**Compost Process and Organic Fertilizers Application in China**

Guanghui Yu, Wei Ran and Qirong Shen

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/62324

#### **Abstract**

Composting is an inexpensive and sustainable treatment for solid wastes. The compost‐ ing industry has been growing rapidly because of a boom in the animal industry in China over the past decades. In this chapter, we introduce composting process and status in China, especially in Jiangsu Province. Meanwhile, the developed novel spectroscopy techniques are also introduced, which are more suitable for assessment of compost maturity than the conventional techniques in view of ease of sample preparation, rapid spectrum acquisition, and nondestructive nature of the analysis. These novel spectrosco‐ py techniques include near-infrared reflectance spectroscopy (NIRS)––partial least squares (PLS) analysis and fluorescence excitation–emission matrix (EEM) spectrosco‐ py––parallel factor (PARAFAC) analysis. In addition, organic fertilizer amendments can not only improve soil fertility but also offset chemical fertilizers' nanoscale changes. Emerging cutting-edge technologies of synchrotron-based X-ray absorption fine structure (XAFS) spectroscopy and nanoscale secondary ion mass spectrometry (NanoSIMS) were used to identify the composition of organic carbon and minerals and their correlations, respectively. Recently, investigators have shown that organic fertilizer amendments could enhance the production of highly reactive minerals, for example, allophane, imogolite, and ferrihydrite, which further benefit for soil carbon storage and soil fertility improve‐ ment.

**Keywords:** compost process, compost maturity, China, solid waste treatment, soil fer‐ tility, spectroscopy techniques, nanominerals, short-range ordered minerals, soil car‐ bon storage

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*4.4. Immunocytochemistry*

Live-cell surface staining was performed as described previously [38]. The primary antibody used was mouse anti-rhodopsin 4D2 (Merck-Millipore). The secondary antibodies used were Cy3-conjugated anti-mouse IgG antibody (Thermo Fisher Scientific, Waltham, MA, USA). After antibody staining, the cells were fixed in 4% paraformaldehyde, and then the slides were mounted with Mowiol and visualized by a fluorescence microscopy M1 imager (Carl Zeiss, Oberkochen, Deutschland).

#### *4.5. Dual Luciferase Reporter Gene Assay*

All odorants were purchased from FUJIFILM Wako Chemicals (Osaka, Japan) and TCI chemicals (Tokyo, Japan). The Dual-Glo Luciferase Assay (Promega) was used to determine the activities of firefly and Renilla luciferase in Hana3A cells as previously described [38]. Briefly, firefly luciferase, driven by a cAMP response element promoter (CRE-Luc; Stratagene), was used to measure the OR activation levels. For each well of a 96-well plate, 5 ng SV40-RL, 10 ng CRE-Luc, 5 ng mouse RTP1, 2.5 ng M3 receptor 3, and 5 ng of Rho-tagged receptor plasmid DNA were transfected. Normalized activity for each

well was further calculated as (Luc)/(Rluc). Luc and Rluc are the luminescence of firefly luciferase and Renilla luminescence, respectively. The basal activity was averaged from six wells in the absence of odorants and further corrected by subtracting that of the control empty vector. Odorant-induced activity was averaged from at least three wells and further corrected by subtracting the basal activity of that receptor. Odorant-induced responses were normalized to that of WT. EC50 value was calculated using GraphPad Prism software and data shown in Supplementary Data S2.


**1. Introduction**

Water is one of the most essential natural resources on which all life depends. However, various economic activities have an indispensable impact on the environment through different pathways [1]. Take China as an example: in recent years, along with high-speed economic development and urbanization, China's limited freshwater resources have been drastically reduced and, at the same time, increasing water pollution poses a serious threat to human survival and security and has become a significant obstacle to human health and sustainable socio-economic development. From the perspective of China's actual national conditions, water resources are relatively scarce. In addition, as China is undergoing a period of rapid socio-economic development, the demand for water resources is accelerating. Although China has 2.8 trillion water resources [2], which seems to be very rich, the per capita share of water resources is only 2400 cubic meters due to its large population [3], and account for less than one-quarter of the world's total per capita water resources. In addition, the discharge of industrial wastewater and domestic sewage into water bodies without treatment has led to the severe pollution of various water bodies, including rivers and lakes, thus seriously damaging the ecological environment, biodiversity, and the ecological and service functions of water bodies [4]. According to previous studies, only a small number of rivers worldwide are not affected by water pollution [5]. At present, the pollution and

**Citation:** Wu, J.; Wang, Z. A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory. *Water* **2022**, *14*, 610. https://doi.org/ 10.3390/w14040610

Academic Editors: Nigel W. T. Quinn, Ariel Dinar, Iddo Kan and Vamsi Krishna Sridharan

Received: 7 December 2021 Accepted: 10 February 2022 Published: 17 February 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

eutrophication of rivers in China are severe. According to the 2019 statistics from China's State Environmental Protection Administration, the seven major water systems in China in descending order of pollution level are listed as follows: the Liaohe River basin, the Haihe River basin, the Huaihe River basin, the Yellow River basin, the Songhua River basin, the Pearl River basin, and the Yangtze River basin, with more than 70% of the Liaohe, Haihe, Huaihe, and Yellow River basins being polluted. Huang et al. [6] conducted an analysis of water quality data from 2424 water quality observation stations in China from 2003–2018 and concluded that the quality of river water in China showed significant spatial differences, with 17.2% of sampling sites in eastern China showing poor water quality during the period of 2016–2018, compared to 4.6% in the western region. Moreover, 24.4% of the sampling sites in coastal areas (buffer zone of 20 km from the coastline) showed poor water quality. Although the Chinese government has invested a great deal of money into the treatment and management of polluted water bodies, the pollution proportion of water resources is still quite impressive, which has brought severe economic and social costs to China's water environment remediation [7]. Water quality prediction is a necessary tool for water environment planning, management, and control; an important element of water pollution research; and a fundamental part of water environmental protection and management. Thus, it is vital to find a reasonable and effective water quality prediction method. At the same time, predicting future water quality is a prerequisite for preventing rapid changes in water quality and proposing countermeasures. Therefore, the accurate prediction of water quality changes can not only effectively ensure the safety of people's drinking water, but can also have a positive impact on guiding fishery production and protecting biodiversity.

Research into water quality prediction dates back to the 1920s. Streeter and Phelps developed a coupled model based on biochemical oxygen demand and dissolved oxygen when they studied pollution sources in the Ohio River. They proposed a one-dimensional steady-state oxygen balance water quality model (the S-P model). Since then, many scholars have supplemented and revised their theories [8–10]. At present, the research methods of water quality prediction are mainly divided into two categories: one is to use theoretical mathematical model and physical model to predict the development trend of water quality mechanism [11], the other is a non-mechanistic prediction method that builds mathematical statistical prediction models based on historical data. The mechanistic prediction method analyses the physical, chemical, and biological changes of each factor in the water resource cycle; establishes a mathematical model reflecting the relationship between the substances; and solves the corresponding mathematical equations to predict the trend of water quality changes. For example, Zhang et al. incorporated the operation rules of dams or sluices into the reservoir regulation module, used an improved SWAT model to simulate the water quantity and quality in the Huaihe River basin, and compared the results with those of the original SWAT model. The results showed that the improved SWAT model was more accurate in simulating the water quantity and quality in the Huaihe River basin [12]. Peng et al. used the Environmental Fluid Dynamics Code (EFDC) model coupled with a geographic information system (GIS) model to simulate the water quality of the lower Charles River, and the results showed that the accuracy of the model was improved compared with the original EFDC model [13]. The mechanistic models of river water quality tend to provide a more comprehensive description of water quality changes, as they consider the effects of physical, chemical, and biological processes on the spatial and temporal transport and transformation patterns of pollutants in river waters; however, at the same time, most of these models are complex and require a great deal of basic information and data (numerical model uses a large amount of water quality data as the basis for calculation), and it is difficult to obtain a continuous distribution of water quality in space and time. This has greatly limited the application of these models [14]. In addition, the mechanics of many water environment systems are not fully understood by scholars; hence, it is difficult to describe them accurately using exclusively mechanistic modelling. In contrast, non-mechanical water quality modelling is a black-box approach to a particular

water quality system, which is modelled by mathematical statistics or other mathematical methods to make predictions about water quality. Commonly used non-mechanical water quality simulation prediction methods include regression models, probability statistical models, grey prediction models, time series models, etc.

In recent years, neural networks and other machine learning algorithms have been applied by many researchers in the field of water quality prediction and have achieved good prediction results. The SOTA table of the progress of research based on water quality prediction is shown in Table 1 (distinguishing between mechanistic and non-mechanistic models).

**Table 1.** Overview of water quality prediction research.

**Table 1.** *Cont*.

Based on the strong noise immunity of the simultaneous wavelet transform, the simultaneous wavelet transform is used to denoise the dataset, followed by an improved sparrow search algorithm to optimize the hyperparameters of the LSTM. The mean absolute error (MAE) of the model for predicting the water quality of Yongding River was 0.4727, which is

much lower than other models.

Song et al. (2021) [23] Haihe River SWT-ISSA-LSTM

**Table 1.** *Cont*.

Archana et al. used the depth belief network in unsupervised learning to study the PH, dissolved oxygen, turbidity, and other water quality parameters of the Chaskaman reservoir for prediction and analysis [31]. The results show that this method performs better than the classical method for prediction. Wang et al. introduced the Holt–Winters seasonal model based on the ARIMA model and predicted the total phosphorus and total nitrogen in the reservoir. The results showed that the model had a prediction accuracy of 97.5% and had many advantages, such as fast learning speed [32]. Mohamed et al. analyzed the irrigation water quality index in Egypt by means of an integrated evaluation method and an artificial neural network model. In addition, the ARIMA model was developed to predict IWQI in Bahr El-Baqar drain, Egypt [33]. Shi et al. proposed a combination of the wavelet artificial neural network (WANN) model and the high-frequency alternative measurement of water quality anomaly detection and early warning method [34]. Li et al. proposed an EEMD-SVR water quality prediction model to predict the water quality of Jialing River in China. The model first decomposes water quality indicators, such as DO, into each IMF component by the EEMD algorithm, and then builds the SVR model based on each IMF component. The results showed that the hybrid model outperformed the standard SVR model and BPNN model in a variety of evaluation indicators [35]. Ewaid et al. established a multiple linear regression model according to the specified weight and predicted the water quality of the Euphrates River [36]. Xu combined wavelet transform and BPNN to establish a short-term wavelet neural network water quality prediction model and used the model to predict the water quality of intensive freshwater pearl culture ponds in Duchang County, Jiangxi Province, China. The results showed that the RMSE of the model was 3.822 in DO metrics, which was much lower than that of the BPNN and ELman models, showing desirable performance [37]. Qin et al. developed a PSO-WSVR model and used a particle swarm algorithm to optimize the parameters of the weighted support vector regression machine to predict water quality in Yixing, China. The results showed that the model reduced RMSE, MAE, MAPE, and MSE by 46.74%, 17.86%, 43.62%, and 67.84%, respectively, compared with the standard SVR model [38]. Tizro et al. used the ARIMA model to study nine water quality parameters of Hor Rood River [39]. Faruk established an ARIMA-ANN model with 108 months of water quality data from the Büyük Menderes River in Turkey from 1996–2004. The model consisted of two parts: firstly, the ARIMA model was used to model the linear part of the dataset, and then the artificial neural network was used to model the nonlinear part of the water quality series based on the fact that the ARIMA model could not solve the nonlinear part of the water quality series well. The results showed that the correlation coefficients between the predicted values of the hybrid model and the observed data for boron, dissolved oxygen, and water temperature were 0.902, 0.893, and 0.909, respectively [40]. Zhang et al. developed an ARIMA-RBFNN model to predict the total nitrogen (TN) and total phosphorus (TP) of Chagan Lake. The results showed that the RMSE values of this hybrid model were 0.139 and 0.036 for TN and TP indicators, respectively, which were improved compared to the ARIMA and RBFNN models [41]. Than et al. developed the LSTM-MA model, classified the water quality of Dongnai River from 2012 to 2019, predicted the water quality in the next two years, and proved that the LSTM-MA hybrid model has a quicker training time and more precise prediction than ARIMA, NAR, NAR-MA, and LSTM models [42]. Jian et al. first used an improved grey correlation (IGRA) to extract the features of water quality information and subsequently used LSTM to predict the water quality of Taihu Lake and Victoria Harbor; the results showed that the RMSE values of the model were 0.07 and 0.067, which were lower than those of the BPNN and ARIMA models, showing good performance [43]. Hameed et al. used an RBF neural network (RBFNN) and BPNN model to forecast and compare the water quality in Malaysia, respectively. The results showed that the RMSE of BPNN was 0.867 and the RMSE of RBFNN was 0.0194, and the RBF neural network outperformed the BP neural network model in terms of prediction accuracy [44].

In summary, although scholars have proposed a large number of research methods in the field of water quality prediction, the prediction results of traditional statistical models are not satisfactory for time series with large fluctuations and long-term trends. For example, the regression analysis model is relatively simple, but its requirements for statistical data are high, demanding a large sample and data with a good distribution pattern; the time series model has a relatively sound theoretical basis, but its prediction accuracy is poor; the grey prediction model is suitable for the case of small and discontinuous historical data, but the model is susceptible to the influence of unstable data, resulting in a large prediction error; the support vector machine is suitable for small samples, but it is more sensitive to the choice of parameters and kernel functions. In addition, traditional single deep learning models, such as back Propagation neural network (BPNN) and RBFNN, lack the memory ability for historical information. Moreover, most of the missing data filling methods cannot effectively handle the time-series information in the dataset, resulting in large errors in the estimation of missing values. Therefore, this study attempts to use an artificial neural network to fill in the missing information of water quality, comprehensively apply wavelet transform and the LSTM model to the field of water quality prediction, and compare the prediction results with ANN-LSTM, ARIMA, NARNN, CNN-LSTM, and DWT-CNN-LSTM models so as to prove the effectiveness of the proposed model.

This study is divided into the following parts: Section 2 introduces the artificial neural network model, wavelet transform, long-short term memory network model, and error evaluation index; Section 3 takes the Jinjiang River Basin as the research object, constructs the ANN-WT-LSTM model for water quality prediction, and compares the prediction results with the NAR neural network model, ANN-LSTM model, and ARIMA model; and the conclusion and research prospects are presented in Section 5.