Impact of continuous low water stage on the breeding environment of Oncomelania hupensis: a case study of Poyang Lake area in China

Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater

Identifying high-risk factors (heavy metals (HMs) and pollution sources) by coupling receptor models and health risk assessment model (HRA) is a novel approach within the field of risk assessment. However, this coupled model ignores the contribution of spatial differentiation to high-risk factors, resulting in the assessment being subjective. Taking Dongting Plain (DTP) as an example, a coupling framework by jointly using the positive matrix factorization model (PMF), HRA, Monte Carlo simulation, and geo-detector was developed, aiming to identify high-risk factors in groundwater, and further explore key environmental variables influencing the spatial heterogeneity of high-risk factors. The results showed that at least 82.86 % of non-carcinogenic risks and 97.41 % of carcinogenic risks were unacceptable for people of all ages, especially infants and children. According to the relationships among HMs, pollution sources, and health risks, As and natural sources were defined as high-risk HMs and sources, respectively. The interactions among Holocene thickness, oxidation-reduction potential, and dissolved organic carbon emerged as the primary drivers of spatial variability in high-risk factors, with their combined explanatory power reaching up to 74%. This proposed framework provides a scientific reference for future studies and a practical reference for environmental authorities in developing effective pollution management measures.

Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system

Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01-0.03 m/year) and increased (0.17-1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river-lake systems.

Three Gorges Dam: Potential differential drivers and trend in the spatio-temporal evolution of the change in snail density based on a Bayesian spatial-temporal model and 5-year longitudinal study

BACKGROUND

Snail abundance varies spatially and temporally. Few studies have elucidated the different effects of the determinants affecting snail density between upstream and downstream areas of the Three Gorges Dam (TGD). We therefore investigated the differential drivers of changes in snail density in these areas, as well as the spatial-temporal effects of these changes.

METHODS

A snail survey was conducted at 200 sites over a 5-year period to monitor dynamic changes in snail abundance within the Yangtze River basin. Data on corresponding variables that might affect snail abundance, such as meteorology, vegetation, terrain and economy, were collected from multiple data sources. A Bayesian spatial-temporal modeling framework was constructed to explore the differential determinants driving the change in snail density and the spatial-temporal effects of the change.

RESULTS

Volatility in snail density was unambiguously detected in the downstream area of the TGD, while a small increment in volatility was detected in the upstream area. Regarding the downstream area of the TGD, snail density was positively associated with the average minimum temperature in January of the same year, the annual Normalized Difference Vegetation Index (NDVI) of the previous year and the second, third and fourth quartile, respectively, of average annual relative humidity of the previous year. Snail density was negatively associated with the average maximum temperature in July of the previous year and annual nighttime light of the previous year. An approximately inverted "U" curve of relative risk was detected among sites with a greater average annual ground surface temperature in the previous year. Regarding the upstream area, snail density was positively associated with NDVI and with the second, third and fourth quartile, respectively, of total precipitation of the previous year. Snail density was negatively associated with slope.

CONCLUSIONS

This study demonstrated a rebound in snail density between 2015 and 2019. In particular, temperature, humidity, vegetation and human activity were the main drivers affecting snail abundance in the downstream area of the TGD, while precipitation, slope and vegetation were the main drivers affecting snail abundance in the upstream area. These findings can assist authorities to develop and perform more precise strategies for surveys and control of snail populations.

Three Gorges Dam: the changing trend of snail density in the Yangtze River basin between 1990 and 2019

BACKGROUND

The area of Oncomelania hupensis snail remains around 3.6 billion m², with newly emerging and reemergent habitats continuing to appear in recent years. This study aimed to explore the long-term dynamics of snail density before and after the operation of Three Gorges Dam (TGD).

METHODS

Data of snail survey between 1990 and 2019 were collected from electronic databases and national schistosomiasis surveillance. Meta-analysis was conducted to estimate the snail density. Joinpoint model was used to identify the changing trend and inflection point. Inverse distance weighted interpolation (IDW) was used to determine the spatial distribution of recent snail density.

RESULTS

A total of 3777 snail survey sites with a precise location of village or beach were identified. For the downstream area, snail density peaked in 1998 (1.635/0.11 m², 95% CI: 1.220, 2.189) and fluctuated at a relatively high level before 2003, then declined steadily from 2003 to 2012. Snail density maintained lower than 0.150/0.11 m² between 2012 and 2019. Joinpoint model identified the inflection of 2003, and a significant decreasing trend from 2003 to 2012 with an annual percentage change (APC) being - 20.56% (95% CI: - 24.15, - 16.80). For the upstream area, snail density peaked in 2005 (0.760/0.11 m², 95% CI: 0.479, 1.207) and was generally greater than 0.300/0.11 m² before 2005. Snail density was generally lower than 0.150/0.11 m² after 2011. Snail density showed a significant decreasing trend from 1990 to 2019 with an APC being - 6.05% (95% CI: - 7.97, - 7.09), and no inflection was identified. IDW showed the areas with a high snail density existed in Poyang Lake, Dongting Lake, Jianghan Plain, and the Anhui branch of the Yangtze River between 2015 and 2019.

CONCLUSIONS

Snail density exhibited a fluctuating downward trend in the Yangtze River basin. In the downstream area, the operation of TGD accelerated the decline of snail density during the first decade period, then snail density fluctuated at a relatively low level. There still exists local areas with a high snail density. Long-term control and monitoring of snails need to be insisted on and strengthened.

Habitat configuration of the Yangtze finless porpoise in Poyang Lake under a shifting hydrological regime

Habitats of freshwater cetaceans are under increasing threats of deterioration globally. A complete understanding of long-term variations of habitat configurations is therefore critical. Poyang Lake in China contains a large and stable population of the Yangtze finless porpoise, a critically endangered freshwater cetacean species. However, constant water decline and intensified human activities in the lake since 2000 have led to uncertainty for porpoise conservation. We address this issue via remote sensing and hydrodynamic modeling of nine environmental variables during different seasons over the past two decades. The MaxEnt model was used to extrapolate changes in likely habitat configurations of the porpoise, and MARXAN algorithms delineated habitat protection priorities in different seasons. Results illustrate that flow velocity, water depth, Chl-a concentration, distance to grassland and boats greatly affect the porpoise distribution. Shifts in these environmental variables can lead to significant habitat decreases in all seasons. In particular, unstable hydrological regimes may force the porpoises to live in habitats with lower water depths for suitable flow velocity conditions in the dry season, and habitats are increasingly infringed by grassland and mudflats. High protection priority areas such as the northern channel and the estuaries of the tributaries urgently need long-term systematic and targeted surveys of ecosystem functionality and flexible management of anthropogenic activities. Combining remote sensing with hydrodynamic and species distribution models can also assist in understanding the situation of other aquatic species.

Potential impact of flooding on schistosomiasis in Poyang Lake regions based on multi-source remote sensing images

Background

Flooding is considered to be one of the most important factors contributing to the rebound of Oncomelania hupensis, a small tropical freshwater snail and the only intermediate host of Schistosoma japonicum, in endemic foci. The aim of this study was to assess the risk of intestinal schistosomiasis transmission impacted by flooding in the region around Poyang Lake using multi-source remote sensing images.

Methods

Normalized Difference Vegetation Index (NDVI) data collected by the Landsat 8 satellite were used as an ecological and geographical suitability indicator of O. hupensis habitats in the Poyang Lake region. The expansion of the water body due to flooding was estimated using dual-polarized threshold calculations based on dual-polarized synthetic aperture radar (SAR). The image data were captured from the Sentinel-1B satellite in May 2020 before the flood and in July 2020 during the flood. A spatial database of the distribution of snail habitats was created using the 2016 snail survey in Jiangxi Province. The potential spread of O. hupensis snails after the flood was predicted by an overlay analysis of the NDVI maps in the flood-affected areas around Poyang Lake. The risk of schistosomiasis transmission was classified based on O. hupensis snail density data and the related NDVI.

Results

The surface area of Poyang Lake was approximately 2207 km² in May 2020 before the flood and 4403 km² in July 2020 during the period of peak flooding; this was estimated to be a 99.5% expansion of the water body due to flooding. After the flood, potential snail habitats were predicted to be concentrated in areas neighboring existing habitats in the marshlands of Poyang Lake. The areas with high risk of schistosomiasis transmission were predicted to be mainly distributed in Yongxiu, Xinjian, Yugan and Poyang (District) along the shores of Poyang Lake. By comparing the predictive results and actual snail distribution, we estimated the predictive accuracy of the model to be 87%, which meant the 87% of actual snail distribution was correctly identified as snail habitats in the model predictions.

Conclusions

Data on water body expansion due to flooding and environmental factors pertaining to snail breeding may be rapidly extracted from Landsat 8 and Sentinel-1B remote sensing images. Applying multi-source remote sensing data for the timely and effective assessment of potential schistosomiasis transmission risk caused by snail spread during flooding is feasible and will be of great significance for more precision control of schistosomiasis.