Study on nitrogen removal from rice paddy field drainage by interaction of plant species and hydraulic conditions in eco-ditches

With the continuous development of China’s modern economy and agricultural society, the discharge of rural sewage has been recognized as a major threat to the safety of the rural ecological environment. This study discussed the purification efficiency of a tower-shaped integrated ecological purification device (TIEPD)—consisting of a measuring tank, detention tank and three-stage purification unit—towards various common pollutants in rural areas during operation and tested the stability and efficiency of the TIEPD under different rural life events (fair activity days and nonfair activity days) and different precipitation intensities (light rain, moderate rain and heavy rain). The results showed that the average removal efficiencies of the TIEPD towards chemical oxygen demand, ammonia nitrogen, total nitrogen and total phosphorus were 69%, 67%, 54% and 73%, respectively. The average effluent concentration of each pollutant can meet the standard of the discharge of pollutants in China. The system exhibited good stability in removing pollutants and good ecological and economic benefits. This study provides the treatment of domestic sewage in the upper reaches of the Yangtze River and in mountainous areas of China and strengthens the prevention and control of rural nonpoint source pollution.

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“…The removal efficiencies of COD, AN, TN and TP were calculated using the following equation [ 34 ]:

where R is the removal efficiency, %, and

and

are the concentrations of COD, AN, TN and TP at the inlet and outlet sampling points, respectively, mg/L.…”

Section: Methodsmentioning

confidence: 99%

Performance of a Tower-Shaped Integrated Ecological Purification Device for Pollutants Removal from Domestic Sewage in Rural Areas

Yan

Zhang

Wang

et al. 2022

IJERPH

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“…However, the average utilization rate of N fertilizer is only 35% [1]. Increasing N applications can increase rice yield, but excessive N application causes a series of environmental problems, such as greenhouse gas emissions, soil acidification, and water pollution [2,3]. Therefore, the scientific and rational application of N fertilizer is of great significance for green and high-yielding rice and for improving N-use efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the objectives of this study were to: (1) assess the potential of rice canopy image parameters to monitor rice N status; (2) compare and analyze models based on image and spectral parameters to estimate rice N status; and (3) determine the accuracy, advantages, and disadvantages of the models constructed using different image and spectral parameters. The overall aim of our research was to provide a reference for the fast, inexpensive, and non-destructive monitoring of the N status of rice crops.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Nitrogen Nutrition Status of Rice Plants Using Spectral and Image Technologies

Chun

Liu

et al. 2020

Preprint

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Background: We aimed to investigate methods to estimate the nitrogen (N) nutrition status of rice plants using data obtained using a digital camera and a spectroradiometer. The overall aim was to compare the advantages and potential of image technology and spectral technology to monitor rice N indexes accurately, inexpensively, and in real time to optimize fertilization strategies. We conducted field trials of rice plants grown with different levels of N fertilizer in 2018 to 2019. Spectral information and images of the rice canopy were obtained at the jointing stage and the booting stage. Various image and spectral characteristic parameters were selected to construct models to estimate rice N status. Results: The determination coefficients of the models constructed using the ratio vegetation index (RVI[800,720]) and cover canopy (CC) as dependent variables were highly significant. The jointing stage was the best observation period. Among the models using spectral parameters, those constructed using RVI[800,720] to estimate rice N indexes had the highest coefficient of determination (R2) values (0.79, 0.60, and 0.61 for the models to estimate leaf area index(LAI), aboveground biomass(AGB), and plant N accumulation(PNA), respectively) (P < 0.01). Among the models using image data, those using CC to predict rice N indexes showed the highest R2 values (0.76, 0.66, and 0.73 for the models to estimate LAI, AGB, and PNA, respectively) (P < 0.01). The model using the spectral parameter RVI[800,720] had a good fit and stability in estimating leaf area index (LAI; R2 = 0.7989, root mean square error (RMSE) = 0.4427, relative RMSE (RRMSE) = 11.45), and the model using the image parameter CC had a good fit in predicting plant nitrogen accumulation (PNA; R2 = 0.6768, RMSE = 11.6925 g·m-2, RRMSE = 121.78). Conclusions: Spectral and image parameters can be used as technical parameters to estimate biomass. The spectral parameter RVI[800,720] can be used to accurately estimate LAI, and the image parameter CC can be used to accurately estimate PNA, although its stability is poor. These technical parameters can be used for fast and non-destructive monitoring of rice plant biomass.

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“…However, the average utilization rate of N fertilizer is only 35% [1]. Increasing N applications can increase rice yield, but excessive N application causes a series of environmental problems, such as greenhouse gas emissions, soil acidification, and water pollution [2,3]. In addition to nitrogen management, rice breeding also plays an important role in the process of increasing rice yield.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the objectives of this study were to: (1) assess the potential of rice canopy image parameters to monitor hybrid rice and conventional rice N status; (2) compare and analyze models based on image and spectral parameters to estimate rice N status; and (3) determine the accuracy, advantages, and disadvantages of the models constructed using different image and spectral parameters. The overall aim of our research was to provide a reference for the fast, inexpensive, and non-destructive monitoring of the N status of rice crops.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the nitrogen nutrition status of rice plants using spectral and image technologies

Chun

Zhong

et al. 2020

Preprint

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Background: We aimed to investigate methods to estimate the nitrogen (N) nutrition status of rice plants using data obtained using a digital camera and a spectroradiometer. The overall aim was to compare the advantages and potential of image technology and spectral technology to monitor rice N indexes accurately, inexpensively, and in real time to optimize fertilization strategies. Realizing the technical selection of definite spectrum or image diagnosis aiming at different rice nitrogen nutrition indexes. We conducted field trials of rice plants grown with different levels of N fertilizer in 2018 to 2019. Spectral information and images of the rice canopy were obtained, various image and spectral characteristic parameters were selected to construct models to estimate rice N status.Results: The determination coefficients of the models constructed using the ratio vegetation index (RVI[800,550]) and cover canopy (CC) as dependent variables were most significant. Among the models using spectral parameters, those constructed using RVI[800,550] to estimate rice N indexes had the obviously coefficient of determination (R2) values, which were 0.69, 0.58, and 0.65 for the models to estimate leaf area index(LAI), aboveground biomass(AGB), and plant N accumulation(PNA). As for image parameter, those using CC to predict rice N indexes showed the highest R2 values (0.76, 0.65, and 0.71 for the models to estimate LAI, AGB, and PNA, respectively) (P < 0.01). The model using the spectral parameter RVI[800,550] had a good fit and stability in estimating plant nitrogen accumulation (R2 = 0.65, root mean square error (RMSE) = 1.35 g·m-2, relative RMSE (RRMSE) = 14.05%), and the model using the image parameter CC had a good fit in predicting leaf area index (R2 = 0.76, RMSE = 0.28, RRMSE = 7.26%) and aboveground biomass (R2 = 0.65, RMSE = 22.03 g·m-2, RRMSE = 7.52%). Different detection technology should be adopted for different rice varieties and rice N nutrition indexes. Conclusions: Spectral and image parameters can be used as technical parameters to estimate rice N status. The spectral parameter RVI[800,550] can be used to accurately estimate plant nitrogen accumulation, and the image parameter CC can be used to accurately estimate leaf area index and aboveground biomass.

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Study on nitrogen removal from rice paddy field drainage by interaction of plant species and hydraulic conditions in eco-ditches

Cited by 14 publications

References 23 publications

Performance of a Tower-Shaped Integrated Ecological Purification Device for Pollutants Removal from Domestic Sewage in Rural Areas

Performance of a Tower-Shaped Integrated Ecological Purification Device for Pollutants Removal from Domestic Sewage in Rural Areas

Monitoring the Nitrogen Nutrition Status of Rice Plants Using Spectral and Image Technologies

Monitoring the nitrogen nutrition status of rice plants using spectral and image technologies

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