Heavy metal elements in farmland soil can be absorbed by crops and endanger food security. To assess the risk of heavy metal elements in farmland soil to crops in Shaanxi Province, we collected 693 soil samples and analyzed the concentrations of nine heavy metals (As, Hg, Pb, Cd, Cr, Mn, Cu, Zn, and Ni). According to the National Standard (GB 15619-2018) of the People’s Republic of China, the proportions of soil sample points in which the concentration of heavy metals was higher than the risk screening value were 2.02% (Cd), 0.29% (Cr), 0.29% (Zn), 2.31% (Cu), 1.15% (Ni), and 0.14% (Pb). The proportions of areas in which the concentration of heavy metal was higher than the background value were as follows, from largest to smallest: Zn (53.20%) > Mn (49.86%) > Cd (29.51%) > Hg (26.77%) > As (26.58%) > Ni (14.95%) > Cu (13.90%) > Pb (6.49%) > Cr (1.40%). The assessment of the risk of heavy metal exposure (geo-accumulation index (I_geo), pollution load index (PLI), and potential ecological risk index (RI)) determined that Hg was the most concerning heavy metal in the farmland soil of Shaanxi Province. Moreover, 11.56% of these areas had Hg contamination, and they were mainly distributed in the western Guanzhong region. The farmland soil in the Guanzhong region was the most contaminated, followed by the southern Shaanxi region and then the northern Shaanxi region. The main sources of heavy metal contamination causing large-scale farmland soil pollution are agricultural production activities, transportation, and air pollution caused by coal combustion in Shaanxi Province. Therefore, sustainable strategies for the prevention and control of heavy metal pollution and agricultural development must be applied in different regions. Heavy metal pollution should be managed, and relevant policies should be created and enforced, such as the standardization of the use of qualified pesticides and fertilizers, improved treatment of livestock and poultry manure, development of the clean energy industry structure, and promotion of renewable energy vehicles. In terms of the high-quality development of agriculture, developing modern and local agriculture in different regions should be based on local geographical, climatic, and economic conditions.

The Chinese Loess Plateau has undertaken a large-scale “Grain for Green” project since 1999. Understanding how reforestation affects soil moisture is crucial for ecological construction and the region’s revegetation. In this study, soil sensors were installed to monitor the soil moisture content (SMC) and soil desiccation intensity in a 0–200 cm soil profile online during the growing season, with farmland as a control and Robinia (R.) pseudoacacia L., Pinus (P.) tabulaeformis Carr., Populus (P.) alba L., and Ulmus (U.) pumila L. were selected. The results showed that the SMC increased with soil depth, and the soil moisture storage (SMS) in the 0–200 cm soil profile was ranked as R. pseudoacacia L. (424.3 mm) < farmland (479.8 mm) < U. pumila L. (569.8 mm) < P. alba L. (583.9 mm) < P. tabulaeformis Carr. (589.8 mm). Secondly, the percentages of inefficient water and gravimetric water in soil moisture were ranked as R. pseudoacacia L. (63%) > farmland (49%) > U. pumila L. (43%) > P. alba L. (17%) > P. tabulaeformis Carr. (11%). The soil desiccation intensity of artificial forests was heavy in June, light in April and July, and no desiccation in the other months. Moderate desiccation was discovered in the 0–40 cm soil layer and mild desiccation occurred in the 40–60 cm soil layer. Additionally, the representative soil layer for SMS in farmland for P. tabulaeformis Carr., U. pumila L., and R. pseudoacacia L. was the 90 cm soil layer, and the SMS representative soil layer for P. alba L. was the 70 cm soil layer. In brief, an SMS deficit occurred after the conversion of the farmland to R. pseudoacacia L., but there was an SMS surplus after the conversion of the farmland to P. alba L., U. pumila L., and P. tabulaeformis Carr. This suggests that the artificial forest species could be optimized by introducing P. tabulaeformis Carr. instead of R. pseudoacacia L., and the degradation of R. pseudoacacia L. could be suppressed by the application of a nitrogen fertilizer. Our research demonstrated that soil moisture depletion patterns were closely related to artificial forest species, and attention should be paid to the vegetation restoration and maintenance of afforestation achievements in water-constrained arid regions in the future.

The wild giant panda habitat is inaccessible and far away from the main areas of human activity, so environmental pollutants entering the habitat are mainly the result of external migration and spread through the atmospheric advection and diffusion processes and particulate matter deposition. To research the variation, transmission route, chemical characteristics, and source of PM_2.5 in the habitat of wild giant pandas, we set up a PM_2.5 sampling point near the Shaanxi Foping National Nature Reserve (SFNNR), which is the area with the highest population density of wild giant pandas in the Qinling Mountains. The 12-month average concentration of PM_2.5 was 11.3 ± 7.9 μg/m³ from July 2021 to June 2022, and the 12-month average concentration did not exceed the limit value set in the standard. In the results of our analysis of element concentrations, As and Pb were much lower than the limit standard. Si, S, P, and Cl accounted for 99.60% of nonmetallic elements, while the proportion of the six metallic elements, Na, Mg, Al, K, Ca, and Fe, was 96.27%. According to the analysis results of enrichment factor (EF) and pollutant emission sources, there were four sources for the total elements in PM_2.5, which were mainly distributed in the areas around the reserve. These included dust, coal combustion, biomass burning, and traffic-related emissions, which contributed 55.10%, 24.78%, 11.91%, and 8.22% of the total element mass in PM_2.5, respectively. Additionally, Pb, Cu, Zn, As, Sc, Co, Ga, Mg, and, especially, Se were severely affected by human activities (coal burning, biomass burning, and traffic-related emissions). In the villages and towns around the wild giant panda habitat, the majority of energy for cooking and heating comes from coal and biomass burning, and older vehicles with high emissions are used more frequently. Therefore, to better protect the health of and reduce the impact of environmental pollution on wild giant pandas, we put forward relevant recommendations, including upgrading the energy structure of towns and villages near the habitat to increase the proportion of clean energy, such as photovoltaic power generation, natural gas, etc.; decreasing the combustion of coal and biomass; encouraging the upgrading of agricultural diesel machines and older vehicles used in these areas; and setting limits on vehicle emissions in areas surrounding the habitat.