Spatiotemporal Distribution Research Articles

Bridging spatiotemporal feature gap for video salient object detection

The mutual transfer of spatiotemporal features is the main challenge for the two-stream video salient object detection. Current methods adopt the spatiotemporal feature interaction to achieve it. However, these methods still have two issues: modal feature gap and layer feature gap. To address these, we propose a Bridging Spatiotemporal feature Gap Network (BSGNet) with a global correspondence interaction and gate filtering (GCGF) module, a global-local distribution consistency (GLDC) module, and a modality-layer feature fusion framework (MLFF). Compared with previous works, BSGNet not only explores more effective interaction by GCGF, but also bridges modality and layer feature gaps by GLDC and MLFF. Firstly, GCGF achieves the spatiotemporal feature interaction by modeling intra-modal and inter-modal global correspondences. Besides, GCGF employs a gate mechanism to control the proportion of message transfer between appearance and motion information, which characterizes the contribution provided by spatiotemporal features. Secondly, at both global and local levels, GLDC pushes the spatiotemporal feature distribution between same scenes, and pulls the spatiotemporal feature distribution between different scenes. This can enhance the distribution consistency to align spatiotemporal features and bridge modal feature gap. Finally, MLFF designs an inter-modal and inter-layer feature fusion framework to bridge the layer feature gap brought by the different modalities and different receptive fields. Extensive experiments on five benchmarks reveal that our BSGNet outperforms state-of-the-arts.

Resolving biology’s dark matter: species richness, spatiotemporal distribution, and community composition of a dark taxon

BackgroundZoology’s dark matter comprises hyperdiverse, poorly known taxa that are numerically dominant but largely unstudied, even in temperate regions where charismatic taxa are well understood. Dark taxa are everywhere, but high diversity, abundance, and small size have historically stymied their study. We demonstrate how entomological dark matter can be elucidated using high-throughput DNA barcoding (“megabarcoding”). We reveal the high abundance and diversity of scuttle flies (Diptera: Phoridae) in Sweden using 31,800 specimens from 37 sites across four seasonal periods. We investigate the number of scuttle fly species in Sweden and the environmental factors driving community changes across time and space.ResultsSwedish scuttle fly diversity is much higher than previously known, with 549 putative species detected, compared to 374 previously recorded species. Hierarchical Modelling of Species Communities reveals that scuttle fly communities are highly structured by latitude and strongly driven by climatic factors. Large dissimilarities between sites and seasons are driven by turnover rather than nestedness. Climate change is predicted to significantly affect the 47% of species that show significant responses to mean annual temperature. Results were robust regardless of whether haplotype diversity or species-proxies were used as response variables. Additionally, species-level models of common taxa adequately predict overall species richness.ConclusionsUnderstanding the bulk of the diversity around us is imperative during an era of biodiversity change. We show that dark insect taxa can be efficiently characterised and surveyed with megabarcoding. Undersampling of rare taxa and choice of operational taxonomic units do not alter the main ecological inferences, making it an opportune time to tackle zoology’s dark matter.

The effects of climate and population on human land use patterns in Europe from 22ka to 9ka ago

Between 22ka and 9ka ago, after the Last Glacial Maximum (LGM) and during the transition to the Holocene, mobile hunter-gatherer populations, differentiated by their stone tool assemblages, periodically dispersed and contracted across Europe. It is well understood that climate played an important role in human distributions and population sizes during the post-LGM Late Pleistocene and Early Holocene, but the question remains as to whether increasing population sizes drove early human dispersal. Here, using a spatiotemporal species distribution model to infer the fundamental and potential human climatic niche space of Late Upper Paleolithic, Final Paleolithic and Early Mesolithic Europe, and hypotheses derived from the Ideal Distribution Model, we test i) how changes in climate affected the size and extent of the projected potential human niche space, ii) for effects of changes in size of projected potential niche on regional human population sizes, iii) whether increasing human population sizes drove human dispersal into less climatically suitable habitats, and iv) whether populations associated with different high-order material culture groupings (macro-level technocomplexes) occupied different climatic spaces. We find that changes in climate correlate strongly with the size and extent of the projected potential human niche space, that increases in the projected human niche space correlate with increases in human population, that human population size is just beginning to become large enough to influence land use and dispersal patterns at the Pleistocene-Holocene transition, and that archaeological technocultural entities overlap in their fundamental climatic niche space. This overlap implies that changing tools forms cannot readily been seen as reflecting adaptations to changing climates.

Epidemiological characteristics, diagnosis and treatment effect of rifampicin-resistant pulmonary tuberculosis (RR-PTB) in Guizhou Province

BackgroundRifampicin-resistant pulmonary tuberculosis (RR-PTB) presents a significant threat to global public health security. China bears a substantial burden of RR-PTB cases globally, with Guizhou Province experiencing particularly alarming trends, marked by a continual increase in patient numbers. Understanding the population characteristics and treatment modalities for RR-PTB is crucial for mitigating morbidity and mortality associated with this disease.MethodsWe gathered epidemiological, diagnostic, and treatment data of all RR-PTB cases recorded in Guizhou Province from January 1, 2017 to December 31, 2023. Utilizing composition ratios as the analytical metric, we employed Chi-square tests to examine the spatiotemporal distribution patterns of RR-PTB patients and the evolving trends among different patient classifications over the study period.ResultsIn our study, 3396 cases of RR-PTB were analyzed, with an average age of 45 years. The number of RR-PTB patients rose significantly from 176 in 2017 to 960 in 2023, peaking notably among individuals aged 23–28 and 44–54, with a rising proportion in the 51–80 age group (P < 0.001). Since 2021, there has been a notable increase in the proportion of female patients. While individuals of Han ethnic group comprised the largest group, their proportion decreased over time (P < 0.001). Conversely, the Miao ethnicity showed an increasing trend (P < 0.05). The majority of patients were farmers, with their proportion showing an upward trajectory (P < 0.001), while students represented 4.33% of the cases. Geographically, most patients were registered in Guiyang and Zunyi, with a declining trend (P < 0.001), yet household addresses primarily clustered in Bijie, Tongren, and Zunyi. The proportion of floating population patients gradually decreased, alongside an increase in newly treated patients and those without prior anti-tuberculosis therapy. Additionally, there was a notable rise in molecular biological diagnostic drug sensitivity (real-time PCR and melting curve analysis) (P < 0.001). However, the cure rate declined, coupled with an increasing proportion of RR-PTB patients lost to follow-up and untreated (P < 0.05).ConclusionsEnhanced surveillance is crucial for detecting tuberculosis patients aged 23–28 and 44–54 years. The distribution of cases varies among nationalities and occupations, potentially influenced by cultural and environmental factors. Regional patterns in RR-PTB incidence suggest tailored prevention and control strategies are necessary. Despite molecular tests advances, challenges persist with low cure rates and high loss to follow-up. Strengthening long-term management, resource allocation, and social support systems for RR-PTB patients is essential.

Spatio-temporal distribution and source analysis of nutrients and antibiotics in the waters of a mariculture area in Haizhou Bay, Yellow Sea

Due to the continuous expansion of China's urban areas, there has been a growing trend of discharging marine aquaculture tail water directly into the sea. This phenomenon has had a significant impact on the marine ecological environment.To determine the status of pollutant discharge from marine aquaculture tailwater and facilitate its treatment.This paper analyses the spatiotemporal distribution characteristics of nutrients and antibiotics in the pond culture mode of Penaeus monodon. The influent, tail water, and adjacent seawater of the aquaculture concentration area in the port industrial zone of Guanyun County, Lianyungang City were monitored. The study aims to explore potential correlations between the variables.The data indicates that nitrogen and phosphorus are the primary pollutants in the Lianyungang aquaculture sea area. In 2022, the average total nitrogen content was 104.475 mg/L, and the average total phosphorus content was 3.83 mg/L.Sulfanilamide and norethindrone antibiotics were found in the culture tailings. The residual content of sulfonamide antibiotics ranged from 0.6 to 33.4 ng/L, while the residual content of norethindrone antibiotics ranged from 0.15 to 1.0 ng/L.This paper analyses the monitoring data statistically to provide basic support for the protection of the marine ecological environment in Haizhou Bay. Additionally, to enhance the environmental management of China's coastal waters, it is necessary to formulate an environmental management policy for coastal waters and provide a scientific basis for the implementation of the total amount of pollutants discharged into the sea.

Spatiotemporal distribution of neurotoxin β-N-methylamino-L-alanine and 2,4-diaminobutyric acid in offshore aquaculture area of Shandong province, China

The neurotoxin β-N-methylamino-L-alanine (BMAA) has been widely detected in aquatic environments and got the public’s attention due to its potential risk to human neurodegenerative diseases. Three cruises in spring, summer and autumn seasons were carried out in Laizhou Bay (LZB), Sishili Bay (SSLB), Sanggou Bay (SGB), Jiaozhou Bay (JZB) and Haizhou Bay (HZB) in 2023. Results showed that the temporal distribution pattern of BMAA in plankton varied in the survey bays. In LZB, the highest average concentration of BMAA in phytoplankton occurred in spring. The highest average concentration of BMAA in phytoplankton was detected in summer in SSLB, JZB and HZB. However, BMAA was only detected in phytoplankton at the R2 station in SGB in spring. The highest average concentration of BMAA in zooplankton was observed in spring in LZB, SSLB and SGB. Zooplankton accumulated the highest average concentration of BMAA in JZB and HZB in summer and autumn, respectively. The BMAA was widely detected in marine mollusks throughout the investigative period. In addition, Mantel test and RDA analysis results indicated that DIN/DIP strongly impacted on the spatiotemporal distribution of BMAA in phytoplankton, in JZB and SSLB. The spatiotemporal distribution of BMAA in plankton was correlated with temperature and DO in JZB. More field cruises should be conducted to explore the environmental drivers of the neurotoxin BMAA in marine ecosystems in future studies.

Genomic surveillance as a scalable framework for precision phage therapy against antibiotic-resistant pathogens

Phage therapy is gaining increasing interest in the fight against critically antibiotic-resistant nosocomial pathogens. However, the narrow host range of bacteriophages hampers the development of broadly effective phage therapeutics and demands precision approaches. Here, we combine large-scale phylogeographic analysis with high-throughput phage typing to guide the development of precision phage cocktails targeting carbapenem-resistant Acinetobacter baumannii, a top-priority pathogen. Our analysis reveals that a few strain types dominate infections in each world region, with their geographical distribution remaining stable within 6 years. As we demonstrate in Eastern Europe, this spatiotemporal distribution enables preemptive preparation of region-specific phage collections that target most local infections. Finally, we showcase the efficacy of phage cocktails against prevalent strain types using invitro and animal infection models. Ultimately, genomic surveillance identifies patients benefiting from the same phages across geographical scales, thus providing a scalable framework for precision phage therapy.

Spatiotemporal analysis of the association between Kawasaki disease incidence and PM2.5 exposure: a nationwide database study in Japan

BackgroundKawasaki disease (KD) is an acute vasculitis primarily affecting children. While some studies suggest a link between KD and PM2.5 exposure, findings remain inconsistent. This study aimed to perform spatiotemporal...

Multi‐Layer Cloud Detection and Distributions Over the Asia–Pacific Region Based on Geostationary Satellite Imagers

AbstractA large portion of cloud scenes over the globe shows multiple layers composed of different phases, in general with ice clouds on the top and liquid water clouds beneath. Such multi‐layer (ML) clouds constitute major challenges in cloud observations and weather and climate modeling. This study improved a threshold algorithm for detecting ice‐over‐water ML clouds using geostationary satellites. Optimal thresholds were established for the spectral characteristics of the Advanced Himawari Imager (AHI) and the Advanced Geostationary Radiation Imager (AGRI), accounting for differences between land and ocean surfaces. Validation with collocated space radar and lidar measurements indicated the identification accuracies of approximately 82% over the land and 76% over the ocean. Annual distributions of ML clouds inferred by AHI and AGRI exhibited strong similarity. Furthermore, 6 years of hourly observations revealed distinct monthly and daily variations in ice‐over‐water clouds over the Asia–Pacific region. The ML cloud monthly variations were similar to those of the seasonal convection cycle, with occurrence frequencies over the typical regions higher in summer (maximum ∼27%) and lower (minimum 6%–10%) in winter. Regarding daily variations, ice‐over‐water clouds occurred more frequently around local noon over most of the six time zones (from UTC + 06 to UTC + 11) throughout all seasons. The refined spatiotemporal distribution of ML clouds, particularly the daily variations, is possible to improve our understanding of cloud vertical distributions and radiative effects, and has the potential to promote subsequent validation and parameterization of cloud overlapping in global climate modeling.

Development and observation of a three-dimensional scanning coaxial Mie lidar for dynamic monitoring of near-surface aerosol plumes

Accurate three-dimensional spatiotemporal distribution information on near-surface aerosols is of great significance for environmental research. In this study, a 3D scanning coaxial Mie lidar (3D-STML) was developed to achieve a fast three-dimensional scanning observation of aerosol diffusion processes in near-surface areas. 3D-STML generates high-spatiotemporal resolution images of aerosol extinction coefficient in real-time and captures the dynamic changes of aerosols in near real-time. By optimizing the design of the light guide mirror and the telescope sub-mirror, the system has a small overlap. Based on this, a highly stable and high-speed mechanical rotation mechanism was developed to enable three-dimensional observations. The integration of a solid-state high-repetition-rate pulsed laser and a coaxial, optical system for the transmitter and receiver ensures rapid tracking of aerosol plumes. To meet the observation requirements of near-surface aerosols, an aerosol inversion algorithm combining the Fernald and Klett methods was designed and developed. For aerosol plume monitoring needs, an aerosol plume-tracking algorithm based on Kalman filtering was developed to track the spatiotemporal evolution of aerosols automatically. Experimental results demonstrated that 3D-STML is capable of detecting aerosols in a range from 15 m to 4 km, with a distance resolution of 1.5 m and a time resolution of 0.083 s. It can effectively track and capture aerosol plumes. It can be used for large-scale, long-term observation of near-surface aerosols and for monitoring the spatiotemporal evolution of aerosol plumes.

Identifying the spatio-temporal distribution characteristics of offshore wind turbines in China from Sentinel-1 imagery using deep learning

ABSTRACT Offshore wind power is a crucial clean energy source for coastal countries and advances blue economies. Accurate spatial mapping of offshore wind turbines supports energy assessment and the sustainable development of marine resources. Offshore wind power has developed rapidly in China, but the spatiotemporal distribution characteristics of offshore wind turbines and farms remain poorly understood. Extracting nearshore small targets from Sentinel-1 synthetic aperture radar images remain challenging due to noise from the rough sea surface background and the diverse substrate types in offshore wind turbine (OWT) distributions. Here, to address these issues, we first created a dataset of China’s offshore wind turbines (COWTs) that contained typical substrate types (e.g. seawater and tidal flats). Then, a deep learning model integrating an attention mechanism and receptive fields was used to accurately detect COWTs. The well-trained model was used to detect the changes in COWTs from 2015 to 2022. Our model detected an increase in the number of offshore wind farms in China from 7 to 114 (clusters counted as an offshore wind farm), while COWTs increased from 305 to 6451 from 2015 to 2022. The Taiwan Strait exhibited the highest wind speeds, yet the Jiangsu and Guangdong regions had the most installed turbines. Over 90% of COWTs were located in areas with offshore wind energy resources less than 500 W/m3, indicating a mismatch between COWT installation and wind resource distribution. This study demonstrated temporal and geographical generalizability, which is promising for global wind power detection. Our analysis of spatiotemporal variation in COWTs facilitates informed decision-making for future field establishment and sustainable marine planning.

Molecular diversity and seasonal dynamics of Ostreococcus (Mamiellophyceae, Chlorophyta) in typical mariculture bays based on metabarcoding analysis

Ostreococcus (Mamiellophyceae, Chlorophyta) is a cosmopolitan genus of marine pico-phytoplankton and the smallest free-living photosynthetic eukaryotes with cell size of 1–2 μm. To understand the diversity and spatio-temporal distribution of Ostreococcus in the Rongcheng coastal regions in northern China, metabarcoding analysis based on the 18S rDNA V4 molecular marker was applied to study the molecular diversity and seasonal dynamics of Ostreococcus in three typical mariculture bays (Rongcheng Bay, Lidao Bay and Sanggou Bay). A total of 103 amplicon sequence variants (ASVs) annotated as Ostreococcus were detected in these three typical mariculture bays throughout the year. The top five ASVs in terms of abundance were ASV4, ASV9, ASV14, ASV28 and ASV109, totally occupying 99.1% of Ostreococcus reads. Phylogenetic analysis showed that these five dominant ASVs represented two Ostreococcus ecotypes (OI and OII) and were grouped into four Ostreococcus clades including Ostreococcus lucimarinus (ASV9) and Ostreococcus tauri (ASV28 and ASV109) in OI, and Ostreococcus sp. RC1 (ASV4) and Ostreococcus sp. RC2 (ASV14) in OII, which provided direct evidence to support the co-existence of two ecotypes in the Rongcheng coastal regions. Five dominant ASVs in OI and OII exhibited two distinct seasonal distribution patterns. Three dominant ASVs (ASV9, ASV28 and ASV109) in OI could be detected in all four seasons of the year, exhibiting native distribution properties, while two ASVs (ASV4 and ASV14) in OII decreased sharply in winter and could not be detected in spring, exhibiting characteristics of alien inputs. The composition, succession and association of Ostreococcus community were mainly driven by water temperature in these mariculture bays. This study helps us systematically understand the molecular diversity and distribution patterns of Ostreococcus in typical mariculture bays in northern China, laying the foundation for understanding and revealing the ecological functions of pico-phytoplankton.

Environmental DNA reveals the spatiotemporal distribution and migration characteristics of the Yangtze finless porpoise, the sole aquatic mammal in the Yangtze River

The Yangtze Finless Porpoise (YFP) is one of the 13 global flagship species identified by the World Wildlife Fund and is classified as “Critically Endangered.” It is also the only extant aquatic mammal in the Yangtze River. In this study, 44 sampling points were deployed across the middle and lower reaches of the Yangtze River, with vertical sampling sections established in four key areas. Using environmental DNA (eDNA) and species distribution model(SDM), we explored the spatiotemporal distribution of YFPs and predicted their potential suitable habitats. The results indicate that the YFP has a relatively wide distribution during the flood season but exhibits clustering behavior during the dry season, showing a patchy distribution and a migratory trend from the midstream to downstream of the main channel. Predictions using the MAXENT model reveal varying trends in suitable habitat under different scenarios. Overall, YFP's potential habitat is expected to expand by 2050, but due to rising temperatures, it will contract by 2070. Elevation (dem, 65.4%), human footprint index (hfp, 8.8%), and isothermality (bio3, 8%) are key factors influencing habitat suitability. These findings demonstrate that eDNA is an effective tool for monitoring large aquatic organisms and provide scientific evidence for the conservation of the YFP.

Model order reduction for the input–output behavior of a geothermal energy storage

In this article, we consider a geothermal energy storage system in which the spatio-temporal temperature distribution is modeled by a heat equation with a time-dependent convection term. Such storage systems are often embedded in residential heating systems. The control and management of such systems requires knowledge of aggregated characteristics of the temperature distribution in the storage. These describe the input–output behavior of the storage, the associated energy flows, and their response to charging and discharging processes. Our aim is to derive an efficient, approximate description of these characteristics by using low-dimensional systems of ordinary differential equations (ODEs). This leads to a model order reduction problem for a large-scale linear system of ODEs resulting from the semidiscretization of the heat equation combined with a linear algebraic output equation. In a first step, we approximated the nonautonomous system of ODEs by a linear time-invariant system. Then, we applied Lyapunov balanced truncation model order reduction to approximate the output by a reduced-order system that has only a few state equations but almost the same input–output behavior. The results of our extensive numerical experiments show the efficiency of the applied model order reduction method. We found that only a few suitably chosen ODEs are sufficient to achieve good approximations of the input–output behavior of the storage.

Light-absorbing black carbon and brown carbon components of smoke aerosol from DSCOVR EPIC measurements over North America and central Africa

Abstract. Wildfires and agricultural burning generate seemingly increasing smoke aerosol emissions, impacting societal and natural ecosystems. To understand smoke's effects on climate and public health, we analyzed the spatiotemporal distribution of smoke aerosols, focusing on two major light-absorbing components, namely black carbon (BC) and brown carbon (BrC) aerosols. Using NASA's Earth Polychromatic Imaging Camera (EPIC) instrument aboard NOAA's Deep Space Climate Observatory (DSCOVR) spacecraft, we inferred BC and BrC volume fractions and particle mass concentrations based on spectral absorption provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm with 1–2 h temporal resolution and ∼ 10 km spatial resolution over North America and central Africa. Our analyses of regional smoke properties reveal distinct characteristics for aerosol optical depth (AOD) at 443 nm, spectral single-scattering albedo (SSA), aerosol layer height (ALH), and BC and BrC amounts. Smoke aerosols in North America showed extremely high AOD up to 6, with elevated ALH (6–7 km) and significant BrC components up to 250 mg m−2 along the transport paths, whereas the smoke aerosols in central Africa exhibited stronger light absorption (i.e., lower SSA) and lower AOD, resulting in higher-BC mass concentrations and similar BrC mass concentrations than the cases in North America. Seasonal burning source locations in central Africa, following the seasonal shift in the Intertropical Convergence Zone and diurnal variations in smoke amounts, were also captured. A comparison of retrieved AOD443, SSA443, SSA680, and ALH with collocated AERONET and CALIOP measurements shows agreement with RMSE values of 0.2, 0.03–0.04, 0.02–0.04, and 0.8–1.3 km, respectively. An analysis of the spatiotemporal average reveals distinct geographical characteristics in smoke properties closely linked to burning types and meteorological conditions. Forest wildfires over western North America generated smoke with a small-BC volume fraction of 0.011 and a high ALH with large variability (2.2 ± 1.2 km), whereas smoke from wildfires and agricultural burning over Mexico region shows more absorption and low ALH. Smoke from savanna fires over central Africa had the most absorption, with a high-BC volume fraction (0.015) and low ALH with a small variation (1.8 ± 0.6 km) among the analyzed regions. Tropical forest smoke was less absorbing and had a high variance in ALH. We also quantify the estimation uncertainties related to the assumptions of BC and BrC refractive indices. The MAIAC EPIC smoke properties with BC and BrC volume and mass fractions and assessment of the layer height provide observational constraints for radiative forcing modeling and air quality and health studies.

Are regional groundwater models suitable for simulating wetlands, rivers and intermittence? The example of the French AquiFR platform

Predicting and managing water resources at regional scale under different climate and socio-economic scenarios is crucial to support drinking water supply and other sectors. At the same time, protecting rivers and wetlands from pollutions and droughts is essential and must include groundwater given its contribution to surface water. Yet, assessing temporal and spatial variability of groundwater contributions to surface water is constrained due to limited observations. This study aims to quantify the spatio-temporal distribution of groundwater discharge (hereafter called “GW discharge”) zones, i.e. the groundwater flow to rivers and wetlands, estimated by calibrated regional groundwater flow models (French AquiFR platform). We compare simulation results with two types of surface observations: (i) the spatial distribution of surface water (BD TOPO, RAMSAR and Natura 2000 database) and (ii) an innovative datasets, the river intermittence observed in headwaters (ONDE network). Results show that simulated GW discharge zones are consistent with the observed location of rivers and wetlands. Time variations in GW discharge are well correlated with the intermittence observed at 396 of the 515 selected stations. Of these, groundwater model continues to feed surface water upstream of the station for ∼75 % of observed river drying up events, which may be consistent with a small alluvial flow. The groundwater withdrawals are shown to have a strong impact on the GW discharge and thus on the river intermittence.

Aerosol composition, air quality, and boundary layer dynamics in the urban background of Stuttgart in winter

Abstract. Aerosol distributions are of great relevance for air quality, especially for cities like Stuttgart, which has limited air exchange due to its location in a basin. We collected a comprehensive set of data from remote sensing and in situ methods including radiosondes for the urban background of downtown Stuttgart to determine the impact of boundary layer mixing processes on local air quality and to evaluate the simulation results of the high-resolution large eddy simulation (LES) model PALM-4U at 10 m grid spacing. Stagnant meteorological conditions caused accumulation of aerosols, and chemical composition analysis shows that ammonium nitrate (37 ± 9 %) and organic aerosol (OA; 34 ± 9 %) dominated during this winter study. Case studies show that clouds during previous nights can weaken temperature inversion and accelerate boundary layer mixing after sunrise by up to 3 h. This is important for ground-level aerosol dilution during the morning rush hour. Furthermore, our observations validate results of the LES model PALM-4U in terms of boundary layer heights and aerosol mixing for 48 h. The simulated aerosol concentrations follow the trend of our observations but are still underestimated by a factor of 4.5 ± 2.1 due to missing secondary aerosol formation processes and uncertainties of emissions and boundary conditions in the model. This paper firstly evaluates the PALM-4U model performance in simulating aerosol spatio-temporal distributions, which can help to improve the LES model and to better understand sources and sinks for air pollution as well as the role of horizontal and vertical transport.

Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Soil moisture content (SMC), as a pivotal component in the energy and matter exchange processes within the soil-plant-atmosphere continuum, plays a crucial role in surface water dynamics, energy fluxes, and carbon cycling within ecosystems. The development of remote sensing technology has offered new perspectives for monitoring soil moisture at regional scales. Unmanned aerial vehicles (UAV) equip with multispectral have distinct advantages for vegetation monitoring, including rapidity and cost-effectiveness, which has superior applicability and practicality. Therefore, in a 5a "Daya" late-maturing citrus orchard, the vegetation index (VI) and texture feature (TF) information of citrus canopy based on UAV multi-spectral images were extracted, and soil and plant analyzer development (SPAD) of citrus was collected. These different data sources were integrated into the framework of the random forest algorithm (RF) and genetic algorithm-optimized random forest (GA-RF) to evaluate the accuracy of surface SMC (SSMC) estimation in citrus orchard. The Biswas model was utilized to simulate the root zone SMC (RSMC). The spatiotemporal variations of SMC in citrus orchard were analyzed, and the potential of low-cost sensor-equipped drones in rapidly acquiring spatial and temporal distribution information of SMC at a large regional scale was explored. The results indicated that the GA-RF models outperformed the RF models in estimating citrus orchard SMC (with R2 ranging from 0.502 to 0.949 and RMSE ranging from 0.552 % to 3.166 % for GA-RF, compared to R2 ranging from 0.430 to 0.936 and RMSE ranging from 0.587 % to 3.449 % for the RF). The GA-RF models using VI+SPAD as inputs exhibited the best performance for SMC at depths of 5 cm, 10 cm, 20 cm and 40 cm (SMC5, SMC10, SMC20 and SMC40) across citrus growth stages (R2 ranging from 0.793 to 0.949 at 5 cm, R2 ranging from 0.702 to 0.938 at 10 cm, R2 ranging from 0.714 to 0.927 at 20 cm). In bud bust to flowering, young fruit and fruit maturation stages (stage Ⅰ, ⅠⅠ and ⅠⅤ), all models demonstrated good accuracy in estimating SMC at depth of 10 cm (R2 ranging from 0.567 to 0.908 in stage Ⅰ, with R2 ranging from 0.681 to 0.916 in stage ⅠⅠ and R2 ranging from 0.579 to 0.938 in stage ⅠⅤ). In fruit expansion stage (stage III), the models performed best in predicting SMC5 (R2 ranging from 0.698 to 0.861). The Biswas model was constructed to simulate SMC40 by utilizing the inverted SMC10 and SMC20, thereby generating spatiotemporal distribution maps of SMC at different depths in citrus orchard. The SSMC was susceptible to environmental factors, exhibiting significant spatiotemporal heterogeneity. In summary, this study illustrated that the integration of multiple data sources into GA-RF enhanced the estimation performance of SMC at different growth stages of late-maturing citrus orchard in the Southwest China. Additionally, it enabled the rapid and efficient monitoring of spatiotemporal variations in SMC, providing an effective method and practical foundation for precision irrigation and improved water use efficiency.

Evolution of hydrochemical characteristics and the influence of environmental background in the Hailar River basin, China.

Understanding the evolution of hydrochemical characteristics in river systems is essential for environmental assessment and water resource management. This study explores the spatiotemporal distribution and the determinants of hydrochemical characteristics in the Hailar River basin, China, over an extensive period. Our results revealed that CODMn and CODCr were the primary concerns for long-term river management, with exceedance rates of 42.92% and 50.62%, respectively. These exceedances were predominantly driven by interactions between riparian soils and surface water, rather than anthropogenic pollution, as suggested by the strong correlations between dissolved organic carbon and soil water-extractable organic carbon, and the limited human footprint in this region. Piper trilinear and Gibbs diagram analysis further revealed that long-term rack weathering shaped the basin's hydrochemical characteristics, resulting in distinct HCO3--Ca2+ and HCO3--Ca2+-Na+ signatures. In addition, APCS-MLR analysis identified that elevated of CODMn and CODCr levels were mainly attributed to the interactions with adjacent soils, which are extensively covered by forests and grasslands. In contrast, leaching and migration processes contributed significantly on total dissolved solids and total phosphorus. The study also found that environmental self-purification processes played a key role in regulating Fe concentrations. This investigation provides a nuanced understanding of the environmental background's influence on hydrochemistry and dissolved organic matter (DOM) in the Hailar River basin, which offers valuable insights and methodologies for the rational assessment of water quality and aquatic ecosystem health in similar riverine systems.

Spatiotemporal climate variability and food security implications in the Central Ethiopia Region

Studies focusing on the spatiotemporal distribution of climatic parameters and meteorological drought are of paramount significance for countries like Ethiopia, where climate change and variability cause major losses to rain-dependent agriculture. In this study, the National Meteorology Institute of Ethiopia provided an Enhanced National Climate Services (ENACTS) dataset at a spatial resolution of approximately 4 km by 4 km over 38 years (1981–2018) was used to study climate trends, spatiotemporal variability, and meteorological drought in the Central Ethiopia Region. Coefficient of variation (CV), Standardized Rainfall Anomaly (SRA), Standardized Precipitation Index (SPI), Mann-Kendall trend test, and Sen's slope were used for the analysis. The findings suggest that Belg rainfall (also known as "small-rain") varied greatly in space and time over the study area, with area-averaged CV of 29 % and pixel-level CVs ranging from 63 to 93 %. The average precipitation during Belg experienced a 15 % decrease from 2000 to 2019 compared to the preceding two decades, from 1981 to 1999. The maximum temperature has increased significantly during the Annual, Belg, and Bega seasons. The SPI and SRA showed that there have been multiple drought episodes with rising negative rainfall anomalies, with a drought occurring every 2.9 years during the Kiremt (also called "big rain", spanning from June to September) and Belg seasons. The growing negative rainfall anomaly, high CV, and highly significant increase in mean maximum temperature during the Belg season is concerning for food security and poverty eradication. The notable rise in rainfall during the June (sowing period) and November (harvesting period) also hurts crop production during the main cropping season. Thus, developing appropriate adaptation strategies and policies oriented toward climate-resilient agriculture is crucial to meet the global sustainable development goals (SDGs) and Africa Union's Agenda 2063.