The acquisition of the precise spatial distribution of precipitation is of great importance and necessity in many fields, such as hydrology, meteorology and ecological environments. However, in the ...arid and semiarid regions of Northwest China, especially over mountainous areas such as the Heihe River basin (HRB), the scarcity and uneven distribution of rainfall stations have created certain challenges in gathering information that accurately describes the spatial distribution of precipitation for use in applications. In this study, the accuracy of precipitation estimates from eight high-resolution gridded precipitation products (CMORPHv1-CRT, CRU TSv.4.05, ERA5, GSMaP_NRT, IMERG V06B-Final, MSWEPv2.0, PERSIANN-CDR and TRMM 3B42v7) are comprehensively evaluated by referring to the precipitation observations from 23 stations over the HRB using six indices (root mean square error, standard deviation, Pearson correlation coefficient, relative deviation, mean error and Kling–Gupta efficiency) from different spatial and temporal scales. The results show that at an annual scale, MSWEP has the highest accuracy over the entire basin, while PERSIANN, CRU and ERA5 show the most accurate results in the upper, middle and lower reaches of the HRB, respectively. At a seasonal scale, the performance of IMERG, CRU and ERA5 is superior to that of the other products in all seasons in the upper, middle and lower reaches, respectively. Over the entire HRB, PERSIANN displays the smallest deviation in all seasons except for spring. TRMM shows the highest accuracy in spring and autumn, while MSWEP and CRU show the highest accuracy in summer and winter, respectively. At a monthly scale, TRMM is superior to the other products, with a relatively stronger correlation almost every month, while GSMaP is inferior to the other products. Moreover, MSWEP and PERSIANN perform relatively best, with favorable statistical results around almost every station, while GSMaP shows the worse performance. In addition, ERA5 tends to overestimate higher values, while GSMaP tends to overestimate lower values over the entire basin. Moreover, the overestimation of ERA5 tends to appear in the upper reach area, while that of GSMaP tends to appear in the lower reach area. Only CRU and PERSIANN yield underestimations of precipitation, with the strongest tendency appearing in the upper reach area. The results of this study display some findings on the uncertainties of several frequently used precipitation datasets in the high mountains and poorly gauged regions in the HRB and will be helpful to researchers in various fields who need high-resolution gridded precipitation datasets over the HRB, as well as to data producers who want to improve their products.
Levels of legacy brominated flame retardants (BFRs), including tetrabromobisphenol A (TBBPA), hexabromocyclododecane isomers (HBCDs) and polybrominated diphenyl ethers (PBDEs), and six currently used ...novel BFRs were determined in house dust and office dust collected from a community in Beijing, China. This is the first study where the three kinds of legacy BFRs and novel BFRs were all measured in dust samples from China. HPLC-MS/MS was used for the detection of TBBPA and HBCDs, and the other BFRs were tested on a GC-NCI/MS. Decabromodiphenyl ethane (DBDPE), PBDEs, HBCD and TBBPA were found to be the main BFRs in the dust samples, with median levels of 709, 241, 199 and 26.7ng/g dust, respectively. Due to the high density of electronic equipment used in offices, levels of BFRs in office dust were found to be higher than those in house dust. DBDPE, as a replacement of PBDEs, was the predominant BFR, and the median level of DBDPE was not only several orders of magnitude higher than that of other novel BFRs but also 3 to 27 times higher than that of the three legacy BFRs, indicating that the consumption pattern of BFRs in the Chinese market has shifted from PBDEs to PBDE alternatives. Median estimated daily intakes (EDIs) of BFRs through dust ingestion for adults (>20years) and toddlers (<2years) were in the range of 2.8×10−5–0.201ng/kg body weight (bw)/day and 5.7×10−4–2.52ng/kg bw/day, respectively. The body burden of BFRs for toddlers was far higher than that for adults; however, a comparison between EDIs and threshold values suggested that daily intakes of BFRs for both adults and toddlers were unlikely to raise significant health concerns.
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•BFRs were measured in house dust and office dust collected from Beijing, China.•Levels of DBDPE were higher than those of any other BFRs in this study.•The consumption pattern of BFRs in China may have shifted from PBDEs to non-PBDE BFRs.•The daily intake of BFRs is unlikely to raise significant health concerns.
Capacitive deionization (CDI) is a promising technology for desalination due to its advantages of low driven energy and environmental friendliness. However, the ion removal capacity (IRC) of CDI is ...insufficient for practical application because such a capacity is limited by the available surface area of the carbon electrode for ion absorption. Thus, the development of a novel desalination technology with high IRC and low cost is vital. Here, a membrane-free hybrid capacitive deionization system (HCDI) with hollow carbon@MnO2 (HC@MnO2) to capture sodium via redox reaction and hollow carbon sphere with net positive surface charges (PHC) for chloride adsorption is introduced. The as-obtained HC@MnO2 with unique structure and high conductivity can improve the utilization of MnO2 pseudocapacitive electrodes. Meanwhile, the PHC can selectively adsorb Cl– and prevent the adsorption of Na+ due to electrostatic repulsion. As expected, the membrane-free HCDI system demonstrates excellent desalination performance. The system’s IRC and maximum removal rate are 30.7 mg g–1 and 7.8 mg g–1 min–1, respectively. Moreover, the proposed system has a low cost because of the absence of expensive ion exchange membranes (IEM), which is suitable for practical application. The excellent performance of this HCDI makes it a promising desalination technology for future use.
A high-performance potassium-ion battery anode is achieved by using nitrogen-doped soft carbon frameworks with high electronic and ionic conductivity.
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•N-doped soft carbon frameworks ...have been fabricated by MgO template method.•The N-doped soft carbon shows rapid electron transfer and K+ diffusion.•The N-doped soft carbon anode presents a superior rate performance and ultra-stable cycle life.•The ordered N-doped carbon clusters with enlarged interlayer distance may responsible for the superior rate performance.
Potassium-ion batteries (PIBs) have been regarded as one of the most promising alternatives to traditional lithium-ion batteries because of the low cost and abundant reserves of potassium resources. However, it is challenging to achieve suitable anode materials with long cycle life and high rate performance. Herein, nitrogen-doped soft carbon frameworks built of well-interconnected nanocapsules have been fabricated as facile and effective anodes for PIBs. The anode delivers a high specific capacity of 293 mAh g−1 at 0.05 A g−1 and 151 mAh g−1 at 5 A g−1 with a rate capability of 51.5%. It retains 85.5% capacity retention at 1 A g−1 after 500 cycles. The excellent rate performance can be mainly ascribed to the high ionic and electronic conductivity, resulted from the ordered nitrogen-doped carbon clusters with enlarged interlayer distance. The interconnected hierarchically porous structure further promotes K+ diffusion kinetics.
The surge of carbon dioxide emission plays a dominant role in global warming and climate change, posing an enormous threat to the development of human being and a profound impact on the global ...ecosystem. Thus, it is essential to analyze the carbon dioxide emission change trend through an accurate prediction to inform reasonable energy-saving emission reduction measures and effectively control the carbon dioxide emission from the source. This paper proposed a hybrid model by combining the random forest and extreme learning machine together for the carbon dioxide emission forecasting in this paper; the random forest is applied for influential factors analysis and the extreme learning machine for the prediction. To improve the performance of the prediction model, moth-flame optimization is adopted to optimize initial weight and bias in extreme learning machine. A case study whose data is derived from Hebei Province, China, during the period 1995–2015 is conducted to verify the effectiveness of the proposed model. Results show that the novel model outperforms the compared parallel models in carbon dioxide emission prediction and has the potential to improve the accuracy of CO
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emission forecasting.
The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. ...The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to estimate the state. However, the clock offsets in range measurements in asynchronous networks are unavoidable, leading to performance degradation for state estimators. Therefore, range difference measurements have been adopted to solve the RBL problem. However, existing approaches struggle to achieve desirable performance while maintaining computational efficiency. To address this issue, a new closed‐form state estimator for asynchronous networks is introduced. The proposed algorithm leverages the Taylor‐series expansion technique to enhance accuracy while keeping computational overhead low. Numerical experiments demonstrate that the proposed method achieves state‐of‐the‐art performance with high computational efficiency under small Gaussian noises.
This paper proposes a lightweight rigid body localization algorithm to obtain reliable position and orientation estimators for rigid bodies. Simulations show that the closed‐form algorithm achieves the CRLB performance over a small noise region.
With ongoing global warming, heatwave-related disasters are on the rise, exerting a multifaceted impact on both the natural ecosystem and human society. While temperature has been extensively studied ...in the effects of extreme heat on human health, humidity has often been ignored. It is crucial to consider the combined influence of temperature and humidity when assessing heatwave risks and safeguarding human well-being. This study, leveraging remote sensing products and reanalysis data, presented the first analysis of the spatiotemporal variations in global human-perceived heatwaves on a seasonal scale from 2000 to 2020, and further employed the Random Forest (RF) regression model to quantitatively assess the explanatory power of seven driving factors. The study found that since the 21st century (1) changes in Heat Index (HI) have varied significantly worldwide, with the majority of regions witnessing an increase, particularly at higher latitudes. The largest HI-increasing area was observed in the second quarter (S2), while the overall HI increase peaked in the third quarter (S3); (2) except for the decreasing area of none-risk regions, the regions under all other risk levels expanded (the proportion of high-risk areas in the world increased from 2.97% to 3.69% in S2, and from 0.04% to 0.35% in the fourth quarter (S4)); (3) aspect demonstrated the greatest explanatory power for the global heatwave distribution (0.69–0.76), followed by land-use coverage (LUCC, 0.48–0.55) and precipitation (0.16–0.43), while the explanatory power of slope and nighttime light (NTL) was rather low; (4) over the years, the explanatory power of each factor for heatwave distribution underwent a minor decrease without significant trend, but exhibited seasonal periodicity. Climatic factors and LUCC were most impactful in the first quarter (S1), while DEM and other human factors dominated in S2; and (5) interaction factors showed no significant trends over the years, but the explanatory power of DEM and slope increased notably when interacting with climate factor, aspect, and LUCC, respectively. The interactions between the aspect and LUCC with precipitation yielded the highest explanatory power (above 0.85) across all interactions. To effectively tackle heatwave risks, it is suggested to concentrate on high-latitude regions, reinforce land use and urban planning with eco-friendly strategies, scrutinize the interplay between precipitation and heatwaves, capitalize on topographic data for devising well-informed prevention measures, and tailor response strategies to accommodate seasonal fluctuations. This study offers valuable insights for enhancing climate change adaptation, disaster prevention, and mitigation strategies, ultimately contributing to the alleviation of extreme heatwaves and risk reduction.
Decabromodiphenyl ether (BDE-209) and its substitute decabromodiphenyl ethane (DBDPE) are heavily used in various industrial products as flame retardant. They have been found to be persistent in the ...environment and have adverse health effects in humans. Although some former studies have reported toxic effects of BDE-209, the study of DBDPE's toxic effects is still in its infancy, and the effects of DBDPE on hepatotoxicity are also unclear. This study aimed to evaluate and compare the hepatotoxicity induced by BDE-209 and DBDPE using a rat model. Sprague-Dawley rats were administered DBDPE or BDE-209 (5, 50, 500 mg/kg bodyweight) intragastrically once a day for 28 days. Twenty-four hours after the end of treatment, the rats were sacrificed, and body liver weight, blood biochemical parameters, liver pathology, oxidative stress, inflammation, pregnane X receptor (PXR), constitutive androstane receptor (CAR), and changes in cytochrome P450 (CYP3A) enzymes were measured. Our results showed that both BDE-209 and DBDPE could cause liver morphological changes, induce oxidative stress, increase γ-glutamyl transferase and glucose levels in serum, and down-regulate PXR, CAR, and CYP3A expression. In addition, BDE-209 was found to increase liver weight and the ratio of liver/body weight, lead to elevated total bilirubin and indirect bilirubin levels in serum, and induce inflammation. The present study indicated that BDE-209 and DBDPE may interfere with normal metabolism in rats through oxidative stress and inflammation, which inhibit PXR and CAR to induce the expression of CYP3A enzymes, and finally produce hepatotoxic effects and cause liver damage in rats. Comparatively, our results show that the damage caused by BDE-209 was more serious than that caused by DBDPE.
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•Hepatotoxicity induced by BDE-209 and DBDPE in rats was studied and compared.•Both BDE-209 and DBDPE can cause liver damage whereas DBDPE is less toxic.•BDE-209 and DBDPE may interfere metabolism in rats through oxidative stress and inflammation.
The research on the relationship between environmental regulation and technological innovation has a long history, and so far no conclusion has been reached. This article uses 2008-2017 China’s ...statistical data for empirical research, uses technology input to measure technological innovation level, and uses innocuous treatment of domestic waste and sulfur dioxide emissions as explanatory variables to perform regression. The empirical results have obtained the positive impact of environmental regulation intensity on the level of technological innovation, indicating that environmental regulation promotes technological innovation.
To reduce the cost of production and the pollution of the environment that is due to the overapplication of herbicide in paddy fields, the location information of rice seedlings and weeds must be ...detected in site-specific weed management (SSWM). With the development of deep learning, a semantic segmentation method with the SegNet that is based on fully convolutional network (FCN) was proposed. In this paper, RGB color images of seedling rice were captured in paddy field, and ground truth (GT) images were obtained by manually labeled the pixels in the RGB images with three separate categories, namely, rice seedlings, background, and weeds. The class weight coefficients were calculated to solve the problem of the unbalance of the number of the classification category. GT images and RGB images were used for data training and data testing. Eighty percent of the samples were randomly selected as the training dataset and 20% of samples were used as the test dataset. The proposed method was compared with a classical semantic segmentation model, namely, FCN, and U-Net models. The average accuracy rate of the SegNet method was 92.7%, whereas the average accuracy rates of the FCN and U-Net methods were 89.5% and 70.8%, respectively. The proposed SegNet method realized higher classification accuracy and could effectively classify the pixels of rice seedlings, background, and weeds in the paddy field images and acquire the positions of their regions.
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