Efficient smartphone-based measurement of phosphorus in water.

Agricultural runoff is one of the main sources of excess phosphorus (P) in different water bodies, subsequently leading to eutrophication and harmful algal blooms. To effectively monitor P levels in water, there is a need for simple measurement tools and extensive public involvement to enable regular and widespread sampling. Several smartphone-based P measurement methods have been reported, which extract red-green-blue (RGB) values from colorimetric reactions to build statistical regression models for P quantification. However, these methods typically require meticulous light conditions, involve initial equipment investment, and have undergone limited testing for large-scale applications. To overcome these limitations, this study developed a smartphone-based, equipment-free and facile P colorimetric analysis method. Following the standard procedure of the ascorbic acid approach, colorimetric reactions were captured by a smartphone camera, and RGB values were extracted using Python code for modeling. Different indoor light conditions, phone types, containers, and types of water samples were examined, resulting in a collection of 1922 images. The best regression model, employing random forest with RGB values and container types as inputs, achieved an R2 of 0.97 and an RMSE of 0.051 for P concentrations ranging from 0.01 to 1.0 mg P/L. Additionally, the optimal classification model could estimate the level of P below 0.1 mg P/L with an accuracy of 95.2 (or 77.4 % for <0.05 mg P/L). The strong performance of the developed models, which are also available freely online, suggests an easy and effective tool for more frequent P measurement and greater public involvement.

Phosphate removal by low-cost industrial byproduct iron shavings: Efficacy and longevity.

Iron shavings (IS) are low-cost industrial byproducts that show great potential in removing phosphorus (P) from contaminated water. This work investigates the effectiveness of IS for P (PO4-P) removal and emphasizes its pretreatment and longevity. A 4-d pretreatment of IS with 2.5 % NaCl resulted in a significant increase in P adsorption capacity, from approximately 1.0 to 2.5 mg/g. In column tests, the P removal efficiency remained above 60 % over 60 d, with a capacity of 4.1 mg P/g. Longevity tests involved seven adsorption-regeneration cycles, with an effective IS regeneration by 1 N NaOH and neutralization by HCl solution (pH=2), and the P adsorption capacity only slightly decreased from 2.14 to 1.75 mg P/g. To significantly improve the IS regeneration operation, we employed induction heating and compared an intermittent 10-s induction heating with an isothermal hot NaOH (85 ℃) treatment in 10-min desorption tests (95.3 % versus 56.6 % regeneration). We further found that IH completely regenerated IS in 5 min with 100 s of IH application, but 30 min were needed for hot NaOH (85 ℃) treatment. SEM/EDX, XRD, and XPS tests were conducted to track the changes in the morphology, crystallinity, and surface oxidation products of IS in the cycle tests. Notably, IS surface changed from coarse to smooth with fewer reactive sites and a higher conversion of amorphous Fe oxides to more crystalline Fe3O4, resulting in lower reactivity and fewer exposed Fe0 sites over multiple cycles. All of these mechanisms contributed to the deterioration in P removal capacity. Overall, this study provides a solid foundation for applying low-cost IS in effectively removing P from agricultural runoff.

Short-term Lake Erie algal bloom prediction by classification and regression models.

The recent outbreaks of harmful algal blooms in the western Lake Erie Basin (WLEB) have drawn tremendous attention to bloom prediction for better control and management. Many weekly to annual bloom prediction models have been reported, but they only employ small datasets, have limited types of input features, build linear regression or probabilistic models, or require complex process-based computations. To address these limitations, we conducted a comprehensive literature review, complied a large dataset containing chlorophyll-a index (from 2002 to 2019) as the output and a novel combination of riverine (the Maumee & Detroit Rivers) and meteorological (WLEB) features as the input, and built machine learning-based classification and regression models for 10-d scale bloom predictions. By analyzing the feature importance, we identified 8 most important features for the HAB control, including nitrogen loads, time, water levels, soluble reactive phosphorus load, and solar irradiance. Here, both long- and short-term nitrogen loads were for the first time considered in HAB models for Lake Erie. Based on these features, the 2-, 3-, and 4-level random forest classification models achieved an accuracy of 89.6%, 77.0%, and 66.7%, respectively, and the regression model achieved an R2 value of 0.69. In addition, long-short term memory (LSTM) was implemented to predict temporal trends of four short-term features (N, solar irradiance, and two water levels) and achieved the Nash-Sutcliffe efficiency of 0.12-0.97. Feeding the LSTM model predictions for these features into the 2-level classification model reached an accuracy of 86.0% for predicting the HABs in 2017-2018, suggesting that we can provide short-term HAB forecasts even when the feature values are not available.

Dual-Arm Robot Trajectory Planning Based on Deep Reinforcement Learning under Complex Environment.

In this article, the trajectory planning of the two manipulators of the dual-arm robot is studied to approach the patient in a complex environment with deep reinforcement learning algorithms. The shape of the human body and bed is complex which may lead to the collision between the human and the robot. Because the sparse reward the robot obtains from the environment may not support the robot to accomplish the task, a neural network is trained to control the manipulators of the robot to prepare to hold the patient up by using a proximal policy optimization algorithm with a continuous reward function. Firstly, considering the realistic scene, the 3D simulation environment is built to conduct the research. Secondly, inspired by the idea of the artificial potential field, a new reward and punishment function was proposed to help the robot obtain enough rewards to explore the environment. The function is consisting of four parts which include the reward guidance function, collision detection, obstacle avoidance function, and time function. Where the reward guidance function is used to guide the robot to approach the targets to hold the patient, the collision detection and obstacle avoidance function are complementary to each other and are used to avoid obstacles, and the time function is used to reduce the number of training episode. Finally, after the robot is trained to reach the targets, the training results are analyzed. Compared with the DDPG algorithm, the PPO algorithm reduces about 4 million steps for training to converge. Moreover, compared with the other reward and punishment functions, the function used in this paper will obtain many more rewards at the same training time. Apart from that, it will take much less time to converge, and the episode length will be shorter; so, the advantage of the algorithm used in this paper is verified.

The efficacy of pH-dependent leaching tests to provide a reasonable estimate of post-carbonation leaching.

pH is a vital factor related to the heavy metal leaching from wastes. Over time, waste materials may be naturally weathered in the presence of water and carbon dioxide, reducing their pH and altering their mineralogy. Here we evaluate whether conducting a pH-dependent leaching test on wastes expected to carbonate sufficiently reflects the leaching of these wastes upon carbonation. Certain elements, such as Al and Sb, exhibited different leaching trends for carbonated and un-carbonated samples of two different waste materials. XRD results observed different mineral phases as a result of carbonation in incineration bottom ash. The application of pH-dependent leaching tests on fresh waste samples (at neutral pH values) were found to potentially mischaracterize leaching from carbonated waste samples at similar pH values for some elements and waste materials.