Spatial-temporal patterns and influencing factors for hemorrhagic fever with renal syndrome: A 16-year national surveillance analysis in China

Background

China is confronted with the significant menace posed by hemorrhagic fever with renal syndrome (HFRS). Nevertheless, the long-term spatial-temporal variations, regional prevalence patterns, and fundamental determinants' mechanisms for HFRS remain inadequately elucidated.

Methods

Newly diagnosed cases of HFRS from January 2004 to December 2019 were acquired from the China Public Health Science Data repository. We used Age-period-cohort and Bayesian Spacetime Hierarchy models to identify high-risk populations and regions in mainland China. Additionally, the Geographical Detector model was employed to quantify the determinant powers of significant driver factors to the disease.

Results

A total of 199,799 cases of HFRS were reported in mainland China during 2004-2019. The incidence of HFRS declined from 1.93 per 100,000 in 2004 to 0.69 per 100,000 in 2019. The incidence demonstrated an inverted U-shaped trend with advancing age, peaking in the 50-54 age group, with higher incidences observed among individuals aged 20-74 years. Hyperendemic areas were mainly concentrated in the northeastern regions of China, while some western provinces exhibited a potential upward trend. Geographical detector model identified that the spatial variations of HFRS were significantly associated with the relative humidity (Q = 0.36), forest cover (Q = 0.26), rainfall (Q = 0.18), temperature (Q = 0.16), and the surface water resources (Q = 0.14).

Conclusions

This study offered comprehensive examinations of epidemic patterns, identified high-risk areas quantitatively, and analyzed factors influencing HFRS transmission in China. The findings may contribute to the necessary implementations for the effective prevention and control of HFRS.

Wastewater surveillance for antibiotics and resistance genes in a river catchment: Spatiotemporal variations and the main drivers

Wastewater-based epidemiology (WBE) is used for mining information about public health such as antibiotics resistance. This study investigated the distribution profiles of six types of antibiotic resistance genes (ARGs) in wastewater and rivers in Wuhu City, China. The levels of ARGs found in the Qingyijiang River were significantly higher than other rivers, and were comparable to effluent levels. Among the ARGs, sulfonamides ARGs and intI1 were the predominant in both wastewaters and rivers. Additionally, the concentrations of ARGs were higher on weekends as opposed to weekdays. Their distribution patterns remained consistent inter-week and inter-season using linear regression analysis (p < 0.001). Interestingly, the occurrence levels of ARGs in wastewaters during spring were significantly higher than in autumn, although insignificant in rivers. The apparent removal rate of ARGs in domestic wastewater sources ranged from 61.52-99.29%, except for qepA (-1.91% to 81.09%), whereas the removal rates in mixed domestic and industrial wastewaters showed a marked decrease (-92.94% to 76.67%). A correlation network analysis revealed that azithromycin and erythromycin were key antibiotics, while blaNDM-1, tetM, tetB, and ermB were identified as key ARGs. Sulfonamide and fluoroquinolone antibiotics, and tetracycline and macrolide ARGs were the primary contributors. Linear mixed models demonstrated that socio-economic variables positively impacted the occurrence levels of ARGs, whereas wastewater flow and river runoff were the negative drivers for their concentrations in wastewaters and surface waters, respectively. Overall, this WBE study contributes to the understanding of spatiotemporal profiles and main drivers of the occurrence of ARGs in wastewater and receiving water.

Spatiotemporal patterns and drivers of the urban air pollution island effect for 2273 cities in China

Air pollution levels tend to be higher in urban areas than in surrounding rural areas, and this air pollution has a negative effect on human health. However, the spatiotemporal patterns of urban-rural air pollution differences and the determinants of these differences remain unclear. Here, we calculate the Urban Air Pollution Island (UAPI) intensity for PM2.5 and PM10 on a monthly, seasonal, and annual scale for 2273 cities in China from 2000 to 2020. Subsequently, we analyze the influence of urban characteristics using a combined approach of a two-way fixed effects model and a spatial Durbin model. Results show a strong downward trend in the UAPI intensity since 2013, with reductions ranging from 42 % to 61 % until 2020, for both pollutants and in summer as well as winter. Consistently, the proportion of the cities experiencing the UAPI phenomenon decreased from 94.5 % to 77.3 % for both PM2.5 and PM10. We find a significant influence of urban morphology on UAPI. Specifically, urban sprawl, polycentric development, and an increase in urban green spaces are associated with a reduction in UAPI, while dense urban areas intensify it. Our study also reveals a robust inverted U-shaped relationship between stages of economic development and UAPI. Moreover, economic development and air pollution itself show spillover effects that oppose their direct impacts. These results suggest that urban and regional planning and more ambitious climate change mitigation policies could be more effective strategies for mitigating air pollution in cities than end-of-pipe control.

A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters

It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.

Matching end-of-life household vehicle generation and recycling capacity in Chinese cities: A spatio-temporal analysis for 2022-2050

End-of-life vehicles (ELVs) present both opportunities and challenges for the environment and the economy, where effective recycling management plays a decisive role. Recently, the primary focus of recycling management has shifted from simply meeting demand to refining and optimizing processes at the city-scale. However, the mismatch in recycling capacity has become a significant obstacle to maximizing environmental and economic benefits. To reveal this issue and propose improvements in the context of China, this study simulates end-of-life internal combustion engine vehicles (ICEVs) and new energy vehicles (NEVs) at the city-scale from 2021 to 2050, and analyzes their spatio-temporal pattern and recycling capacity matching. The results indicate that the number of ELVs in China will continue to increase, peaking between 3.5 and 3.7 million. This growth will be mainly driven by third- to fifth-tier cities, as well as central and southwestern cities. Regarding recycling capacity matching, most cities possess excess dismantling capacity, while first-tier cities face coordination problems in battery collection. Spatial coordination across cities or provinces is a viable approach for dismantling enterprises and should be prioritized over indiscriminate deregistration or establishing new facilities. The absence of initiative within the recycling system results in uncoordinated battery collection. Implementing a recycling-sharing mechanism and establishing a reuse market can effectively tackle this problem by leveraging market incentives. These analyses provide practical suggestions to maximize the environmental and economic benefits of resource recycling, thereby contributing to the UN's 2030 Sustainable Development Goals (SDGs).

Effect of permafrost degradation on carbon sequestration of alpine ecosystems

Permafrost degradation profoundly affects carbon storage in alpine ecosystems, and the response characteristics of carbon sequestration are likely to differ at the different stages of permafrost degradation. Furthermore, the sensitivity of different stages of permafrost degradation to climate change is likely to vary. However, related research is lacking so far on the Qinghai-Tibetan Plateau (QTP). To investigate these issues, the Shule River headwaters on the northeastern margin of the QTP was selected. We applied InVEST and Noah-MP land surface models in combination with remote sensing and field survey data to reveal the dynamics of different carbon (vegetation carbon, soil organic carbon (SOC), and ecosystem carbon) pools from 2001 to 2020. A space-for-time analysis was used to explore the response characteristics of carbon sequestration along a gradient of permafrost degradation, ranging from lightly degraded permafrost (H-SP) to severely degraded permafrost (U-EUP), and to analyze the sensitivity of the permafrost degradation gradient to climate change. Our results showed that: (1) the sensitivity of mean annual ground temperature (MAGT) to climatic variables in the U-EUP was stronger than that in the H-SP and S-TP, respectively; (2) rising MAGT led to permafrost degradation, but increasing annual precipitation promoted permafrost conservation; (3) vegetation carbon, SOC, and ecosystem carbon had similar spatial distribution patterns, with their storage decreasing from the mountain area to the valley; (4) alpine ecosystems acted as carbon sinks with the rate of 0.34 Mg‧ha-1‧a-1 during 2001-2020, of which vegetation carbon and SOC accumulations accounted for 10.65 % and 89.35 %, respectively; and (5) the effects of permafrost degradation from H-SP to U-EUP on carbon density changed from promotion to inhibition.

A holistic assessment of spatiotemporal variation, driving factors, and risks influencing river water quality in the northeastern Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is the source for many of the most important rivers in Asia. It is also an essential ecological barrier in China and has the characteristic of regional water conservation. Given this importance, we analyzed the spatiotemporal distribution patterns and trends of 10 water quality parameters. These measurements were taken monthly from 67 monitoring stations in the northeastern QTP from 2015 to 2019. To evaluate water quality trends, major factors influencing water quality, and water quality risks, we used a series of analytical approaches including Mann-Kendall test, Boruta algorithm, and interval fuzzy number-based set-pair analysis (IFN-SPA). The results revealed that almost all water monitoring stations in the northeastern QTP were alkaline. From 2015 to 2019, the water temperature and dissolved oxygen of most monitoring stations were significantly reduced. Chemical oxygen demand, permanganate index, five-day biochemical oxygen demand, total phosphorus, and fluoride all showed a downward trend across this same time frame. The annual average total nitrogen (TN) concentration fluctuation did not significantly decrease across the measured time frame. Water quality index (WQI-DET) indicated bad or poor water quality in the study area; however, water quality index without TN (WQI-DET') reversed the water quality value. The difference between the two indexes suggested that TN was a significant parameter affecting river water quality in the northeastern QTP. Both Spearman correlation and Boruta algorithm show that elevation, urban land, cropland, temperature, and precipitation influence the overall water quality status in the northeastern QTP. The results showed that between 2015 and 2019, most rivers monitored had a relatively low risk of degradation in water quality. This study provides a new perspective on river water quality management, pollutant control, and risk assessment in an area like the QTP that has sensitive and fragile ecology.

Detection of social-ecological drivers and impact thresholds of ecological degradation and ecological restoration in the last three decades

Special consideration should be given to the differential coupling relationships between natural and anthropogenic factors on ecological degradation and ecological restoration. However, few studies have focused on how to quantify the contribution rate of social-ecological interactions to vegetation growth and determine the impact thresholds of vegetation coverage at the county scale. Notably, it is more conducive to evaluating the impact of anthropogenic factors on vegetation coverage by integrating ecological land use policy into the research framework. This study combined remote sensing technology, as well as the Geo-detector model and elasticity coefficient to identify the key factors affecting ecological degradation and ecological restoration and quantitatively determine the impact thresholds from the aspects of climate change, topography, hydrological condition, human disturbance, and ecological land use policy. The results showed that ecosystems shifted from severe degradation (1990-2000) to restoration (2000-2010) and then to slight degradation (2010-2020). Meteorological factors and topographic factors revealed a stronger impact on ecological degradation and ecological restoration before the implementation of large-scale ecological engineering, and then they were most affected by ecological land use policy. In addition, the ecological thresholds of some factors were found in this study. Specifically, when average annual precipitation and slope reached the threshold of 523 mm and 5° respectively under ecological degradation, they had the greatest influence on vegetation coverage. Under ecological degradation and ecological restoration, the threshold of altitude was 1500 mm, and the threshold of drainage density was 10 and 14, respectively. The information from this study is expected to enhance the practical value of ecological research and provide an important reference for ecological standards and sustainable environmental management.

Spatial-temporal patterns and influencing factors of ecological land degradation-restoration in Guangdong-Hong Kong-Macao Greater Bay Area

Despite the fact that urban agglomerations have undergone extensive ecological land coverage modifications, exploration of the patterns and driving mechanisms associated with ecological land degradation (ELD) and ecological land restoration (ELR) in urban agglomerations is still limited. This study combined remote sensing technology, as well as landscape index and geographical detector to characterize the spatiotemporal patterns of ELD (isolating, adjacent, and enclosing degradation) and ELR (outlying, edge-expansion, and infilling restoration) in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1990 to 2019. Subsequently, the contributions, interactions, and driver changes were quantified. The results showed an ecological land shift from over-exploitation to balanced co-existence, which was facilitated by a spatiotemporal pattern transition from adjacent degradation-led (1990-2010) to edge-expansion restoration-led (2010-2019). Land urbanization rate and population density showed a stronger promoting effect on ELD than natural factors, while tertiary industry, topography, and soil conditions were more significant in ELR. The factors' nonlinear interaction enhanced the degradation-restoration pattern evolution and continued to increase over time-particularly the interaction between construction land expansion and other drivers. Additionally, from 2010 to 2019, 80% of the ELR socio-economic factors turned from negative to positive and gradually became to play a significant role. This study is expected to help ecological protection and restoration planners/managers recognize the factors' interactions and variations, and ultimately improve the ecological network structure that is designed to integrate the city with the ecosystem.

Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China

Background

China is one of the world’s fastest-aging countries. Population aging and social-economic development show close relations. This study aims to illustrate the spatial-temporal distribution and movement of gravity centers of population aging and social-economic factors and thier spatial interaction across the provinces in China.

Methods

Factors of elderly population rate (EPR), elderly dependency ratio (EDR), per capita gross regional product (GRP_pc), and urban population rate (UPR) were collected. Distribution patterns were detected by using global spatial autocorrelation, Kernel density estimation, and coefficient of variation. Further, Arc GIS software was used to find the gravity centers and their movement trends yearly from 2002 to 2018. The spatial interaction between the variables was investigated based on bivariate spatial autocorrelation analysis.

Results

The results showed a larger variety of global spatial autocorrelation indexed by Moran’s I and stable trends of dispersion degree without obvious convergence in EPR and EDR. Furthermore, the gravity centers of the proportion of EPR and EDR moved northeastward. In contrast, the economic and urbanization factors showed a southwestward movement, which exhibited an reverse trend compared to population aging indicators. Moreover, the movement rates of EPR and EDR (15.12 and 18.75 km/year, respectively) were higher than that of GRP_pc (13.79 km/year) and UPR (6.89 km/year) annually during the study period. Further, the bivariate spatial autocorrelation variation is in line with the movement trends of gravity centers which showed a polarization trend of population aging and social-economic factors that the difference between southwest and northeast directions and exhibited a tendency to expand in China.

Conclusions

In sum, our findings revealed the difference in spatio-temporal distribution and variation between population aging and social-economic factors in China. It further indicates that the opposite movements of gravity centers and the change of the BiLISA in space which may result in the increase of the economic burden of the elderly care in northern China. Hence, future development policy should focus on the social-economic growth and distribution of old-aged supporting resources, especially in northern China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11032-z.

Discovering dynamic patterns from infectious disease data using dynamic mode decomposition

BACKGROUND

The development and application of quantitative methods to understand disease dynamics and plan interventions is becoming increasingly important in the push toward eradication of human infectious diseases, exemplified by the ongoing effort to stop the spread of poliomyelitis.

METHODS

Dynamic mode decomposition (DMD) is a recently developed method focused on discovering coherent spatial-temporal modes in high-dimensional data collected from complex systems with time dynamics. The algorithm has a number of advantages including a rigorous connection to the analysis of nonlinear systems, an equation-free architecture, and the ability to efficiently handle high-dimensional data.

RESULTS

We demonstrate the method on three different infectious disease sets including Google Flu Trends data, pre-vaccination measles in the UK, and paralytic poliomyelitis wild type-1 cases in Nigeria. For each case, we describe the utility of the method for surveillance and resource allocation.

CONCLUSIONS

We demonstrate how DMD can aid in the analysis of spatial-temporal disease data. DMD is poised to be an effective and efficient computational analysis tool for the study of infectious disease.