Environmental Chemistry Letters - Wastewater from the uranium mining industry contains toxic arsenate (AsO43–), selenate (SeO42–), and molybdate (MoO42–) that can be removed by... 相似文献
● A novel framework integrating quantile regression with machine learning is proposed.● It aims to identify factors driving observations to upper boundary of relationship.● Increasing N:P and TN concentration help fulfill the effect of TP on CHL.● Wetter and warmer decrease potential and increase eutrophication control difficulty.● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships. 相似文献
● Lipid can promote PA production on a target from food waste.● PA productivity reached 6.23 g/(L∙d) from co-fermentation of lipid and food waste.● Lipid promoted the hydrolysis and utilization of protein in food waste.● Prevotella , Veillonella and norank _f _Propioni bacteriaceae were enriched.● Main pathway of PA production was the succinate pathway. Food waste (FW) is a promising renewable low-cost biomass substrate for enhancing the economic feasibility of fermentative propionate production. Although lipids, a common component of food waste, can be used as a carbon source to enhance the production of volatile fatty acids (VFAs) during co-fermentation, few studies have evaluated the potential for directional propionate production from the co-fermentation of lipids and FW. In this study, co-fermentation experiments were conducted using different combinations of lipids and FW for VFA production. The contributions of lipids and FW to propionate production, hydrolysis of substrates, and microbial composition during co-fermentation were evaluated. The results revealed that lipids shifted the fermentation type of FW from butyric to propionic acid fermentation. Based on the estimated propionate production kinetic parameters, the maximum propionate productivity increased significantly with an increase in lipid content, reaching 6.23 g propionate/(L∙d) at a lipid content of 50%. Propionate-producing bacteria Prevotella, Veillonella, and norank_f_Propionibacteriaceae were enriched in the presence of lipids, and the succinate pathway was identified as a prominent fermentation route for propionate production. Moreover, the Kyoto Encyclopedia of Genes and Genomes functional annotation revealed that the expression of functional genes associated with amino acid metabolism was enhanced by the presence of lipids. Collectively, these findings will contribute to gaining a better understanding of targeted propionate production from FW. 相似文献
● Established a quantification method of pollutant emission standard.● Predicted the SO2 emission intensity of single coking enterprises in China. ● Evaluated the influence of pollutant discharge standard on prediction accuracy.● Analyzed the SO2 emissions of Chinese provincial and municipal coking enterprises. Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO2 emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and theR2 increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO2 emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO2 emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants. 相似文献
Controlled experiment of Leymus chinensis seedlings grown in the environmental growth chambers at 3 soil moisture levels and 3 temperature levels was conducted in order to improve the understanding how leaf photosynthetic parameters will respond to climatic change. The results indicated that soil drought and high temperature decreased the photochemical efficiency of photosystem ( Fw/Fm ), the overall photochemical quantum yield of PSIl(yield), the coefficient of photochemical fluorescence quenching(qp), but increased the coefficient of non-photochemical fluorescence quenching( q, ). Severe soil drought would decrease Fv/Fm and yield by 3.12% and 37.04% under 26% condition, respectively, and 6.60% and 73.33% under 32% condition, respectively,suggesting that higher temperature may enhance the negative effects of soil drought. All the soil drought treatments resulted in the decline in leaf nitrogen content. There was no significant effect of temperature on leaf nitrogen level,but higher temperature significantly reduced the root nitrogen content and the ratio of root nitrogen to leaf nitrogen,indicating the different strategies of adaptation to soil drought and temperature. It was also implied that higher temperature would enhance the effect of soil drought on leaf photosynthetic capacity, decrease the adaptability of Leymus chinensis to drought. 相似文献