With the acceleration of urban industrialization and urbanization processes, land use patterns have undergone significant changes. In Northern Anhui, a typical region where agriculture and industry intersect, land use modifications exert a particularly significant influence on carbon sequestration capacities. However, research into how land use transformations affect carbon storage within this region is still lacking. Exploring the relationship between land use changes and carbon storage in depth can not only provide reliable data for scientific management and policy formulation but also offer important references for achieving “dual carbon” goals. This study combines the PLUS model and the InVEST model to comprehensively evaluate and forecast land utilization and carbon sequestration shifts across six cities in Northern Anhui from 2000 to 2030. By constructing four scenarios—natural development, farmland protection, economic development, and sustainable development—the potential impacts of different land use patterns on carbon sequestration capacity were simulated. The findings suggest that between 2000 and 2020, the farmland area in Northern Anhui continuously decreased, while the construction land area significantly increased; carbon storage decreased by 8.53 million tons between 2000 and 2020, which was primarily caused by the transformation of agricultural land into areas for construction. Except for the farmland protection scenario, carbon storage decreased to varying degrees in all other scenarios, with the sustainable development scenario showing the most notable mitigation in carbon storage reduction. As Northern Anhui undergoes rapid development, the significant loss of farmland caused by industrialization and urbanization is the main reason for the decline in carbon sequestration capacity.

Riverine heavy metal (HM) pollution, a critical global environmental issue, severely affects water quality, ecosystem health, and human well-being. The Huaihe River, once among China’s most polluted, has seen water quality improvements due to strict pollution controls, yet the extent of HM pollution reduction remains uncertain. Here, we investigated the distribution, sources, and potential ecological and health risks of nine typical HMs (Cr, Mn, Ni, Cu, Zn, As, Cd, Pb, and Hg) in surface water and sediment in the Anhui section of the river. Seasonal variations in HM concentrations were observed, with most values below drinking water safety limits, except for Mn and Cd at specific sites and seasons. Indices including the HPI, HEI, HQ, and HI showed low contamination and health risks, yet children are more vulnerable to non-carcinogenic hazards, notably from Cd and As. Sediment HMs trends decreased as Mn > Zn > Cr > Pb > Ni > Cu > As > Cd > Hg, with moderate pollution from Cd, Mn, and Pb based on CF, EF, and I_geo assessments. PLI and NPI suggested moderate ecological risks in midstream areas due to HM accumulation. The correlation analysis and PCA revealed that HMs in uncontaminated sediments were mainly of geogenic origin, while contaminated sediments were largely influenced by anthropogenic activities, including agricultural runoff, industrial waste, and domestic sewage discharge. Overall, our findings highlight that control of anthropogenic activities within the Huaihe River basin is essential for reducing HM pollution in the river.

Global urbanization has caused widespread ecological degradation, yet habitat quality in agricultural plains remains understudied. This study addresses this gap by assessing and predicting land use and habitat quality changes in the Northern Anhui Plain from 2000 to 2030 using the PLUS and InVEST models under four scenarios (natural development, farmland protection, economic development, and sustainable development). The optimal parameters-based geographical detector (OPGD) was employed to identify driving factors. Results show that farmland continuously shrank while built-up land expanded, and habitat quality remained low and declined over time, with low-grade areas expanding. All four 2030 scenarios exhibited declines, with the farmland protection scenario yielding the highest habitat quality and the economic development scenario the lowest. The optimal spatial scale was 4 km, and discretization algorithms and break numbers significantly influenced driver analysis. Locational factors had relatively higher explanatory power, though the overall q-statistic was moderately low, indicating limited single-factor explanation. The study reveals the spatiotemporal dynamics and driving mechanisms of habitat quality in this farmland-dominated plain, providing useful insights for spatial planning and policy-making to support sustainable development in agricultural regions.