Xiaohong Chen’s research while affiliated with Sun Yat-Sen University and other places

The impacts of landscape patterns on river water quality are commonly acknowledged, but understanding the complex processes by which landscape patterns affect water quality is still limited, especially in densely populated urban areas. Exploring the mechanisms through which landscape characteristics influence water quality changes in urbanized rivers will benefit regional water resource protection and landscape-scale resource development and utilization. Utilizing daily water quality monitoring data from rivers in the urbanized area of the Pearl River Delta in 2020, our research employed canonical analysis and partial least squares structural equation modeling (PLS-SEM) to explore the processes and mechanisms of the influence of urbanized river landscape patterns on surface water quality. The results indicated that total nitrogen (TN) was the critical indicator limiting the water quality of rivers in the Pearl River Delta. The landscape composition and configuration indexes exhibited non-linear variations with scale, and the landscape fragmentation was higher closer to the river. Landscape patterns had the most significant influence on water quality under the characteristic scale of a 5.50 km circular buffer zone, and landscape composition dominated the change of water quality of urbanized rivers, among which 30.64% of the percentage patch area of construction (C_PLAND) contributed 46.40% to the explanation rate of water quality change, which was the key landscape index affecting water quality. Moreover, landscape patterns had a higher interpretive rate of 39.29% on water quality in the wet season compared to 36.62% in the dry season. Landscape composition had an indirect negative impact on water quality, with a value of 0.47, by affecting the processes of runoff and nutrient migration driven by human activities, while landscape configuration had an indirect negative impact on water quality, with a value of 0.11. Our research quantified the impacts of landscape patterns driven by human activities on surface water quality and proposed management measures to optimize the allocation of landscape resources in riparian zones of urbanized rivers. The results provide a scientific basis for water quality management and protection in urbanized rivers.

Landslides pose a formidable natural hazard. Accurate risk assessment of landslides triggered by precipitation heavily relies on hydrometeorological factors, specifically precipitation and soil moisture. However, the insufficient ground-based observations and the coarse spatio-temporal resolution hinder the performance of landslide prediction. It is not clear what hydrometeorological thresholds and triggering mechanisms are more likely to trigger landslides in China, particularly in the context of rapid urbanization. To address these questions, this study investigated 1504 shallow landslide events in Chinese urban and non-urban areas from 2007 to 2019. It utilized daily 1 km soil moisture at various depths (20–100 cm) and multi-source precipitation datasets, including gauge-based gridded dataset, in conjunction with three multi-source fusion precipitation products (Multi-Source Weighted-Ensemble Precipitation − MSWEP, the Climate Hazards Group InfraRed Precipitation with Station dataset − CHIRPS, and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement − GPM-IMERG), along with dynamic urban impervious area datasets. It aims to determine the optimal multi-source precipitation predictor, the critical soil moisture depth that triggers landslides, and to establish the hydrometeorological thresholds for landslides. Additionally, the impact of urbanization on landslide occurrences was estimated by comparing antecedent precipitation accumulation, soil moisture, and impervious surface ratio dynamics between urban and non-urban areas. The results indicated that a combination of 2-day cumulative CHIRPS precipitation and 100 cm soil moisture provided the most accurate predictions for landslides in urban regions of China (accuracy = 88.5 %), outperforming interpolated ground-based observations and other fusion products. Specifically, landslides become more prone when antecedent cumulative rainfall surpasses 97.42 mm in 2 days and soil moisture exceeds 39.6 % m/m saturation in China. Urban areas experienced high antecedent precipitation levels, lower precipitation (64.40 mm) threshold and soil moisture threshold (38.9 %), and shorter durations at landslide sites compared to non-urban areas (71.90 mm, 41.4 %, and 7 days, respectively). The process of urbanization is observed to decrease soil moisture levels while concurrently elevating rainfall amounts. This phenomenon, combined with anthropogenic activities, including distance from roads and urban impervious surface expansion, contributes to 44.6 % of the causes of landslides by reducing slope stability and increasing the presence of loose material. These findings have implications for landslide warnings in urban areas with limited measurements and contribute to understanding urbanization’s impact on landslide risks in developing nations.

Global warming has accelerated the interregional hydrological cycle, resulting in a significant increase in the frequency and intensity of extreme events worldwide. These events often involve a combination of spatial and temporal factors, giving rise to compound events. Among them are rapid transitions from dry and hot conditions to wet (DHW) events, which can have more severe impacts on human societies and ecosystems than individual extreme events. Urbanization not only heightens the likelihood of disasters but also exacerbates the exposure of affected populations. However, there has been insufficient attention given to understanding the connections between these successive compound events and the heightened risks posed by the increasing urbanization. In this study, we used bias-corrected daily precipitation and maximum temperature data simulated by the Coupled Model Intercomparison Project Phase 6 (CMIP6) model, along with historical and future daily runoff data simulated by the Variable Infiltration Capacity (VIC) model. We systematically investigated the spatiotemporal changes (i.e., frequency, duration, intensity, and compound probability) in DHW events in China during the upper historical period (1979–2014) and under two medium and high emission scenarios (2015–2100) − Shared Socioeconomic Pathways (SSP245 and SSP585). Furthermore, we examined how extreme runoff responds to variations in maximum temperature and daily precipitation during DHW events. We also evaluated the potential population and urban exposure to DHW events using dynamic future population and urban expansion data to assess the potential risks. Our results indicate that the multi-model ensemble predicts varying spatial patterns of future DHW events under SSP245 and SSP585 scenarios. The frequency and duration of these events are expected to decrease by approximately 20–25% in the Northwest region under both scenarios. At the same time, the middle and lower plains of the Yangtze and Yellow River Basins experience increased occurrence, broader geographical impact, and higher DHW event intensity alongside urban expansion and population growth in these regions. Specifically, event intensity is anticipated to increase by a factor of approximately 7–11. Temporally, we expect short-duration, high-intensity DHW events to occur in the Yangtze and Yellow River basins around 2023, 2038, and 2058, respectively. The primary driver for the future rise in population exposure to DHW events is the expected increase in event frequency in the middle and lower plains of the Yangtze and Yellow River basins, concurrent with population growth in these regions. Under the medium emission scenario, the total exposed population to DHW events in China is predicted to be 1.61 times higher than the historical baseline. In contrast, the high-emission scenario estimates a total population of 2.41 times higher than the historical baseline period. Most of the DHW events occurred in regions that exhibit a positive dependence between high temperature and high runoff events, serving as the primary driver of DHW events. Urbanization has a positive impact on DHW events, with the effect under high emissions being approximately 30% higher than during the historical base period. Additionally, it is indicated that future DHW events will exhibit higher sensitivity to global warming, with intensity projected to increase approximately fourfold and the exposed population to rise by about 1.5 times for every 1 °C of global warming. This research enhances our understanding of forecasting future compound hydrometeorological extreme events under different scenarios and provides insight into the role of climate change and urbanization in shaping these events in China.

Natural hazards could have devastating consequences globally, making hazard assessment and spatial prediction crucial for enhancing the resilience of urbanized regions. However, current disaster prediction and assessment research often neglect the compound effects between multiple geohazards highly in urbanized regions. To address the concern, we employed comparative methodology, evaluating four machine learning algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Back Propagation Neural Network (BP), and Long Short-Term Memory (LSTM)—in the creation of Geohazard Susceptibility Maps (GSM) for the highly urbanized Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Additionally, the study investigated the triggering mechanisms and the compound interaction between multiple geohazards using the conditional vine copula model. The results showed that the XGBoost model outperformed other models (AUC = 0.89) for predicting multiple geohazards. Geohazards were predominantly concentrated in urban areas in the GBA, with surface subsidence being the most severe, followed by collapse and landslide. The primary triggers for multi-geohazards include distance to roads, slope length, and lithology, with slope length and lithology identified as the primary causative factors in urban areas. Urbanization within the GBA increased the probability of multi-geohazards by 10%, compared to their univariate counterparts. Urban regions exhibited increased risks of landslides, surface subsidence, and collapse by approximately 31%, 44%, and 32%, respectively compared to non-urban regions. Additionally, compound geohazards in the GBA were primarily triggered by heavy rainfall, resulting in the formation of landslide-collapse and collapse-landslide geohazard chains. The probability of compound geohazards is approximately 5% lower than that of univariate geohazards. This is because compound geohazards necessitate a higher cumulative rainfall, and the rainfall threshold was approximately 2–3 times higher than that of univariate geohazards. In the cascading hazard pattern, the occurrence of primary geohazards during local heavy rain increased the probability of secondary geohazards by approximately 10%. The study provides essential insights for mitigating compound geohazards in urbanized areas.

Extreme precipitation events (EPEs) have garnered considerable social concerns due to their hazardous and destructive nature. To identify the possible causes of EPEs in China, this paper presents an in-depth investigation of how EPEs coincide with atmospheric conditions, i.e., atmospheric instability, moisture availability and moisture convergence, as well as driving variables, i.e., vertical velocity, relative humidity and air temperature. Specifically, the classic coincidence probability is devised to explicitly relate 72-h EPEs to convective available potential energy (CAPE), precipitable water (PW), vertical velocity at 700 hPa (verV), relative humidity at 700 hPa (RH), average air temperature between 850 and 500 hPa (Tavg) as well as air temperature difference between 850 and 500 hPa (Tdiff). The results show that at the annual timescale, EPEs in Southeast and Southwest China are dominantly controlled by verV, in North and Central China by PW and in Northwest China by CAPE roughly. At the seasonal timescale, the spatial distributions of coincidence probability values and “competition” among atmospheric conditions and driving variables exhibit similar patterns as observed throughout the entire year except for December–January–February. Moreover, the diagnostic plots generated for three case study regions in China provide valuable insights into the temporal evolution of precipitation events, cumulative distribution function curves of influential factors and dominant controlling factors of EPEs. This paper contributes to understandings of the dominant controlling factors of EPEs for the whole of China. The spatial patterns of EPEs and their related atmospheric conditions and driving variables yield useful information for rainstorm forecasting and disaster risk management.

Breast cancer is the most frequently diagnosed female cancer worldwide. Environmental contaminant exposure is suspected to be crucial, but the broad-spectrum communal properties that these suspected contaminants all share remain to be explored, especially in source and drinking water. In this work, we focused on the Pearl River Basin, which has the highest breast cancer incidence and mortality in China, and hypothesized that the breast cancer risk in this area is associated with its water source. Our objective was to resolve the possible communal properties that are associated with breast cancer from water mixture extracts of source and drinking water and to identify the key drivers by utilizing the latest epidemiology data, performing an exhaustive water toxicological and chemical characterization, and combining partial least-squares path statistics modeling (PLS-PM). We proposed a path for a drinking water-toxicity-induced breast cancer risk and confirmed its association with estrogen-receptor- and thiol-depletion-relevant mechanisms. The breast cancer incidence risk was associated with water-mixture-promoted mammalian cell proliferation (i.e., estrogenic effect), while the mortality risk was associated with a greater thiol depletion (i.e., oxidative stress). Endocrine-disrupting chemicals (EDCs) and dissolved organic matter (DOM) from anthropogenic sources in drinking water are key drivers for estrogenic effects and oxidative stress, respectively. The PLS-PM standardized effects of the DOM and EDCs in treated water on the breast cancer incidence and mortality were −0.07 and 0.31, and 0.35 and 0.31, respectively, further revealing that EDCs strongly influence the incidence risk, whereas the mortality risk resulted from the joint effects of EDCs and DOM. This study clearly shows an association between the breast cancer risk and drinking water toxicity in a high-prevalence area of China, broadening the future perspectives for water-contaminant-specific breast cancer prevention research.

Understanding the multiscale impacts and drivers of urban agglomeration landscape patterns for ecosystem services (ESs), especially water‐related ecosystem services (WESs), is essential for the development of regional ecological management. However, the multiscale impacts and driving mechanisms of urban agglomeration landscape patterns for ESs have not been adequately explained. In this study, multivariate data were employed, and the InVEST model, trend test method, coupled GeoDetector and geographically and temporally weighted regression (GTWR) method were utilized to comprehensively explore the spatial and temporal changes in landscape patterns and WESs in the Pearl River Delta urban agglomeration (PRDUA) at various grid and administrative scales from 1990 to 2020 and to determine the driving mechanisms affecting WESs. The results indicated that the variation characteristics of landscape patterns and WESs in the PRDUA were consistent, forming a binary spatial structure of core and peripheral areas in an inverted “U” shape around the estuary of the Pearl River. The relationship between landscape patterns and WESs weakened with the increase of scale, and the correlation coefficient decreased by approximately 0.10 from 5 km to 10 km grid scale. Additionally, precipitation (PRE) was the main factor controlling WESs changes in the PRDUA, explaining more than 50% of the changes in WESs, and the regression coefficients ranged from 0.0825 to 0.1584. Changes in WESs were the result of the combined effects of natural factors, including PRE, landscape pattern, elevation, slope, and socioeconomic factors, such as population and gross domestic product (GDP). Overall, these findings could contribute to optimizing regional landscape patterns and fostering sustainable development of the ecological environment in urban agglomerations.