Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework.

Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a "backward pollution source tracing" (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.

Assessing thermodynamic parameter sensitivity for simulating temperature responses of soil nitrification.

Soil nitrification responses to temperature have major implications for the global nitrogen cycle. Temperature sensitivity of soil nitrification has been modeled using several mathematical models, yet the extent to which model-generated thermodynamic parameters are accurate and sensitive in describing temperature sensitivity is unclear. In this study, we performed global sensitivity analysis to identify the key thermodynamic parameters that are most influential when simulating the temperature response of the soil nitrification potential (NP) across two different temperature gradients (4-40 °C and 20-45 °C) which are imposed upon sixteen different soils with square root growth (SQRT) and macromolecular rate theory (MMRT) models. We found that two thermodynamic parameters stand out as moderately to highly sensitive, and are uniquely identifiable in each model, regardless of the temperature range. The minimum and maximum measured temperatures seem to have no impact on the list of sensitive parameters but do influence the parameter ranges, especially for the SQRT model. However, parameters that control the minimum temperature and curvature of the NP response curve (Tmin and ΔC‡P) were found to have little to no sensitivity to SQRT and MMRT model outputs, respectively. We show that the parameter sensitivity and range of measured temperatures influence the complementary model's ability to describe the temperature sensitivity of soil nitrification. Our proposed framework enhances the accurate interpretation of existing thermodynamic parameters that explain the temperature sensitivity of soil biochemical processes, and provides methodological recommendations for future temperature sensitivity studies.

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC.

This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH3-N and NO3-N. Results indicate that the integrated FME-GLUE-based model, with good Nash-Sutcliffe coefficients (0.53-0.69) and correlation coefficients (0.76-0.83), successfully simulates the concentrations of ON-N, NH3-N and NO3-N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH3-N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO3-N simulation, which was measured using global sensitivity.