Investigating the impact of weather on stroke in summer.

The objective of this study is to explore how changes in weather contribute to an increase in hospital admissions for stroke in summer. We collected 96,509 cases of stroke hospitalization data in Tianjin from 2016 to 2022 summer, along with corresponding meteorological data. The generalized additive model and distributed lag nonlinear model were used to analyze the lag and cumulative effects of temperature on stroke hospitalization. The research results show both the cold effect and the heat effect in summer would increase the risk of hospitalization. The effect of daily maximum temperature on stroke hospitalization was immediate when the temperature was higher, and delayed when the temperature was lower. However, the risk of stroke hospitalization increased more significantly with increasing temperature than with decreasing temperature. In the presence of one or more of the following three weather changes: sharp temperature increase, sharp temperature decrease, continuous high temperature, the daily number of stroke inpatients were higher than the average in the same period. 83% of the Inpatient-heavy events within the study period were caused by a combination of dramatic temperature changes and continuous high temperatures. In 48% of Inpatient-heavy events, continuous high temperature weather above 30℃ for at least 4 consecutive days were observed. And 55% of high temperature weather was accompanied by high humidity. When the daily relative humidity was greater than 70% and the daily maximum temperature was between 26 and 28℃ or more than 34℃, or the daily maximum temperature changes over 10℃ within 48 h, the number of daily inpatients was more than 1.2 times of the average daily inpatients. More attention should be paid to the combined effects of continuous high temperature and sudden temperature changes in summer stroke prevention.

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.

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.

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.

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.

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.

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.

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 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.

[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.

[Source and Cause Analysis of High Concentration of Inorganic Aerosol During Two Typical Pollution Processes in Winter over Tianjin].

Inorganic aerosol is the main component of haze days in winter over Tianjin. In this study, two typical high concentrations of secondary inorganic aerosol (SIA) processes, defined as CASE1 and CASE2, were selected during polluted days in January 2020 over Tianjin, and the effects of meteorological factors, regional transport, and chemical processes were comprehensively investigated combined with observations and numerical models (WRF-NAQPMS). The average SIA concentrations in CASE1 and CASE2 were 76.8 µg·m-3 and 66.0 µg·m-3, respectively, and the nitrate concentration was higher than that of sulfate and ammonium, which were typical nitrate-dominated pollution processes. Meteorological conditions played a role in inorganic aerosol formation. The temperature of approximately -6-0℃ and 2-4℃ and the relative humidity of 50%-60% and 80%-100% would be suitable conditions for the high SIA concentration (>80 µg·m-3) in CASE1, whereas the temperature of approximately 2-4℃ and the relative humidity of 60%-70% would be suitable in CASE2. The average contribution rates of external sources to SIA in the CASE1 and CASE2 processes were 62.3% and 22.1%, which were regional transport-dominant processes and local emission-dominant processes, respectively. The contribution of the local emission of CASE1 to nitrate and sulfate was 16.2 µg·m-3 and 8.2 µg·m-3, respectively, higher than that of external sources (31.7 µg·m-3 and 8.8 µg·m-3). the local contribution of CASE2 to nitrate and sulfate was 29.3 µg·m-3 and 25.1 µg·m-3, respectively, whereas the contribution from external sources was 8.1 µg·m-3 and 9.4 µg·m-3, respectively. The quantitative result indicated that local formation and regional transport resulted in higher nitrate concentration than sulfate in CASE1, in contrast to only local sources in CASE2. The gas phase reaction was the main source of inorganic aerosol formation, contributing 48.9% and 57.8% in CASE1 and CASE2, respectively, whereas the heterogeneous reactions were also important processes, with contribution rates of 48.1% and 42.2% to SIA. The effect of aqueous phase reaction was negligible.

[Multi-scenario Simulation of Construction Land Expansion and Its Impact on Ecosystem Carbon Storage in Beijing-Tianjin-Hebei Urban Agglomeration].

It is of great practical significance for regional sustainable development and ecological construction to quantitatively analyze the impact of construction land expansion on terrestrial ecosystem carbon storage and to explore the optimization scheme of simulating construction land expansion to improve future ecosystem carbon storage. Based on the land use and cover change (LUCC) and other geospatial data of the Beijing-Tianjin-Hebei Urban Agglomeration from 2000 to 2020, this study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the patch-generating land-use simulation (PLUS) model to assess and analyze the changes in ecosystem carbon stocks and spatial patterns regionally. In this study, we performed linear regression analysis to investigate the relationship between urban land expansion and changes in ecosystem carbon stocks for varying urban land proportion levels during two distinct time intervals, 2000-2010 and 2010-2020, which was conducted at a spatial resolution of 2 km. Three distinct urban land expansion scenarios were subjected to simulation to forecast the prospective land use pattern by 2030. Subsequently, we quantified the ramifications of these scenarios on ecosystem carbon stocks during the period from 2020 to 2030. The results were as follows:① In the Beijing-Tianjin-Hebei Urban Agglomeration, the ecosystem carbon stocks exhibited notable variations over the study period, with values of 2 088.02, 2 106.78, and 2 121.25 Tg recorded for the years 2000, 2010, and 2020, respectively, resulting in a cumulative carbon sequestration of 33.23 Tg C during the study duration. It is noteworthy that forest carbon storage emerged as the dominant contributor, with an increase from 1 010.17 Tg in 2000 to 1 136.53 Tg in 2020. Throughout the study period, the spatial distribution of carbon stocks displayed relative stability. Regions characterized by lower carbon content were concentrated in the vicinity of the Bohai Rim region and in proximity to cities such as Beijing, Tianjin, and Shijiazhuang, as well as rural settlements. In contrast, grid units with moderate and high carbon stocks were predominantly situated in the western Taihang Mountain and the northern Yanshan Mountain. Additionally, there was a tendency of increasing carbon stocks in the Taihang Mountain and Yanshan Mountain region, whereas those surrounding major urban centers such as Beijing, Tianjin, Shijiazhuang, and Tangshan experienced a notable decline in carbon stocks. Such reductions were most pronounced in regions undergoing urban land expansion during the study period. ② In grid units with an urban land proportion exceeding 10% at each level, a strong correlation was observed between urban land expansion and changes in carbon stocks during both the 2000-2010 and 2010-2020 periods. The changes in urban land proportion adequately explained the variations in carbon stocks. However, the explanatory power of urban land on carbon stocks decreased during the 2010-2020 period, indicating that other factors played a more substantial role in influencing carbon stocks during this time. The regression coefficients for both periods exhibited a fluctuating upward trend. In comparison to that during the 2000-2010 period, the impact of urban land expansion on carbon stocks was relatively smaller during 2010-2020, indicating a weakening influence. ③ In light of three distinct development scenarios, namely natural development (Scenario Ⅰ), a 15% reduction in the rate of urban land expansion (Scenario Ⅱ), and a 30% reduction in the rate of urban land expansion (Scenario Ⅲ), the projected ecosystem carbon stocks for the Beijing-Tianjin-Hebei Urban Agglomeration in the year 2030 were estimated to be 2 129.12, 2 133.55, and 2 139.10 Tg, respectively. These projections indicated an increase of 7.88, 12.30, and 17.85 Tg in comparison to the current carbon stocks. All scenarios demonstrated that the terrestrial ecosystem would play a role of carbon sink, particularly with the greatest carbon sink observed in the scenario with a 30% reduction in urban land expansion. The fit performance between urban land expansion and carbon stock changes during the 2020-2030 period was significantly better than that during the 2000-2010 and 2010-2020 periods, and the regression coefficients showed a fluctuating increase with an increase in urban land proportion. Across grid units with different urban land proportion levels, the regression coefficients exhibited the order of Scenario Ⅰ < Scenario Ⅱ < Scenario Ⅲ. In pursuit of the carbon peaking and carbon neutrality goals, the Beijing-Tianjin-Hebei Urban Agglomeration should prioritize scenarios with reduced rates of urban land expansion, especially in regions with higher urban land proportions.

[Spatial Variability and Source Apportionment of PM2.5 Carbon Components in Tianjin].

The contents of eight carbonaceous subfractions were determined by simultaneously collecting PM2.5 samples from four sites in different functional areas of Tianjin in 2021. The results showed that the organic carbon (OC) concentration was 3.7 µg·m-3 to 4.4 µg·m-3, and the elemental carbon (EC) concentration was 1.6 µg·m-3 to 1.7 µg·m-3, with the highest OC concentration in the central urban area. There was no significant difference in EC concentration. The concentration of PM2.5 showed the distribution characteristics of the surrounding city>central city>peripheral area. The OC/EC minimum ratio method was used to estimate the concentrations of secondary organic carbon (SOC) in PM2.5, and the results showed that the secondary pollution was more prominent in the surrounding city, with SOC accounting for 48.8%. The correlation between carbon subcomponents in each functional area showed the characteristics of the peripheral area>central area>surrounding area, all showing the strongest correlation between EC1 and OC2 and EC1 and OC4. By including the carbon component concentration into the positive definite matrix factorization (PMF) model for source apportionment, the results showed that road dust sources(9.7%-23.5%), coal-combustion sources (10.2%-13.3%), diesel vehicle exhaust (12.6%-20.2%)and gasoline vehicle exhaust (18.9%-38.8%)were the main sources of carbon components in PM2.5 in Tianjin. The pollution sources of carbon components were different in different functional areas, with the central city and peripheral areas mainly affected by gasoline vehicle exhaust; the surrounding city was more prominently affected by the secondary pollution and diesel vehicle exhaust.

[Effects of Land Use Types on Water Quality at Different Buffer Scales: Tianjin Section of the Haihe River Basin as an Example].

It is important to explore the relationship between land use types and water quality to improve the surface water environment. Based on monthly water quality monitoring data from 16 nationally controlled surface water quality monitoring stations in Tianjin and land use data in 2021, GIS spatial analysis and mathematical and statistical methods were used to study the influence of land use types on surface water quality in buffer zones at different scales. The results showed that:① the land use types in the study area were mainly construction land, farmland, and water areas, which had significant effects on river water quality. Except for water temperature (WT) and pH, the farmland, construction land, and water areas were negatively correlated with each water quality indicator; forest land and grassland were positively correlated with dissolved oxygen (DO) and total nitrogen (TN) and negatively correlated with other water quality indicators. ② The water quality indicators showed obvious spatial differences in different seasons. The pH, DO and TN concentrations were higher in the dry season, whereas the permanganate index, ammonia nitrogen (NH4+-N), and total phosphorus (TP) concentrations were higher in the rainy season. ③ The results of the RDA analysis showed that the 800 m buffer zone land use had the greatest explanatory power for water quality changes in the dry season (50.4%), whereas the 3 000 m buffer zone land use could explain the water quality changes in the rainy season to the greatest extent (49.6%); from the average explanation rate of the dry and rainy seasons, the 3 000 m buffer zone was the best impact scale (50.0%) on water quality indicators in Tianjin. ④ The partial least squares regression (PLSR) analysis showed that the most important variables affecting surface water quality changes were construction land, farmland, and water areas. The predictive ability of the PLSR model of most water quality indicators was stronger in the dry season than that in the rainy season. In the dry season, all water quality indicators, except WT and pH, were most influenced by farmland. In the rainy season, construction land had the greatest influence on WT and NH4+-N concentrations, and the most important influencing factor for the remaining water quality indicators was still farmland. This study showed that the rational planning of land use types within 3 000 m of rivers or lakes was beneficial to improving the water quality of surface water.

Competitive and synergic evolution of the water-food-ecology system: A case study of the Beijing-Tianjin-Hebei region, China.

A vital approach to attaining sustainable development lies in the in-depth examination of both competition and synergy between these subsystems from a water-food-ecology (WFE) system perspective, while previous or existing studies have limitations in to quantitative characterize and evaluation the cooperative and competitive relationships between different systems. In this study, an evaluation indicator system is constructed from the two dimensions of resources and efficiency, and the WFE synergic development capacity (WFE-SDC) is proposed by integrating the order degree of the coupled system, enables a multidimensional and comprehensive quantitative assessment of the sustainable development of the WFE system. Then a synergic evolution model is constructed to explore the competitive and synergic evolution of the WFE system in the Beijing-Tianjin-Hebei region. The following key insights were obtained: (1) The WFE-SDC (range of 0-1) shows a fluctuating increase, indicating a shift from mild dysfunctional recession to intermediate synergic development (0.24 to 0.72). (2) Principal factors impeding WFE-SDC encompass diversion water, ecology water consumption, grain demand, reclaimed water consumption, and outbound water, both come from resource dimension, with a combined impediment degree of over 46 %, and the improvement of efficiency dimension may improve the WFE-SDC. (3) The water subsystem acts as a driving force for synergic development, fostering cooperation within the food and ecology subsystems, although they mainly operate in a competitive state. (4) Within the WFE system, Beijing, Tianjin, and Hebei exhibited mutual cooperation and significantly contributed to one another's development. Beijing has played a pivotal role in the progress of both Tianjin and Hebei. This study offers valuable insights for the formulation of policies and the application of technical approaches for the sustainable development of the WFE system in relevant regions.

[Analysis of O3 Pollution Affected by a Succession of Three Landfall Typhoons in 2020 in Eastern China].

Based on air quality monitoring, surface meteorological data, wind profile radar observation, and the HYSPLIT model, the characteristics and causes of O3 pollution in eastern China during the period of the typhoons BAVI, MAYSAK, and HAISHEN from August 26 to September 8, 2020 were analyzed. The results showed that during the succession of the three landfall typhoons, the O3 pollution sites in Beijing Tianjin Hebei and its surrounding areas (BTHS) and the Yangtze River Delta (YRD) exceeded 50%. During the HAISHEN period, O3 pollution days in the two regions reached 2.22 d and 2.97 d, respectively, with significant persistence characteristics. The location of the typhoon had an obvious influence on O3 concentration. When the typhoons were located within the 24h warning line, the O3 concentrations in BTHS and YRD were relatively low. When the typhoons were located between the 24 h and 48 h warning lines, the O3 concentration in BTHS was the highest. When the typhoons moved north of 34°N, the YRD was most prone to regional O3 pollution. O3 pollution in Shanghai mainly occurred under the control of the northward air flow to the west side of the typhoons, and the regional transport from the upstream area had a significant impact on the increase in O3 and its precursor concentrations. The downdraft below 1 000 m maintained O3 at a high concentration at night. In Jinan, O3 pollution mainly occurred under the control of the subtropical high and typhoon periphery. The downdraft prevailed in the middle and lower levels during the O3 pollution. From August 28 to 30, under the control of the subtropical high, the pollutants were mainly accumulated locally, and some of them were transmitted within the province, showing a "double high" phenomenon of O3 and PM2.5. From September 5 to 8, under the influence of HAISHEN peripheral circulation, the regional transport was obvious, and the O3 concentration increased earlier than that of PM2.5.

[Spatial-temporal Evolution of Ecosystem Health and Its Influencing Factors in Beijing-Tianjin-Hebei Region].

Exploring ecosystem health and its influencing factors is of great significance for promoting regional sustainable development. An ecosystem health assessment model was constructed, and the spatial-temporal evolution characteristics of ecosystem health in the Beijing-Tianjin-Hebei Region in 2000, 2010, and 2020 were analyzed. The geographical detector and GWR were used to identify the dominant factors affecting ecosystem health. The main conclusions were as follows:during the study period, the index of ecosystem natural health in the Beijing-Tianjin-Hebei Region was generally better in the north and west than that in the southeast, and it showed an overall upward trend. The index of ecosystem services in the Beijing-Tianjin-Hebei Region presented as a spatial differentiation pattern of high in the north and low in the south, and it showed a downward trend. The ecosystem health level in the Beijing-Tianjin-Hebei Region showed a trend of rising first and then declining, showing significant heterogeneity in spatial distribution. The ecological health level in the central urban area of large cities was mostly poor, and the ecosystem health level in the Yanshan and Taihang Mountains and Bohai Rim was better. During the study period, the spatial pattern of ecosystem health in the Beijing-Tianjin-Hebei Region remained relatively stable. The hot spots and sub-hot spots were mainly distributed in the northern mountainous areas of Hebei Province and the Taihang Mountains, and the cold spots and sub-cold spots were mainly distributed in the southeast plain and the surrounding areas of some big cities. Population density, annual average temperature, per capita cultivated land area, and urbanization level were the dominant factors of ecosystem health in the Beijing-Tianjin-Hebei Region, and they were all negatively correlated with ecosystem health.

Evaluation on evolution of spatial relationship between carbon emissions and economic growth in Beijing-Tianjin-Hebei region.

The Beijing-Tianjin-Hebei region is not only an important economic center in China, but also one of the major regions contributing to China's carbon emissions. Revealing the spatial distribution between carbon emissions and economic growth is essential for the formulation of low-carbon development policies. Following the principle from macro to micro, this paper investigates the spatial evolution trend and distribution characteristics between carbon emissions and economic growth in the Beijing-Tianjin-Hebei region from 2005 to 2020 by applying imbalance index model, the rank-scale rule, and decoupling index model. The results show that the imbalance index of carbon emissions decreased between 0.0601 and 0.0533 in a fluctuating way, indicating that the imbalance of spatial distribution of carbon emissions decreases. The imbalance index of economic growth increased between 0.0738 and 0.0851, indicating that economic growth has become more disequilibrated, and the spatial evolution of carbon emissions is not coordinated with economic growth. The Zipf dimension of carbon emissions declined from 1.1806 in 2005 to 0.9594 in 2020, and carbon emissions declined in big cities and increased in cities of the middle order. The Zipf dimension of economic growth increased from 1.1384 in 2005 to 1.2388 in 2020, and the economic growth monopoly in big cities increased. The decoupling coefficient of carbon emissions to economic growth declined, and the driving effect of economic growth on carbon emissions diminished. It is recommended that the Beijing-Tianjin-Hebei region should coordinate the allocation of factors and coordinate industrial adjustment. Hebei should accelerate industrial upgrading and establish a diversified industrial system.

Seasonal variation and dissolved organic matter influence on the distribution, transformation, and environmental risk of pharmaceuticals and personal care products in coastal zone: A case study of Tianjin, China.

Pharmaceuticals and personal care products (PPCPs) are emerging contaminants that have raised urgent environmental issues. The dissolved organic matter (DOM) plays a pivotal role on PPCPs' migration and transformation. To obtain a comprehensive understanding of the occurrence and distribution of PPCPs, a seasonal sampling focused on the riverine system in coastal zone, Tianjin, Bohai Rim was conducted. The distribution and transformation of thirty-three PPCPs and their interaction with DOM were investigated, and their sources and ecological risks were further evaluated. The total concentration of PPCPs ranges from 0.01 to 197.20 µg/L, and such value is affected by regional temperature, DOM and land use types. PPCPs migration at soil-water interface is controlled by temperature, sunlight, water flow and DOM. PPCPs have a high affinity to the protein-like DOM, while the humus-like DOM plays a negative influence and facilitates PPCPs' degradation. It is also found that protein-like DOM can represent point source pollution, while humus-like substances indicate non-point source (NPS) emission. Specific PPCPs can be used as markers to trace the source of domestic discharge. Additionally, daily use PPCPs such as ketoprofen, caffeine and iopromide are estimated to be the main risk substances, and their ecological risk varies on space, season and river hydraulic condition.

Forecasting the energy demand and CO2 emissions of industrial sectors in China's Beijing-Tianjin-Hebei region under energy transition.

As the world's greatest energy consumer, China's energy consumption and transition have become a focus of attention. The most significant location for regional integration in the north of China is the Beijing-Tianjin-Hebei region, where the industrial sector dominates its energy consumption. Forecasting the energy demand and structure of industrial sectors in China's Beijing-Tianjin-Hebei region may help to promote the energy transition and CO2 emission mitigation. This study conducts a model based on the year 2020 using the Long-Range Energy Alternatives Planning System (LEAP) software and sets two scenarios (baseline scenario and emission peak scenario) to forecast the future energy demand and CO2 emissions of industrial sectors in China's Beijing-Tianjin-Hebei region until the year 2035. Moreover, the industrial sectors are classified into traditional high-energy-consuming industries, emerging manufacturing industries, daily-related light industries, and other industries. The forecasting results show that (1) The industrial energy demand of the entire Beijing-Tianjin-Hebei region will grow from 234 Mtce in 2020 to 317 Mtce in 2035, and the corresponding energy structure will shift from coal-based to electricity-based; (2) at the provincial level, all three provinces will experience an increase in industrial energy demand between 2020 and 2035, with Hebei experiencing the fastest average annual growth rate of 2.18% and the largest share of over 80%, and Beijing experiencing the highest average annual electrification rate of 70%; (3) at the industrial sector level, the electricity and natural gas will gradually replace other energy sources as the main energy source for industry. The most representative industrial sub-sector in Beijing, Tianjin, and Hebei provinces are all traditional high-energy-consuming industries, which will account for more than 90% of the total energy demand in both Tianjin and Hebei by 2035.