Ancient Chinese silk paintings represent a remarkable fusion of art and culture, functioning as key artifacts with historical, present, and future significance. However, inappropriate preservation has resulted in different types of deterioration, including mold infestation, which causes pigment fading, discoloration, structural fragility, and breakdown. This paper proposed the Spectral-Guided Restoration Asymmetric Autoencoder (MoldSGR-AsyAutoencoder) for hyperspectral virtual restoration of mold-affected silk paintings. Through the mold spectral response analysis, the spectral invariant characteristics of mold spots on silk paintings in the near-infrared (NIR) were found. The similarity discrimination strategy based on spectral-spatial features was developed. An asymmetric autoencoder model with multistage feature extraction was then designed to achieve pixel-level hyperspectral virtual recovery of mold-affected regions. Experimental results demonstrate that this method achieved excellent virtual restoration in both simulated and real-mold-affected regions, providing a robust theoretical foundation and technical support for the hyperspectral virtual restoration of mold-affected regions on silk paintings.

Long term remote sensing data contains information from four dimensions:time,space,and spectrum.Currently,the description is only based on spatial and spectral dimensions,and there is no unified concept to describe long-term remote sensing data.In traditional research,using a three-dimensional cube model to store long-term remote sensing data would separately store data from different time periods,which cannot meet the efficient storage,fast retrieval,and deep analysis of its time dimension,and thus cannot fully explore the characteristics of different land features changing over time.Essentially,it is still discrete three-dimensional data,and the four-dimensional information of long-term data has not been effectively managed and analyzed uniformly.Therefore,based on the research foundation of predecessors,the concept of time spectrum has emerged,bringing new breakthroughs to the organization and storage of remote sensing data,and improving the utilization value of long-term remote sensing data.Similar to the concept of spectra,remote sensing feature sequences at different times constitute spectrograms. Remote sensing spectrogram theory mainly describes the information of remote sensing data in spectral,temporal,and spatial dimensions.By analyzing the temporal spectrum of remote sensing images,the changing characteristics of surface objects at different time scales and spectral ranges can be revealed,which can be used in various application fields.Experts and scholars in the remote sensing field have fully recognized the scientific value and broad application prospects of MDD. Land change detection and fine classification of crops require the use of multiple spectral information from different time periods.Unlike traditional 3D datasets,the study of remote sensing data has been elevated from 3D to 4D,providing a different way for land features to be represented.This helps to solve the problem of"same spectrum foreign objects,same object different spectrum".The analysis of cultivated land types based on spatiotemporal spectra also has the characteristics of complete phenology.It can effectively eliminate false changes caused by seasonal factors.The MDD format has significantly improved the efficiency and accuracy of remote sensing analysis,and has been widely promoted and applied in multiple industry user units,generating good social and economic benefits. This article summarizes and generalizes the basic concepts,main methods,and techniques of remote sensing spectrogram theory,introduces the application fields and latest research progress of remote sensing spectrogram theory,and finally looks forward to the future research directions of spectrogram theory.The future research directions in this field include the study of spatiotemporal consistency and data fusion of multi-source data,the research of deep learning time-frequency analysis methods,and the research of real-time and high-resolution analysis. In the field of next-generation remote sensing data storage,the multidimensional data format developed by Chinese scientists is expected to meet the needs of real-time online data processing,be used to develop a new spatiotemporal spectral integrated data organization and storage mode,and serve as one of the technical foundations of remote sensing cloud platforms.This innovation can improve the storage,retrieval,and sharing efficiency of remote sensing big data,providing key support for China to build an independent and controllable remote sensing cloud platform.

Painting and calligraphy possess significant historical value and play a crucial role in the transmission of human cultural heritage. However, the presence of mildew greatly impacts the preservation of painting and calligraphy, thereby affecting their cultural value and legacy. Traditional mildew detection methods rely on manual visual inspection and/or chemical analysis, which are limited by inefficiency, subjectivity, the need for nondestructiveness, and low accuracy. Here, we overcome these limitations by developing a new mildew spectral index (MSIndex) using hyperspectral imaging technology and the mildew characteristics of ancient Chinese silk paintings. This approach provides support for the rapid, accurate, and nondestructive extraction and identification of mildew in ancient Chinese silk paintings. We first analyzed the mildew spectra on ancient Chinese silk paintings and optimized the spectral characteristics of mildew based on the hyperspectral data. Then, using this analysis, we constructed the MSIndex to detect mildew. We tested the proposed mildew detection method on the hyperspectral dataset of Shen Qinglan Tieluo on Qing Dynasty (1796-1805), followed by the evaluation of its generalization ability using the hyperspectral dataset of the Portrait of Pa & ntilde;cika Arhat (dated 1756) as an independent validation set. The results suggested that the proposed MSIndex was robust and effective with an overall accuracy of 94.17 % in mildew detection. The MSIndex was also capable of detecting mildew regions even in complex environments, such as those involving other pigments or diseases. This method can help professionals make accurate restoration plans for ancient Chinese silk paintings and support the preservation of cultural heritage. (c) 2025 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The voltage gradient plays a crucial role in the process of electro-bioremediation for petroleum-contaminated soil. However, the micro-ecological response mechanisms of relevance have been scarcely documented. This study compared petroleum degradation characteristics, soil physicochemical properties, and bacterial micro- biome indicators under 0.5 V cm- 1, 1 V cm- 1, and 2 V cm-1 conditions to elucidate the interaction mechanism among soil micro-ecological factors. The findings indicated that the treatment at 1 V cm- 1 resulted in the most effective synergistic enhancement of electrokinetics and bioremediation, yielding a peak petroleum degradation ratio of 43.54 f 1.64% over 105 days. The improvement in biodegradation resulted from the direct stimulation of bio-metabolism by higher ratios of "window condition" (RWC, 0.5331) and the indirect sustenance of microbial physiological activity by favorable soil conditions. The 1 V cm- 1 voltage gradient either maintained or fostered the soil microbiome's response to the remediation system. The structural equation models (SEMs) demonstrated that variations in microbiome properties across different voltage gradients resulted from the influences of effective current intensity, soil pH, redox potential (Eh), dissolved organic carbon (DOC), and electrical conductivity (EC). Optimizing voltage gradients is a practical approach for developing effective micro- ecosystems to efficiently remediate petroleum-contaminated soil and implement electro-bioremediation in various engineering applications.

Paper based cultural relics hold crucial significance to the historical and cultural heritage and the continuation of the national spirit in China. At the same time, the aging of popet seriously affects the longevity of artifacts. Paper viscosity is an important indicator reflecting the degree of paper aging. Traditional paper viscosity measurement methods are destructive experiments that cause inevitable secondary damage to valuable cultural relics. Hyperspectral remote sensing technology can achieve non destructive and rapid analysis to address this issue, providing an effective approach to establishing a paper viscosity inversion model. In this study, drying and moist-heat paper aging were taken as the research subjects, and110 groups of simulated paper aging samples with measured viscosity content were obtained. Through spectral filtering, a hyperspectral database of paper aging was established, Based on this, nine data processing methods, including original spectrum first order derivative, original spectrum second-order derivative, original spectrum reciprocal logarithm, original spectrum reciprocal logarithm first-order derivative, multiplicative scatter correction, multiplicative scatter correction spectrum first-order derivative. multiplicative scatter correction spectrum second-order derivative, multiplicative scatter correction spectrum reciprocal logarithm, and continuous wavelet transform were analyzed. well as two feature selection methods, competitive adaptative. reweighted sampling (CARS) and correlation coefficients (R). were analyzed in combination as input for the models to identify the best model through dataset partitioning validation, Research results have shown: (1) In the original spectrum, the correlation at 430 nm is the highest with viscosity, with an R-value of 0.75. After data processing, the R-value at 430 increases to 0.874 following the application of the reciprocal logarithm first-order derivative of the original spectrum method. which significantly enhances the paper viscosity information in the spectrums (2) After the original spectrum is transformed into the second-order derivative, the correlation at 578 nm is the highest with an R-value of 0.57, significantly lower than the highest R-value (0.75) in the original spectral data. This finding indicates that the second-order derivative is unsuitable for paper viscosity estimation, Meanwhile, the maximum correlation coefficients of other transformation methods are all higher than the original spectrum, demonstrating the effectiveness of spectral transformations (3) For the original spectrum. fecomposition scale increases, the paper viscosity information contained in the spectrum decreases, and the highest correlation Joefficient occurs at 484nm at the decomposition scale of 2, with a value of 0.873, (4) Among different input combinations, the model with the highest accuracy uses the reciprocal logarithm first-order derivative of the original spectrum as input. Based the CARS method, the support vector regression (SVR). random forest (RF), and AdaBoost models have R-2 values of 0.96. 1.93. and 0.93. respectively, and RMSE values of 14.80. 18.33. and 19.79 ml. g(-1) for the validation dataset, We Recommend prioritizing using the reciprocal logarithm first-order derivative of the original spectrum as inputs and the SVR model with the CARS feature selection algorithm for paper viscosity inversion, The above research findings demonstrate the applicability of hyperspectral technology for non-destructive analysis of paper viscosity, providing a scientific basis for the Jestoration work of paper based cultural relics.

Painted cultural relics hold significant historical value and are crucial in transmitting human culture. However, mold is a common issue for paper or silk-based relics, which not only affects their preservation and longevity but also conceals the texture, patterns, and color information, hindering cultural value and heritage. Currently, the virtual restoration of painting relics primarily involves filling in the RGB based on neighborhood information, which might cause color distortion and other problems. Another approach considers mold as noise and employs maximum noise separation for its removal; however, eliminating the mold components and implementing the inverse transformation often leads to more loss of information. To effectively acquire virtual restoration for mold removal from ancient paintings, the spectral characteristics of mold were analyzed. Based on the spectral features of mold and the cultural relic restoration philosophy of maintaining originality, a 3D CNN artifact restoration network was proposed. This network is capable of learning features in the near-infrared spectrum (NIR) and spatial dimensions to reconstruct the reflectance of visible spectrum, achieving the virtual restoration for mold removal of calligraphic and art relics. Using an ancient painting from the Qing Dynasty as a test subject, the proposed method was compared with the Inpainting, Criminisi, and inverse MNF transformation methods across three regions. Visual analysis, quantitative evaluation (the root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MEA), and a classification application were used to assess the restoration accuracy. The visual results and quantitative analyses demonstrated that the proposed 3D CNN method effectively removes or mitigates mold while restoring the artwork to its authentic color in various backgrounds. Furthermore, the color classification results indicated that the images restored with 3D CNN had the highest classification accuracy, with overall accuracies of 89.51%, 92.24%, and 93.63%, and Kappa coefficients of 0.88, 0.91, and 0.93, respectively. This research provides technological support for the digitalization and restoration of cultural artifacts, thereby contributing to the preservation and transmission of cultural heritage.

Dissolved organic matter (DOM) is easy to combine with residual pesticides and affect their morphology and environmental behavior. Given that the binding mechanism between DOM and the typical herbicide glyphosate in soil is not yet clear, this study used adsorption experiments, multispectral techniques, density functional theory, and pot experiments to reveal the interaction mechanism between DOM and glyphosate on Mollisol in farmland and their impact on the environment. The results show that the adsorption of glyphosate by Mollisol is a multilayer heterogeneous chemical adsorption process. After adding DOM, due to the early formation of DOM and glyphosate complex, the adsorption process gradually became dominated by single-layer chemical adsorption, and the adsorption capacity increased by 1.06 times. Glyphosate can quench the endogenous fluorescence of humic substances through a static quenching process dominated by hydrogen bonds and van der Waals forces, and instead enhance the fluorescence intensity of protein substances by affecting the molecular environment of protein molecules. The binding of glyphosate to protein is earlier, of which affinity stronger than that of humic acid. In this process, two main functional groups (C-O in aromatic groups and C-O in alcohols, ethers and esters) exist at the binding sites of glyphosate and DOM. Moreover, the complexation of DOM and glyphosate can effectively alleviate the negative impact of glyphosate on the soil. This study has certain theoretical guidance significance for understanding the environmental behavior of glyphosate and improving the sustainable utilization of Mollisol.

Soil organic carbon (SOC) potentially interacts with microbial metabolism and may affect the degradation of petroleum-derived carbon (PDC) in the electro-bioremediation of petroleum-contaminated soil. This study evaluated the interactions among organic carbon, soil properties, and microbial communities to explore the role of SOC during the electro-bioremediation process. The results showed that petroleum degradation exerted superposition and synergistic electrokinetic and bioremediation effects, as exemplified by the EB and EB-PR tests, owing to the maintenance and enhancement of SOC utilization (P/S value), respectively. The highest P/S value (2.0-2.4) was found in the electrochemical oxidation zone due to low SOC consumption. In the biological oxidation zones, electric stimulation enhanced the degradation of PDC and SOC, with higher average P/S values than those of the Bio test. Soil pH, Eh, inorganic ions, and bioavailable petroleum fractions were the main factors reshaping the microbial communities. SOC metabolism effectively buffered the stress of environmental factors and pollutants while maintaining functional bacterial abundance, microbial alpha diversity, and community similarity, thus saving the weakened PDC biodegradation efficiency in the EB and EB-PR tests. The study of the effect of SOC metabolism on petroleum biodegradation contributes to the development of sustainable low-carbon electro-bioremediation technology.

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling and pollutant transmission, and thus on regional water quality. A full understanding of the water quality response to the LULC pattern is critical for water resource management and improvement of the natural environment in Tianjin. In this study, surface water monitoring station data and LULC data from 2021 to 2022 were used to investigate the surface water quality in Tianjin. A cluster analysis was conducted to compare water quality among monitoring stations, a factor analysis was conducted to identify potential pollution sources, and an entropy weight calculation was used to analyze the impact of the land use pattern on water quality. The mean total nitrogen (TN) concentration exceeded the class Ⅴ water quality standard throughout the year, and the correlation coefficient of the relationship between dissolved oxygen (DO) and pH exceeded 0.5 throughout the year, with other water quality parameters showing seasonal changes. On the basis of their good water quality, the water quality monitoring stations near large water source areas were distinguished from those near areas with other LULC patterns via the cluster analysis. The factor analysis results indicated that the surface water in Tianjin suffered from nutrient and organic pollution, with high loadings of ammonia nitrogen (NH3N), TN, and total phosphorus (TP). Water pollution was more serious in areas near built-up land, especially in the central urban area. The entropy weight calculation results revealed that water, built-up land, and cultivated/built-up land had the greatest impact on NH3N, while cultivated land had the greatest impact on electrical conductivity (EC). This study discusses the seasonal changes of surface water and impact of land use/land cover pattern on water quality at a macro scale, and highlighted the need to improve surface water quality in Tianjin. The results provide guidance for the sustainable utilization and management of local water resources.

Electrical stimulation during electro-bioremediation has the potential to enhance the biodegradation of poly -cyclic aromatic hydrocarbons (PAHs) in soil. In this study, we investigated the effective current intensity, referred to as the "window condition," which promotes the activity of functional microflora. Electro-bioremediation was performed on PAH-contaminated soil using various polarity reversal frequencies (PRF) to study the remediation mechanism. The "window condition" in this study was 20-40 mA. The enhancement of PAH degradation, particularly that of high-PAHs, increased with higher PRF, which accompanied the increasing ratio of the "window condition" to the overall electrification time (RWC). Electro-bioremediation with a 10 -min-ute (EBPR-10 m) and 30-minute (EBPR-30 m) polarity reversal (PR) periods achieved the highest ratios of PAH degradation at 43.9 +/- 2.3 % and 37.9 +/- 1.8 %, respectively, with RWC of 0.4919 and 0.4056, respectively, after 60 days. The increase in PRF improved the soil physicochemical properties, which were conducive to maintaining effective electrokinetic and biodegradation processes. Redundancy analysis (RDA) and Pearson correlation an-alyses demonstrated that soil electrical conductivity (EC), dissolved organic carbon (DOC), temperature, and the proportion of macroparticle components (0.195-2.500 um) in soil colloids (Ps/Po) were key factors in explaining the persistence of microbial abundance and community structure. Structural equation models (SEMs) further illustrated the mechanism behind the enhanced PAH biodegradation, including (i) the direct stimulation of microbial activity because of increased RWC and (ii) the indirect optimization of microbial function by improving soil physicochemical properties under optimal PRF.

Total suspended sediments (TSS) are an important water quality indicator. The spatiotemporal distribution patterns of TSS concentrations in summer in the Peace-Athabasca Delta (PAD) remain unclear between 2000 and 2020. Among empirical and machine learning models, the random forest (RF) model exhibits the best performance for the PAD with a coefficient of determination, normalized root-means-square error, and relative root-mean-square error of 0.92, 7.51%, and 27.36%, respectively. Our study analyzes the spatiotemporal TSS distribution based on 21 years of Landsat remote sensing data and the RF model. The results showed that the TSS concentrations of the Peace River were elevated in 2000, 2016, 2019, and 2020. The long-term TSS concentration distribution in the PAD showed a decreasing trend from west to east, and the TSS concentrations ranged from 6 to 80 mg/L level and remained stable in Lake Athabasca during the investigated period. Moreover, 57.14% of the PAD area exhibited no significant change between 2000 and 2020. The results of our study provide detailed information for the local government on the TSS concentrations in the PAD. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

Total petroleum hydrocarbon (TPH) pollution in oilfield soils is a worldwide environmental problem. In this study, we analysed the spatial variation of residual TPH components and the ecological risk they pose. The soils of five selected oilfields in China, across 11 degrees of latitude and 17 degrees of longitude were selected for the investigation. The results showed that the non-zonal composition of the residual TPHs in the soil was similar to the that of the crude oil input. Principal component analysis (PCA) suggested that the effect of zonal environmental factors explained 81.5% of the variability in the residual indexes of saturated and aromatic hydrocarbons. The first principal component, the soil clay and organic matter, correlated positively with the residual TPH index. The second principal component, the accumulated temperature, however, correlated negatively with the residual TPH index in the soil. Moreover, the application of the soil quality index (SoQI) and a Monte Carlo simulation for estimating the residual TPH content suggested that the ecological risk caused by residual TPHs in the soil decreased when the oilfield latitude and clay and organic matter content in the oilfield soil were lower. This study provides a basis for the assessment and monitoring of ecological risk in oilfield soils worldwide.

Due to differences in environmental factors, the phenology of the same crop is different every year, causing divergent performances of the classifier built by spectral or time-series features Here, we proposed a random forest classifier (RFC) based on an asymmetric double S curve model fitted by accumulated temperature (AT) and Vegetation Index (VI), which can be applied in different years without ground samples. We built AT and VI time series from Moderate Resolution Imaging Spectroradiometer 8-day composites of land surface temperatures and Sentinel-2 and Landsat-8, respectively. The RFC was trained by characteristics from the asymmetric double S curve. We prepared RFC by ground samples of 2018 and 2019 and then mapped crops of the same region in 2017. Results indicated that, compared with diverse VI-AT series, the overall accuracy based on universal normalized vegetation index (UNVI) was the best of all (2017: F1 = 0.91, 2018: F1 = 0.92, 2019: F1 = 0.91) and better than that based on the UNVI-TIME series (2017: F1 = 0.84, 2018: F1 = 0.81, 2019: F1 = 0.88). It proved that the classification features from the VI-AT series have smaller intra-class differences in 2017, 2018, and 2019.

为较精确地预测含油固体颗粒在低填充率螺旋推进式脱附炉内的传热系数,文章基于马尔可夫传热模型,提出了该传热系数由覆盖体系传热系数和敞开体系传热系数两部分组成,建立了预测螺旋推进式脱附炉总传热系数模型,并以实验室配制的油污土壤颗粒,分别在填充率为15％和25％,螺旋转速为2,5,8 r/min,炉壁温度为300℃的6种工况条件下,测量了炉壁与颗粒间的传热系数,对模型进行了验证.实验结果表明:该模型预测的平均相对误差为7.51％,覆盖体系传热是主要的传热途径,在该实验操作参数范围内,供能占比为65％以上;降低填充率、减小螺旋直径和增大搅拌强度有助于提高脱附炉的传热性能.

In this study, we aimed to address the attenuation of electrokinetic fluxes that occur during plant (tall fescue)based electrokinetic remediation of oil-contaminated soil. Following 60 days of treatment, the concentration of water-soluble cations and anions in the electrokinetics-assisted phytoremediation treatment (EK-P) were 20.03 mg/kg and 15.7 mg/kg higher than that in the electrokinetic (EK) treatment, respectively. At the electrode, plants were able to alleviate the ion aggregation effect caused by the electrokinetics, reduce the conversion of soluble ions to insoluble ones, and reduce the decay of water-soluble ions. In addition, the zeta potential of EK-P was 5.05 mV lower than that of EK. Plants maintained the stability of the soil colloid and reduced the movement of the peak of colloidal particle size from small to large particles, thereby reducing the amount of colloidal deposition. Finally, the EK-P current was 22.49% higher than that in EK while the electrokinetic effect was maintained. Meanwhile, electrokinetics increased plant biomass by 20.21%. Electrokinetics was found to create a synergy with the plants, an effect that eventually enhanced the rate of oil degradation.

Monitoring the spatio-temporal dynamics of the Eastern Plain Lake (EPL) is vital to the local environment and economy. However, due to the limitations and efficiency of traditional image formats in storing and processing large amounts of images and optimal threshold adjustments are often necessary for water/non-water separation based on traditional multi-band/spectral water indexes over large areas and in the long-term, previous studies have either been on a short period or mainly focused on water inundation dynamics of several lakes. To address these issues, a multi-dimensional dataset (MDD) storage format was used to efficiently organize more than ~7000 time series composite MODIS images. Furthermore, a universal normalized water index (UNWI) was developed based on full-spectrum information to simplify optimal threshold adjustments. Consequently, the present study analyzed the patterns of spatio-temporal water dynamic patterns and potential driving factors of inundation changes at large lakes (>5 km2) in the EPL during 2000–2020 through MDD and UNWI. In terms of annual inundation patterns, the numbers of lakes that experienced significant (p < 0.05) decreases (17 lakes) and increases (43 lakes) were highest for Class IV lakes among six geographical classes. Variation in intra-annual inundation in Classes I and II is correlated with consumption of chemical fertilizers (CCF), while precipitation accounted for the most change in lake area in Class III. This spatio-temporal analysis of lakes provides a necessary foundation for the sustainable development and continuous investigations of the EPL.

This study comprehensively evaluates the ecotoxicity of high-concentration heavy petroleum (HCHP)-contaminated soil before and after thermal desorption (TD) remediation at different temperatures and times. The results showed that the detoxification of contaminated soil was effectively achieved by extending the remediation duration at 400-600 °C. After treatment at 400 °C for 60 min, the toxicological indicators including bioluminescence EC50 (acute toxicity), seed germination ratio (Gr) and plant biomass of Brassica juncea (subacute toxicity), and diversity of the microbial community (chronic toxicity) reached a maximum. The value of the SOS-Induction Factor (SOSIF), characterizing genotoxicity was below 1.5, indicating that it was non-toxic. Pearson's correlation analysis illustrated that the water-soluble fraction (WSF), ALK1-3 and ARO1-3 of petroleum hydrocarbons were the primary sources of ecotoxicity. Notably, although the total ratio of petroleum removed from the soil reached 87.26 ± 4.38 %-98.69 ± 1.61 % under high-temperature thermal desorption (HTTD, 500-600 °C), the ecotoxicity was not lower than that at 400 °C. The pyrolysis products of petroleum macromolecules and extreme changes in soil properties were the leading causes of soil ecotoxicity following HTTD. The inconsistency between the removal of petroleum pollutants and ecological health risks reveals the significance of soil ecotoxicological assessments for identifying TD remediation endpoints and process optimization.

The key to enhancing the efficacy of bioremediation of hydrocarbon-contaminated soil is the precise and highly efficient screening of functional isolates. Low screening effectiveness, narrow screening range and an unstable structure of the constructed microflora during bioremediation are the shortcomings of the traditional shaking culture (TSC) method. To improve the secondary screening of isolates and microflora implemented for alkane degradation, this work evaluated the characterization relationship between bacterial function and enzyme activity and devised an enzyme activity assay (EAA) method. The results indicated a substantial positive correlation (r = 0.97) between 24 candidate isolates and their whole enzymes, proving that whole enzyme activity properly reflects the metabolic functions of microorganisms. The functional analysis of the isolates demonstrated that the EAA method in conjunction with microbial abundance and metabolite determination could broaden the screening range of functional isolates, including aliphatic acid-metabolizing isolates (isolates H4 and H7) and aliphatic acid-sensitive isolates (isolate H2) with n-hexadecane degradation ability. The EAA method also guided the construction of functional microflora and optimized the mode of application using combinations of alkane-degrading bacteria and aliphatic acid-degrading bacteria successively (e.g., F1+H7+H7). The combinations maintained a high abundance of functional isolates and stable α diversity and community composition throughout the experiment, which contributed to more advanced alkane degradation and mineralization ability (p < 0.01). Assuming a workload of 100 tests, the screening efficiency of the EAA method is more than 16 times that of the TSC method, and the greater the quantity of isolates, the higher the screening efficiency, enabling high-throughput screening. In conclusion, the EAA method has a broad-spectrum, accurate and highly efficient screening ability for functional isolates and microflora, which can provide intensive technical support for the development of bioremediation materials and the application of bioremediation technology.

Total phosphorus (TP) is a significant indicator of water eutrophication. As a typical macrophytic lake, Lake Baiyangdian is of considerable importance to the North China Plain’s ecosystem. However, the lake’s eutrophication is severe, threatening the local ecological environment. The correlation between chlorophyll and TP provides a mechanism for TP prediction. In view of the absorption and reflection characteristics of the chlorophyll concentrations in inland water, we propose a method to predict TP concentration in a macrophytic lake with spectral characteristics dominated by chlorophyll. In this study, water spectra noise is removed by discrete wavelet transform (DWT), and chlorophyll-sensitive bands are selected by gray correlation analysis (GRA). To verify the effectiveness of the chlorophyll-sensitive bands for TP concentration prediction, three different machine learning (ML) algorithms were used to build prediction models, including partial least squares (PLS), random forest (RF) and adaptive boosting (AdaBoost). The results indicate that the PLS model performs well in terms of TP concentration prediction, with the least time consumption: the coefficient of determination (R2) and root mean square error (RMSE) are 0.821 and 0.028 mg/L in the training dataset, and 0.741 and 0.029 mg/L in the testing dataset, respectively. Compared with the empirical model, the method proposed herein considers the correlation between chlorophyll and TP concentration, as well as a higher accuracy. The results indicate that chlorophyll-sensitive bands are effective for predicting TP concentration.