ABSTRACT
Non-point source pollution(NSP) poses a great threat to water ecosystem health. The quantitative estimation of spatial distribution characteristics and accurate identification of critical source areas(CSAs) of NSP are the basis for its efficient and accurate control. The export coefficient model(ECM) has been widely used to assess NSP, but this model should be improved because it ignores pollutant loss in transport processes. In this study, the ECM, which refines the physical transport processes of pollutants through quantifying the loss rate of pollutants in runoff, sediment, and infiltration, was improved to assess NSP and identify CSAs. The simulation accuracy among Johnes ECM, frequent ECM, and improved ECM were analyzed, and the effects of the three models on the simulation results of both spatial distribution characteristics and CSAs were explored. The study showed that:â the simulation error of the improved ECM(-6.79%) was significantly lower than that of the Johnes ECM(50.44%) and the frequent ECM(-84.01%), and this improved ECM increased the simulation accuracy of NSP. â¡ The spatial distribution characteristics and CSAs of NSP obtained from Johnes, frequent, and improved ECMs were significantly different, and the simulation results of improved ECM were more consistent with the spatial characteristics of NSP in the watershed. The NSP was high in the southeast and low in the northwest of the basin, and the NSP mainly came from urban and cultivated land. ⢠Based on the improved ECM, the CSAs of NSP in the basin were mainly distributed in Changping, Shahe, Shigezhuang, the north of Wenquan, and the west of Malianwa Street, accounting for 6.71% of the area. This study can provide an effective tool and scientific reference for the assessment and control of NSP in data-limited regions.
ABSTRACT
Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile sensors to adaptively sense and monitor their environments. In this work, we investigate the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow field. We uncover connections between trajectories optimized over a finite-time horizon and finite-time Lyapunov exponents of the background flow, confirming that energy-efficient trajectories exploit invariant coherent structures in the flow. We demonstrate our findings on the unsteady double gyre vector field, which is a canonical model for chaotic mixing in the ocean. We present an exhaustive search through critical MPC parameters including the prediction horizon, maximum sensor actuation, and relative penalty on the accumulated state error and actuation effort. We find that even relatively short prediction horizons can often yield energy-efficient trajectories. We also explore these connections on a three-dimensional flow and ocean flow data from the Gulf of Mexico. These results are promising for the adaptive planning of energy-efficient trajectories for swarms of mobile sensors in distributed sensing and monitoring.
ABSTRACT
Non-point source pollution has become an important factor affecting the aquatic ecological environment and human health, and the analysis of spatial-temporal variations in non-point source pollution risks is an important prerequisite for pollution control. Based on land-use and land-cover data from 1980 to 2020, the potential non-point source pollution index (PNPI) model was applied in the upper Beiyun River Basin using different weighting methods. The results showed that:â The potential risk of non-point source pollution is high in the southeast and low in the northwest of the basin. Between 1980 and 2020, the total area of extremely high-risk and high-risk non-point source pollution regions showed a decreasing trend, and the main types of land use for extremely high-risk and high-risk regions gradually evolved from paddy fields, drylands, and orchards to urban and rural residential land; â¡ The weighting of the land use index determined by the mean-square deviation decision, entropy, coefficient of variation, and expert scoring methods was largest among the three PNPI indices, with average weightings of 0.46, 0.53, 0.45, and 0.48, respectively. However, the weightings for runoff and distance indices determined by different weighting methods were notably different, and the proportions of regions with different levels of non-point source pollution risk also varied; ⢠The exponential function method, which describes the relationship between source factors and transport factors by constructing the exponential functions of land use, runoff, and distance indices, provided results that are more consistent with the spatial distribution characteristics of non-point source pollution risk in the basin. The proportions of extremely low-risk and extremely high-risk regions are 54.22% and 6.23%, respectively. These results provide scientific reference for risk analysis and the control of non-point source pollution in this basin.
