Search

Farmland soil quality is a crucial determinant for agricultural productivity, food safety, and human well-being. Among the various contaminants, heavy metals have emerged as pervasive factors significantly impacting farmland quality, attracting widespread societal concern. In this study, we systematically applied multivariate statistical analysis, geostatistical methods, and the positive matrix factorization (PMF) source apportionment technique to elucidate the sources and contributions of eight heavy metals (Cd, Hg, As, Pb, Cr, Cu, Zn, Ni) in farmland soils within an industrialized town. Our findings reveal that Cd, Hg, Pb, and Zn exhibit pollution or enrichment in farmland soils compared to natural background levels, with Hg and Cd surpassing 164.3% and 50.2%, respectively. Notably, Zn demonstrates discernible point-source pollution. Source apportionment results highlight industrial point sources, coal combustion, and agriculture as the primary anthropogenic contributors to heavy metal contamination, with zinc-plating enterprises being the predominant industrial point sources. Addressing the specific issue of point-source pollution from Zn in industrial activities, further analysis establishes a correlation between soil Zn content and the distance from zinc-plating enterprises. Utilizing an atmospheric transport model, we observe that the impact of industrial activities on soil Zn is limited when the distance exceeds 1.5 km, emphasizing the importance of monitoring Zn pollution within areas less than 1.5 km. This study pioneers a progressive source apportionment approach, considering the origins of different heavy metals, pollution levels, distance factors, and the cost-effectiveness of environmental measures. The insights gained provide scientifically sound strategies for future decision making in environmental protection. [ABSTRACT FROM AUTHOR]

Accurately estimated reference evapotranspiration (ET0) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET0 estimation with missing Rs measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A‐P formula. This paper proposes a new method for estimating ET0 with missing Rs by combining machine learning with physical‐based P‐M models (PM‐ET0). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in Rs and ET0 estimates at 80 national Rs measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET0 (ML‐ET0) to compare with the PM‐ET0. Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET0 estimation without Rs measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest Rs and ET0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both Rs and ET0, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET0 outperformed the PM‐ET0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET0. However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing Rs measurements. [ABSTRACT FROM AUTHOR]

The EI is an important pillar of modern economic development and a key factor in ensuring national strategic energy security. The upgrading of China's Energy Industry (EI) faces a series of problems and challenges, such as excessive energy production, difficulty in energy technology innovation, and low energy efficiency. The significance of this study lies in its endeavor to tackle these challenges by focusing on several facets, including the promotion of innovation in energy technology and the enhancement of energy management. Utilizing the Kuznets Curve (KC) theory and considering energy export restrictions (ER) as a constraint, the study analyzed the impact of China's EI upgrading. It analyzed the relationship between changes in indicators such as EI resource utilization rate, degree of ERs, export proportion, and resource allocation proportion of energy enterprises and the impact of China's EI upgrading. According to the experimental results, it can be concluded that when the resource utilization rate was between 30% and 60%, the energy export volume showed a significant growth trend. As it gradually approached saturation, the contribution of energy production growth began to weaken and showed an inverted U-shape. Examining how the expansion of Economic Relations (ERs) affects the enhancement of China's Economic Infrastructure (EI) within the framework of the Knowledge Capital (KC) can provide valuable insights. It offers guidance on striking a balance between exports and domestic demand in China's economic development, facilitating the formulation of export policies, supporting structural adjustments and advancements in China's EI, and contributing to the promotion of sustainable development (SD). Additionally, this analysis aids in preventing potential issues and safeguarding both the ecological environment and the interests of citizens. [ABSTRACT FROM AUTHOR]

Based on the linear relationship between solar radiation and sunshine duration, the Angstrom model is widely used to estimate solar radiation from routinely observed meteorological variables for energy exploitation. However, the relationship may have changed in quickly developing regions in the recent decades under global “dimming” and “brightening” context, with increasing aerosols due to industrial pollutions. Solar radiation stations under different climate conditions in six large cities in China are selected to test this hypothesis. Analysis of the related meteorological items shows that Guiyang has the lowest solar radiation with the average annual value of 10.5 MJm −2 d −1 , while Lhasa on the Tibetan Plateau has the highest of 20.1 MJm −2 d −1 . Both radiation and sunshine hours decreased from 1961 to 2010, but at different rates. A moving linear regression method is used to investigate the changes in the relationship between radiation and sunshine duration, the results indicate an abrupt change in the correlation coefficients in 1980–1990s, which can be attributed to the aerosol load resulting from air pollution caused by the industrial development in 1980s under China's Open Door Policy. The sky condition has been changing from clean to dirty, thus the relationship between solar radiation and duration changes in the 1980's and has recovered in the recent decades. This finding implies that it might not necessarily be right to use long data sets for model calibration. Further investigation confirms that the Angstrom model performs the best with higher NSE (nash-sutcliffe efficiency) of 0.914 and lower MAPE (mean absolute percentage error) and RMSE (root mean square error) values of 13.7 w/m 2 and 23.9 w/m 2 respectively, when calibrated with a 10-year data set. In contrast, the model performs worst when it is calibrated with a 40-year data set, with NSE , MAPE and RMSE values of 0.891, 15.1 w/m 2 and 25.3 w/m 2 , respectively. Based on the findings of this research, a 10-year data set is recommended as the national standard for model calibration in rapidly developing regions of China. Further analogous investigations are needed in other industrial regions to make an international standard for Angstrom model calibration. [ABSTRACT FROM AUTHOR]

• Correlations were analyzed between meteorological factors and solar radiation. • Some meteorological factors were exchangeable in solar radiation estimation. • Machine learning methods could improve the accuracy in radiation estimation. • The locally optimal combinations of input meteorological factors were determined. • The extreme learning machine is more suitable for radiation estimation in China. The values of global solar radiation are important fundamental data for potential evapotranspiration estimation, solar energy utilization, climate change study, crop growth model, and etc. This research tried to explore the optimal combination of input meteorological factors and the machine learning methods for the estimation of daily solar radiation under different climatic conditions so as to improve the estimation accuracy. Based on the correlation between meteorological factors, different meteorological factor input combinations were established and the support vector machine method was used to estimate global solar radiation at 80 weather stations in four climatic regions of China mainland. The results showed that, the optimal combinations of input meteorological factors were different in the four different climatic zones in China mainland. Three meteorological factors of sunshine hours, extraterrestrial radiation, and air temperature had greater impacts on the solar radiation estimation. Adding the factor of precipitation could obviously improve the estimation accuracy in humid regions, but not remarkably in arid regions. Wind speed had very little influence on solar radiation estimation. The accuracies of machine learning methods were better than the Angstrom-Prescott formula and the multiple linear regression method. Among them, support vector machine and extreme learning machine were more appropriate. In some sites, the root mean square error of support vector machine method was even 20% less than that of the Angstrom-Prescott formula. In general, reasonable division of the areas and establishment of appropriate input combinations of meteorological factors according to the climatic conditions, combined with machine learning methods, can effectively improve the accuracy of solar radiation estimation. [ABSTRACT FROM AUTHOR]

Photosynthetic rate of winter wheat was measured in 1995 and 1996 at Lhasa (29°41′N, 91°20′E, 3688 m a.s.l.) on the Tibetan Plateau under the extreme conditions of the highest solar radiation and lowest CO2 partial pressure on earth. Similar measurements were also made in the North China Plain (Yucheng, 36°57′N, 116°36′E, 28 m a.s.l.) in 1998. It has been shown that photosynthesis was greatly inhibited by high light intensities on the plateau. As diurnal changes in temperature was great, temperature also played an evident role in causing diurnal variation of photosynthesis on the plateau.A mathematical description of declination of maximum photosynthesis with light intensities due to photoinhibition or photooxidation in plant was developed. The relation is integrated into a leaf photosynthesis model, which includes the rate-limiting biochemical processes of photosynthesis and physiological processes of stomatal regulation. Photosynthesis of wheat under field conditions on Tibetan Plateau was simulated. The model was also verified with the observation of photosynthesis of winter wheat leaves in the North China Plain. The result shows that the model can simulate photosynthetic rate of crops in field fairly well. This model provides a method to calculate plant productivity, especially for plants under plateau climate with high solar radiation and it is also applicable to plant grown in places at low altitudes. [Copyright &y& Elsevier]

• Transition zones are the hotspots of bioclimatic changes under global warming. • Global warming extends growing season lengths but increases heat and water stresses. • Sustainable grasslands in China shrink and shift northwestward with global warming. Bioclimatic response to global warming is a key linkage between anthropogenic climate change and ecosystem sustainability, particularly for the impact-prone climate transition zones. This paper examines changes in heat-water conditions from 2006 to 2100 across China, using the fifth generation climate reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and bias-adjusted climate projections by the Coupled Model Intercomparison Project Phase 5 (CMIP5). We then assess the effects of these changes on habitat suitability for responsive ecosystems using a species distribution model. The results reveal significant changes in bioclimatic environments across dry–wet climate transition zones under the present-day climate and the RCP8.5 scenario. By the end of the 21st century, local growing season length (GSL) could extend by 60 days and 30 % of the GSL could be under heat stress. Water conditions worsen overall in both the air and the soil, despite some improvements in the middle parts of transition zones. With altered heat-water conditions, sustainable grasslands shift northwestward, with a reduction of approximately 131 million km2 (46.6 % relative to 2006–2010) in total area. These changes primarily arise from alterations in winter and spring heat-water conditions. Our findings highlight the climate transition zones as hotspots of ecosystem vulnerability demanding particular attention in impact assessment and adaptation studies. [ABSTRACT FROM AUTHOR]

• Two solutions were established with or without R s inputs for daily ET 0 estimation. • FAO56 recommended larger coefficient a but smaller b of the A-P formula in China. • The transfer error from estimated R s to ET 0 was reduced by using the P-M model. • SVM-ET 0 model obtained larger errors than P-M model with estimated R s at test stages. Accurate estimation of reference evapotranspiration (ET 0) is of great importance for regional water resources planning and irrigation scheduling. The FAO56 recommended Penman-Monteith (P-M) model is widely adopted as the standard method for ET 0 estimation, but its application is usually restricted by limited meteorological data worldwide, especially global solar radiation (R s). This study provided two possible solutions to deal with the missing R s data in ET 0 estimation in China mainland. In the first solution, R s data were estimated with the Ångström-Prescott (A-P) formula and daily sunshine hours. The values of two A-P formula fundamental coefficients a and b were obtained through three ways: (1) estimated based on limited R s measurements at 80 solar radiation measurement stations (or site-calibrated); (2) recommended by the FAO-56 manual (or FAO-recommended); and (3) estimated based on the altitude and latitude of each weather station through the support vector machine algorithm (or SVM-estimated). The second solution used the SVM algorithm and available weather variables without R s. The results showed that the FAO-recommended coefficients a and b were separately overestimated and underestimated in China mainland, which generated the largest simulation errors of R s. However, the transfer errors from R s estimations to ET 0 estimations were reduced by using the P-M model for all of the three kinds of coefficients. Compared with the R s -based models, the estimation accuracy of the SVM-ET 0 model yielded the highest accuracy both at the training stage (R2 = 0.979; RMSE = 0.273 mm d−1) and the testing stage (R2 = 0.973; RMSE = 0.302 mm d−1). Generally, both the P-M and the machine-learning-based methods could be used for the ET 0 estimation, when only R s data were missing. However, considering the complexity in the programming, the P-M model combining with the A-P formula with the SVM-estimated A-P coefficients is recommended for daily ET 0 estimation in China. [ABSTRACT FROM AUTHOR]

This paper presents a new analysis of inequality of eddy diffusivities for sensible heat (KH) and water vapor (KW). It is shown that the same set of equations, established on the principle of dual-source diffusion, can be applied to both local and regional advection. Various advection scenarios are discussed using a formula that relates KH/KW to the Bowen ratio of the advective source and the observed gradient Bowen ratio (βg) near the ground surface. A similar analysis can also be performed for eddy diffusivities for trace gases.The ratio KH/KW, observed at a well-watered wheat field in the North China Plains, was mostly greater than unity when βg was negative and smaller than unity when βg was positive. The pattern was consistent with the theoretical analysis of the ratio under the influence of regional advection. Some degree of local advection was also suggested by the data. Despite inequality of the eddy diffusivities, there was little systematic bias between the evapotranspiration rates measured with a Bowen ratio/energy balance and an eddy covariance system. [Copyright &y& Elsevier]

Background and Aim: Gallbladder adenomatous polyp is a pre-cancerous neoplasm, and it is difficult to classify from cholesterol polyps before cholecystectomy. The study aimed to clarify the risk characteristics of gallbladder adenomas and establish a prediction model to differentiate gallbladder adenomas from cholesterol polyp lesions., Methods: From May 2019 to December 2021, the patients underwent cholecystectomy in the Shanghai Eastern Hepatobiliary Surgery Hospital were retrospectively reviewed. According to the permanent pathology test, the patients were divided into adenomas and cholesterol polyps groups. All the included cases received ultrasound equipment examinations before cholecystectomy and their clinical information were completely recorded. Then the patients' baseline characteristics and ultrasound imaging variables were analyzed by logistic regression. Finally, a predictive model for gallbladder adenomas will be established and assessed based on the independent risk factors., Results: A total of 423 cases including 296 cholesterol polyps and 127 gallbladder adenomas were analyzed in detail. Multivariate logistic regression analysis revealed that solitary polyp lesion (OR = 2.954, 95% CI 1.759-4.960, P < 0.001), the maximal diameter of lesions (OR = 1.244, 95% CI 1.169-1.324, P < 0.001), and irregular shape of polyp lesions (OR = 5.549, 95% CI 1.979-15.560, P = 0.001) were the independent predictive factors of gallbladder adenomas. According to the results, regression equation of logit(P) = -3.828 + 1.083*number of gallbladder polyps lesions (GPLs) + 0.218*diameter of GPLs + 1.714*shape of GPLs was established. Area under the curve (AUC) was 0.828 (95% CI 0.782-0.874, P < 0.001). When logit P > 0.204, the sensitivity of estimating adenoma was 79.5%, the specificity of recognizing adenoma was 70.6%, and the whole correct ratio was 73.3%. While the AUC of diameter (10 mm) being a predictive factor in this study was only 0.790 (95% CI 0.741-0.839, P < 0.001). And the sensitivity and specificity of 10 mm as the optimal diagnostic cutoff value to diagnose adenomas were 74.8% and 65.9%, respectively., Conclusions: The risk factors of solitary polyp lesion, larger diameter, and irregular morphology feature of polyp lesions were significantly related to gallbladder adenomas. And the predictive model established in the study can effectively identify adenomas from cholesterol polyps and help patients to select the optimal treatment protocol., (© 2022 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

The Universal Thermal Climate Index (UTCI) is believed to be a very powerful tool for providing information on human thermal perception in the domain of public health, but the solar radiation as an input variable is difficult to access. Thus, this study aimed to explore the optimal strategy on estimation of solar radiation to increase the accuracy in UTCI calculation, and to identify the spatial and temporal variation in UTCI over China. With daily meteorological data collected in 35 tourism cities in China from 1961 to 2020, two sunshine-based Angstrom and Ogelman models, and two temperature-based Bristow and Hargreaves models, together with neural network and support vector machine-learning methods, were tested against radiation measurements. The results indicated that temperature-based models performed the worst with the lowest NSE and highest RMSE . The machine-learning methods performed better in calibration, but the predictive ability decreased significantly in validation due to big data requirements. In contrast, the sunshine-based Angstrom model performed best with high NSE (Nash-Sutcliffe Efficiency) of 0.84 and low RMSE (Root Mean Square Error) of 35.4 J/m 2 s in validation, which resulted in a small RMSE of about 1.2 °C in UTCI calculation. Thus, Angstrom model was selected as the optimal strategy on radiation estimation for UTCI calculation over China. The spatial distribution of UTCI showed that days under no thermal stress were high in tourism cities in central China within a range from 135 to 225 days, while the largest values occurred in Kunming and Lijiang in southwest China. In addition, days under no thermal stress during a year have decreased in most tourism cities of China, which could be attributed to the asymmetric changes in significant decrease in frost days and slightly increase in hot days. However, days under no thermal stress in summer time have indeed decreased, accompanying with increasing days under strong stress, especially in the developed regions such as Yangze River Delta and Zhujiang River Delta. Based on the study, we conclude that UTCI can successfully depict the overall spatial distribution and temporal change of the thermal environments in the tourism cities over China, and can be recommend as an efficient index in the operational services for assessing and predicting thermal perception for public health. However, extreme cold and heat stress in the tourism cities of China were not revealed by UTCI due to mismatch of the daily UTCI with category at hourly scale, which makes it an urgent task to redefine category at daily scale in the next research work.

Purpose: To investigate potential preoperative predictors of urethral or apical positive surgical margin (PSM) and the value of apical prostate biopsy in predicting urethral/apical margin status after radical prostatectomy (RP)., Methods: A total of 531 patients who underwent RP during 2010 to 2017 at West China Hospital were enrolled in this retrospective study. Preoperative and postoperative factors including age, BMI, PSA, clinical T stage and biopsy Gleason score were analyzed. Univariate analysis and logistic regression were used to find out the potential predictive factors for PSM. Two logistic regression models were built to evaluate the role of apical prostate biopsy in predicting urethral/apical margin status., Results: The overall PSM rate was about 30.1% (160/531) and 97 of them were reported urethral/apical PSM. The incidence of urethral or apical PSM in patients with positive cores in the apical prostate was higher than those without (23.0% vs 9.9%, P < .001). We further found that the multivariable model with positive apical prostate biopsy could significantly increase the predictive value of urethral or apical PSM status (AUC: 0.744 vs 0.783, P = .016). Our analysis also showed that neo-adjuvant hormone therapy was an independent protective factor for urethral or apical PSM in patients with positive apical prostate biopsy, but not all patients., Conclusion: This study revealed the necessity of apical prostate biopsy to predict the risk of apical or urethral PSM. In clinical practice, neo-adjuvant hormone therapy should be given when patients with positive apical prostate biopsy to reduce the presence of PSM, especially patients with high/very high risk prostate cancer.