Calibration of chemical and biological changes in cocomposting of biowastes using near-infrared spectroscopy

Regulatory effects of different anionic surfactants on the transformation of heavy metal fractions and reduction of heavy metal resistance genes in chicken manure compost

Surfactants are widely used as a passivating agent in heavy metal passivation process, but their effect on transformation of heavy metal fraction and reduction of heavy metal resistance genes (MRGs) in composting process is still unknown. The aim of this study was to compare the effects of two anionic surfactants (rhamnolipid and sodium dodecyl sulfate) on heavy metal passivation and resistance gene reduction in chicken manure composting. The results showed that the addition of surfactant can effectively enhance degradation of organic matter (OM). Both surfactants could effectively reduce the bioavailability of heavy metals (HMs) and the relative abundance of resistance genes, especially rhamnolipids. The potential functional bacteria affecting heavy metal passivation were identified by the changes of microbial community. Redundancy analysis (RDA) showed that protease (PRT) activity was the key factor affecting the fractions of the second group of HMs including ZnF1, CuF1, CuF2, PbF1 and PbF3. These findings indicate that addition of anionic surfactants can reduce the bioavailability of HMs and the abundance of resistance genes in compost products, which is of guiding significance for the reduction of health risks in the harmless utilization of livestock and poultry manure.

Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods

Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R² values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.

Earthworm-Collembola interactions affecting water-soluble nutrients, fauna and physiochemistry in a mesocosm manure-straw composting experiment

A mesocosm fermentation experiment was undertaken to investigate interactions between Eisenia fetida and Collembola affecting composting processes. Earthworms, Collembola, respiration, water soluble nutrients and compost characteristics (near infrared spectra - NIRS) were monitored on four occasions over 136 days. Earthworms were the main drivers of early changes in composts, increasing the general abundance of Collembola, although responses varied with species. Earthworms accelerated substrate mineralisation and release of soluble nutrients whilst also changing compost characteristics. Collembola alone had little direct effect on soluble nutrient concentrations or respiration; they did however alter compost characteristics (NIR spectra). Earthworm-Collembola interactions affecting respiration and soluble nutrients were mainly antagonistic in the early stages of composting but synergistic in later stages. In the later stages of composting, the higher abundance of Collembola when combined with earthworms resulted in greater concentrations of soluble nitrate and phosphate. These findings emphasise the importance in vermicomposting practice of different invertebrate groups having access to feedstock at appropriate stages of the process. The high concentrations of soluble nutrients released during vermicomposting indicate the need for control measures to avoid off-site pollution and loss of this resource.

Enzymatic activities triggered by the succession of microbiota steered fiber degradation and humification during co-composting of chicken manure and rice husk

The carbon to nitrogen ratio (C/N) is well known for its importance in the composting process, however the fiber degradation and humification associated with enzymatic activity and microbial variation derived from different C/N ratios are poorly studied. Here, we designed two treatments of chicken manure with 15% (initial C/N ratio 9.61) and 50% (initial C/N ratio 17.3) rice husk to adjust the moisture of mixtures for turning feasibly by towable fertilizer turner in industrial level. Compared to the C/N ratio 9.61, the suitable C/N ratio of 17.3 significantly enhanced the composting efficiency and the final germination index (23.7%). Moreover, the suitable C/N ratio increased the relative abundance of Bacilli, which played an important role during the mesophilic and thermophilic phases. Bacilli abundance was related to cellulose and β-glycosidase activities, thus improved fiber degradation and humification. This study not only seeks a swift method in industrial level to process chicken manure but also provides insight into the enzymatic activity of microbial community related to high-efficient composting.

Production of an innovative biowaste-derived fertilizer: Rapid monitoring of physical-chemical parameters by hyperspectral imaging

In this work the possibility to apply hyperspectral imaging as a fast and non-destructive technique for the monitoring of the production process at pilot plant scale of an innovative biowaste-derived fertilizer was explored. Different mixtures of urban organic waste, farm organic residues, biochar and vegetable active principles were selected and utilized in two different European countries, Italy and Spain, for the production of the innovative fertilizer. The biowaste-derived fertilizer samples were collected from the pilot plant piles at different curing time and acquired by the hyperspectral imaging device. Spectra have been collected in the near infrared wavelength range (1000-1700 nm). Conventional analyses were carried out on the same samples in order to find correlations between the physical-chemical parameters detected at laboratory scale, and the acquired reflectance spectra. The investigated parameters were: pH, electrical conductivity, soluble total organic carbon and soluble total nitrogen. Hyperspectral data were processed adopting chemometric strategies through the application of principal component analysis, for exploratory purposes, and partial least squares analysis to establish correlations between spectral features and measured physical-chemical parameters. Good correlations, with R² ranging between 0.85 and 0.96, were obtained for all the investigated parameters. Results showed as the proposed approach, based on hyperspectral imaging, is suitable to be adopted for a rapid and non-destructive monitoring of waste-derived fertilizer production.

Concentration estimation of heavy metal in soils from typical sewage irrigation area of Shandong Province, China using reflectance spectroscopy

Since sewage irrigation can markedly disturb the status of heavy metals in soils, a convenient and accurate technique for heavy metal concentration estimation is of utmost importance in the cropland using wastewater for irrigation. This study therefore assessed the feasibility of visible and near infrared reflectance (VINR) spectroscopy for predicting heavy metal contents including Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg in the north plain of Longkou city, Shandong Province, China. A total of 70 topsoil samples were taken for in situ spectra measurement and chemical analysis. Stepwise multiple linear regression (SMLR) and principal component regression (PCR) algorithms were applied to establish the associations between heavy metals and reflectance spectral data pretreated by different transformation methods. Based on the criteria that minimal root mean square error (RMSE), maximal coefficient of determination (R ²) for calibration, and greater ratio of standard error of performance to standard deviation (RPD) is related to the optimal model, SMLR model using first deviation data (RD₁) provided the best prediction for the contents of Ni, Pb, As, Cd, and Hg, calibration using SNV data for Cr and continuum removal spectra for Zn, while PCR equation employed RD₁ values was fit for prediction of the contents of Cu. The determination coefficients of all the reasonable models were beyond 0.6, and RPD indicated a fair or good result. In general, first deviation preprocessing tool outperformed other methods in this study, while raw spectra reflectance performed unsatisfactory in all models. Overall, VINR reflectance spectroscopy technique could be applicable to the rapid concentration assessment of heavy metals in soils of the study area.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km² agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses

Due to a boom in the dairy industry in Northeast China, the hay industry has been developing rapidly. Thus, it is very important to evaluate the hay quality with a rapid and accurate method. In this research, a novel technique that combines near infrared spectroscopy (NIRs) with three different statistical analyses (MLR, PCR and PLS) was used to predict the chemical quality of sheepgrass (Leymus chinensis) in Heilongjiang Province, China including the concentrations of crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF). Firstly, the linear partial least squares regression (PLS) was performed on the spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the MLR evaluation method for CP has a potential to be used for industry requirements, as it needs less sophisticated and cheaper instrumentation using only a few wavelengths. Results show that in terms of CP, ADF and NDF, (i) the prediction accuracy in terms of CP, ADF and NDF using PLS was obviously improved compared to the PCR algorithm, and comparable or even better than results generated using the MLR algorithm; (ii) the predictions were worse compared to laboratory-based spectra with the MLR algorithmin, and poor predictions were obtained (R2, 0.62, RPD, 0.9) using MLR in terms of NDF; (iii) a satisfactory accuracy with R2 and RPD by PLS method of 0.91, 3.2 for CP, 0.89, 3.1 for ADF and 0.88, 3.0 for NDF, respectively, was obtained. Our results highlight the use of the combined NIRs-PLS method could be applied as a valuable technique to rapidly and accurately evaluate the quality of sheepgrass hay.

Rapid and accurate evaluation of the quality of commercial organic fertilizers using near infrared spectroscopy

The composting industry has been growing rapidly in China because of a boom in the animal industry. Therefore, a rapid and accurate assessment of the quality of commercial organic fertilizers is of the utmost importance. In this study, a novel technique that combines near infrared (NIR) spectroscopy with partial least squares (PLS) analysis is developed for rapidly and accurately assessing commercial organic fertilizers quality. A total of 104 commercial organic fertilizers were collected from full-scale compost factories in Jiangsu Province, east China. In general, the NIR-PLS technique showed accurate predictions of the total organic matter, water soluble organic nitrogen, pH, and germination index; less accurate results of the moisture, total nitrogen, and electrical conductivity; and the least accurate results for water soluble organic carbon. Our results suggested the combined NIR-PLS technique could be applied as a valuable tool to rapidly and accurately assess the quality of commercial organic fertilizers.

Fluorescence excitation-emission spectroscopy with regional integration analysis for assessment of compost maturity

Composting of animal manures is believed as an alternative way for directly recycling them in farms, and therefore assessment of compost maturity is crucial for achieving high quality compost. Fluorescence excitation-emission matrices (EEMs) combined with regional integration analysis is presented to assess compost maturity. The results showed that the EEM contours of water-extract organic matter (WEOM) from immature composts exhibited four peaks at excitation/emission (Ex/Em) of 220/340nm, 280/340nm, 220/410nm, and 330/410nm, whereas EEM contour of WEOM from mature composts had only two peaks at Ex/Em of 230/420nm and 330/420nm. Pearson correlation demonstrated that peaks intensity rather than their ratios had a significantly correlation with the common indices assessing compost maturity, whereas the normalized excitation-emission area volumes (Φ(i,n)s) from regional integration analysis had a stronger correlation with the common indices assessing compost maturity than peaks intensity. It is concluded that the Φ(i,n)s from regional integration analysis are more suitable to assess the maturity of compost than the intensities of peaks. Therefore, the fluorescence spectroscopy combined with regional integration analysis can be used as a valuable industrial and research tool for assessing compost maturity, given its high sensitivity and selectivity.

Comparison between UV spectroscopy and Nirs to assess humification process during sewage sludge and green wastes co-composting

The humification of organic matter during composting was studied by the quantification and monitoring of the evolution of humic substances (Humic Acid-HA and Fulvic Acid-FA) by UV spectra deconvolution (UVSD) and near-infrared reflectance spectroscopy (NIRS) methods. The final aim of this work was to compare UVSD to NIRS method, already applied on the same compost samples in previous studies. Finally, UVSD predictions were good for HA and HA/FA (r(2) of 0.828 and 0.531) but very bad for FA (r(2) of 0.092). In contrary, all NIRS correlations were accurate and significant with r(2) of 0.817, 0.806 and 0.864 for HA, FA and HA/FA ratio respectively. From these results, HA/FA ratio being a well-used index of compost maturity, UVSD and NIRS represent two invaluable tools for the monitoring of the composting process. However, we can note that NIRS predictions were more accurate than UVSD calibrations.

Different analysis techniques for fluorescence excitation-emission matrix spectroscopy to assess compost maturity

Assessment of compost maturity is essential for achieving high quality compost. In this study, fluorescence excitation-emission matrix spectroscopy combined with different analysis techniques was applied to improve the sensitivity of compost maturity assessment. Results showed that composts in two parallel piles could be believed mature after 37d when combined with the evolution of temperature, chemical and biological indices in the two piles. Pearson correlation between the common maturity indices and fluorescence analysis parameters demonstrated that fluorescence regional integration (FRI) had a higher correlation coefficient than that of fluorescence intensities and the ratios of peaks, suggesting that FRI technique is more suitable to characterize the maturity of compost than the other two analysis techniques, i.e., peak intensity and peak ratio. Furthermore, the fluorescence spectroscopy combined with FRI analysis could be used as a valuable industrial and research tool for assessing compost maturity.

Functional changes in culturable microbial communities during a co-composting process: carbon source utilization and co-metabolism

Microbial communities in sewage sludge and green waste co-composting were investigated using culture-dependent methods and community level physiological profiles (CLPP) with Biolog Microplate. Different microbial groups characterized each stage of composting. Bacterial densities were high from beginning to end of composting, whereas actinomycete densities increased only after bio-oxidation phase i.e. after 40days. Fungal populations become particularly high during the last stage of decomposition. Cluster analyses of metabolic profiles revealed a similar separation between two groups of composts at 67days for bacteria and fungi. Principal component analysis (PCA) applied to bacterial and fungal CLPP data showed a chronological distribution of composts with two phases. The first one (before 67days), where the composts were characterized by the rapid decomposition of non-humic biodegradable organic matter, was significantly correlated to the decrease of C, C/N, organic matter (OM), fulvic acid (FA), respiration, cellulase, protease, phenoloxidase, alkaline and acid phosphatases activities. The second phase corresponding to the formation of polycondensed humic-like substances was significantly correlated to humic acid (HA) content, pH and HA/FA. The influent substrates selected on both factorial maps showed that microbial communities could adapt their metabolic capacities to the particular environment. The first phase seems to be focused on easily degradable substrate utilization whereas the maturation phase appears as multiple metabolisms, which induce the release of metabolites and their polymerization leading to humification processes.