Background: Early puberty has been associated with various adverse health outcomes. We aimed to characterize the spatiotemporal patterns of age at menarche or spermarche in China and to identify potential associated factors. Methods: We leveraged data from six consecutive nation-wide cross-sectional surveys in China conducted in 1995, 2000, 2005, 2010, 2014, and 2019. A total of 519 940 girls aged 9 to 18 years and 392 813 boys aged 11 to 18 years from 30 provinces in Mainland China were included. Probabilistic analysis was used to estimate the median ages at spermarche or menarche as surrogate measures of pubertal development. We used Bayesian spatiotemporally varying series models with the spatiotemporal variance partitioning index to assess the spatiotemporal heterogeneity of ages at menarche or spermarche across 30 provinces and to quantify the relative contributions of the socioeconomic and environmental factors. Results: Almost all provinces experienced decreases in the median ages at spermarche and menarche during the study period. Regional disparities were identified, as a faster decreasing trend was generally observed in eastern provinces when compared with western provinces. Socioeconomic factors explained 34.58% of the spatiotemporal heterogeneity for boys and 34.17% for girls, while environmental factors explained 55.68 and 56.22%, respectively. Among these, temperature, nighttime light, nitrogen dioxide, relative humidity, and illiteracy rate showed the largest contributions (each >5%). In general, higher levels of economic indicators and environmental stressors were observed in provinces with lower initial ages at spermarche and menarche and/or faster declining trends. Conclusions: A significant trend toward early puberty was observed in China over the past 25 years, especially in eastern provinces. Both socioeconomic and environmental factors were strongly associated with the observed spatiotemporal patterns.

Kaluginia wangi sp. nov., collected from China, is described and illustrated. The species belongs to the tribe Boreoheptagyiini (Diamesinae). The genus Kaluginia is newly recorded from China. Phylogenetic analyses based on five molecular markers (18S, 28S, CAD, COI-5p, and COI-3p) confirm the new species belongs to a highly supported clade together with the known species Kaluginia lebetiformis. Morphologically, the two species are somewhat similar but can be distinguished by the number of antennal flagellomeres, and the structure of the hypopygium. Through careful slide preparation of the holotype and integrated morphological and molecular cross-validation, this study revealed that variations in slide-mounting techniques can produce morphological artifacts, thereby directly affecting taxonomic conclusions. These findings highlight that for taxa characterized by fine morphological structures, meticulous slide preparation and thorough verification are essential for ensuring robust taxonomic outcomes.

ABSTRACT Soil Fe cycling is co‐regulated by texture and land use intensity (LUI), but their synergistic mechanism and depth‐dependent effects remain unclear. We analyzed 68 samples (0–200 cm) from one cultivated (CL, high LUI, silty loam, clay = 11%–18%) and three non‐cultivated (AC1–AC3, low LUI, sandy loam, clay = 2.65%–9.00%) profiles in Thailand's Mun River Basin, using hierarchical regression, slope difference tests, and CV decomposition. Results showed: (1) Soil texture best explains Fe content variation—silty loam maintains stable high Fe (25.4–39.7 g kg −1 ) via clay adsorption, while sandy loam has variable Fe (1.1–118.5 g kg −1 ) with severe depletion in topsoil. (2) The top 1.2 m is a δ 56 Fe‐sensitive zone (CV = 35.7%), more variable than deep soil (CV = 21.8%). (3) Texture ×LUI synergy is statistically significant (Fe: Δ R 2 = 0.09, p < 0.01; δ 56 Fe: Δ R 2 = 0.13, p < 0.001). (4) Fe content (static pool) and δ 56 Fe (dynamic tracer) are complementary, with LUI acting as a “switch” for texture‐Fe relationships. This study clarifies the vertical differentiation and synergistic controls of Fe biogeochemistry in tropical soils, and strengthens the mechanistic basis for land degradation assessment and restoration in tropical agroecosystems.

Abstract. Within the Healthy Cities and Sustainable Development Goals (SDGs) agendas, socioeconomic data are fundamental for tracking regional development. China, however, lacks a complete, long-term subnational socioeconomic dataset due to severe spatiotemporal missingness in official statistical yearbooks. We compiled 35 official socioeconomic indicators for 366 Chinese cities from 2000 to 2021, incorporated remote-sensing-derived covariates as auxiliary information, and applied a Bayesian spatiotemporal interacting varying intercepts (BSTIVI) model to capture the target variables’ spatial, temporal, and coupled spatiotemporal dependence. Model performance was evaluated using global Bayesian criteria and cross-validation, while local error distributions and temporal trends were visualized to examine imputation outcomes. Based on the completed dataset, we further derived a composite development index using entropy weighting and assessed spatial inequality with the Gini coefficient, coefficient of variation and hotspot analysis. The results show that BSTIVI achieved markedly better fit than traditional multiple linear regression (MLR). In cross-validation, 32 of 35 indicators achieved R2 >= 0.95, RMSE and MAE remained low. The resulting data product showed strong imputation performance in both spatial and temporal dimensions. Analyses of the completed dataset revealed marked spatial inequality and clustering in urban socioeconomic development across China during 2000–2021. We ultimately produced the first long-term city-level socioeconomic dataset for China, comprising 35 indicators and one composite index, with Bayesian credible intervals for imputed values. This study provides both a new city-level data resource for China and a transferable framework for imputing missing subnational socioeconomic data worldwide, thereby supporting Earth system research and SDG implementation.

Cadmium (Cd) contamination poses a significant threat to both crop production and human health, with certain regions, or hotspots, experiencing heightened risks. This study explores the spatial distribution of Cd hotspots and assesses contamination patterns and ecological risks in Henan Province, China, using geographic information system (GIS) and decision tree models. A total of 385 topsoil samples were collected and analyzed through GIS spatial analysis, decision tree analysis, correlation analysis, and ecological risk assessments to identify areas of high contamination and determine the key factors influencing Cd distribution. Cd concentrations in the topsoil ranged from 0.043 to 5.209 mg/kg, with an average of 0.19 mg/kg, significantly exceeding the Henan background value of 0.074 mg/kg. The enrichment factor (EF) analysis revealed that over 90% of the soil samples exhibited mild to moderate Cd contamination, with the highest EF values indicating significant anthropogenic influence near metal mines and industrial enterprises. The ecological risk (Er) index showed an uneven distribution of contamination, with moderate to elevated risks, particularly in the central and southern regions. Decision tree analysis identified soil organic matter (SOM) as the primary factor influencing Cd content in low-risk areas, where Cd concentrations peaked at 0.212 mg/kg in soils with SOM > 22.41 g/kg. In contrast, in low SOM areas (<= 22.41 g/kg), pH, SiO2, CaO, and MgO significantly influenced Cd accumulation. These findings provide critical insights into Cd hotspots, informing future environmental policy and strategies for contamination control and ecological risk mitigation in the region.

Background:China has made progress in reducing maternal mortality ratio (MMR), yet county-level spatiotemporal heterogeneity persists. This study aims to identify spatiotemporal disparities in MMR and quantify the impacts of various administrative levels on these disparities. Methods:We analyzed county-level MMR panel data from 1996 to 2015, employing the spatial Gini coefficient, Anselin Local Moran's I, and Getis-Ord Gi* to assess spatiotemporal disparities related to spatial inequity and geographic clustering. Additionally, we applied a Bayesian multiscale spatiotemporally varying intercepts (BMSTVI) model to unveil the national temporal trend and multiple sub-national spatial patterns in maternal mortality risk. We further quantified the relative contributions of five sub-national administrative levels using the spatiotemporal variance partitioning index (STVPI). Results:Results suggested that from 1996 to 2015, the proportion of MMR in counties achieving Sustainable Development Goals (SDGs) increased from 27.05% to 93.40%, yet spatiotemporal disparities remained. The spatial Gini coefficient and geographic clustering analyses indicated temporally varying but spatially stable inequities patterns, highlighting the Spatial Inequity Lock-in (SILI) effect. Hotspot analysis identified sensitive and exemplary counties, underscoring the need for targeted regional interventions. The BMSTVI model indicated a declining trend in MMR risk over 20 years, with the most substantial reduction from 2003 to 2012. While the geographic distribution of high-risk areas remained relatively stable, analyses at finer administrative levels enabled more precise identification of affected locations and improved intervention effectiveness. Finally, the STVPI revealed that spatial effects contributed 83.91% (95% CIs: 78.66%-89.47%) to MMR variations, far exceeding the 11.60% (95% CIs: 7.27%-16.55%) from temporal effects. The contribution from the administrative county-level was the highest (29.15%, 95% CIs: 19.69%-35.06%), followed by contributions from the seven geographical regions (14.10%, 95% CIs: 6.61%-34.06%), rural-urban differences (13.77%, 95% CIs: 4.93%-39.2%), provincial level (12.41%, 95% CIs: 8.06%-16.85%), and city level (11.21%, 95% CIs: 7.53%-13.84%). Discussion:These findings underscore the crucial need for region-specific, time-sensitive policies to achieve maternal health equity across Chinese counties. This study provides a robust empirical foundation for a multi-tiered adaptive policy framework grounded in systematic spatiotemporal assessment across macro, meso, and micro scales to guide targeted maternal health interventions globally.

Nebkhas, which play a fundamental role in stabilizing ecosystems in arid and semi-arid regions, are currently threatened by global warming and anthropogenic activities. This study focuses on a tamarisk-nebkha profile situated in the lower reaches of the Shule River Basin, an arid region in northwestern China. Using radioactive dating and the physicochemical properties of sediments, this study reconstructed changes in the nebkha’s hydro-ecological conditions over the past decades. The results revealed a significant decline in fine particle fraction, carbonate content, and low-frequency magnetic susceptibility, along with a notable increase in the Si2O3/Al2O3 ratio, since the 1990s. These findings indicate the intensification of the desertification process and the degradation of hydrological conditions within the nebkha. Primary factors contributing to these transformations include the steadily rising temperature, which leads to an increased evaporation rate, and a substantial rise in human water consumption. These indicate an elevated risk of future nebkha reactivation. This reactivation, in turn, could potentially accelerate the process of regional desertification and lead to an ecological crisis.

Background:Regional disparities in healthcare resource allocation across space and time present significant challenges to the global achievement of SDG 3, SDG 10, and SDG 11. To this end, we proposed a joint spatiotemporal evaluation framework to assess the synergistic efficiency of multiple healthcare resources. Methods:Using China as a case study, we analyzed data from 365 cities (2000-2021) on three key healthcare resource indicators: hospitals, hospital beds, and physicians. A composite healthcare resource score was constructed using the entropy weight method. We developed a three-dimensional joint spatiotemporal evaluation framework incorporating spatial Gini coefficient, emerging hotspot analysis, and Bayesian spatiotemporally varying coefficients (BSTVC) model with spatiotemporal variance partitioning index (STVPI) to evaluate spatiotemporal equity, agglomeration, and influencing factors. Individual indicators were evaluated to validate the framework's robustness. Results:(i) Spatiotemporal description: The composite indicator, weighted by hospitals (25%), hospital beds (46%), and physicians (29%), showed only a modest increase from 2000 to 2021, with persistently lower values in western and northern regions. (ii) Common spatiotemporal equity: The spatial Gini coefficient for the composite indicator increased annually by 0.34%, mirroring trends in hospital beds (0.34%) and physicians (0.26%) but contrasting with hospitals (-0.32%). This suggested that declining equity was mainly driven by hospital beds and physicians, partially offset by the more balanced distribution of hospitals. (iii) Common spatiotemporal agglomeration: Hotspot intensity for the composite indicator was lower than that for hospitals but higher than that for hospital beds and physicians. Cold spots were more concentrated for the composite indicator than for any individual indicator, with less than 10% overlap across the three indicators, indicating weak regional synergy. (iv) Common spatiotemporal drivers: BSTVC and STVPI methods revealed consistent patterns of explainable percentages across four healthcare resource indicators, with population density (37.96%, 95% CI: 30.05-43.05%) and employed population density (31.63%, 30.69-33.83%) emerging as dominant common drivers, supporting unified and coordinated policy interventions. Discussion:We proposed a joint spatiotemporal evaluation framework to quantify both common and differentiated allocation patterns and driving factors across multiple healthcare resource indicators, highlighting the necessity for type-specific, temporally responsive, and spatially adaptive interventions to support dynamic monitoring and precise regulation of regional healthcare resource allocation globally.

To comprehensively assess regional landslide hazards, we propose a geospatial approach that jointly evaluates both the probability of occurrence (susceptibility) and potential destructive power (intensity) within a single framework, overcoming the limitations of previous studies that treated these two disaster scenarios independently. Focusing on the largest landslide event triggered by the Wenchuan earthquake in China, we collected landslide occurrence and count data at the slope unit level, alongside 18 environmental factors, including seismic data. To enable this multi-hazard single-framework evaluation, we employed two Bayesian spatial joint regressions: the spatial shared component model (SSCM) and the spatial shared hyperparameter model (SSHM). This joint assessment focuses on three key components: identifying shared influencing factors, capturing shared spatial autocorrelated random effects, and jointly predicting susceptibility and intensity maps. Additionally, we enhanced the traditional absolute intensity index into the relative intensity by accounting for slope unit size. Both Bayesian SSCM and SSHM, incorporating multiple environmental drivers (seismic, topographical, geological, hydrological, and human activities), successfully evaluated landslide susceptibility and intensity under a single analytical frame. SSHM outperformed SSCM in terms of model fit and predictive accuracy, as revealed by cross-validation. While SSCM overfitted the landslide distribution of spatial autocorrelated random effect, SSHM provided a smoother, more spatially diverse representation. Both models consistently identified slope as the shared key factor influencing susceptibility and intensity, with the top four additional environmental factors varying slightly but all related to seismic activity. The concurrent susceptibility and absolute intensity maps produced by both models exhibited similar patterns, while relative intensity mapping identified new high-hazard areas within smaller slope units that were previously overlooked by susceptibility and absolute intensity. We established a Bayesian-based single modeling framework for joint hazard assessment and prediction of regional susceptibility and intensity, providing a cutting-edge geospatial paradigm for multi-objective hazard assessment in global environmental disaster management.

BackgroundThe global rise of private hospitals is crucial for achieving universal health coverage, yet the development of public and private hospitals remains uncoordinated. This study explores the co-evolution of private and public hospitals, focusing on their spatiotemporal disparities, geospatial interactions, and the social determinants under policy guidance.MethodsWe used Sichuan province, China, as a case study and collected hospital-level annual report data from 2002 to 2020. Spatiotemporal analyses examined the co-evolution of public and private hospitals across different hierarchical levels. The Gini coefficient assessed the spatial equity of hospital bed resources, while spatial accessibility was measured using the provider-to-population ratio at district and county levels. Trend analysis quantified changes in accessibility over time. Fixed-effects models identified social determinants influencing hospital resource allocation.ResultsBetween 2002 and 2020, the proportion of districts/counties in Sichuan with more than 4.8 hospital beds per 1,000 population increased significantly, from 5.46% to 43.72%. The equity of medical bed resources also improved across the province. The proportion of districts/counties with more than 3.3 public hospital beds per 1,000 population rose from 12.57% to 50.27%, and the share of districts/counties where private hospitals made up 25% or more of total beds grew from 2.19% to 53.01%. Geospatial interaction maps revealed regional disparities: complementarity in advantaged areas, persistent deficits in remote regions, and geographical compression of public hospitals in urban centers. Our analysis further showed that private hospital accessibility positively correlates with population density, per capita GDP, and government health expenditure, while public hospital accessibility is positively linked to per capita GDP, urbanization, and health expenditure. However, public primary hospital accessibility negatively correlates with per capita GDP.DiscussionWhile private hospitals have rapidly expanded bed capacity, policy biases and market incentives have caused a structural imbalance, with a shortage of high-end services and an excess of low-end resources. In contrast, public hospitals have upgraded hierarchically, concentrating high-quality resources in urban areas. However, basic medical supply remains insufficient in remote regions, exacerbating disparities in healthcare accessibility and quality, and hindering the achievement of universal health coverage.

Trace metals (TMs) in soil have garnered widespread attention due to their adverse impacts on crop production and human health. In this study, 385 topsoil samples (0-20 cm) and 118 deep soil samples (150-200 cm) were collected from Nanyang City to investigate the spatial distribution, contamination, and source allocation of TMs. Geographic information system analysis, contamination factor (CF), geo-accumulation index (Igeo), and positive matrix factorization (PMF) model were utilized. The results showed that the mean contents of Cu, Hg, Pb, and Zn in the study area's topsoil (25.50, 0.035, 28.51, and 79.46 mg kg-1) exceeded background values. CF and Igeo results indicated that over 60% of soil samples were contaminated by Hg, the main contaminant in soil. By combining correlation and PMF analysis, three sources of TMs were identified for (a) Pb (75.4%), Hg (63.7%), and Zn (55.6%), primarily associated with industrial emissions and atmospheric deposition; (b) Co (57.5%), Cr (50.8%), Cu (59.2%), and Ni (46.4%) mainly originated from natural sources; and (c) As (72.8%) primarily from agricultural activities. The proportions of the three sources were 36.27%, 39.76%, and 23.97%, respectively. Anthropogenic sources contributed the most to soil TMs (60.24%), higher than natural sources, indicating substantial accumulation of TMs in topsoil due to significant anthropogenic activities. This study provided useful information for environmental management planning, decision-making, and contamination assessment.

Coilia nasus are an important fish resource in the Yangtze River, and the Yangtze River Estuary is a crucial migration pathway for them. In this study, we used otolith microchemistry to analyze the strontium/calcium (Sr/Ca) ratios and Sr contents in the sagitta otolith of C. nasus from the south branch (SB) and north branch (NB) of the Yangtze River Estuary and obtained the diversity of migration patterns and spawning ground distribution for C. nasus. The results indicate that C. nasus from both branches include two types of habitat history: freshwater (F)–brackish water (B) (Type I) and F-B seawater (S) (Type II), with Type I being dominant at 62.50% in both branches. The C. nasus from the SB comprise six migration patterns, while that from the NB has seven migration patterns. The C. nasus from both branches hatch in F habitats. At the time of capture, the C. nasus from the SB predominantly remain in F, accounting for 62.5%, while C. nasus from the NB primarily stay in B, accounting for 87.5%. Throughout the migration process, C. nasus from both branches switch between different habitats, with C. nasus from the NB exhibiting more frequent transitions between F and B, showing a greater reliance on the estuarine brackish habitat. The radius of the first blue region near the core (Lf) and freshwater coefficient (Fc) of the otolith for C. nasus from both branches are divided into three groups: long-distance freshwater dependence (LD), medium-distance freshwater dependence (MD), and short-distance freshwater dependence (SD), with the LD only appearing in the SB, while the NB is primarily represented by MD. There is a correlation between the differences in Lf among different groups of C. nasus and the differences in the distance from the spawning grounds of C. nasus in different sections of the Yangtze River to the estuary (DYRE), reflecting the distribution pattern of C. nasus spawning grounds in different sections of the Yangtze River. This study provides theoretical guidance for the protection of migration pathways and maintenance of spawning grounds for C. nasus, which have significant practical value in the precise management of C. nasus resources in the Yangtze River Estuary.

The Huangling region is located in the central part of the Chinese Loess Plateau, which is sensitive to climate change due to the transitional characteristics of the natural environmental zone in which it is located. In this study, we utilized a spore–pollen analysis of the Tianjiahe (TJH) profile in Huangling to apply the pollen–climate factor conversion function method. This approach allowed us to quantitatively reconstruct the paleotemperature and paleoprecipitation of the Huangling area during the Middle and Late Holocene. The results show that the Huangling area experienced four climatic stages during the Middle and Late Holocene, including mild and slightly humid → warm and humid → warm and slightly humid → warm and humid. Except for the period of 5.3–4.72 kaBP, during which the climate was relatively cool and dry compared to the present, the climate in the remaining period (4.72–0.03 kaBP) was warmer and more humid than that of the present. The above results provide an important insight for further exploring the mechanism of paleoclimate change and predicting future climate change.

BackgroundWhile stationary links between childhood hand, foot and mouth disease (HFMD) and air pollution are known, a comprehensive study on their heterogeneous relationships (nonstationarity), jointly considering numerical, temporal and spatial dimensions, has not been reported.MethodsMonthly HFMD incidence and air pollution data were collected at the county level from Sichuan-Chongqing, China (2009-2011), alongside meteorological and social environmental covariates. Key influential factors were identified using random forest (RF) under the stationary assumption. Factors' numerically, temporally, and spatially heterogeneous relationships with HFMD were assessed using generalized additive model (GAM) and geographically and temporally weighted regression (GTWR).ResultsOur findings highlighted the relatively higher stationary contributions of fine particulate matter (PM2.5) and ozone (O3) to HFMD incidence across Sichuan-Chongqing counties. We further uncovered heterogeneous impacts of PM2.5 and O3 from three nonstationary perspectives. Numerically, PM2.5 showed an inverse 'V'-shaped relationship with HFMD incidence, while O3 exhibited a complex pattern, with increased HFMD incidence at low PM2.5 and moderate O3 concentrations. Temporally, PM2.5's impact peaked in autumn and was weakest in spring, whereas O3's effect was strongest in summer. Spatially, hotspot mapping revealed high-risk clusters for PM2.5 impact across all seasons, with notable geographical variations, and for O3 in spring, summer, and autumn, concentrated in specific regions of Sichuan-Chongqing.ConclusionsThis study underscores the nuanced and three-perspective heterogeneous influences of air pollution on HFMD in small areas, emphasizing the need for differentiated, localized, and time-sensitive prevention and control strategies to enhance the precision of dynamic early warnings and predictive models for HFMD and other infectious diseases, particularly in the fields of environmental and spatial epidemiology.

EDITORIAL article Front. Public Health, 04 January 2024Sec. Health Economics Volume 11 - 2023 | https://doi.org/10.3389/fpubh.2023.1349985

BACKGROUND:Ensuring universal health coverage and equitable access to health services requires a comprehensive understanding of spatiotemporal heterogeneity in healthcare resources, especially in small areas. The absence of a structured spatiotemporal evaluation framework in existing studies inspired us to propose a conceptual framework encompassing three perspectives: spatiotemporal inequalities, hotspots, and determinants. METHODS:To demonstrate our three-perspective conceptual framework, we employed three state-of-the-art methods and analyzed 10 years' worth of Chinese county-level hospital bed data. First, we depicted spatial inequalities of hospital beds within provinces and their temporal inequalities through the spatial Gini coefficient. Next, we identified different types of spatiotemporal hotspots and coldspots at the county level using the emerging hot spot analysis (Getis-Ord Gi* statistics). Finally, we explored the spatiotemporally heterogeneous impacts of socioeconomic and environmental factors on hospital beds using the Bayesian spatiotemporally varying coefficients (STVC) model and quantified factors' spatiotemporal explainable percentages with the spatiotemporal variance partitioning index (STVPI). RESULTS:Spatial inequalities map revealed significant disparities in hospital beds, with gradual improvements observed in 21 provinces over time. Seven types of hot and cold spots among 24.78% counties highlighted the persistent presence of the regional Matthew effect in both high- and low-level hospital bed counties. Socioeconomic factors contributed 36.85% (95% credible intervals [CIs]: 31.84-42.50%) of county-level hospital beds, while environmental factors accounted for 59.12% (53.80-63.83%). Factors' space-scale variation explained 75.71% (68.94-81.55%), whereas time-scale variation contributed 20.25% (14.14-27.36%). Additionally, six factors (GDP, first industrial output, local general budget revenue, road, river, and slope) were identified as the spatiotemporal determinants, collectively explaining over 84% of the variations. CONCLUSIONS:Three-perspective framework enables global policymakers and stakeholders to identify health services disparities at the micro-level, pinpoint regions needing targeted interventions, and create differentiated strategies aligned with their unique spatiotemporal determinants, significantly aiding in achieving sustainable healthcare development.

Hypophthalmichthys nobilis are widely distributed in the Yangtze River basin and its related lakes. They are an important economic fish species and are a famous cultured species known as the “Four Famous Domestic Fishes” in China. Currently, with the fishing ban in the Yangtze River basin, fishing for H. nobilis in the natural water bodies of the Yangtze River basin has been completely prohibited. In order to identify the sources of H. nobilis appearing in the market, further control and accountability is necessary to trace the sources of H. nobilis in the Yangtze River basin and its related water bodies. Therefore, this study identified and traced different sources of H. nobilis through muscle element fingerprint analysis (EFA). The results show that H. nobilis from different stations have characteristic element compositions. The characteristic element of H. nobilis from Wuhan (WH) is Pb, which is significantly higher than that in other stations; the characteristic element from Anqing (AQ) is Hg, which is significantly higher than that in other stations; and the characteristic element from Taihu (TH) is Al, which is significantly higher than that in other water areas. Multivariate analysis selected different spatial distribution patterns in four discriminative element ratios (Pb/Ca, Cr/Ca, Na/Ca, and Al/Ca) in the muscle of H. nobilis in the Yangtze River basin and its related lakes. This study suggests that the screened discriminative elements can be used to visually distinguish different sources of H. nobilis and to quickly trace and verify the origin of newly emerging samples. Therefore, the use of selected discriminative element fingerprint features to trace the origin of new samples has been proven to be feasible. By further discriminating and verifying the muscle element fingerprints of new samples, the discrimination rate is high. Therefore, a multivariate analysis of muscle element fingerprints can be used for tracing the origins of samples of unknown origin in market supervision.

Recent studies have linked the cardiovascular events with the exposure to ambient fine particulate matter (PM2.5); however, the impact of PM2.5 chemical components on acute myocardial infarction (AMI) case fatality remains poorly understood. To address this gap, we included 178,340 hospitalised patients with AMI utilising the inpatient discharge database from Sichuan, Shanxi, Guangxi, and Guangdong, China spanning 2014-2019. We evaluated exposure to PM2.5 and its components (black carbon (BC), organic matter (OM), sulphate (SO2 4 ), nitrate (NO3 ), and ammonium (NH+4 )) using bilinear interpolation based on the patient's residential address. We used mixed-effects logistic regression models to investigate the associations of PM2.5 and its five components with in-hospital AMI case fatality. Per interquartile range (IQR) increment in short-term exposure (7-day average) to overall PM2.5 (odds ratio (OR): 1.086, 95 % confidence interval (CI): 1.045-1.128), SO24 (1.063, 1.024-1.104), BC (1.055, 1.023-1.089), OM (1.052, 1.019-1.086, and NO 3 (1.045, 1.003-1.089) were significantly associated with high risk of in-hospital AMI case fatality. The ORs per IQR increment in long-term exposure (annual average) were 1.323 (95% CI: 1.255-1.394) for PM2.5, followed by BC (1.271, 1.210-1.335), OM (1.243, 1.188-1.300), SO24 (1.212, 1.157-1.270), NO 3 (1.116, 1.075-1.159), and NH+4 (1.068, 1.031-1.106). Our study suggests that PM2.5 chemical components might be important risk factors for in- hospital AMI case fatality, highlighting the importance of targeted reduction of PM2.5 emissions, particularly BC, OM, and SO2 4.

Bayesian calibration of building energy simulation (BES) has gained growing attention for its capability to tackle uncertainties and narrow the gap between simulated and measured results using expert knowledge. However, how output or parameter correlations influence single-output Bayesian calibration (SOBC) and multiple-output Bayesian calibration (MOBC) for BES has not been investigated and compared. Hence, this paper intends to determine the impacts of output or parameter correlations and data informativeness on BC of BES. Aiming to leverage multiple outputs' correlations while comparing with traditional SOBC in calibration performance and computation cost, we also developed the MOBC model. Compared to including weakly correlated outputs or parameters, the results show that strongly correlated outputs or parameters in MOBC reduce the Coefficient of Variation of the Root Mean Squared Error (CVRMSE) by 5.56% and 4.729% respectively and bring notably better Continuous Ranked Probability Score (CRPS) results. Conversely, including strongly correlated parameters in SOBC causes worse model performance. These results reflect that SOBC has parameter identifiability issues that MOBC can solve. The findings contribute to our better understanding of the impacts of (1) output or parameter correlations and (2) different data streams' informativeness on the calibration performance of SOBC and our developed MOBC for BES.

The central section of China's South-to-North Water Diversion Project has been designated as a national water conservation area, and the soil ecological security in its associated watersheds is of great importance. A total of 204 soil samples (0–20 cm depth) were obtained from the Laoguanhe River Basin. The concentrations of seven elements (Cd, Cr, Cu, Ni, Zn, Pb and Hg) were determined using inductively coupled plasma mass spectrometry and atomic fluorescence spectrometry following a near-total acid dissolution. Data analyses (including potential ecological risk, principal component analysis, geostatistical analysis and the positive matrix factorization model) were applied to evaluate the contamination of soil heavy metals and to identify their sources. The research results demonstrated that the mean contents of these seven elements exceeded background values for Henan Province, China, indicating human disturbance. Ecological risk evaluation revealed that Cd was the most frequently detected and the heavy metal causing the most pollution. Principal component analysis indicated that Cr, Ni and Cu stem from natural sources, while Zn and Cd are predominantly influenced by agricultural activities. Additionally, industrial activities and atmospheric deposition were responsible for the excess presence of Pb and Hg. The study suggests taking measures to control Cd sources in agricultural areas, reducing input of heavy metals into the river and providing scientific support for managing water quality.