Ma, HuaLiu, XiaotongLei, QiuliangLuo, JiafaDi, HongDu, XinzhongZhao, YingZhang, XuejunLiu, Hongbin2024-11-062024-11-052024-112024-11-04https://hdl.handle.net/10182/17809This study employed the Net Anthropogenic Phosphorus Inputs (NAPI) model to assess the impact of human activities on phosphorus input in a watershed, analyzing county-level statistical data and NAPI model parameters from 1991 to 2020. The Monte Carlo method was used for a quantitative analysis of the model parameters’ effects on each NAPI component and the overall simulation results. The sensitivity index method identified each component’s sensitive parameters. The study found that the lowest NAPI value was 454 kg/(km²·a) in 1991 and the highest was 1336 kg/(km²·a) in 2003. NAPI in Ningxia showed an overall upward trend from 1991 to 1999, a slight decrease from 1999 to 2003, and a slight increase from 2003 to 2020, with fertilizer being the main contributing factor, accounting for 77.4% of the total input. On a spatial scale, NAPI in Ningxia was significantly correlated with land use patterns, showing higher values in the northern and southern regions compared to the central part. The NAPI values derived from Monte Carlo simulations with appropriate parameters ranged from −24.83% to 31.49%. The study highlighted the net food and feed imports component as having the highest uncertainty, impacting simulation results within a range of −23.89% to 53.98%. It was observed that the larger a component’s proportion in the NAPI model, the more sensitive its parameters, with the phosphorus fertilizer (Pfer) component’s parameters being notably more sensitive than those of the food/feed phosphorus input and the non-food phosphorus input (Pnf) components. These findings can inform phosphorus pollution control policies in Northwest China, while the selection of sensitive parameters provides a useful reference for future NAPI research in other regions.13 pagesen© 2024 The authors. Licensee MDPI, Basel, Switzerland.model uncertaintyMonte Carlo methodnet anthropogenic phosphorus inputs (NAPIs)non-point pollutionsensitivity analysisspatio-temporal distributionJournal Article10.3390/w162231602073-4441ANZSRC::370704 Surface water hydrologyANZSRC::400411 Water treatment processesANZSRC::400513 Water resources engineeringANZSRC::401101 Air pollution modelling and controlANZSRC::410304 Environmental biotechnology diagnostics (incl. biosensors)ANZSRC::300410 Crop and pasture waste water usehttps://creativecommons.org/licenses/by/4.0/Attribution