Spatiotemporal heterogeneity and long-term impact of meteorological, environmental, and socio-economic factors on scrub typhus in China from 2006 to 2018

BACKGROUND

Large-scale outbreaks of scrub typhus combined with its emergence in new areas as a vector-borne rickettsiosis highlight the ongoing neglect of this disease. This study aims to explore the long-term changes and regional leading factors of scrub typhus in China, with the goal of providing valuable insights for disease prevention and control.

METHODS

This study utilized a Bayesian space-time hierarchical model (BSTHM) to examine the spatiotemporal heterogeneity of scrub typhus and analyze the relationship between environmental factors and scrub typhus in southern and northern China from 2006 to 2018. Additionally, a GeoDetector model was employed to assess the predominant influences of geographical and socioeconomic factors in both regions.

RESULTS

Scrub typhus exhibits a seasonal pattern, typically occurring during the summer and autumn months (June to November), with a peak in October. Geographically, the high-risk regions, or hot spots, are concentrated in the south, while the low-risk regions, or cold spots, are located in the north. Moreover, the distribution of scrub typhus is influenced by environment and socio-economic factors. In the north and south, the dominant factors are the monthly normalized vegetation index (NDVI) and temperature. An increase in NDVI per interquartile range (IQR) leads to a 7.580% decrease in scrub typhus risk in northern China, and a 19.180% increase in the southern. Similarly, of 1 IQR increase in temperature reduces the risk of scrub typhus by 10.720% in the north but increases it by 15.800% in the south. In terms of geographical and socio-economic factors, illiteracy rate and altitude are the key determinants in the respective areas, with q-values of 0.844 and 0.882.

CONCLUSIONS

These results indicated that appropriate climate, environment, and social conditions would increase the risk of scrub typhus. This study provided helpful suggestions and a basis for reasonably allocating resources and controlling the occurrence of scrub typhus.

Revealing spatiotemporal inequalities, hotspots, and determinants in healthcare resource distribution: insights from hospital beds panel data in 2308 Chinese counties

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.

Modeling spatiotemporal heterogeneity in interval-censored traffic incident time to normal flow by leveraging crowdsourced data: A geographically and temporally weighted proportional hazard analysis

Non-recurrent traffic congestion arising from traffic incidents is unpredictable but should be addressed efficiently to mitigate its adverse impacts on safety and travel time reliability. Numerous studies have been conducted about incident clearance time, while the recovery time, due to the limitations of data collection, is often inadvertently neglected in assessing incident-induced duration (i.e., the time from incident occurrence to the normal flow of traffic). Overlooking the recovery time is likely to underestimate the total incident-induced impact. Furthermore, the spatiotemporal heterogeneity of observed factors is not adequately captured in incident duration models. To address these gaps, this study specifically investigated traffic crashes as they reflect safety issues and are the primary cause of non-recurrent congestion. The emerging crowdsourced traffic reports were harnessed to estimate crash recovery time, which can complement the blind zone of fixed detectors. A geographically and temporally weighted proportional hazard (GWTPH) model was developed to untangle factors associated with the interval-censored crash duration. The results show that the GWTPH model outperforms the global model in goodness-of-fit. Many factors present a spatiotemporally heterogeneous effect. For example, the global model merely revealed that deploying dynamic message signs (DMS) shortened the crash time to normal. Notably, the GWTPH model highlights an average reduction of 32.8% with a standard deviation of 31% in time to normal. The study's findings and application of new spatiotemporal techniques are valuable for practitioners to localize strategies for incident management. For instance, deploying DMS can be very helpful in corridors when incidents happen, especially during peak hours.

Decoding spatiotemporal dynamics in atmospheric CO2 in Chinese cities: Insights from satellite remote sensing and geographically and temporally weighted regression analysis

Understanding the primary factors influencing atmospheric carbon dioxide (CO2) dynamics is essential for addressing global climate change. However, these factors' spatial and temporal impact is seldom considered in the extant literature. This study employs a geographically and temporally weighted regression (GTWR) to examine the magnitude and direction of the effects that human activities, ecological conditions, and meteorological parameters exert on atmospheric CO2 variations. This examination encompasses 356 Chinese cities, utilizing satellite-derived column-averaged dry air mole fraction of carbon dioxide (XCO2) data from 2010 to 2019. Our findings reveal that cities in eastern China predominantly exhibit higher atmospheric CO2 concentrations, with most average values exceeding 399.34 ppm. Conversely, western regions generally maintain levels below 398.98 ppm. Trend analyses show a consistent increase over the decade, with a rate of 2.25-2.54 ppm/yr, coupled with pronounced seasonal variations. Anthropogenic emissions are found to amplify atmospheric CO2 concentrations during springs (overall-averaged GTWR coefficient of 0.72 ppm), autumns (0.51 ppm), and winters (0.87 ppm), especially in western cities. Vegetative activities can effectively reduce atmospheric CO2 during summers nationwide (temporally-averaged GTWR coefficients of -5.67 ~ -0.41 ppm) and autumn in southern cities (-1.32-0.00 ppm). From a meteorological perspective, increased summer relative humidity (overall-averaged coefficient of 2.41 ppm) and precipitation (1.57 ppm) can intensify atmospheric CO2 in most Chinese Cities. At the same time, warmer winter temperatures (-0.63 ppm) can mitigate it. Wind speed generally reduces atmospheric CO2 levels during spring (-1.02 ppm), autumn (-1.55 ppm), and winter (-1.76 ppm). Yet, it can heighten atmospheric CO2 concentrations during summers, particularly in eastern cities (0.07 ppm). The relationships between atmospheric CO2 concentrations and their influencing factors present significant spatial and seasonal variations. These findings offer comprehensive guidance for regions in developing targeted carbon emission control policies and enable a more practical approach to sustainable development.

Land subsidence prediction in Zhengzhou's main urban area using the GTWR and LSTM models combined with the Attention Mechanism

In recent years, due to urbanization and human activities, groundwater overexploitation has become increasingly severe, resulting in some degrees of land subsidence and, consequently, causing a series of geological disasters and other environmental issues. Therefore, large-scale and high-precision land subsidence prediction is of great importance for the prevention and control of geological disasters. However, the existing prediction models and methods ignore the effects of the spatiotemporal non-stationary relationships between the influencing factors and the accumulated land subsidence, causing the poor accuracy of the predicted land subsidence results. In this context, a Geographically and Temporally Weighted Regression combined with the Long Short-Term Memory (LSTM)-multivariable and Attention Mechanism (AM) (GTWR-LSTMm-AM) was proposed to more accurately predict the deformation of time series land subsidence in this study. The small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) was used to reveal the temporal deformation information of Zhengzhou's main urban area, then the GTWR model was used to assess the spatiotemporal non-stationarity relationships between the accumulated land subsidence and its influencing factors monthly groundwater stability level, monthly precipitation and Normalized Difference Vegetation Index (NDVI) data, and to determine the corresponding weight matrix. In addition, we introduced an LSTM model with AM to extract key information from the time-series land subsidence data and adjusted the dynamic weights of the three selected influencing factors to predict the land subsidence in Zhengzhou's main urban area. The prediction accuracy R2 of the GTWR-LSTMm-AM model reaches 0.972, which is higher than 0.929 of the LSTMm model. The prediction accuracy RMSE is less than 3 mm and reaches 2.403 mm. In addition, we determined the importance of the impact factor on the subsidence results by randomly interrupting the impact factor time series, disclosuring that the monthly groundwater level contributed the most to the land subsidence in Zhengzhou's main urban area.

Dynamic characteristics of net anthropogenic phosphorus input to the upper Yangtze River Basin from 1989 to 2019: Focus on the phosphate ore rich area in China

Phosphorus (P), a non-renewable essential resource, faces heavy exploitation and contributes to eutrophication in aquatic environments. Assessing P input is vital for a healthier P cycle in the Upper Yangtze River (UYR), a phosphate ore rich basin, where P mining and P chemical enterprises have prominent pollution problems. This study modified the net anthropogenic phosphorus input (NAPI) model to include ore mining P input (Pore). We analyzed the evolutionary characteristics of P input in five sub-basins of UYR from 1989 to 2019 using prefecture-level data, and assessed the uncertainty of the data. NAPI in all sub-basins exhibited an upward and then downward trend during 1989-2019, with the inflection point occurring in 2015 or 2016, showing a net increase of about 1.1 times (568-1162 kg P km-2 yr-1) in the whole UYR basin. Among the components of NAPI, P fertilizer inputs (Pfer) and food/non-food and feed P inputs (Pf/nf&feed) contributed comparably, though the growth rate of Pfer was most notable basin-wide. Pore proportion increased significantly (about 3-fold), with a peak of 20%, especially in Wujiang sub-basin. The multi-year (1989-2019) average NAPI in UYR rose sequentially from west to east, with hotspot areas mainly concentrated in the Sichuan-Chongqing urban agglomeration and cities of Hubei province. The regional P input closely related to the population density and the level of agricultural development, certainly the phosphate mining was also unignorable. This study emphasizes that based on current status of NAPI development in UYR, targeted management for different regions should focus on improving agricultural P use efficiency and rational exploitation of P mineral resources.

Modeling Ventilator-Induced Lung Injury and Neutrophil Infiltration to Infer Injury Interdependence

Acute respiratory distress syndrome (ARDS) and ventilator-induced lung injury (VILI) are heterogeneous conditions. The spatiotemporal evolution of these heterogeneities is complex, and it is difficult to elucidate the mechanisms driving its progression. Through previous quantitative analyses, we explored the distributions of cellular injury and neutrophil infiltration in experimental VILI and discovered that VILI progression is characterized by both the formation of new injury in quasi-random locations and the expansion of existing injury clusters. Distributions of neutrophil infiltration do not correlate with cell injury progression and suggest a systemic response. To further examine the dynamics of VILI, we have developed a novel computational model that simulates damage (cellular injury progression and neutrophil infiltration) using a stochastic approach. Optimization of the model parameters to fit experimental data reveals that the range and strength of interdependence between existing and new damaged regions both increase as mechanical ventilation patterns become more injurious. The interdependence of cellular injury can be attributed to mechanical tethering forces, while the interdependence of neutrophils is likely due to longer-range cell signaling pathways.

Influencing factors and spatiotemporal heterogeneity of net carbon sink of conservation tillage: evidence from China

Conservation tillage is an important reform of traditional tillage, which has significant carbon sequestration and emission reduction effects. It is important to investigate the influencing factors and spatiotemporal heterogeneity of net carbon sink of conservation tillage for realizing the "dual carbon" target, and facilitating agricultural sustainable development. This study used the coefficient accounting method to calculate the carbon sink and carbon emission of conservation tillage in China from 2000 to 2019, respectively. Based on this, the net carbon sink of conservation tillage was measured. Then, the spatiotemporal heterogeneity of influencing factors on net carbon sink of conservation tillage was analyzed by using the geographically and temporally weighted regression model. The results showed that (1) the net carbon sink of conservation tillage in China was significant and had potential to have a constant rise; (2) spatially, the net carbon sink of conservation tillage changed more variably in longitudinal direction. Specifically, the promotion effect of conservation tillage machinery gradually decreased from west to east. The planting structure and conservation tillage promotion intensity played key roles in improving net carbon sink of conservation tillage. (3) Temporally, the effect of conservation tillage machinery showed positive effect of decreasing yearly, while the positive effect of promotion intensity increased year by year. Planting structure and economic development negatively affected improvement on the net carbon sink of conservation tillage and the negative effect increased year by year. Additionally, the effect of education on the net carbon sink shifted from positive to negative over time. The study aims to provide a reference for the government to promote conservation tillage according to local conditions and to achieve the "dual carbon" target.

The spatiotemporal heterogeneity of the biophysical microenvironment during hematopoietic stem cell development: from embryo to adult

Hematopoietic stem cells (HSCs) with the ability to self-renew and differentiate are responsible for maintaining the supply of all types of blood cells. The complex and delicate microenvironment surrounding HSCs is called the HSC niche and can provide physical, chemical, and biological stimuli to regulate the survival, maintenance, proliferation, and differentiation of HSCs. Currently, the exploration of the biophysical regulation of HSCs remains in its infancy. There is evidence that HSCs are susceptible to biophysical stimuli, suggesting that the construction of engineered niche biophysical microenvironments is a promising way to regulate the fate of HSCs in vitro and ultimately contribute to clinical applications. In this review, we introduced the spatiotemporal heterogeneous biophysical microenvironment during HSC development, homeostasis, and malignancy. Furthermore, we illustrated how these biophysical cues contribute to HSC behaviors, as well as the possible mechanotransduction mechanisms from the extracellular microenvironment into cells. Comprehending the important functions of these biophysical regulatory factors will provide novel approaches to resolve clinical problems.

Spatial-temporal patterns and influencing factors for pulmonary tuberculosis transmission in China: an analysis based on 15 years of surveillance data

Profiting from a series of anti-tuberculosis programs in China, the number of tuberculosis (TB) cases has diminished dramatically in the past decades. However, long-term spatial-temporal variations, regional trends of prevalence, and mechanisms of determinant factors remain unclear. Age-period-cohort analysis and Bayesian space-time hierarchy statistics were conducted to identify high-risk populations and areas in mainland China, and the geographical detector model was used to evaluate the important drivers of the disease. The prevalence of pulmonary TB has declined from 73.3/100,000 in 2004 to 55.45/100,000 in 2018. A bimodal distribution was found in age groups, and the birth cohorts before 1978 had relative higher risk. The high-risk areas were mainly distributed in western China and south-central China, and several provinces in eastern China showed a potential increasing trend, including Beijing, Shanghai, Liaoning, and Guangdong province. The index of night light (Q = 0.46), the population density (Q = 0.41), PM10 (Q = 0.38), urbanization rate (Q = 0.32), and PM 2.5 (Q = 0.31) contributed substantially to the spatial distribution of pulmonary tuberculosis. The identifications of epidemic patterns, high-risk areas and influence factors would help design targeted intervention measures to achieve milestones of the end TB strategy.

Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia

BACKGROUND

In Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in Sarawak.

METHODS

The spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted regression.

RESULTS

In the first 8 months of 2021, COVID-19 severely affected Sarawak's central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts' rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence rates.

CONCLUSION

This research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-informed judgments and implement targeted interventions by having an in-depth understanding of the spatial patterns and relationships between COVID-19 incidence rates and socio-demographic characteristics. This will effectively help in mitigating the spread of the disease.

Characteristics of anthropogenic heat with different modeling ideas and its driving effect on urban heat islands in seven typical Chinese cities

Anthropogenic heat (AH), an essential urban heat source, is often overlooked or simplified in research on the multiple temporal and spatial driving mechanisms of the urban heat island (UHI), and case studies investigating the impacts of different AH connotations are scarce. This study estimated the AH in seven typical Chinese cities based on a remote sensing surface energy balance model (AH_seb) and an energy consumption inventory-machine learning model (AH_inv). The intensity of the surface UHI was extracted using land surface temperatures, and then the linear mixed-effects model and geographic detectors were used to analyze the driving effect of AH on the UHI. Despite the similar shapes of the spatial profile curves, the AH derived from the two models differed in both temporal and spatial characteristics, which was more typical in winter and in urban centers, and AH_inv had a more notable central spread feature than AH_seb. The AH driving effects on UHI were notably influenced by spatial and temporal heterogeneity, particularly in regions with distinct background climates. However, after controlling for the random effects of the background climate, AH still exhibited a considerable enhancing effect on the UHI. AH_seb outperformed AH_inv in terms of linear positive correlation and interpretation rate for UHI. Meanwhile, interactions with other potential factors enhanced AH driving effects. Consequently, UHI mitigation must be tailored to the local context by integrating multiple drivers, and for the heating effects of AH, it is necessary to develop specific mitigation measures by limiting the conversion of AH_inv to AH_seb in addition to reducing the heat production. The findings offer guidance for analyzing and optimizing urban thermal climates with a focus on AH or energy consumption control.

Ecological performance assessment of ecologically fragile areas: a perspective of spatiotemporal analysis

Sustainable development in ecologically fragile areas (EFAs) has faced significant challenges in recent years, but the traditional analytical approaches fail to provide an ideal assessment for ecological performance due to spatiotemporal variability in EFAs. This paper evaluates the ecological performance of EFAs based on a modified ecological footprint model, and ecological footprint intensity (EFI) is considered an essential indicator to measure ecological performance, especially for EFAs. Empirically, taking the Π-shaped Curve Area in the Yellow River basin of China as the study area, the spatiotemporal heterogeneity of EFI of 17 cities in the area is analyzed. Then, the extended STIRPAT and geographically and temporally weighted regression (GTWR) models are employed to explore the spatiotemporal heterogeneity of the factors driving EFI. The results show that from 2006 to 2019, the overall level of EFI in the area has decreased; EFI of the area offers a significant spatial agglomeration effect; results of the GTWR model suggest that factors driving EFI have spatiotemporal heterogeneity; the impact of population size, openness, marketization, technology, industrial structure rationalization, and information communication level on EFI was two-sided, while that of affluence, government scale, environmental regulation, and industrial structure advancement show inhibitory impact with the intensity of inhibition varying across periods and cities.

The role of nutrients, wind speed, and rainfall in determining the composition of the algal community of shallow lakes in the Taoge water system, upstream from Lake Taihu, China

Gaining a deeper understanding of factors that influence changes in phytoplankton community has significant implications for shallow lake management. The present study examined changes in the algae community of three shallow eutrophic lakes of the Taoge water system between 2008 and 2018 and the related factors influencing these changes. The composition of the algal community varied significantly during this period with the relative diatom biomass in lakes Changdanghu and Gehu increasing between 2014 and 2016 and again decreasing after 2017. However, relative cyanobacteria biomass initially decreased and later increased; meanwhile, the proportion of biomass of other phyla decreased continuously in the study period. Lake Zhushanhu showed similar trends, although it eventually returned to its initial state with absolute Microcystis dominance. Furthermore, the analysis of driving factors revealed that the concentrations of total nitrogen (TN), nitrate (NO₃), and orthophosphate (PO₄) were significantly associated with a significant increase in Microcystis biomass. Meteorological conditions also influenced changes in total algal and diatom biomasses, which were inversely related to the daily mean and daily maximum wind speeds. Monthly cumulative precipitation was only significantly associated with diatom biomass. Meanwhile, rainfall primarily affected the algal community structure between 2013 and 2017; an increase in the relative biomass of diatoms coincided with increased precipitation. Coordinating nitrogen and phosphorous use within the Taoge water system should improve lake habitat management; a broader perspective in attempts to control global and regional climate change may be needed.

Epidemiological Characteristics and Spatiotemporal Distribution Patterns of Human Norovirus Outbreaks in China, 2012-2018

OBJECTIVE

To clarify the epidemiological characteristics and spatial distribution patterns of human norovirus outbreaks in China, identify high-risk areas, and provide guidance for epidemic prevention and control.

METHODS

This study analyzed 964 human norovirus outbreaks involving 50,548 cases in 26 provinces reported from 2012 to 2018. Epidemiological analysis and spatiotemporal scanning analysis were conducted to analyze the distribution of norovirus outbreaks in China.

RESULTS

The outbreaks showed typical seasonality, with more outbreaks in winter and fewer in summer, and the total number of infected cases increased over time. Schools, especially middle schools and primary schools, are the most common settings of norovirus outbreaks, with the major transmission route being life contact. More outbreaks occurred in southeast coastal areas in China and showed significant spatial aggregation. The highly clustered areas of norovirus outbreaks have expanded northeast over time.

CONCLUSION

By identifying the epidemiological characteristics and high-risk areas of norovirus outbreaks, this study provides important scientific support for the development of preventive and control measures for norovirus outbreaks, which is conducive to the administrative management of high-risk settings and reduction of disease burden in susceptible areas.

Spatiotemporal heterogeneity and attributions of streamflow and baseflow changes across the headstreams of the Tarim River Basin, Northwest China

Understanding spatiotemporal heterogeneity of streamflow and baseflow and revealing their changes contributed by climatic factors and human activities in the alpine region of inland river basin are critical for regional water management. However, the hydrology heterogeneity in the alpine region has remained unclear, which limits the scientific understanding of the interaction mechanism between the hydrological cycle and terrain, and further constrains the effective utilization of regional water resources in the water-shortage areas. In this study, the hydrological process and regimes for headstreams of Tarim River Basin (HTRB) during 1985-2011 were simulated by the Soil and Water Assessment Tool. We systematically characterized the spatial and temporal patterns of streamflow and baseflow through geostatistical and trend analyses, and subsequently investigated their heterogeneity responses to climate change and human activities at different sub-basins and elevation zones. Results show that the spatial distributions of streamflow and baseflow are highly related to terrain and river direction. Increased trends in precipitation enhanced with altitude, whereas decreased trends in potential evapotranspiration (PET) weakened with altitude, meanwhile, increased trends in streamflow and baseflow of HTRB are most pronounced in mid-altitude areas during 1985-2011. The climate elasticities of streamflow and baseflow are highly reliant on the altitudinal gradient. Increases in streamflow and baseflow in high-lying areas are more sensitive to precipitation variation, while they are more sensitive to PET change in low-lying areas. The magnitude and change rate with altitude bands of the precipitation has greater effects on streamflow and baseflow variations than those of PET. Furthermore, the percentage of sub-basins where climate changes dominate streamflow variation in each elevation band increases with height but decreases abruptly at elevations above 5000 m. The percentage of sub-basins where climate changes dominate baseflow variations gradually decreases in elevation bands above 3000 m. Our results indicate that climate change rather than human activities dominants the variation in streamflow and baseflow in most sub-basins and elevation bands.

Spatiotemporal heterogeneity and influencing factors on urbanization and eco-environment coupling mechanism in China

High-quality urbanization is the core for realizing human well-beings, for which reason investigating how the relationship evolves between urbanization and eco-environment is of crucial importance. Differing from the rationale of revealing spatial spillover effects using traditional tests, we consider spatial network characteristics to enrich the notion of local coupling and telecoupling from a relational perspective. First, we adopt coupling coordination degree model (CCDM) and decoupling model (DM) to calculate the urbanization and eco-environment coupling coordination degree (UECCD) and the decoupling index (DI) in 30 provinces and municipalities of China from 2008 to 2017. Second, we use gravity model to construct urbanization and eco-environment coupling coordination network (UECCN), in which provinces are nodes and spatial connection relationships of UECCD are edges between nodes. Third, we introduce social network analysis (SNA) to reveal spatial network characteristics of UECCN without using local spatiotemporal heterogeneity. Finally, we employ spatial econometric model to reveal factors that influence urbanization and eco-environment coupling effect. The major findings and conclusions of this study are summarized as follows. (1) The main subclasses of UECCD and DI are basically uncoordinated patterns with eco-environment lagging and weak decoupling, respectively. (2) Only two spatial agglomeration types of UECCD exist, the high-high (H-H) clustering in Shanghai and the low-low (L-L) clustering in western China, whereas no significant spatial agglomeration effect is observed among most provinces. (3) The distribution characteristics of UECCN are sparse in western China and dense in eastern China, and the spatial correlation strength of UECCN improves. (4) Technological innovation plays a critical role in promoting UECCD, while the total population, per capita disposable income, coupling network structure, and environmental regulations exert significant impact on UECCD. Collectively, we propose to prioritize governance provinces with low UECCD in western China as well as adequately utilize the positive externalities of key node provinces in eastern China. Equally importantly, we suggest that it is also critical to fully exert a driving force of technological innovation on improving the UECCD by promoting renewable energy utilization.

The dynamic relationships between landscape structure and ecosystem services: An empirical analysis from the Wuhan metropolitan area, China

Environmental managers have been striving to optimize landscape structure to achieve a sustained supply of ecosystem services (ESs). However, we still lack a full understanding of the relationships between landscape structure and ESs due to the absence of thorough investigations on the variability of these relationships in space and time. To fill this critical gap, we assessed landscape structure alongside four important ESs (agricultural production (AP), carbon sequestration (CS), soil conservation (SC), and water retention (WR)) in the Wuhan metropolitan area (WMA), and then analyzed the spatiotemporal impacts of landscape structure on ESs from 2000 to 2020 using Geographically and Temporally Weighted Regression. The results show only AP maintained a stable growth trend over the past two decades, while the other ESs fluctuated considerably with a noticeable decline in SC and WR. The importance of landscape structure in influencing ESs varies by time and place, depending on the local landscape composition and configuration. In general, landscape composition has a stronger and less temporally stable impact on ESs compared to configuration. Furthermore, increases in landscape diversity, as measured through Shannon's diversity index, and the percentage of woodlands were found to contribute to the simultaneous benefits of multiple ESs, but in most cases the effects of landscape structure on different ESs were different or even opposite, suggesting that trade-offs are critical in landscape management. The findings highlight the complex response of ESs to dramatically changing landscapes in the WMA and can guide decision-makers in precise spatial arrangement and temporal adjustments to improve current landscape management.

Exploring the spatiotemporal heterogeneity and influencing factors of agricultural carbon footprint and carbon footprint intensity: Embodying carbon sink effect

Due to the combined effects of carbon emission and carbon sink, agriculture is acknowledged as an essential contributor to achieve the Chinese government's carbon neutrality goal of 2060, and carbon footprint (CF) and carbon footprint intensity are substantial indicators to reveal the carbon emission level. For these reasons, the Theil index technique and extended STIRPAT model were employed to evaluate their spatiotemporal heterogeneity and influencing factors using panel data from 31 provinces for the period 1997-2019. The findings revealed that the CF showed an increasing trend with an annual growth rate of 24.6 %. The carbon footprint intensity (CFI) indicated an evident spatiotemporal heterogeneity and transferred over time, with an average growth rate of 19.82 %. The CFI Theil index and its contribution rate both confirmed that intra-regional difference is the main source of the overall difference, among which, the CFI Theil index displayed the distribution feature of "western (11.50 %) > central (11.12 %) > eastern (10.56 %) > northeast (6.61 %). The contribution rate of CFI illustrated the spatial pattern of "eastern (33.74 %) > central (21.07 %) > western (19.87 %) > northeast (5.24 %). Furthermore, the influencing effects of GDP per capita, planting structure, population density and urbanization level on CF and CFI also demonstrate evident spatiotemporal heterogeneity.

Spatiotemporal analysis of urban road congestion during and post COVID-19 pandemic in Shanghai, China

Coronavirus Disease 2019 (COVID-19) has become one of the most serious global health crises in decades and tremendously influence the human mobility. Many residents changed their travel behavior during and after the pandemic, especially for a certain percentage of public transport users who chose to drive their owned vehicles. Thus, urban roadway congestion has been getting worse, and the spatiotemporal congestion patterns has changed significantly. Understanding spatiotemporal heterogeneity of urban roadway congestion during and post the pandemic is essential for mobility management. In this study, an analytical framework was proposed to investigate the spatiotemporal heterogeneity of urban roadway congestion in Shanghai, China. First, the matrix of average speed in each traffic analysis zones (TAZs) was calculated to extract spatiotemporal heterogeneity variation features. Second, the heterogenous component of each TAZ was extracted from the overall traffic characteristics using robust principal component analysis (RPCA). Third, clustering analysis was employed to explain the spatiotemporal distribution of heterogeneous traffic characteristics. Finally, fluctuation features of these characteristics were analyzed by iterated cumulative sums of squares (ICSS). The case study results suggested that the urban road traffic state evolution was complicated and varied significantly in different zones and periods during the long-term pandemic. Compared with suburban areas, traffic conditions in city central areas are more susceptible to the pandemic and other events. In some areas, the heterogeneous component shows opposite characteristics on working days and holidays with others. The key time nodes of state change for different areas have commonness and individuality. The proposed analytical framework and empirical results contribute to the policy decision-making of urban road transportation system during and post the COVID-19 pandemic.

Exploring the optimal crop planting structure to balance water saving, food security and incomes under the spatiotemporal heterogeneity of the agricultural climate

Crop planting provided foods, generated incomes, and consumed water resources to different extents under different spatiotemporal agroclimatic conditions. For balancing three aspects, targeting the rice, maize, wheat, and sorghum planted in Liaoning during the recent two decades, we established an integrated research framework consisting of water footprint (WF) accounting, clustering analysis, and fuzzy optimization programming to quantify the temporal trends and spatial distribution of water footprints, and optimized the planting structure under the different spatiotemporal agroclimatic conditions. Results showed that the maximum water footprint differences were 4166.73 m³/t and 4790.71 m³/t in spatial distribution and temporal series, respectively. Based on precipitation, we established 12 agroclimatic scenarios according to K-Means clustering. The fuzzy optimization result indicated that the planting area percent ranges of maize, wheat, rice, and sorghum in Liaoning province were 4.96%-98.62%, 0.00%-8.55%, 0.00%-18.18%, and 0.00%-95.04%, respectively under the different spatiotemporal conditions. This study's methods and results help make targeted decisions related to grain planting structure while considering the complex spatial-temporal conditions.

Exploring the effect of urban traffic development on PM2.5 pollution in emerging economies: fresh evidence from China

Urban traffic congestion and haze pollution have become the main obstacles to the development of most cities in emerging economies. It is not clear how urban traffic development processes impact on PM_2.5 concentration for the cities of emerging economies. Motivated by exploring the relationship between urban traffic development and PM_2.5 pollution, 30 provinces in China (a representative emerging economy) from 2007 to 2016 were taken as examples, and threshold regression model and geographically temporally weighted regression model were used to explore the nonlinear relationship and their spatio-temporal heterogeneity. These empirical researches demonstrated that the impact of urban traffic development on PM_2.5 pollution has a significant threshold effect. That is, when the road area crosses the threshold, it will significantly aggravate the regional PM_2.5 pollution. Meanwhile, regional economic development also shows a significant threshold effect. Moreover, the relationship between urban traffic development and PM_2.5 pollution in various Chinese provinces presents significant spatial heterogeneity. Specifically, the Chinese provinces are divided into four categories, and urban planning should be designed for different types for the sustainable development of the economy and environment. Our results not only contribute to advancing the existing literature, but also merit particular attention from urban planners in emerging economies.

Detecting urban landscape factors controlling seasonal land surface temperature: from the perspective of urban function zones

Understanding the impact on the thermal effect by urbanization is of great significance for urban thermal regulation and is essential for determining the relationship between the urban heat island (UHI) effect and the complexities of urban function and landscape structure. For this purpose, we conducted case research in the metropolitan region of Beijing, China, and nearly 5000 urban blocks assigned different urban function zones (UFZs) were identified as the basic spatial analysis units. The seasonal land surface temperature (LST) retrieved from remote sensing data was used to represent the UHI characteristics of the study area, and the surface biophysical parameters, building forms, and filtered landscape pattern metrics were selected as the urban landscape factors. Then, the effects of urban function and landscape structure on the UHI effect were examined based on the optimal results of the ordinary least squares and geographically weighted regression models. The results indicated that (1) Significant spatiotemporal heterogeneity of the LST was found in the study area, and there was an obvious temperature gradient with "working-living-resting" UFZs. (2) All types of urban landscape factors showed a significant contribution to the seasonal LST, in the order of surface biophysical factors > building forms > landscape factors; however, their contributions varied in different seasons. (3) The major contributing factors showed a certain difference due to the variation of urban function and landscape complexity. This study expands the understanding on the complex relationship among urban landscape, function, and thermal environment, which could benefit urban landscape planning for UHI alleviation.

The varying driving forces of urban land expansion in China: Insights from a spatial-temporal analysis

The impacts of socioeconomic development on urban land expansion in China vary across space and time; however, comprehensive investigation of this issue remains scarce in the existing literature. This study used a geographically and temporally weighted regression model (GTWR) to examine the spatiotemporally heterogeneous impacts of socioeconomic factors on urban land expansion in China using a newly available annual urban land-use dataset from 2000 to 2015. We found that although the eastern region has maintained its leading role (53.79%) in terms of urban expansion, the share of the central (20.34%) and western (16.13%) regions is gradually increasing. Cities with a higher administrative status tended to expand more rapidly; however, increasingly expansion has also taken place in the prefecture-level cities in recent years. We further found that Gross domestic product (GDP) growth, population density, and capital investment positively contributed to the expansion, although the directions and strengths of association between these factors and urban expansion varied across space and time. Industrial structure and foreign direct investment (FDI) showed a similar variation change trend, with the number of cities evidencing a negative relationship rapidly expanding and increasingly being seen not just in northwest China but also in the southeast during the study period. We also found that the correlation between public finance expenditure and urban expansion presented significant north-south differentiation. It is worth noting that governmental behavior plays a significant role in driving urban land expansion. Our empirical study confirmed the spatiotemporal heterogeneous effects of socioeconomic factors on urban expansion in China, providing useful insights for city governments and urban planners.

Using geographically temporally weighted regression to assess the contribution of corruption governance to global PM2.5

In the face of the global haze crisis, exploring the driving force of political factors for controlling minute atmospheric particles has become essential for managing PM_2.5 pollution. In this study, the political drivers of haze pollution were examined by combining kernel density estimation, exploratory spatial data analysis, and a geographically temporally weighted regression model. The results showed that global haze pollution was increasing annually, and that differences and similarities in PM_2.5 pollution between different countries coexisted. Furthermore, the multi-dimensional driving elements of haze pollution showed obvious spatial and temporal non-stationarity, and different driving factors present multiple distribution trends. In general, the strengthening of anti-corruption measures addressed PM_2.5 concentration, but the direction and intensity of political drivers differed due to factors such as economic development, national culture, and natural conditions. Therefore, African countries should endeavor to control corruption, so as to achieve economic development and control haze pollution. However, from the perspective of political factors, it is likely North American countries will not be able to effectively control haze pollution.

Spatiotemporal variations in the incidence of bacillary dysentery and long-term effects associated with meteorological and socioeconomic factors in China from 2013 to 2017

Bacillary dysentery is a global public health problem that exhibits manifest spatiotemporal heterogeneity. However, long-term variations and regional determinant factors remain unclear. In this study, the Bayesian space-time hierarchy model was used to identify the long-term spatiotemporal heterogeneity of the incidence of bacillary dysentery and quantify the associations of meteorological factors with the incidence of bacillary dysentery in northern and southern China from 2013 to 2017. GeoDetector was used to quantify the determinant powers of socioeconomic factors in the two regions. The results showed that the incidence of bacillary dysentery peaked in summer (June to August), indicating temporal seasonality. Geographically, the hot spots (high-risk areas) were distributed in northwestern China (Xinjiang, Gansu, and Ningxia) and northern China (including Beijing, Tianjin, and Hebei), whereas the cold spots (low-risk areas) were concentrated in southeastern China (Jiangsu, Zhejiang, Fujian, and Guangdong). Moreover, significant regional differences were found among the meteorological and socioeconomic factors. Average temperature was the dominant meteorological factor in both northern and southern China. In northern and southern China, a 1 °C increase in the average temperature led to an increase of 1.01% and 4.26% in bacillary dysentery risk, respectively. The dominant socioeconomic factors in northern and southern China were per capita gross domestic product and the number of health technicians, with q statistic values of 0.81 and 0.49, respectively. These findings suggest that hot, moist, and overcrowded environments or poor health conditions increase the risk of bacillary dysentery. This study provides suggestions and serves as a basis for surveillance efforts. Further, the suggestions may aid in the control of bacillary dysentery and in the implementation of disease prevention policies.

Rationalization of water quality monitoring locations under spatiotemporal heterogeneity of diffuse pollution using seasonal export coefficient

Water quality is continuously changing because of anthropogenic origin of point and diffuses (non-point) pollution sources. Most of the time diffuse sources are not considered for rationalization of sampling sites as their accurate estimation is tedious and data intensive. The estimation of diffuse pollution is conventionally carried out using observed water quality data. These conventional approaches are data intensive and demands detailed information for a considerably long-time horizon and hence becomes challenging to implement in data-scarce regions. Also, diffuse pollution sources are characterized by spatio-temporal heterogeneity as they depend upon seasonal behavior of precipitation. The present study proposes an innovative semi-empirical approach of Seasonal Export Coefficients (SECs) for estimation of diffuse pollution loads, especially for tropical countries like India. This approach takes into account the effect of seasonality on the estimation of diffuse pollution loads, by considering seasonal heterogeneity of terrain and precipitation impact factors and land use applications. This seasonal heterogeneity is then tested for its possible impact on rationalization of water quality monitoring locations for Kali River basin in India. The SECs are estimated for available water quality dataset of 1999-2000 and are further used for simulation of nutrient loading for experimental years 2004-2005, 2009-2010, and 2014-2015. The resulting SECs for Kali river basin are: 2.03 (agricultural), 1.44 (fallow), and 0.92 (settlement) for monsoonal nitrate; while for non-monsoonal nitrate, SECs are 0.51 (agricultural), 0.23 (fallow), and 0.10 (settlement). The monsoonal phosphate SECs for land use classes - agricultural, fallow and settlement are 1.01, 0.68, and 0.25, while non-monsoonal phosphate SECs are 0.27, 0.14 and, 0.03 respectively. The seasonal variation of diffuse pollution sources is effectively captured by SECs. The proposed approach, by considering both point and diffuse pollution, is found efficient in determining optimum locations and number of monitoring sites where seasonal variations are found evident during experimental years.

Spatiotemporal heterogeneity of PM2.5 and its relationship with urbanization in North China from 2000 to 2017

Fine particulate matter (PM_2.5) pollution is becoming an increasing global concern due to rapid urbanization and socioeconomic development, especially in North China. Although North China experiences poor air quality and high PM_2.5 concentrations, their spatial heterogeneity and relationship with the relative spatial risks of air pollution have not been explored. Therefore, in this study, the temporal variation trends (slope values) of the PM_2.5 concentrations in North China from 2000 to 2017 were first quantified using the unitary linear regression model, and the Bayesian space-time hierarchy model was introduced to characterize their spatiotemporal heterogeneity. The spatial lag model was then used to examine the determinant power of urbanization and other socioeconomic factors. Additionally, the correlation between the spatial relative risks (probability of a region becoming more/less polluted relative to the average PM_2.5 concentrations of the study area), and the temporal variation trends of the PM_2.5 concentrations were quantified using the bivariate local indicators of spatial association model. The results showed that the PM_2.5 concentrations increased during 2000-2017, and peaked in 2007 and 2013. Spatially, the cities at high risk of PM_2.5 pollution were mainly clustered in southeastern Hebei, northern Henan, and western Shandong where the slope values were low, as demonstrated by the value of Moran's I (-0.56). Moreover, urbanization and road density were both positively correlated with PM_2.5 pollution, while the proportion of tertiary industry was negatively correlated. Furthermore, a notable increasing trend was observed in some cities, such as Tianjin, Zaozhuang, Qingdao, and Xinyang. These findings can contribute to the development of effective policies from the perspective of rapid urbanization to relieve and reduce PM_2.5 pollution.

Dynamic assessment of PM2.5 exposure and health risk using remote sensing and geo-spatial big data

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM_2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM_2.5 estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM_2.5 concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM_2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM_2.5 concentration greater than 80 μg/m³ in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM_2.5 were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM_2.5 estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated.

Spatial and temporal heterogeneity in the structure and function of sediment bacterial communities of a tropical mangrove forest

Bacterial communities of mangrove sediments are well appreciated for their role in nutrient cycling. However, spatiotemporal variability in these communities over large geographical scale remains understudied. We investigated sediment bacterial communities and their metabolic potential in an intertidal mangrove forest of India, Bhitarkanika, using high-throughput sequencing of 16S rRNA genes and community-level physiological profiling. Bulk surface sediments from five different locations representing riverine and bay sites were collected over three seasons. Seasonality largely explained the variation in the structural and metabolic patterns of the sediment bacterial communities. Freshwater Actinobacteria were more abundant in monsoon, whereas γ-Proteobacteria demonstrated higher abundance in summer. Distinct differences in the bacterial community composition were noted between riverine and bay sites. For example, salt-loving marine bacteria affiliated to Oceanospirillales were more prominent in the bay sites than the riverine sites. L-asparagine, N-acetyl-D-glucosamine, and D-mannitol were the preferentially utilized carbon sources by bacterial communities. Bacterial community composition was largely governed by salinity and organic carbon content of the sediments. Modeling analysis revealed that the abundance of δ-Proteobacteria increased with salinity, whereas β-Proteobacteria displayed an opposite trend. Metabolic mapping of taxonomic data predicted biogeochemical functions such as xylan and chitin degradation, ammonia oxidation, nitrite reduction, and sulfate reduction in the bacterial communities suggesting their role in carbon, nitrogen, and sulfur cycling in mangrove sediments. This study has provided valuable clues about spatiotemporal heterogeneity in the structural and metabolic patterns of bacterial communities and their environmental determinants in a tropical mangrove forest.

Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach

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We modelled MERS epidemic in the Middle East region up to September 2014.

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We assessed spatiotemporal variation in zoonotic and human transmission.

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Spring 2014 wave showed a 17-fold and 3-fold increase in the above transmissions.

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Zoonotic transmission has a larger spatial heterogeneity than human transmission.

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Human transmission is more frequent than expected (75% of cases vs. 34%).

MERS coronavirus cases notified in the Middle East region since the identification of the virus in 2012 have displayed variations in time and across geography. Through a combined modelling approach, we estimate the rates of generation of cases along the zoonotic and human-to-human transmission routes and assess their spatiotemporal heterogeneity. We consider all cases notified to WHO from March 2012 to mid-September 2014. We use a stochastic modelling of the time series of case incidence in the Middle East region to estimate time- and space-dependent zoonotic and human-to-human transmission parameters. The model also accounts for possible lack of identification of secondary transmissions among notified cases. This approach is combined with the analysis of imported cases out of the region to assess the rate of underreporting of cases. Out of a total of 32 possible models, based on different parameterisation and scenario considered, the best-fit model is characterised by a large heterogeneity in time and across space for both zoonotic and human-to-human transmission. The variation in time that occurred during Spring 2014 led to a 17-fold and 3-fold increase in the two transmissions, respectively, bringing the reproductive rate to values above 1 during that period for all regions under study. The model suggests that 75% of MERS-CoV cases are secondary cases (human-to-human transmission), which is substantially higher than the 34% of reported cases with an epidemiological link to another case. Overall, estimated reporting rate is 0.26. Our findings show a higher level of spatial heterogeneity in zoonotic transmission compared to human-to-human, highlighting the strong environmental component of the epidemic. Since sporadic introductions are predicted to be a small proportion of notified cases and are responsible for triggering secondary transmissions, a more comprehensive understanding of zoonotic source and path of transmission could be critical to limit the epidemic spread.

Non-equilibrium spatial dynamics of ecosystems

Ecological systems show tremendous variability across temporal and spatial scales. It is this variability that ecologists try to predict and that managers attempt to harness in order to mitigate risk. However, the foundations of ecological science and its mainstream agenda focus on equilibrium dynamics to describe the balance of nature. Despite a rich body of literature on non-equilibrium ecological dynamics, we lack a well-developed set of predictions that can relate the spatiotemporal heterogeneity of natural systems to their underlying ecological processes. We argue that ecology needs to expand its current toolbox for the study of non-equilibrium ecosystems in order to both understand and manage their spatiotemporal variability. We review current approaches and outstanding questions related to the study of spatial dynamics and its application to natural ecosystems, including the design of reserves networks. We close by emphasizing the importance of ecosystem function as a key component of a non-equilibrium ecological theory, and of spatial synchrony as a central phenomenon for its inference in natural systems.