To investigate the status of heavy metal pollution and the associated ecological and health risks in farmland surrounding the Xicheng lead and zinc mining area, we collected soil samples (0–20 cm) and measured the level of As, Ni, Cu, Zn, Cd, Pb, Hg, and Cr. The characteristics of soil heavy metals, pollution levels, and ecological, and health risks were quantitatively assessed using Monte Carlo simulation in conjunction with the geo-accumulation index (Igeo), pollution index (Pi), ecological risk index (Er), and human health risk assessment model. The results indicated that the average concentrations of As, Ni, Cu, Zn, Cd, Pb, Hg, and Cr in the soil of the study area were 26.92, 39.46, 31.18, 340.23, 1.13, 184.61, 0.34, and 71.15 mg·kg⁻¹, respectively. These values were significantly higher than the soil background levels reported for Gansu Province. The average Igeo for Hg was 3.27, and the average Er was 679.13, indicating that Hg had the highest accumulation and posed the greatest ecological risk in the study area. The average single-factor pollution index for Cd was 2.52, marking it as the heavy metal with the highest pollution level. The cumulative probability of health risk derived from Monte Carlo simulation indicates that the non-carcinogenic hazard indices for adults and children were 2.79 × 10⁻¹ and 1.67, respectively, with 94.82% of children exceeding the non-carcinogenic risk threshold. The carcinogenic risk indices (TCR) for adults and children were 8.09 × 10⁻⁵ and 4.60 × 10⁻⁴, respectively, and 100% of the samples exceeded the TCR standard for children. As and Cd were identified as the primary contributors to both carcinogenic and non-carcinogenic risks. The findings enhance our understanding of heavy metal contamination in farmland soils and establish an empirical framework for developing targeted remediation approaches and sustainable land management practices.

The summer monsoon transition zone (SMTZ) in China represents an unusual land type with an agro-pasture ecotone, and it is a climate-sensitive region. Changes in climate aridity and changes in meteorological drought are mutually related yet fundamentally different. In this study, potential evapotranspiration (E_TO) is calculated using Penman–Monteith, based on China’s national meteorological stations data from 1961 to 2013. An E_TO-based climate aridity index (I_AI) and E_TO-based standardized precipitation evapotranspiration index (SPEI) are used as the metrics for climate aridity and meteorological drought, respectively. The result shows a significant difference between climate aridity and meteorological drought in the SMTZ, compared with the monsoon and non-monsoon zone. This difference varies on different time scales (1–48 months), and the greatest differences between I_AI and SPEI are on seasonal and monthly scales (1–12 months), but lower at longer time scales (>12 months). The first reason for the difference is the desynchronicity of meteorological drought and the background climate. After the background climate becomes a relatively arid state (such as 0.96 < I_AI < 1) from a semi-arid state (0.50 < I_AI < 0.80), the continued arid state with weak I_AI fluctuations eventually results in increasingly severe meteorological droughts, or the recurrence of equally severe droughts with drastic reduction. Consequently, the onset of the most severe climate aridity is two to seven months (mostly three to four months) ahead of the onset of the most severe drought events, until the climate returns to a semi-arid state. Second, climate aridity represents the average state of the background climate over a long time period and changes gently, while meteorological droughts are stochastic climate events and change drastically. These findings indicate that I_AI can serve as a predictor of the onset of meteorological drought events, especially in the SMTZ, but it fails to characterize the progression of meteorological drought events well. Therefore, this result is of great significance for drought prediction and early warning.

This study employs land use and energy consumption data from Gansu Province spanning the years 2005 to 2020 and utilizes models to estimate carbon emissions and the corresponding carbon compensation values. The research calculated the carbon emissions and compensation for various administrative regions over different time periods, revealing the following insights: (1) There is a notable increasing trend in net carbon emissions due to land-use changes in Gansu Province, characterized by an initial swift rise, followed by a more gradual growth pattern. Construction land is identified as the primary contributor to carbon emissions, increasing from 26 million tons in 2005 to 55.3 million tons in 2020, which is an increase of 1.80 times; meanwhile, forested areas, as significant carbon sinks, show a slight increase in carbon absorption from 2.33 million tons in 2005 to 2.35 million tons in 2020. (2) The municipalities with high net carbon emissions are predominantly Lanzhou, Qingyang City, and Jiuquan City, which are marked by high levels of carbon emissions and low compensation rates. In contrast, regions with lower net carbon emissions are mainly found in the Gannan Tibetan Autonomous Prefecture, where emissions are minimal and compensation rates are relatively high. A similarity in the spatial distribution patterns of both net carbon emission intensity and total net carbon emissions is observed among these cities. Alterations in land use have a significant impact on regional carbon emissions. Investigating the spatiotemporal variations of land-use change and carbon compensation in Gansu Province is essential for comprehending the dynamics of regional carbon emissions, developing effective emission reduction strategies, and fostering low-carbon development.