This study explored the spatial pattern of heavy metals in Beijing agricultural soils using Moran’s I statistic of spatial autocorrelation. The global Moran’s I result showed that the spatial dependence of Cr, Ni, Zn, and Hg changed with different spatial weight matrixes, and they had significant and positive global spatial correlations based on distance weight. The spatial dependence of the four metals was scale-dependent on distance, but these scale effects existed within a threshold distance of 13 km, 32 km, 50 km, and 29 km, respectively for Cr, Ni, Zn, and Hg. The maximal spatial positive correlation range was 57 km, 70 km, 57 km, and 55 km for Cr, Ni, Zn, and Hg, respectively and these were not affected by sampling density. Local spatial autocorrelation analysis detected the locations of spatial clusters and spatial outliers and revealed that the pollution of these four metals occurred in significant High-high spatial clusters, Low-high, or even High-low spatial outliers. Thus, three major areas were identified and should be receiving more attention: the first was the northeast region of Beijing, where Cr, Zn, Ni, and Hg had significant increases. The second was the southeast region of Beijing where wastewater irrigation had strongly changed the content of metals, particularly of Cr and Zn, in soils. The third area was the urban fringe around city, where Hg showed a significant increase.

Understanding the effects of intensive agricultural land use activities on water resources is essential for natural resource management and environmental improvement. In this paper, multi-scale nested watersheds were delineated and the relationships between two representative water quality indexes and agricultural land use intensity were assessed and quantified for the year 2000 using multi-scale regression analysis. The results show that the log-transformed nitrate-nitrogen (NO₃-N) index exhibited a relationship with chemical fertilizer input intensity and several natural factors, including soil loss, rainfall and sunlight at the first order watershed scale, while permanganate index (COD_Mn) had a positive relationship with another two input intensities of pesticides and agricultural plastic mulch and organic manure at the fifth order watershed scale. The first order watershed and the fifth order watershed were considered as the watershed adaptive response units for NO₃-N and COD_Mn, respectively. The adjustment of agricultural input and its intensity may be carried out inside the individual watershed adaptive response unit. The multiple linear regression model demonstrated the cause-and-effect relationship between agricultural land use intensity and stream water quality at multiple scales, which is an important factor for the maintenance of stream water quality.

Arbuscular mycorrhizal inoculation can promote plant growth, but specific research on the difference in the symbiosis effect of arbuscular mycorrhizal fungi and plant combination is not yet in-depth. Therefore, this study selected Medicago sativa L., Bromus inermis Leyss, and Festuca arundinacea Schreb., which were commonly used for restoring degraded land in China to inoculate with three AMF separately, to explore the effects of different AMF inoculation on the growth performance and nutrient absorption of different plants and to provide a scientific basis for the research and development of the combination of mycorrhiza and plants. We set up four treatments with inoculation Entrophospora etunicata (EE), Funneliformis mosseae (FM), Rhizophagus intraradices (RI), and non-inoculation. The main research findings are as follows: the three AMF formed a good symbiotic relationship with the three grassland plants, with RI and FM having more significant inoculation effects on plant height, biomass, and tiller number. Compared with C, the aboveground biomass of Medicago sativa L., Bromus inermis Leyss, and Festuca arundinacea Schreb. inoculated with AMF increased by 101.30–174.29%, 51.67–74.14%, and 110.67–174.67%. AMF inoculation enhanced the plant uptake of N, P, and K, and plant P and K contents were significantly correlated with plant biomass. PLS-PM analyses of three plants all showed that AMF inoculation increased plant nutrient uptake and then increased aboveground biomass and underground biomass by increasing plant height and root tillering. This study showed that RI was a more suitable AMF for combination with grassland degradation restoration grass species and proposed the potential mechanism of AMF–plant symbiosis to increase yield.

Extreme rainfall and drought events attributed to climate change are anticipated to occur in the current century, resulting in frequent drying and rewetting cycles (DWCs) in soils, which will, in turn, influence soil properties and microorganisms. Sample plots of Sophora japonica, Pinus tabulaeformis, and Ginkgo biloba were selected, and undisturbed soil columns were collected. CK was the constant drying treatment; the precipitation intensities of R80, R40, and R20 were 80 mm, 40 mm, and 20 mm, respectively, and the total precipitation for the four treatments was 160 mm. Significant differences were observed in the cumulative CO₂ emissions among the various DWC frequencies for the same woodland soils. A significant correlation was observed between the Birch effect and the DWC frequencies of the three woodland soils. A Pearson’s correlation analysis revealed that background nutrient contents were the key factors influencing alpha diversity. In conclusion, DWCs generally increased CO₂ fluxes, cumulative CO₂ emissions, and the Birch effect in addition to decreasing the alpha diversity of soil microorganisms when compared to those in the constant drying treatment.

Recently, land use and land cover change have received increased attention, and an approach is required that can assess agricultural land use intensity on a general basis. This study demonstrated the usefulness of a tool for characterizing and assessing agricultural land use intensity in Beijing mountainous region. An emergy analysis and principal component analysis (PCA) were adopted to obtain agricultural input and output intensity data. Correlation and regression analyses were used to study the relationship among land capability, agricultural input, output intensity, and agricultural system sustainability. Ultimately, the agricultural land use intensity types in the Beijing mountainous region were identified through a cluster analysis. The results produced five indices of agricultural input intensity and five indices of output intensity. Non-renewable energy was the overwhelming input, and grain, meat, eggs, and vegetables were the major outputs of the agricultural system. The results also showed that there was better natural land quality, higher input intensity, greater output intensity, and lower agricultural system sustainability. Eight types of agricultural intensity were classified and assessed, and they may be used to evaluate and monitor sustainable land use and provide baseline measurements of land use intensity for land use analyses and change detection.

As an irreplaceable ecological barrier, an ecological conservation developing area (ECDA) is vital for the integrated construction of urban and rural areas and the optimization and adjustment of industrial structures. However, few empirical studies have been conducted on the spatiotemporal variations of agricultural green development (AGD) in the ECDAs of large cities. Based on the green agricultural traits of Beijing and the accessible data, we evaluated the AGD and analyzed its spatial and temporal heterogeneity in Beijing’s ECDAs by constructing a framework with 13 indicators. The results stated that energy consumption is a vital factor in green agriculture production and that the agricultural output value per unit of arable land area is the key to green agricultural revenue. From 2006 to 2016, the AGD index of the ECDA had an increasing trend, until 2012 when it followed a decreasing tendency. The AGD index of the northern region was higher than in the southern ECDA. The obstacle degree model was used to verify the AGD limiting factors, where poor infrastructure, slow agritourism, low labor productivity, and low resource use efficiency varied by districts in the ECDA. Given these findings, our study is conducive to AGD evaluation at the district (county) level for the ECDAs of large cities and provides important policy implications.