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Eutrophic shallow lakes are generally considered as a contributor to the emission of nitrous oxide (N2O), while regional and global estimates have remained imprecise. This due to a lack of data and insufficient understanding of the multiple contributing factors. This study characterized the spatiotemporal variability in N2O concentrations and N2O diffusive fluxes and the contributing factors in Lake Wuliangsuhai, a typical shallow eutrophic and seasonally frozen lake in Inner Mongolia with cold and arid climate. Dissolved N2O concentrations of the lake exhibited a range of 4.5 to 101.2 nmol/L, displaying significant spatiotemporal variations. The lowest and highest concentrations were measured in summer and winter, respectively. The spatial distribution of N2O flux was consistent with that of N2O concentrations. Additionally, the hotspots of N2O emissions were detected within close to the main inflow of lake. The wide spatial and temporal variation in N2O emissions indicate the complexity and its relative importance of factors influencing emissions. N2O emissions in different lake zones and seasons were regulated by diverse factors. Factors influencing the spatial and temporal distribution of N2O concentrations and fluxes were identified as WT, WD, DO, Chl-a, SD and COD. Interestingly, the same factor demonstrated opposing effects on N2O emission in various seasons or zones. This research improves our understanding of N2O emissions in shallow eutrophic lakes in cold and arid areas.

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The air pollution levels from polychlorodibenzo-p-dioxins/polychlorodibenzofurans (PCDD/Fs) and dioxin-like polychlorobiphenyls (dl-PCBs) in three residential areas located north, west, and south of the Da Nang airport were determined by using passive air samplers containing polyurethane foam (PUF) discs with 3-month sampling intervals from 2017 to 2020. The total toxic equivalents (∑TEQs) of the PCDD/Fs and dl-PCBs, using WHO2005-TEFs, were highest north of the airport (134 to 10610 fg WHO-TEQ/PUF day, with an average of 1108 fg WHO-TEQ/PUF day). The ∑TEQs were lower west of the airport, between 159 and 381 fg WHO-TEQ/PUF day and averaged 230 fg WHO-TEQ/PUF day. The lowest ∑TEQs occurred south of the airport, with ranges of 76 and 331 fg WHO-TEQ/PUF day and an average of 152 fg WHO-TEQ/PUF day. Construction activities, including excavation and transportation of dioxin-contaminated soil north of the airport, have increased airborne PCDD/F and dl-PCB contamination and health risks. The average daily doses of PCDD/Fs and dl-PCBs through inhalation (ADDA) for residents located north of the airport were the highest (10.9 to 3434 fg WHO-TEQ/kg BW/day and average: 597 fg WHO-TEQ/kg BW/day). Residents located west of the airport faced lower health risks (13-123 fg WHO-TEQ/kg BW/day and average: 39 fg WHO-TEQ/kg BW/day). Residents south of the airport were exposed to a minimum of 6.2-107 fg WHO-TEQ/kg BW/day, with an average of 28 fg WHO-TEQ/kg BW/day. The maximum and average ADDA values for residents north of the airport exceeded 10% of the tolerable daily intake (TDI) recommended by the WHO (100-400 fg WHO-TEQ/kg BW/day). In comparison, all the ADDA values for residents located west and south of the airport were less than and within 10% of the TDI.

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Zhari Namco is situated in the alpine grassland belt of northwestern Xizang with a fragile ecological environment. As the third-largest lake in Xizang, there has been a long-term lack of research data concerning its basin water environment. In an effort to elucidate the surface water environment characteristics of the basin and the factors influencing them, an extensive investigation was conducted from August 2021 to June 2022, encompassing periods of high flow, low flow, and base flow. Further, the study also involved comprehensive assessments of the water chemistry characteristics and spatial-temporal variation in lake sampling sites of the basin that were not significant by using mathematical statistics, hydrochemical analysis, correlation analysis, and principal component analysis. The findings revealed the following： ① The water in the Zhari Namco Basin exhibited an alkaline nature, with dominant ionic compositions in the lake comprising Na+, SO42-, and Cl-, whereas the rivers were primarily characterized by Ca2+, HCO3-, and SO42-. ② The main pollutants exceeding established standards included sulfates, arsenic, chlorides, and total phosphorus. The study identified significant spatiotemporal variations in water quality. Temporally, the exceedance of sulfates, arsenic, and total phosphorus was most pronounced during high-flow periods, followed by that during low-flow and base flow periods, with chloride levels showing less temporal variation. Spatially, river water quality surpassed that of the lakes, with arsenic, total phosphorus, TDS, sulfate, chloride, K+, and Na+ concentrations in lakes 1 to 2 orders of magnitude higher than those in rivers. Water qualities exceeding the established standard were primarily found in the lake, with less spatial variations within the lake itself. ③ Hydrochemical processes within the basin were found to be primarily influenced by natural phenomena, including evaporation-concentration and rock weathering. Various elements entered the lakes via surface runoff, where they continuously accumulated under the influence of evaporation-concentration processes, ultimately leading to exceedances. ④ Temporal variations in water quality were primarily attributed to increased elemental loss and intensified evaporation during high-flow periods. The spatial discrepancies in water quality were predominantly a consequence of the differing hydrodynamic conditions between flowing water bodies and enclosed water bodies.

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There is some evidence that the river migration success of Atlantic salmon smolts, on their first migration to sea, varies both spatially and temporally. However, we have only a poor understanding of what may be driving this variation. In this study, we used acoustic telemetry to quantify the spatial and temporal variations in river migration success in Atlantic salmon smolts on their first migration to sea. In total 4120 Atlantic salmon smolts migrating through 22 rivers in Scotland, England, Ireland, and Northern Ireland over multiple years were included in the study. Individuals were defined as successful migrants if detected leaving the river to enter marine waters. The results show significant temporal (up to 4 years) and spatial (river) variations in migration success, with overall between-river migration success varying from 3.4% to 97.0% and between years from 3.4% and 61.0%. Temporal variation in migration success was river specific, with some rivers being more temporally stable (exhibiting little variation between years) than others. Across all rivers and years, individual migration success was predicted positively by body condition and negatively by tag burden. The rate of migration success for a population (migration success standardized to a common river distance [proportion km-1]) was predicted by a number of environmental factors. The proportion of river catchment that comprised wetland and woodland positively predicted migration success, whereas the proportion of grassland and peatland in a catchment negatively predicted the rate of migration success. Although the mechanisms through which these effects may be operating were not directly examined in this study, we discuss some potential routes through which they may occur.

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Ambient air pollution's health impacts are well documented, yet the domestic environment remains underexplored. We aimed to compare indoor versus outdoor (I/O) air quality and estimate the association between indoor/ambient fine particulate matter (PM2.5) exposure and lung function in asthma and chronic obstructive pulmonary disease (COPD) patients. The study involved 24 h monitoring of PM2.5 levels indoors and outdoors, daily peak expiratory flow (PEF), and biweekly symptoms collection from five patients with asthma and COPD (average age of 50 years, 40% male) over a whole year. Data analysis was performed with linear mixed effect models for PEF and generalized estimating equations (GEE) for exacerbations. More than 5 million PM2.5 exposure and meteorological data were collected, demonstrating significant I/O PM2.5 ratio variability with an average ratio of 2.20 (±2.10). Identified indoor PM2.5 sources included tobacco use, open fireplaces, and cooking, resulting in average indoor PM2.5 concentrations of 63.89 µg/m3 (±68.41), significantly exceeding revised World Health Organization (WHO) guidelines. Analysis indicated a correlation between ambient PM2.5 levels and decreased PEF over 0-to-3-day lag, with autumn indoor exposure significantly impacting PEF and wheezing. The study underscores the need to incorporate domestic air quality into public health research and policy-making. A personalized approach is required depending on the living conditions, taking into account the exposure to particulate pollution.

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The monitoring and evaluation of fluoride pollution are essentially important to make sure that concentrations do not exceed threshold limit, especially for surrounding atmosphere and soil, which are located close to the emission source. This study aimed to describe the atmospheric HF and edaphic fluoride distribution from an electrolytic aluminum plant located in Yunnan province, on which the effects of meteorological conditions, time, and topography were explored. Meanwhile, six types of solid waste genereted from different electrolytic aluminum process nodes were characterized to analyze the fluoride content and formation characteristics. The results showed that fluoride in solid waste mainly existed in the form of Na3AlF6, AlF3, CaF2, and SiF4. Spent electrolytes, carbon residue, and workshop dust are critical contributors to fluoride emissions in the primary aluminum production process, and the fluorine content is 17.14 %, 33.30 %, and 31.34 %, respectively. Unorganized emissions from electrolytic aluminum plants and solid waste generation are the primary sources of fluoride in the environment, among which the edaphic fluoride content increases most at the sampling sites S1 and S7. In addition, the atmospheric HF concentration showed significant correlations with wind speed, varying wildly from March to September, with daily average and hourly maximum HF concentrations of 4.32 µg/m3 and 9.0 µg/m3, respectively. The results of the study are crucial for mitigating fluorine pollution in the electrolytic aluminum industry.

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Traditionally, for indoor radon testing, predominantly passive measurements have been used, typically applying the solid-state alpha track-etch method for long-term and the charcoal method for short-term measurements. However, increasingly, affordable consumer-grade active monitors have become available in the last few years, which can generate a concentration time series of an almost arbitrary duration. Firstly, we argue that consumer-grade monitors can well be used for quality-assured indoor radon assessment and consequent reliable decisions. Secondly, we discuss the requirements of quality assurance, which actually allow for reliable decision-making. In particular, as part of a rational strategy, we discuss how to interpret measurement results from low-cost active monitors with high and low sensitivity with respect to deciding on conformity with reference levels that are the annual average concentration of indoor radon. Rigorous analysis shows that temporal variations in radon are a major component of the uncertainty in decision-making, the reliability of which is practically independent of monitor sensitivity. Manufacturers of low-cost radon monitors already provide sufficient reliability and quality of calibration for their devices, which can be used by both professional inspectors and the general public. Therefore, within the suggested measurement strategy and metrologically assured criteria, we only propose to clarify the set and values of the key metrological characteristics of radon monitors as well as to upgrade user-friendly online tools. By implementing clear metrological requirements as well as the rational measurement strategy for the reliable conformity assessment of a room (building) with radon safety requirements, we anticipate significant reductions in testing costs, increased accessibility, and enhanced quality assurance and control (QA/QC) in indoor radon measurements.

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Illicit drugs have become a crucial global social issue, with South Korea experiencing a continuous increase in the number of offenders and drug smuggling. This study employed wastewater-based epidemiology to investigate consumption patterns of 8 illicit drugs and their 7 metabolites during the COVID-19 pandemic (2020-2022) in South Korea. Ten compouds were detected in the wastewater influent. Methamphetamine (METH) was prevalent in samples, followed by amphetamine and ecstasy (MDMA). Interestingly, MDMA and ketamine (KET), which were not detected in previous Korean studies conducted before COVID-19 pandemic, were detected in this study. METH exhibited the highest consumption rates, decreasing from 16.6 to 12.4 mg/day/1000 people between 2020 and 2022, while MDMA increased over the three years (mean: 1.16, 1.24, and 1.62 mg/day/1000 people in 2020, 2021, and 2022, respectively) (p < 0.05). Significant correlations were identified between regional income levels and the consumption rates of METH (p < 0.01), MDMA (p < 0.01), and KET (p < 0.05). Furthermore, METH and MDMA consumption rates in cities were positively correlated with the number of drug offenders arrested and local clubs in those cities. The findings of this study provide valuable insights into shaping regulatory policies related to illicit drugs and future studies.

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Despite continuous efforts, eutrophication is still occurring in freshwater and phosphorus (P) is the most important nutrients that drive the eutrophication in rivers and streams. However, little information is available about the distribution of P fractions in river sediment. Here, the sequential extraction approach was used to evaluate the sediment P fractionation and its content in the anthropogenically damaged river Ganga, India. Different sedimentary P fractions viz. exchangeable (Ex-P), aluminum bound (Al-P), iron bound (Fe-P), calcium bound (Ca-P), and organically bound phosphorus (Org-P), were quantified. Significantly higher level of total P was recorded in pre-monsoon season (438.5 ± 95.8 mg/kg), than other [winter (345.7 ± 110.6 mg/kg), post-monsoon (319.2 ± 136.3 mg/kg), and monsoon (288.6 ± 77.3 mg/kg)] seasons. Different P fractions such as Ex-P, Al-P, Fe-P, Ca-P and Org-P varied from 2.88-12.8 mg/kg, 7.64-98.8 mg/kg, 32.2-179.2 mg/kg, 51.97-286.1 mg/kg and 9.3-143.7 mg/kg, respectively, which correspondingly represented 0.5-10.54%, 3.41-20.18%, 17.27-37.82%, 37.35-60.2%, 4.15-25.88% of the Total P with a rank order of P-fractions was Ca-P > Fe-P > Org-P > Al-P > Ex-P. Bio-available P contributes a considerable portion (37.9-46.0%) of total P which may increase the eutrophication to overlying water. Results demonstrate that inorganic P species control the P bio-availability in both time and space. However, an estimated phosphorus pollution index based on sediment total P content showed no ecological risk of phosphorus to Ganga River sediment.

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Due to rapid urbanization, the Beibu Gulf, a semi-closed gulf in the northwestern South China Sea, faces escalating ecological and environmental threats. Understanding the assembly mechanisms and driving factors of bacterioplankton in the Beibu Gulf is crucial for preserving its ecological functions and services. In the present study, we investigated the spatiotemporal dynamics of bacterioplankton communities and their assembly mechanisms in the Beibu Gulf based on the high-throughput sequencing of the bacterial 16 S rRNA gene. Results showed significantly higher bacterioplankton diversity during the wet season compared to the dry season. Additionally, distinct seasonal variations in bacterioplankton composition were observed, characterized by an increase in Cyanobacteria and Thermoplasmatota and a decrease in Proteobacteria and Bacteroidota during the wet season. Null model analysis revealed that stochastic processes governed bacterioplankton community assembly in the Beibu Gulf, with drift and homogenizing dispersal dominating during the dry and wet seasons, respectively. Enhanced deterministic assembly of bacterioplankton was also observed during the wet season. Redundancy and random forest model analyses identified the physical properties (e.g., temperature) and nutrient content (e.g., nitrate) of water as primary environmental drivers influencing bacterioplankton dynamics. Moreover, variation partitioning and distance-decay of similarity revealed that environmental filtering played a significant role in shaping bacterioplankton variations in this rapidly developed coastal ecosystem. These findings advance our understanding of bacterioplankton assembly in coastal ecosystems and establish a theoretical basis for effective ecological health management amidst ongoing global changes.

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Crash counts are non-negative integer events often analyzed using crash frequency models such as the negative binomial (NB) distribution. However, due to their random and infrequent nature, crash data usually exhibit unique characteristics, such as excess zero observations that the NB distribution cannot adequately model. The negative binomial-Lindley (NBL) and random parameters negative binomial-Lindley (RPNBL) models have been proposed to address this limitation. Despite addressing the issues of excess zero observations, these models may not fully account for unobserved heterogeneity resulting from temporal variations in crash data. In addition, many variables, such as traffic volume, speed, and weather, change with time. Therefore, the analyst often requires disaggregated data to account for their variations. For example, it is recommended to use monthly crash datasets to better account for temporally varying weather variables compared to yearly crash data. Using temporally disaggregated data not only adds the complexity of the temporal variations issue in data but also compounds the issue of excess zero observations. To address these issues, this paper introduces a new variant of the NBL model with coefficients and Lindley parameters that vary by time. The derivations and characteristics of the model are discussed. Then, the model is illustrated using a simulation study. Subsequently, the model is applied to two empirical crash datasets collected on rural principal and minor arterial roads in Texas. These datasets include several time-dependent variables such as monthly traffic volume, standard deviation of speed, and precipitation and exhibit unique characteristics such as excess zero observations. The results of several goodness-of-fit (GOF) measures indicate that using the NBL model with time-dependent parameters enhances the model fit compared to the NB, NBL, and the NB model with time-dependent parameters. Findings derived from crash data collected from both rural minor and principal arterial roads in Texas suggest that the variables denoting the median presence and wider shoulder width are associated with a potential decrease in crash occurrences. Moreover, higher variations in speed and wider road surfaces are linked to a potential increase in crash frequency. Similarly, a higher monthly average daily traffic (Monthly ADT) positively correlates with crash frequency. We also found that it is important to account for temporal variations using time-dependent parameters.

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Sympatric species are often locally adapted to distinct microhabitats. However, temporal variation may cause local maladaptation and species boundary breakdown, especially during extreme climatic events leading to episodic selection. Repeated reciprocal transplants can reveal the interplay between short and long-term patterns of natural selection. To examine evolutionary trajectories of sympatric Monkeyflowers adapted to different niches, Mimulus guttatus and M. laciniatus, we performed three replicated transplants and combined them with previous experiments to leverage a dataset of five transplants spanning 10 years. We performed phenotypic selection analyses on parents and hybrids in parental habitats in Yosemite NP, CA during years of drastically differing snowpack. If there is ecological isolation, then we predicted divergent phenotypic selection between habitats in line with species' differences and local adaptation. We found interannual fluctuations in phenotypic selection, often in unpredicted directions. However, a combined-year analysis detected longer-term divergent selection on flowering time, a key temporally isolating and adaptative trait, suggesting that selection may reinforce species boundaries despite short-term fluctuations. Finally, we found temporal variation in local adaptation with M. laciniatus locally adapted in low snowpack years, while an extremely high snowpack year contributed to average local maladaptation of M. guttatus.

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The aim of this study was to comprehensively understand the water environment quality status and its change trend in the Inner Mongolia section of the Yellow River Basin. To analyze the water quality in recent years,the water quality data in the Yellow River basin from 2003 to 2020 were firstly collected from five typical monitoring stations．Various data analysis methods, including principal component analysis, cluster analysis, and a long short-term memory model, were used along with an improved comprehensive water quality identification index to explore the spatiotemporal characteristics of water quality in the Yellow River Basin. The results showed that the overall water quality in the basin has improved and stabilized over time. In terms of temporal variation, there was a distinction between the wet season and dry season, with a better status observed during the wet season due to increased agricultural irrigation and higher water volume. Spatially, the five monitoring sections could be divided into three categories based on strong natural factors that maintained their temporal characteristics during the wet season； however, significant differences were observed during the dry season due to urban water usage patterns. Analysis using LSTM models revealed that ammonia nitrogen will continue to decline and have a decreasing impact on the comprehensive water quality. These findings provide valuable insights for the comprehensive management of water quality in Inner Mongolia's Yellow River Basin.

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The present study evaluated the cadmium (Cd) levels and temporal variation of Cd in dark muscle, white muscle, and liver of juvenile Thunnus albacares. 72 individuals (Standard length: 50-67 cm; weight: 0.8-2.5 kg) were collected from Indian Oceanic water around Sri Lanka during the period between April 2021 to May 2022. Total Cd levels were analyzed using an Inductively Coupled Plasma Mass Spectrophotometer. The mean Cd levels (mean ± SD mg kg-1 dry weight) in different tissues varied with significantly higher levels in the liver (13.62 ± 0.98, p < 0.05), compared to dark muscle (0.52 ± 0.05), and white muscle (0.42 ± 0.04). Cd levels in liver tissues were positively correlated (p < 0.05) with the fish weight. The Cd levels reported in dark muscles, white muscles, and liver tissues were significantly higher (p < 0.05) during 2nd inter-monsoon than in the other monsoonal regimes and exceeded the maximum permissible level (0.1 mg kg-1 wet weight) set by the European Union (EU). However, the measured Cd levels in white and dark muscles were below the maximum permissible level (0.2 mg kg-1 wet weight) set by FAO/WHO. The Cd levels in all the liver tissues were above the levels set by the EU and FAO/WHO. Accordingly, people should avoid the consumption of liver tissues of T. albacares from the Indian Ocean. A human with a body weight of 60 kg can consume white muscles up to 4.667 kg per week without exceeding the Provisional Tolerable Weekly Intake.

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Grassland plays an indispensable role in the stability and development of terrestrial ecosystems. Quantitatively assessing grassland resilience is of great significance for conducting research on grassland ecosystems. However, the quantitative measurement of resilience is difficult, and research on the spatio-temporal variation of grassland resilience remains incomplete. Utilizing the Global Land Surface Satellite (GLASS) leaf area index (LAI) product derived from MODIS remote sensing data, along with land cover and meteorological data, this paper constructed the grassland resilience index (GRI) in the west Songnen Plain, China, a typical region with salt and alkali soils. This paper analyzed the spatio-temporal changes of the GRI and explored the contribution of climate factors, human activities, and geographical factors to the GRI. The results revealed that from 2000 to 2021, the GRI in the study area ranged from 0.1 to 0.22, with a multi-year average of 0.14. The average GRI exhibited a pattern of high-value aggregations in the north and low-value distributions in the south. Trend analysis indicated that areas with an improved GRI accounted for 59.09% of the total grassland area, but there were still some areas with serious degradation. From 2000 to 2015, the latitude and mean annual temperature (MAT) were principal factors to control the distribution of the GRI. In 2020, the mean annual precipitation (MAP) and MAT played important roles in the distribution of the GRI. From 2000 to 2021, the influence of human activities was consistently less significant compared to geographical location and climate variables.

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Despite evolutionary biology's obsession with natural selection, few studies have evaluated multigenerational series of patterns of selection on a genome-wide scale in natural populations. Here, we report on a 10-y population-genomic survey of the microcrustacean Daphnia pulex. The genome sequences of [Formula: see text]800 isolates provide insights into patterns of selection that cannot be obtained from long-term molecular-evolution studies, including the following: the pervasiveness of near quasi-neutrality across the genome (mean net selection coefficients near zero, but with significant temporal variance about the mean, and little evidence of positive covariance of selection across time intervals); the preponderance of weak positive selection operating on minor alleles; and a genome-wide distribution of numerous small linkage islands of observable selection influencing levels of nucleotide diversity. These results suggest that interannual fluctuating selection is a major determinant of standing levels of variation in natural populations, challenge the conventional paradigm for interpreting patterns of nucleotide diversity and divergence, and motivate the need for the further development of theoretical expressions for the interpretation of population-genomic data.

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Currently, the comprehensive effect of the landscape pattern on river water quality has been widely studied. However, the interactive influences of landscape type, namely composition (COM) and configuration (CON) on water quality variations, as well as the specific landscape driving types affecting water quality variations under different spatial and seasonal scales remain unclear. To further improve the effectiveness of landscape planning and water quality protection, this study collected monthly water samples from the Fengyu River Watershed in southwestern China from 2018 to 2021, the Biota-Environment Matching Analysis (Bioenv) was used to identify key metrics representing landscape COM and CON, respectively. Then, the multiple regression (MLR) and redundancy analysis (RDA) were used to explore the relationship between these landscape metrics and water quality. In addition, this study used a variation partitioning analysis (VPA) to quantify the interactive and independent influence of landscape COM and CON on water quality. Results revealed that construction land and the Shannon's diversity index (SHDI) were the key metrics of landscape COM and CON, respectively, for predicting water pollution concentrations. The interactive contribution was particularly sensitive to seasonal changes in riparian buffer areas (27.66 % to 48.73 %), while it remained relatively stable at the sub-watershed scale (38.22 % to 40.51 %). Moreover, landscape CON had a higher independent contribution to variations on water quality across most spatio-temporal scales. Overall, identifying and managing key landscape type and consequential metrics, matching with the spatio-temporal scale, holds promise for enhancing water quality conservation. Furthermore, this study provides valuable insights into the identification and selection of core landscape metrics.

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Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery. Label data were a ten-class natural vegetation and forest classification (i.e., Acacia, Callitris, Casuarina, Eucalyptus, Grassland, Mangrove, Melaleuca, Plantation, Rainforest and Non-Forest) derived by combining current best-available regional-scale maps of Australian forest types, natural vegetation and land use. The best CNN generated using six Landsat geomedian bands as input produced better results than a pixel-based random forest algorithm, with higher overall accuracy (OA) and weighted mean F1 score for all vegetation classes (93 vs. 87% in both cases) and a higher Kappa score (86 vs. 74%). The trained CNN was used to generate annual vegetation maps for 2000-2019 and evaluated for an independent test area of 100 km × 100 km using statistics describing accuracy regarding the label data and temporal stability. Seventy-six percent of pixels did not change over the 20 years (2000-2019), and year-on-year results were highly correlated (94-97% OA). The accuracy of the CNN model was further verified for the study area using 3456 independent vegetation survey plots where the species of interest had ≥ 50% crown cover. The CNN showed an 81% OA compared with the plot data. The model accuracy was also higher than the label data (76%), which suggests that imperfect training data may not be a major obstacle to CNN-based mapping. Applying the CNN to other regions would help to test the spatial transferability of these techniques and whether they can support the automated production of accurate and comparable annual maps of natural vegetation and forest types required for national reporting.

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The study of long-range transport effects on marine fine particles (PM2.5), particularly in remote sites such as the Dongsha Islands, is pivotal for advancing our understanding of air pollution dynamics on a regional scale and for formulating effective environmental policies. PM2.5 concentrations were examined over three consecutive years and grouped based on their transport routes. The backward trajectory simulation revealed that high PM2.5 concentrations were observed in the West Channel, originating from North and Central China, the Korean Peninsula, and the Japanese Islands, opposed to the East Channel. High PM2.5 concentrations, commonly observed in winter and spring, were mainly attributed to the Asian Northeastern Monsoons. Water-soluble inorganic ions constituted the major components, accounting for 37.8-48.7% of PM2.5, and followed by metal elements (15.5-20.0%), carbons (7.5-13.3%), levoglucosan (0.01-0.17%), and organic aerosols (0.2-2.2%). Secondary inorganic aerosols as the dominant source accounted for 8.3-24.7% of PM2.5, while sea salts were the secondary major contributor. High levoglucosan contribution (3.8-7.2%) in winter and spring was attributed to biomass burning, mainly from the Indochina Peninsula. Chemical mass balance receptor modeling resolved that major sources of PM2.5 were secondary sulfate, sea salts, fugitive dust, and industrial boilers. This study concluded that the long-range transport of PM2.5 gradually increased since fall, contributing 52.1-74.3%, highlighting its substantial impact on PM2.5 in all seasons except summer.

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The vegetation phenology of the Qinghai-Xizang Plateau is changing significantly in the context of climate change. However, there are many hydrothermal factors affecting the phenology, and few studies have focused on the effects of multiple factors on the phenology of the Qinghai-Xizang Plateau, resulting in a lack of understanding of the mechanisms underlying phenological changes on the Qinghai-Xizang Plateau. In this study, we used remote sensing data interpretation to analyze the spatial and temporal variability of grassland phenology on the Qinghai-Xizang Plateau from 2002 to 2021, focusing on precipitation, temperature, altitude, soil, and other aspects to reveal the dominant factors of phenological variability using an interpretable machine learning method (SHAP) and to quantify the interactive effects of multiple factors on phenology. The results showed that:① The growing season start (SOS) of grasslands on the Qinghai-Xizang Plateau mostly ranged from 110 to 150 d, with 56.32 % of grasslands showing an early SOS trend; the growing season end (EOS) mostly ranged from 290-320 d, with 67.65 % of grasslands showing a delayed EOS trend; and the growing season length (LOS) mostly ranged from 120 to 210 d, with 65.50 % of the grasslands showing a trend towards longer growing season lengths. ② SOS in grasslands on the Qinghai-Xizang Plateau was mainly influenced by moisture conditions, in which soil moisture between 10 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the advancement of SOS and peaked at approximately 20 kg·m-2. EOS was mainly influenced by temperature, with higher temperatures in September and October having a stronger effect on EOS latency promotion and peaking at over 8 ℃ and -0.5 ℃, respectively. The main influencing factors of LOS were more consistent with SOS, in which soil moisture between 15 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the prolongation of LOS and peaked at approximately 18 kg·m-2. ③ There was an obvious interactive effect of water and heat and other factors on phenology; after soil moisture reached 20 kg·m-2 in the 0-10 cm soil layer in March, SOS was more advanced in low-precipitation and low-altitude areas. Better moisture conditions were more conducive to EOS delay at temperatures above 0 ℃ in October, and soil moisture in high precipitation areas promoted LOS prolongation more when soil moisture was between 12 and 22 kg·m-2 in 0-10 cm in March. The results also demonstrated that interpretable machine learning methods could provide a new approach to the analysis of the multifactorial effects of phenological change.

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