Background: Industrial and agricultural activities result in elevated levels of potentially toxic elements (PTEs) in the local environment. PTEs can enter the human body through the food chain and pose severe health risks to inhabitants. In this study, PTE levels in maize, soil, and irrigation water were detected, and health risks through maize consumption were evaluated. Methods: Maize, soil, and irrigation water samples were collected in northern Ningxia, China. Inductively coupled plasma-optical emission spectrometry was applied to determine the contents of six PTEs. Bioaccumulation factor was used to reflect the transfer potential of a metal from soil to maize. Health risks associated with maize consumption were assessed by deterministic and probabilistic estimation. Sensitivity analysis was performed to determine variables that pose the greatest effect on health risk results. Results: The levels of Pb and Cr in maize exceeded the standards, while the PTE levels in soil and irrigation water did not exceed the corresponding standards. The bioaccumulation factor values of the six PTEs in maize were all lower than 1 and followed the order of Cd > Zn = As > Cr > Cu > Pb. The hazard index (0.0986) was far less than 1 for all inhabitants implying no obvious non-carcinogenic risk. The carcinogenic risk value was 3.261 × 10^− 5, which was lower than the maximum acceptable level of 1 × 10^− 4 suggested by United States Environmental Protection Agency (USEPA). Females were at greater risk than males, and the age group of below 20 years had the greater risk among all the groups evaluated. Approximately 0.62% of inhabitants exceeded the level for non-carcinogenic risk, while 8.23% exceeded the level for carcinogenic risk. The As concentration and daily intake of maize contributed 35.8, and 29.4% for non-carcinogenic risk results as well as 61.0 and 18.5% for carcinogenic risk results. Conclusions: Maize was contaminated by Pb and Cr, whereas the associated soil and irrigation water were not contaminated by PTEs. Inhabitants would not suffer obvious harmful health risks through maize consumption. Arsenic level and daily intake of maize were the most sensitive factors that impact health risks.

Rice-crab co-culture is an environmentally friendly agricultural and aquaculture technology with high economic and ecological value. In order to clarify the structure and function of soil and water microbial communities in the rice-crab symbiosis system, the standard rice-crab field with a ring groove was used as the research object. High-throughput sequencing was performed with rice field water samples to analyze the species and abundance differences of soil bacteria and fungi. The results showed that the OTU richness and community diversity in soil were significantly higher than those in water, while there were significant differences in soil microbial diversity and OTU richness in water sediments. The dominant species at the bacterial phylum level were Amoebacteria, Cyanobacteria, Actinomycetes, Synechococcus and Greenbacteria, and at the genus level the dominant species were norank_f_norank_o_Chloroplast, unclassified_f_Rhodobacteraceae, LD29, Cyanobium_PCC-6307, and norank_f_MWH-UniP1_aquatic_group. The dominant species at the fungal phylum level are unclassified_k_Fungi, Ascomycota, Rozellomycota, Phaeomycota and Stenotrophomonas, and at the genus level the dominant species are unclassified_k_Fungi, unclassified_p_Rozellomycota, Metschnikowia, Cladosporium, unclassified_p_Chytridiomycota. The dominant phylum may rely on mechanisms such as organic matter catabolism, secretion of secondary metabolites and phototrophic autotrophy, as predicted by functional gene analysis. The main functional genes are related to metabolic functions, including secondary product metabolism, energy metabolism, and amino acid metabolism.

To accurate forecast of water evaporation and transpiration (reference evapotranspiration, ET₀) is imperative in the planning and management of water resources. The Penman-Monteith FAO56 (PM-56) equation which is recommended for estimating ET₀ across the world. However, it requires several climatic variables; the use of the PM-56 equation is restricted by the unavailability of input climatic variables in many locations. In the current study, the potential of k-Nearest Neighbor algorithm (KNN), which is a data mining method for estimating ET₀ were investigated using limited climatic data in a semi-arid environment in China. In addition, a KNN based ET₀ forecast model were tested against the PM-56 equation. The accuracies of the models were evaluated by using three commonly used criteria: root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (r). The results obtained with the KNN-based ET₀ forecast model (through normalization, weighted and K = 3) were better than it without any process. The prediction result is consistent with the PM-56 results, and confirmed the ability of these techniques to provide useful tools in ET₀ modeling in semi-arid environments. Based on the comparison of the overall performances, it was found that t the KNN-based ET₀ forecast model which requires max air temperature, min air temperature and relative humidity, input variables had the best accuracy.

To study the influence mechanism of micro-nano oxygenated irrigation (MNOI) on greenhouse fruit cucumber in arid and semi-arid cold regions, the yield and quality of greenhouse fruit cucumber were evaluated and verified based on 2 years of observation data. Taking fruit cucumber in Ningxia solar greenhouse as the research object, three dissolved oxygen (DO) levels of MNOI (DO; 6, 7.5, and 9 mg L⁻¹, O1, O2, and O3, respectively) and non-oxygenated irrigation (CK, 4 mg L⁻¹) were set up as the control treatment. Through comparative design, the influence mechanism of different levels of aerobic irrigation on the yield and quality of greenhouse fruit cucumber was studied. The main indicators of fruit cucumber yield and quality increased with dissolved oxygen in irrigation water from 4 to 9 mg L⁻¹. In spring–summer (autumn–winter), compared with CK, the leaf area index (LAI) and net photosynthetic rate (A) increased by 28.83% (28.77%) and 44.90% (35.00%), respectively, and Vitamin C, soluble protein, soluble sugar, soluble solids and total acid content increased by 100.00% (51.88%), 37.78% (61.11%), 34.17% (54.17%), 37.07% (78.72%) and 26.92% (30.67%) respectively, while nitrate content decreased by 44.88% (51.15%), and dry matter accumulation (DMA), soil respiration rate (SRR), microbial carbon (MC), and microbial nitrogen (MN) increased by 49.81% (127.25%), 55.22% (110.34%), 117.50% (90.91%) and 70.37% (74.42%) respectively, and yield, irrigation water use efficiency (IWUE) and soil oxygen content (SO) increased by 22.47% (28.04%), 22.39% (28.05%) and 33.21% (35.33%) respectively. A model of DO in irrigation water and SO was established and the applicability of the model was verified with an average relative error of 2% (less than 5%). MNOI increased SO and soil enzyme activity, enriched soil microorganisms, improved soil microenvironment, promoted water nutrient uptake and growth of root system, increased chlorophyll, photosynthesis and DMA, which improved fruit cucumber yield and quality, and the better DO concentration in irrigation water is 9 mg L⁻¹. The research results provide theoretical support for regulating soil water, fertilizer and air environment, and at the same time, provide feasible ways to improve the quality and efficiency of crops in arid and semi-arid cold regions.

Small watersheds are ideal objects for studying the evolution of hydrological and water resources at small scales. Whether precipitation products can meet the runoff simulation of small watersheds is the main purpose of this study. With NOAA-CPC-US precipitation as a reference, in nine small watersheds of the United States, accuracy of the precipitation products such as PERSIANN, PERSIANN-CDR, TRMM-3B42V7, GPM-IMERG, StageIV, and ERA5 is analyzed. By driving the CREST hydrological model using these datasets, the runoff simulation effects were evaluated. Result shows the precipitation products match the NOAA-CPC-US from high to low in the order of StageIV, PERSIANN-CDR, GPM-IMERG, PERSIANN, ERA5, and finally TRMM-3B42 V7. These datasets have relatively low accuracy in the northern high latitude area and the western mountains, while accuracy is better in the central, southern, and eastern parts of the United States. In the runoff simulation effectiveness evaluation, the daily runoff of watersheds is simulated during the same verification period. The results show that: NOAA-CPC-US and StageIV have good simulation effect in small watersheds. In the northern and western United States, the PERSIANN, PERSIANN-CDR, GPM-IMERG, and ERA5 for runoff simulation should be used with caution. TRMM-3B42V7 is not suitable for runoff simulation in small watershed.