Spillover effects of structure-adjustment pollution reduction measures in China's iron and steel industry.

The iron and steel industry (ISI) is a significant source of sulfur dioxide and particulate matter pollution in China. Existing research on regional environmental regulation or ISI emission reduction strategies tends to overlook spillover effects and the enterprise perspective. During the heating season, production limitations in ISI are potential policy measures for achieving structural emission reductions in heavily polluted cities in China's Jing-Jin-Ji and surrounding regions. We adopt a bottom-up modeling approach, incorporating effective production time to describe enterprise behavior and establishing a quantitative trade model based on trade theory. By modeling three types of production restriction policies outlined in policy documents, we evaluate the emission reduction effects of structure-adjustment measures using the example of reduced effective production time for steel-producing enterprises in the air pollution transmission channel in the Beijing-Tianjin-Hebei area. The results indicate the following: (1) Reducing the effective production time of ISI enterprises can help decrease domestic production value and total factor productivity in pollution-intensive industries, including but not limited to ISI. It also leads to reduced emissions of various pollutants in the implementation regions. (2) Due to interprovincial trade and input-output linkages, structural reduction measures in certain regions have implications for almost all other provinces' industrial structures. Differences in initial industrial structures, factor endowments, and geographical locations contribute to varying directions and magnitudes of industrial structural changes. Pollution-intensive industries' share tends to increase higher in less developed regions. (3) Our estimated pollution reduction is smaller compared to the literature evaluating clean air policies in similar regions using top-down strategies. This discrepancy arises because we analyze a single policy tool rather than modeling industry-wide emission fluctuations from the top down. Additionally, our modeling approach allows us to examine dynamic changes in comparative advantages. The increase in production scale for certain industries in policy-affected regions partially offsets the decline in pollution emissions. These findings enhance our understanding of structure-adjustment reduction measures' role and highlight their potential advantages and limitations.

Investigating spatio-temporal variations and contributing factors of land use-related carbon emissions in the Beijing-Tianjin-Hebei Region, China.

The land use change is the primary factor in influencing the regional carbon emissions. Studying the effects of land use change on carbon emissions can provide supports for the development policies of carbon emission. Using land use and energy consumption data, this study measures carbon emissions from land use dynamics in the Beijing-Tianjin-Hebei region from 2000 to 2020. The standard deviation ellipse model is employed to investigate the distribution characteristics of the spatial patterns of carbon emissions, while the Geographically and Temporally Weighted Regression (GTWR) model is used to examine the contributing factors of carbon emissions and their spatial and temporal heterogeneity. Results indicate a consistently increasing trend in carbon emissions from land use in the Beijing-Tianjin-Hebei region from 2000 to 2020. Construction land is characterized with both the primary source and an increasing intensity of carbon emissions. Besides, the spatial distribution of carbon emissions from land use in the Beijing-Tianjin-Hebei region demonstrates an aggregation pattern from in the northeast-southwest direction towards the center, with a greater aggregation trend in the east-west direction compared to that in the south-north direction. During the study period, a positive correlation was documented between carbon emissions and factors including total population, economic development level, land use degree, and landscape patterns. This correlation showed a decreasing trend and reached a stable level at the end of the study period. Moreover, the analysis showed a negative correlation between industrial structure and carbon emissions, which showed an increasing trend and reached a relatively high level at the end of the study period.

Classification of deep and shallow groundwater wells based on machine learning in the Hebei Plain North China.

Accurately determining the extraction volumes from various aquifers is crucial for effectively managing groundwater overexploitation. A key initial step in quantifying extracted groundwater volumes involves the classification of groundwater wells as either deep or shallow. This study evaluated 881,872 groundwater wells in the Hebei Plain, applying machine learning techniques to classify wells with unknown depths. Through the hydrogeological borehole data, the groundwater wells with known depth are divided into deep wells and shallow wells. Four machine learning algorithms-Random Forest, Support Vector Machine, Logistic Regression, and Naive Bayes-were employed to classify groundwater wells with unknown depths. The accuracy of these models was validated using known-depth well classifications. The results reveal that the Random Forest algorithm exhibited the highest performance among the models, achieving an overall accuracy of 91.23%. According to the Random Forest model, 43.51% of groundwater wells with unknown depths were classified as deep, while 56.49% were classified as shallow. The study also found that wells in areas where salinity exceeds 2 g/L are primarily deep groundwater wells. These findings provide valuable technical insight for groundwater well decommissioning and facilitate the assessment of extracted volumes of deep and shallow groundwater.

Rapid discriminant analysis for the origin of specialty yam based on multispectral data fusion strategies.

In order to achieve rapid and effective identification of Hebei yam, a qualitative discrimination model was constructed based on near infrared (NIR), mid infrared (MIR), and microscopic Raman spectra in combination with individual spectra and multispectral data fusion strategies. The results showed that the gray wolf optimizer-support vector machine (GWO-SVM) model constructed by mid-level fusion using the three feature spectra performed the best in distinguishing the geographic origin of the yam, with a prediction accuracy of 100.00% in both the training set and the test set, and an F1 score of 1.00. The results indicated that due to spectral complementarity, NIR, MIR and Raman combined with feature-level fusion can be used as a powerful, non-destructive, fast and feasible tool for geographic origin classification and brand protection of Hebei yam. This work is expected to be a potential method for origin identification analysis and quality monitoring in the food and pharmaceutical industries.

[Estimation of Near-surface Ozone Concentration in the Beijing-Tianjin-Hebei Region Based on XGBoost-LME Model].

High spatiotemporal resolution data on near-surface ozone concentration distribution is of great significance for monitoring and controlling atmospheric ozone pollution and improving the living environment. Using TROPOMI-L3 NO2, HCHO products, and ERA5-land high-resolution data as estimation variables, an XGBoost-LME model was constructed to estimate the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. The results showed that： ① Through correlation analysis, surface 2 m temperature （T2M）, 2 m dewpoint temperature （D2M）, surface solar radiation downwards （SSRD）, tropospheric formaldehyde （HCHO）, and tropospheric nitrogen dioxide （NO2） were important factors affecting the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. Among them, T2M, SSRD, and D2M had strong correlations, with correlation coefficients of 0.82, 0.75, and 0.71, respectively. ② Compared with that of other models, the XGBoost-LME model had the best performance in terms of various indicators. The ten-fold cross-validation evaluation indicators R2, MAE, and RMSE were 0.951, 9.27 µg·m-3, and 13.49 µg·m-3, respectively. At the same time, the model performed well at different time scales. ③ In terms of time, there was a significant seasonal difference in near-surface ozone concentration in the Beijing-Tianjin-Hebei Region in 2019, with the concentration changing in the order of summer > spring > autumn > winter. The monthly average ozone concentration in the region showed an inverted "V" trend, with a slight increase in September. The highest value occurred in July, whereas the lowest value occurred in December. In terms of spatial distribution, the near-surface ozone concentrations in the Beijing-Tianjin-Hebei Region during the months of February and March were generally at the same levels. In January, November, and December, there was a relatively insignificant trend of higher concentrations in the north and lower concentrations in the south. For the remaining months, the spatial distribution of near-surface ozone concentrations in this area predominantly exhibited a pattern of higher concentrations in the south and lower concentrations in the north. High-value areas were predominantly found in the plain regions of the southern part with lower altitudes, dense population, and higher industrial emissions； low-value areas, on the other hand, were primarily located in mountainous areas of the northern part with higher altitudes, sparse population, higher vegetation coverage, and lower industrial emissions.

Changes in climate attributes and harvest area structures jointly determined spatial-temporal variations in water footprint of maize in the Beijing-Tianjin-Hebei region.

Irrigation contributes significantly to boosting crop yield and ensuring food security. However, in the Beijing-Tianjin-Hebei (BTH) region, unsustainable irrigation practices have led to serious outcomes on freshwater resources. Balancing irrigation with crop productivity in this region, currently facing complex challenge, requires a comprehensive understanding of its spatial pattern and thus to seeking for potential optimization of current crop structures. In this study, we employed the concept of water footprint (WFP) to assess the spatial-temporal patterns of water footprint for maize in BTH region at the county level for the years 2005, 2010, 2015, and 2020, untangled the relative impacts on WFP from climate attributes and harvest area structures. Our results showed significant regional heterogeneities in both blue water requirement and green water requirement, ranging from 64.6 mm to 290.7 mm. Yearly anomalies of climate attributes and maize harvest jointly influenced water footprints, with the highest value of 1.06 × 1011 m3 occurring in the year 2015. The green water footprints, linked to precipitation, dominated the total water footprint compared to the blue water footprint associated with irrigation. Additionally, we observed an increasing influence of maize harvest area on the temporal changes in water footprints, with these changes becoming more concentrated in the east-central region over time. Our findings underscore the respective contributions of annual climate attribute changes and harvest area variations at the county level, highlighting regions where urgent interventions are required to enhance the sustainability of water usage for agriculture.

Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS.

To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.

An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model.

A two-stage model integrating a spatiotemporal linear mixed effect (STLME) and a geographic weight regression (GWR) model is proposed to improve the meteorological variables-based aerosol optical depth (AOD) retrieval method (Elterman retrieval model-ERM). The proposed model is referred to as the STG-ERM model. The STG-ERM model is applied over the Beijing-Tianjin-Hebei (BTH) region in China for the years 2019 and 2020. The results show that data coverage increased by 39.0% in 2019 and 40.5% in 2020. Cross-validation of the retrieval results versus multi-angle implementation of atmospheric correction (MAIAC) AOD shows the substantial improvement of the STG-ERM model over earlier meteorological models for AOD estimation, with a determination coefficient (R2) of daily AOD of 0.86, root mean squared prediction error (RMSE) and the relative prediction error (RPE) of 0.10 and 36.14% in 2019 and R2 of 0.86, RMSE of 0.12 and RPE of 37.86% in 2020. The fused annual mean AOD indicates strong spatial variation with high value in south plain and low value in northwestern mountainous areas of the BTH region. The overall spatial seasonal mean AOD ranges from 0.441 to 0.586, demonstrating strongly seasonal variation. The coverage of STG-ERM retrieved AOD, as determined in this exercise by leaving out part of the meteorological data, affects the accuracy of fused AOD. The coverage of the meteorological data has smaller impact on the fused AOD in the districts with low annual mean AOD of less than 0.35 than that in the districts with high annual mean AOD of greater than 0.6. If available, continuous daily meteorological data with high spatiotemporal resolution can improve the model performance and the accuracy of fused AOD. The STG-ERM model may serve as a valuable approach to provide data to fill gaps in satellite-retrieved AOD products.

The impact of China's urbanization on ecosystem service value from the perspective of gross ecosystem product: a case study of Beijing-Tianjin-Hebei region.

Gross ecosystem product (GEP) is an aggregate measure of the monetary value of final ecosystem services, or the direct benefits that people derive from nature. GEP can provide decision makers with clear and competing evidence of the monetary value of ecosystem services. However, the relationship between GEP and urbanization has not been clarified which is not conducive to the decision-making role of GEP in the process of urban sustainable development. This work focused on the 'Beijing-Tianjin-Hebei' (BTH) urban agglomeration as a case study of the dynamics of ecological production amidst rapid economic and urban development, and coupled a spatial-temporal analysis of regional ecological change based on GIS (Geographic Information System) with economic valuation methods using official statistics and survey data. Results showed that from 2000 to 2020, the GEP increased from 1.55 trillion to 2.36 trillion, the value of provisioning services and cultural services increased from 0.51 to 0.71 trillion to 0.10-0.65 trillion. The value of regulation services showed an upward and downward trend (0.94-1.03-0.98) due to the rapid economic development in the Beijing-Tianjin-Hebei region. There were obvious spatial differences in the distribution of the GEP, in which Beijing, Tianjin, Tangshan, Cangzhou GEP accounted for 15%, 14%, 16% and 11%, respectively. During 2000-2020, there is a significant correlation between urbanization index (UI) and GEP in BTH, but the correlation between GEP and UI shows a trend of first increasing and then decreasing. The correlation between UI and EPS and ERS gradually decreases, and the impact of UI on ETS shows a significant positive correlation. In the future, it can be foreseen that urbanization will suppress the increase of GEP.

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region.

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

Assessment of vegetation net primary productivity variation and influencing factors in the Beijing-Tianjin-Hebei region.

Exploring the spatiotemporal variations of vegetation net primary productivity (NPP) and analyzing the relationships between NPP and its influencing factors are vital for ecological protection in the Beijing-Tianjin-Hebei (BTH) region. In this study, we employed the CASA model in conjunction with spatiotemporal analysis techniques to estimate and analyze the spatiotemporal variations of NPP in BTH and different ecological function sub-regions over the past two decades. Subsequently, we established three scenarios (actual, climate-driven and land cover-driven) to assess the influencing factors and quantify their relative contributions. The results indicated that the overall NPP in BTH exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 3.83 gC·m-2a-1. Furthermore, all six sub-regions exhibited an increase. The Bashang Plateau Ecological Protection Zone (BP) exhibited the highest growth rate (5.03 gC·m-2a-1), while the Low Plains Ecological Restoration Zone (LP) exhibited the lowest (2.07 gC·m-2a-1). Geographically, the stability of NPP exhibited a spatial pattern of gradual increase from west to east. Climate and land cover changes collectively increased NPP by 0.04 TgC·a-1 and 0.07 TgC·a-1, respectively, in the BTH region. Climate factors were found to have the greatest influence on NPP variations, contributing 40.49% across the BTH region. This influence exhibited a decreasing trend from northwest to southeast, with precipitation identified as the most influential climatic factor compared to temperature and solar radiation. Land cover change has profound effects on ecosystems, which is an important factor on NPP. From 2000 to 2020, 15.45% area of the BTH region underwent land cover type change, resulting in a total increase in NPP of 1.33 TgC. The conversion of grass into forest brought about the 0.89 TgC increase in NPP, which is the largest of all change types. In the area where land cover had undergone change, the land cover factor has been found to be the dominant factor influencing variations in NPP, with an average contribution of 49.37%. In contrast, in the south-central area where there has been no change in land cover, the residual factor has been identified as the most influential factor influencing variations in NPP. Our study highlights the important role of land cover change in influencing NPP variations in BTH. It also offers a novel approach to elucidating the influences of diverse factors on NPP, which is crucial for the scientific assessment of vegetation productivity and carbon sequestration capacity.

Numerical study of the future PM2.5 concentration under climate change and best-health-effect (BHE) scenario.

The Beijing-Tianjin-Hebei (BTH) is one of the key areas with PM2.5 air pollution in China. Driven by the PM2.5 target accessibility of the Interim Target-1 (IT-1) by World Health Organization (WHO) and China's carbon neutrality, this study explored and quantified the contribution of climate change and anthropogenic emission to future PM2.5 in the region. The experiments considered future climate change scenarios RCP8.5, RCP4.5, and RCP2.6 with the baseline (Base) and reduced emission (EIT1) inventories in 2030, and RCP4.5 climate scenario with 3 emission inventories in 2050, the additional strong control emission scenario called Best-Health-Effect (BHE). Under various climate scenarios, the future air quality research modelling system projected annual PM2.5 concentrations nearing 35 µg/m3 in 2030. However, considering only the effect of emission reduction, the annual PM2.5 concentrations under EIT1 emission scenario is about 35% less than under Base scenario in different key years. The future PM2.5 concentrations are highly related to anthropogenic emission from human activities, while climate change by 2030 or 2050 has little impact on future air quality over the BTH region. The BHE emission reduction is significantly required for China to meet the new PM2.5 guideline value of WHO in the future.

Spatiotemporal dynamics and influencing factors of CO2 emissions under regional collaboration: Evidence from the Beijing-Tianjin-Hebei region in China.

Against the backdrop of global climate change and the "dual carbon" target, cities have a significant responsibility to achieve carbon reduction targets. As a crucial urban agglomeration in northern China, effectively balancing economic growth with CO2 emission reduction to achieve high-quality economic development remains a significant challenge that the Beijing-Tianjin-Hebei region should address both presently and in the future. The objective of this study is to utilize nighttime lighting data and energy consumption information to quantify the CO2 emissions of diverse cities within the Beijing-Tianjin-Hebei region spanning from 2006 to 2020. The research aims to analyze the spatial progression patterns of CO2 emissions across these urban centers, identify key determinants and their interrelations, and delve into the underlying mechanisms pivotal for advancing carbon mitigation strategies within urban agglomerations. The results indicate that: with an exception in Beijing where CO2 emissions slightly decreased compared to 2006, CO2 emissions increased across cities in the Beijing-Tianjin-Hebei region by 2020. High-value CO2 emission areas are primarily concentrated in central of the study area, exhibiting negative spatial correlation characteristics. Based on its urban development positioning, it is imperative for the Beijing-Tianjin-Hebei urban agglomeration to formulate and implement carbon reduction strategies on innovative development, industrial upgrading, and ecological protection among other aspects towards coordinated low-carbon development.

Identification of a new HIV-1 circulating recombinant form (CRF159_01103) derived from CRF103_01B and CRF01_AE in Hebei Province, China.

Recombinant HIV-1 genomes identified in three or more epidemiological unrelated individuals are defined as circulating recombinant forms (CRFs). CRFs can further recombine with other pure subtypes or recombinants to produce secondary recombinants. In this study, a new HIV-1 intersubtype CRF, designated CRF159_01103, isolated from three men who have sex with men with no epidemiological linkage, was identified in Baoding city, Hebei Province, China. CRF159_01103 was derived from CRF103_01B and CRF01_AE. Bayesian molecular clock analysis was performed on the CRF01-AE and CRF103_01B regions of CRF159_01103. The time of origin of CRF159_01103 was predicted to be 2018-2019, indicating that it is a recent recombinant virus. The emergence of CRF159_01103 has increased the complexity of the HIV-1 epidemic in Hebei Province.

Climate and land use changes impact the trajectories of ecosystem service bundles in an urban agglomeration: Intricate interaction trends and driver identification under SSP-RCP scenarios.

The delivery of ecosystem services (ESs), particularly in urban agglomerations, faces substantial threats from impending future climate change and human activity. Assessing ES bundles (ESBs) is critical to understanding the spatial allocation and interactions between multiple ESs. However, dynamic projections of ESBs under various future scenarios are still lacking, and their underlying driving mechanisms have received insufficient attention. This study examined the Beijing-Tianjin-Hebei urban agglomeration and proposed a framework that integrates patch-generating land use simulation into three shared socioeconomic pathway (SSP) scenarios and clustering analysis to assess spatiotemporal variations in seven ESs and ESBs from 1990 to 2050. The spatial trajectories of ESBs were analyzed to identify fluctuating regions susceptible to SSP scenarios. The results indicated that (1) different scenarios exhibited different loss rates of regulating and supporting services, where the mitigation of degradation was most significant under SSP126. The comprehensive ES value was highest under SSP245. (2) Bundles 1 and 2 (dominated by regulating and supporting services) had the largest total proportion under SSP126 (51.92 %). The largest total proportion of Bundles 4 and 5 occurred under SSP585 (48.96 %), with the highest provisioning services. The SSP126 scenario was projected to have the least ESB fluctuation at the grid scale, while the most occurred under SSP585. (3) Notably, synergies between regulating/supporting services were weaker under SSP126 than under either SSP245 or SSP585, while trade-offs between water yield and non-provisioning services were strongest. (4) Forestland and grassland proportions significantly affected carbon sequestration and habitat quality. Climatic factors (precipitation and temperature) acted as the dominant drivers of provisioning services, particularly water yield. Our findings advocate spatial strategies for future regional ES management to address upcoming risks.

Efficiency measurement and inefficiency analysis of low-carbon logistics in the Beijing-Tianjin-Hebei region, China.

Under the dual-carbon goals, enhancing the green development level of logistics industry and realizing its low-carbon transformation are important issues that need to be solved urgently. Amidst the continuous escalation in the total energy consumption of the national logistics industry, the Beijing-Tianjin-Hebei (BTH) region has exhibited a favorable descending trajectory in this respect. It is necessary to investigate the underlying reasons. Based on the panel data from 2012 to 2021, the DEA and Malmquist index are employed to analyze the low-carbon logistics efficiency of the BTH region from both static and dynamic perspectives. Furthermore, the inefficiency analysis is conducted to identify the deficiencies of low-carbon logistics industry in this region. Results show that (1) from the static perspective, the development of low-carbon logistics industry in the BTH region is relatively unbalanced. Compared to Tianjin and Hebei, Beijing's low-carbon logistics efficiency is significantly lower, becoming the focal area for attention; (2) from the dynamic perspective, technological progress is the main reason for the fluctuation of total factor productivity in the BTH region and a constraining factor for further improvements; (3) from the results of inefficiency analysis, the forthcoming emphasis on low-carbon logistics in Beijing should be on optimizing the number of logistics practitioners, transportation efficiency, and energy efficiency. Economic output and energy efficiency are relatively vulnerable aspects in Tianjin and Hebei, respectively, warranting due consideration. The research results of this paper have important practical implications for better developing low-carbon logistics in the BTH region and leveraging its leading role nationwide.

Epidemiological Characterisation of Chronic Diseases Among Civil Servants in Hebei Province, China: A Cross-Sectional Real-World Study of Approximately 50,000 Adults.

Background: The objective of this investigation was to explore the health status and epidemiological features of civil servants working in Hebei Province, China. Methods: Data was collected through a cross-sectional survey that involved 50,039 adult civil servants in Hebei Province. The research was conducted at the Hebei Provincial Medical Examination Centre and included inquiries about demographics, health behaviours, chronic illnesses, and abnormal check-up indicators. Statistical data were used to determine the prevalence of chronic diseases and abnormal health examination indicators. Subgroup analyses by sex were then conducted to calculate the similarities and differences in health status between men and women. Results: The findings of the survey indicate that 14.2% of individuals who work in civil service suffer from obesity. Additionally, a considerable proportion of this group display poor health behaviours, including smoking and alcohol consumption. Hypertension (21.03%), dyslipidaemia (10.88%), and diabetes mellitus (8.56%) were identified as the top three chronic diseases, while cardiovascular diseases were prevalent at 6.27% among civil servants. Ultrasound showed a high prevalence of fatty liver, non-smooth gallbladder and liver cysts (44.61%, 33.24% and 23.26% respectively). The occurrence of pulmonary nodules was alarmingly high at 88.48%, while thyroid nodules were found in as much as 62.65% of patients. Abnormal E/A values≤1, prostate hyperplasia, breast nodules, and carotid plaques were also prevalent, with percentages ranging from 46.76% to 63.04%. In subgroup analyses by gender, the prevalence of chronic diseases and abnormal screening indicators was consistently higher in men than in women, with the exception of thyroid nodules and thyroid surgery. Conclusion: The survey revealed a large proportion of civil servants in Hebei Province, China, suffering from chronic diseases, underscoring the necessity for enhanced attention to chronic disease prevention and management in this group and emphasising the requirement for focused interventions aimed at improving health outcomes.

[Spatial and Temporal Distribution Characteristics of Ozone Concentration and Health Benefit Assessment in the Beijing-Tianjin-Hebei Region from 2015 to 2020].

To evaluate the spatial and temporal distribution characteristics of ambient ozone (O3) in the Beijing-Tianjin-Hebei (BTH) Region, the land use regression (LUR) model and random forest (RF) model were used to simulate the ambient O3 concentration from 2015 to 2020. Meanwhile, all-cause, cardiovascular, and respiratory mortalities as well as economic losses attributed to O3 were also estimated. The results showed that upward trends with fluctuation were observed for ambient O3 concentration, mortalities, and economic losses attributable to O3 exposure in the BTH Region from 2015 to 2020. The areas with high O3 concentration and great changes were concentrated in the central and southwestern regions, whereas the concentration in the northern region was low, and the change degree was small. The spatial distribution of the mortalities was also consistent with the spatial distribution of O3 concentration. From 2015 to 2020, the economic losses regarding all-cause mortality and cardiovascular mortality increased in 13 cities of the BTH Region, whereas the economic losses of respiratory mortality decreased in 4 cities in the BTH Region. The results indicated that the priority areas for O3 control were not uniform. Specifically, Beijing, Tianjin, Hengshui, and Xingtai were vital areas for O3 pollution control in the BTH Region. Differentiated control measures should be adopted based on the characteristics of these target areas to decline O3 concentration and reduce health impacts and economic losses associated with O3 exposure.

The impact of cross-regional social and ecological interactions on ecosystem service synergies.

Increasing socioecological systems (SESs) sustainability requires establishing a reasonable cross-regional social and ecological interaction. In this study, we examine how cross-regional ecological and social interactions affect synergistic effects. Using InVEST and correlation analysis with data from 2010 through 2020, we assessed ESs (i.e., water retention-WR, nutrient retention-NR, and carbon storage-CS) in the Beijing-Tianjin-Hebei (BTH) region. A small watershed, a river network, and settlement development capacity are used to delineate ecological and social interactions units. Based on a Bayesian network model that considers population, economy, and spatial agglomeration patterns between social units, we assessed the potential for achieving a synergistic improvement of ESs and the driving forces behind them. The results show that ESs in the BTH region compete, only a small percentage (6.38%) shows synergetic improvement across CS, WR, and NR. It is beneficial for upstream watersheds to retain water and nutrients, but to maintain carbon storage they may sacrifice water retention. Upstream areas with less development and higher vegetation density have better ecosystem integrity of up- and down-stream watersheds, and can be enhanced with minimal human impact, as social interactions and settlement spatial structures influence ES synergies. There is a higher risk for ecological issues in downstream areas, but greater awareness and collaboration can lead to better ES synergies.

[Spatial Distribution Characteristics of PM2.5 and O3 in Beijing-Tianjin-Hebei Region Based on Time Series Decomposition].

Notably, clear spatial differences occur in the distribution of air pollution among cities in the Beijing-Tianjin-Hebei (BTH) Region. Clarifying the concentration distribution of PM2.5 and O3 at different time scales is helpful to formulate scientific and effective pollution prevention and control measures. Here, the concentrations of PM2.5 and O3 were decomposed using a seasonal-trend decomposition procedure based on the loess (STL) method; their long-term, seasonal, and short-term components were obtained; and their temporal and spatial distribution characteristics were studied. The results showed that the decrease in PM2.5 concentration in the BTH Region from 2017 to 2021 was higher than that of O3. There was a positive correlation between PM2.5 and O3 concentrations in spring and summer and a negative correlation in autumn and winter. The short-term component and seasonal component had the greatest contribution to PM2.5 and O3 concentrations, respectively. There were two principal components in the seasonal and short-term components of PM2.5 and the long-term and short-term components of O3, corresponding to the central and southern part of Hebei Province and the northern part of the BTH Region. Sub-regional distribution of PM2.5 and O3 in the BTH Region at different time scales were found. Compared with that in the original series, the long-term component could better reflect the evolution trend of PM2.5 and O3 concentrations, and the standard deviation (SD) of the seasonal component and short-term component could be used to measure the fluctuation in PM2.5 and O3 concentrations in various cities. The SD of the seasonal and short-term components of the PM2.5 concentration in every city in front of Taihang Mountain was higher, and the SD of the short-term component of the O3 concentration in Tangshan was the highest.