Legacy phosphorus delays the accomplishment of expected phosphorus management object.

Legacy phosphorus (P) in watersheds continuously affects the water quality. The time lag between anthropogenic P input and algal bloom has made P dynamics prediction in aquatic ecosystems more challenging. Whether the legacy P in the Yangtze River Watershed (YRW) exceeds its storage threshold remains unknown, and the continuous impact of legacy P on the water quality has not been analyzed. This study aimed to evaluate variation trends (1970-2018) and influencing factors for accumulated P in the YRW under different economic development periods, quantitatively identify the watershed P storage threshold based on the two split line models and estimate the time required for the return of legacy P to the baseline level using an exponential decay process. The results showed that the P storage threshold of the YRW was surpassed due to intense anthropogenic activities, and the residual P still had an impact on aquatic ecosystems for a long time. The dissolved total P loadings may become the top priority to achieve better P management goals. The time lags for the legacy P restoration would require for about 1000 years to be exhausted. The legacy P in the YRW would continuously undermine the restoration efforts of the water quality. The combined effects of watershed P surplus reductions and depletion of residual P may become essential to better manage P in the future. We still need to strengthen our efforts to make soil legacy P more absorbed by crops and improve sewage treatment capacity to achieve sustainable development of YRW.

A comprehensive method of source apportionment and ecological risk assessment of soil heavy metals: A case study in Qingyuan city, China.

The study combined multiple models to provide a deeper understanding to soil heavy metal contamination and source information, which are essential for controlling pollution and reducing human health risks. In this study, the agricultural soils were collected from the Qingyuan City of China as an example. The multiple models (APCS/MLR, PMF, and GDM) were used to identify and quantitatively apportion the main sources of heavy metal pollution in the area. The results showed that Cu (56.4 %), Ni (70.9 %), B (44.5 %), and Cr (72.8 %) were associated with natural sources, such as soil parent material and soil-forming processes. However, Pb (41.2 %), Zn (61.8 %), Hg (67.0 %), and Cd (69.6 %) were associated with agricultural activities, atmospheric deposition, vehicle exhaust emissions, and vehicle tires, while Mo, Se, and Mn were possibly derived from natural sources, including rock weathering and soil parent materials. Additionally, the network of environmental analysis revealed that soil microbes are far more sensitive to soil heavy metal pollution than herbivores, vegetation, and carnivores. This study can serve as a guideline for reducing the ecological and health risks associated with heavy metals in soil by controlling their preferential sources. Environmental implication Combining multiple models is more effective approach to wide understanding of heavy metal contamination and source information, which is essential for controlling pollution and reducing human health risks. Based on multiple models (APCS/MLR, PMF, and GDM) and network environ analysis, a comprehensive method for apportioning soil heavy metal sources and assessing ecological risk had been provided. Further, the present study can be a guideline for reducing ecological and health risks by heavy metals in soil by controlling preferential sources.