state functional magnetic resonance imaging data were collected from 98 patients with schizophrenia. Brain dynamics included the temporal and spatial variations in functional connectivity density and association with symptom scores were evaluated. Moreover, the spatial association between dynamics and receptors/transporters according to prior molecular imaging in healthy subjects was examined. Patients demonstrated decreased temporal variation and increased spatial variation in perceptual and attentional systems. However, increased temporal variation and decreased spatial variation were revealed in higher order networks and subcortical networks in patients. Specifically, spatial variation in perceptual and attentional systems was associated with symptom severity. Moreover, case-control differences were associated with dopamine, serotonin and mu-opioid receptor densities, serotonin reuptake transporter density, dopamine transporter density, and dopamine synthesis capacity. Therefore, this study implicates the abnormal dynamic interactions between the perceptual system and cortical core networks; in addition, the subcortical regions play a role in the dynamic interaction among the cortical regions in schizophrenia. These convergent findings support the importance of brain dynamics and emphasize the contribution of primary information processing to the pathological mechanism underlying schizophrenia.

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Afforestation as an effective measure for wind and sand control has achieved remarkable results in northern China, and has also greatly changed the land use and vegetation characteristics of the region. It is important to study the spatial and temporal dynamics of soil water content (SWC) in different afforestation years and its temporal stability to understand the dynamic characteristics of SWC during afforestation. In order to reveal the spatiotemporal dynamic characteristics of SWC in desert area Haloxylon ammodendron (HA)plantations, in this study, five restorative-aged HA plantations in desert areas were selected and their SWC was measured in stratified layers for the 0-400 cm soil profile; we also analyzed the spatiotemporal dynamics and temporal stability of the SWC. The results showed that the SWC of HA plantations decreased with the increase in planting age in the measurement period, and the SWC of deep layers increased by more than that of shallow layers with planting age. Spearman's rank correlation coefficients for SWC of 0-400 cm in both 5- and 11-year-old HA plantations reached above 0.8 and were highly significantly correlated; the temporal stability of SWC tends to increase as the depth of the soil layer deepens. In contrast, the temporal stability of SWC in deeper layers (200-400 cm) of 22-, 34- and 46-year-old stands showed a decreasing trend with depth. Based on the relative difference analysis, representative sampling points can be selected to monitor the regional average SWC, but for older HA plantations, the uncertainty factor of stand age should be considered in the regional moisture simulation. This study verified that it is feasible to simulate large-scale SWC in fewer observations for HA plantations younger than 11 years old, while large errors exist for older stands, especially for deeper soils. This will help soil moisture management in HA plantations in arid desert areas.