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Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir.

The deterioration of river water quality as a result of agricultural activities in the Tokachi River basin is concerning. However, it is not easy to reduce fertilization or to decrease agricultural land area while maintaining crop yields as an effort of water quality conservation. We investigated the total nitrogen (T-N) and the total phosphorus (T-P) concentrations and the N:P ratio (atomic and mass) in the Tokachi River basin during 2007-2011 under base flow conditions. The proportion of agricultural land and the Spatial Continuity (SC) of agricultural land were analyzed to examine effective land use management for water quality conservation. T-P showed comparatively low concentrations (0.03-0.26 mg/L) because Andosols in the agricultural land, which have high phosphate absorption coefficients. While the T-N concentrations (0.4-6.6 mg/L) and the N:P mass ratio (6.3-39) tended to high values. T-N concentration and the N:P ratio had positive correlations with the proportion and the SC of agricultural land. In particular, the correlation coefficients of the relationships with the SC of agricultural land were higher than those of the proportion of agricultural land. In watersheds having more than 40 % of agricultural land, it was confirmed that there were some tributaries which had a high proportion of agricultural land but had low SC of agricultural land. Besides, the results suggested that the nitrogen runoff could be suppressed by setting the connectivity of agricultural land with certain scale without reducing the proportion of agricultural land in the Tokachi River basin significantly. [ABSTRACT FROM AUTHOR]

Total nitrogen (N) concentration (N%), N derived from 15N-fertilizer (Ndff%), amount of 15N uptake (ANU) in main organs (leaves, shoots, roots, fruits), and N use efficiency (NUE) were measured to assess N absorption, distribution, and utilization of Red Fuji apple trees across two years using a 15N-enriched urea method. The N% in leaves and fruits decreased while those in shoots and roots increased in both years. The Ndff% and ANU in roots were highest at fruitlet stage than those in leaves, shoots and fruits at mature stages. This suggested that the absorbed 15N by roots was redistributed to new organs. The N% was lower while Ndff% and ANU were greater in 2008 than 2007. The most 15N absorbed was accumulated in the trunks, main and coarse roots and smaller in the fine roots and biennial branches. The NUE in 2007 and 2008 reached 9.9% and 12.2% respectively. [ABSTRACT FROM AUTHOR]

To understand the load characteristics and related mechanisms of surface runoff on two management types of bamboo forests (bamboo timber forest and bamboo shoot forest) slopes (gradient 20) in Zhejiang Province, this study measured the runoff volume, sediment yield, its total nitrogen (TN) and total phosphorus (TP) concentrations of runoff under six artificial simulated rainfall intensity (31. 8-114. 0 mm•h-1). In bamboo timber forest, the total runoff volume and runoff coefficient were higher, but the runoff sediment content and the total sediment yield were far lower, as compared with those in bamboo shoot forest. The runoff TN concentration in bamboo shoot forest decreased with increasing rainfall intensity. Under the same rainfall intensity, the runoff TN concentration in bamboo shoot forest was 5-6 times of that in bamboo timber forest. The runoff TP concentration was higher in bamboo timber forest than in bamboo shoot forest, but the TP loss from the sediment runoff in bamboo shoot forest was hundreds times of that in bamboo timber forest. During the processes of the TN and TP losses from the sediment runoff, the TN and TP concentrations at the prophase of runoff yield played a cardinal role, while the runoff volume and sediment yield at the anaphase played a decisive role. [ABSTRACT FROM AUTHOR]

The article discusses a study on total soil carbon and nitrogen stocks in reclaimed minesoils in southeastern Ohio. It is noted that the amount of carbon in the soil is increased by planting of trees and grasses and reclamation of disturbed minesoils. Reclaimed minesoils used in the study include R78G, which was reclaimed in 1978, and R825GT, reclaimed in 1982 and planted with grass and trees. the study found that soil carbon is increased in disturbed minesoils that were reclaimed. It resulted to better soil quality.

• Total nitrogen content is estimated by an adaptive filtering-based soft sensor; • A moving-horizon estimation algorithm is used to deal with the model mismatch; • Offline measurements are used to correct soft sensor results; • Offline measurement frequency is determined to balance between accuracy and cost. The aquaponic system can satisfy the different needs of aquatic products and plants by utilizing nutrient flow to improve the economic benefit. Nitrogen is a key nutrient element in such process. High nitrogen concentration can worsen water quality, which may further result in mass death of typical fish and/or shellfish. Therefore, it is particularly important to monitor the nitrogen concentration to manage aquatic products and plants growth in aquaponic systems. Sensors for measuring nitrogen concentration are commercially available, but they are often expensive and unreliable in service. At the same time, the research on the soft sensor of nitrogen concentration is very limited. Therefore, this paper proposes a new adaptive filtering-based soft sensor method for real-time estimation of total nitrogen concentration. This method provides accurate prediction by integrating mechanistic model, online measurements (e.g. fish biomass, temperature) and infrequent offline measurement of total nitrogen. A moving-horizon estimation (MHE) algorithm is used for joint state and parameter estimation, thus allowing correction of mismatch between model parameters and the real process. Furthermore, the appropriate frequency of offline nitrogen measurement is determined to balance between the soft sensor accuracy and cost. Through computer simulation study of an aquaponic system, the proposed approach is effective to provide promising real-time prediction performance. By applying the correction method, the RMSE (root-mean-square error) of nitrogen concentration estimation is reduced by 31.86% on average compared to the simulated practical situation. In conclusion, the proposed soft sensor method could provide useful monitoring of the total nitrogen in aquaponics, and such information could be used for optimizing the operations. [ABSTRACT FROM AUTHOR]

• Twelve machine learning algorithms are compared to construct a retrieval model. • The TN concentration is retrieved using the UAV Hyper Spectral Remote Sensing data. • Spatial distribution of TN concentration is retrieved with high resolution. • Providing scientific references for water quality monitoring and management. Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir. [ABSTRACT FROM AUTHOR]