Effects of long-chained perfluoroalkyl acids (PFAAs) on the uptake and bioaccumulation of short-chained PFAAs in two free-floating macrophytes: Eichhornia crassipes and Ceratophyllum demersum.

Short-chained perfluoroalkyl acids (PFAAs, CnF2n+1-R, n ≤ 6) have merged as global concerns due to their extensive application and considerable toxicity. However, long-chained PFAAs (n ≥ 7) featured with high persistence are still ubiquitously observed in aquatic environment. To understand the uptake behavior of short-chained PFAAs in aquatic macrophytes, the uptake kinetics, bioconcentration, and translocation of short-chained PFAAs (3 ≤n ≤ 6) in two typical free-floating macrophytes (Eichhornia crassipes and Ceratophyllum demersum) were investigated in the treatments with and without long-chained PFAAs (7 ≤n ≤ 11). Results showed that short-chained PFAAs can be readily accumulated in both E. crassipes and C. demersum, and the uptake of short-chained PFAAs fit the two-compartment kinetic model well (p < 0.05). In the treatments with long-chained PFAAs, significant concentration decreases of all concerned short-chained PFAAs in E. crassipes and PFAAs with n ≤ 5 in C. demersum were observed. Long-chained PFAAs could hinder the uptake rates, bioconcentration factors, and translocation factors of most short-chained PFAAs in free-floating macrophytes (p < 0.01). Significant correlations between bioconcentration factors and perfluoroalkyl chain length were only observed when long-chained PFAAs were considered (p < 0.01). Our results underlined that the effects of long-chained PFAAs should be taken into consideration in understanding the uptake and bioaccumulation behaviors of short-chained PFAAs.

Characterization of vapor intrusion sites with a deep learning-based data assimilation method.

Appropriate characterization of site soils is essential for accurate risk assessment of soil vapor intrusion (VI). In this study, we develop a data assimilation method based on deep learning (i.e., ES(DL)) to estimate the distribution of soil properties with limited measurements. Two hypothetical VI scenarios are employed to demonstrate site characterization using the ES(DL) method, followed by validation with a laboratory sandbox experiment and then one practical site application. The results show that the ES(DL) method can provide reasonable estimates of the effective diffusion coefficient distributions and corresponding emission rates (into the building) in all four cases. The spatial heterogeneity of site soils can be characterized by considerably enough measurements (i.e., 15 sampling points in the first hypothetical case); otherwise, layered characterization is recommended at the cost of neglecting horizontal heterogeneity of site soils. This new method provides an alternative to characterize VI sites with relatively fewer sampling efforts.

Parameter regionalization based on machine learning optimizes the estimation of reference evapotranspiration in data deficient area.

Reference evapotranspiration (ET0), as one important variable in climatology, hydrology, and agricultural science, plays an important role in the terrestrial hydrological cycle and agricultural irrigation. However, the ET0 estimation process is inaccurate due to the lack of weather stations and historical data. In this study, a new method of ET0 estimation was proposed to improve the ET0 estimation performance in regions with limited data. Four empirical models with different data requirements, Albrecht, Hargreaves-Samani, Priestley-Taylor, and Penman, were applied and optimized the parameters by the Shuffled Complex Evolution-University of Arizona algorithm with the ET0 calculated by the Penman-Monteith model as the reference value at 600 meteorological stations in China. Two machine learning models, Random Forest (RF) and Multiple Linear Regression (MLR) were used to establish the regionalization of the parameter of the empirical model. The result showed that parameter optimization could significantly improve ET0 estimation in different climate regions of China. The Penman model has the strongest physical foundation and the highest estimation accuracy, followed by the Hargeaves-Samani and Priestley-Taylor model. The mass-transfer-based model, Albrecht, could only estimate regional ET0 efficiently after parameter optimization. Based on the more advanced RF machine learning regionalization method that considers complex linear relationships of variables, ET0 estimation in regions lacking data could be improved efficiently. Machine learning could be used to describe the ET0 model parameters in different regions because of the similarity. The combination of machine learning and empirical model could provide a new method for ET0 estimation in data deficient regions.

Past variations and future projection of runoff in typical basins in 10 water zones, China.

Understanding the historical and future changing characteristics of key climatic variables and runoff in 10 major river zones in China is essential for water resources evaluation and management. To this end, the historical and future changing trends of key hydrometeorological variables, including precipitation, potential evapotranspiration, and runoff were analyzed in detail for each water zone across China. The climate elasticity method was also established to quantify the impacts of climate change and human activities on historical runoff variations. The results indicate that the characteristics and causes of runoff variations in China were generally spatially heterogeneous. The runoff in water-scarce river basins of northern China decreased significantly during the period of 1961-2018, variations of which were more sensitive to human activities. For southern water zones in China, the runoff showed no significant trend and climate change was the main influencing factor. On basis of 9 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model ensemble simulations under three different shared socioeconomic pathways (ssp126, ssp245 and ssp585), the future runoff in 10 typical basins of the water zones were projected and the results suggested an increasing trend of runoff over China, thanks to increasing precipitation in the rest 21 century. While under ssp585, the rising air temperature tends to evaporate more water and offset the effect of precipitation increase to some extent, resulting in that the increments of runoff under ssp585 are not necessarily greater than those under ssp245 and ssp126. Overall, our study could be used as a basis to support climate adaptation strategies and policies to cope with future water resources conditions.

Projections of meteorological drought based on CMIP6 multi-model ensemble: A case study of Henan Province, China.

Future changes in drought events are critical for risk assessment and associated policymaking. In this study, the future changes in meteorological droughts in Henan Province, China are explored. Random forests downscaling model is first constructed based on ERA5 reanalysis data and meteorological observations. The model is validated using evaluation indices such as R2 and RMSE, and is shown to be able to capture the relationship between large-scale predictors and monthly precipitation. The validated random forests downscaling model is driven by multiple global climate models (GCMs) from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6) under three emission scenarios for projecting three future drought characteristics (duration, frequency, and intensity). Results show that drought frequency decreases in most areas of Henan while drought duration and intensity increase in various degrees. Some differences are also observed among different emission scenarios, especially under SSP2-4.5, where the magnitudes of changes in drought duration and intensity are lower relative to other scenarios. The decrease in drought frequency in most areas is found to be caused by increases in monthly mean precipitation in this study. Changes in drought duration and intensity are related to a combination of increases in precipitation mean and variability.

Evaluating the contribution of different environmental drivers to changes in evapotranspiration and soil moisture, a case study of the Wudaogou Experimental Station.

Evapotranspiration and soil moisture content (SMC) are key elements of the hydrological cycle. Accurate prediction of the dynamic processes of evapotranspiration and soil water is essential for irrigation and water management. Here, the boosted regression tree (BRT) method was employed to quantify environmental controls on actual evapotranspiration (ETa), potential evapotranspiration (ET0), and SMC using monitoring data from the Wudaogou hydrological experimental station. The results indicated that: (1) the BRT algorithm was effective in predicting the relative control of different environmental factors on ETa, ET0, and SMC; and (2) vapor pressure deficit (VPD) was the most important factor affecting daily ET0, and sunshine duration (SSD) also played a nonnegligible role. The results further explained the phenomenon of the "evaporation paradox" in the study area. SSD could be a leading control on daily ETa, followed by VPD, leaf area index (LAI). (3) Among the underground factors, groundwater level (GL) and LAI played a dominant role in the relative contribution to SMC. Among the aboveground factors, relative humidity (RH) and soil temperature (TS) have a relatively large influence on SMC. (4) The differences in SMC at different depths were determined by multiple influencing factors, including LAI, VPD, and precipitation (P). This study also underscores the importance of vegetation variations to hydrological cycle processes. In general, climate warming and an increase in extreme rainfall events will increase the control of temperature on SMC and weaken the control of P on SMC in the future.

Targeting LRH­1 in hepatoblastoma cell lines causes decreased proliferation.

Hepatoblastoma is the most common malignant liver tumor in children. Since it is often unresectable and exhibits drug resistance, the treatment of advanced hepatoblastoma is challenging. The orphan nuclear receptor liver receptor homolog­1 (LRH­1) serves prominent roles in malignancy; however, to the best of our knowledge, the role of LRH­1 in hepatoblastoma remains unknown. In the present study, human hepatoblastoma cell lines were analyzed; the mRNA and protein expression levels of LRH­1 were significantly higher in HepG2 and HuH6 cells compared with those in HepT1 cells and control THLE­2 cells. Knockdown of LRH­1 resulted in decreased HepG2 and HuH6 cell proliferation via downregulation of cyclin D1 (CCND1) and c­Myc. Furthermore, treatment with an LRH­1 antagonist (LRA) inhibited the proliferation and colony formation of cell lines in a dose­dependent manner, and induced cell cycle arrest at G1 phase through inhibition of CCND1 expression. Finally, LRA treatment enhanced the cytotoxic effects of doxorubicin on hepatoblastoma cells. Collectively, these findings suggested that LRH­1 may have an important role in the progression of hepatoblastoma and implicated LRA as a novel, potential therapeutic agent for the treatment of hepatoblastoma.