GIS- and Multivariate-Based Approaches for Assessing Potential Environmental Hazards in Some Areas of Southwestern Saudi Arabia.

Soil contamination is a major issue that endangers the ecology in most countries. Total concentrations of As, Cd, Co, Cr, Cu, Mn, Ni, Pb, VFe, and Zn were determined by analyzing soil samples from 32 surface soil samples in southwest Saudi Arabia, including certain areas of Al-Baha. Kriging techniques were used to create maps of the distribution of metal. To assess the levels of soil contamination in the research area, principal component analysis (PCA), contamination factors (CF), and pollution load index were used. The results show the stable model gave the best fit to the As and Zn semivariograms. The circular model fits the Cd, Co, and Ni semivariograms the best, while the exponential model fits the Cr, V, and Fe semivariograms the best. For Ni and Pb, respectively, spherical and Gaussian models are fitted. The findings demonstrated two clusters containing different soil heavy metal concentrations. According to the data, there were two different pollution levels in the research region: 36.58% of it is strongly contaminated, while 63.41% of it has a moderate level of contamination (with average levels of these metals 5.28 ± 5.83, 0.81 ± 0.19, 18.65 ± 6.22, 45.15 ± 23.25, 60.55 ± 23.74, 972.30 ± 223.50, 33.45 ± 14.11, 10.05 ± 5.13, 84.15 ± 30.72, 97.40 ± 30.05, and 43,245.00 ± 8942.95 mg kg-1 for As, Cd, Co, Cr, Cu, Mn, Ni, Pb, V, Fe, and Zn, respectively). The research area's poor management practices are reflected in the current results, which raised the concentration of harmful elements in the soil's surface layers. Ultimately, the outcomes of pollution concentration and spatial distribution maps could aid in informing decision-makers when creating suitable heavy metal mitigation strategies.

Enhancing Maize Yield and Soil Health through the Residual Impact of Nanomaterials in Contaminated Soils to Sustain Food.

Studying the impact of residual soil nanomaterials is a promising challenge for sustainable agricultural development to improve soil health and crop productivity. The objective of this study is to assess the long-term impacts of 50, 100, and 250 mg kg-1 soil of nanobiochar (nB) and nano-water treatment residues (nWTR) on the fertility, biological activity, and yield of maize (Zea mays L.) growing in heavy metal-contaminated soils. The results showed that when nB and nWTR were added in larger quantities, the concentrations of lead (Pb), nickel (Ni), cadmium (Cd), and cobalt (Co) extracted with DTPA decreased. With the addition of nB or nWTR, it also showed a significant increase in exchangeable cations, cation exchange capacity (CEC), soil fertility, soil organic matter (OM), microbial biomass carbon (MBC), and a decrease in soil salinity and sodicity. Catalase and dehydrogenase activities rose as nB addition increased, while they decreased when nWTR addition increased. In comparison to the control, the addition of nB and nWTR greatly boosted maize yield by 54.5-61.4% and 61.9-71.4%, respectively. These findings suggest that the researched nanomaterials' residual effect provides an eco-friendly farming method to enhance the qualities of damaged soils and boost maize production. Our research suggested that adding recycling waste in the form of nanoparticles could immobilize heavy metals, improve soil characteristics, and increase the soil's capacity for productivity.

Optimizing sowing window, cultivar choice, and plant density to boost maize yield under RCP8.5 climate scenario of CMIP5.

The impacts of climate change and possible adaptations to food security are a global concern and need greater focus in arid and semi-arid regions. It includes scenario of Coupled Model Intercomparison Phase 5 (CMIP-RCP8.5). For this purpose, two DSSAT maize models (CSM-CERES and CSM-IXIM) were calibrated and tested with two different maize cultivars namely Single Cross 10 (SC10) and Three Way Cross 324 (TW24) using a dataset of three growing seasons in Nile Delta. SC10 is a long-growing cultivar that is resistant to abiotic stresses, whereas TW24 is short and sensitive to such harsh conditions. The calibrated models were then employed to predict maize yield in baseline (1981-2010) and under future time slices (2030s, 2050s, and 2080s) using three Global Climate Models (GCMs) under CMIP5-RCP8.5 scenario. In addition, the use of various adaptation options as shifting planting date, increasing sowing density, and genotypes was included in crop models. Simulation analysis showed that, averaged over three GCMs and two crop models, the yield of late maturity cultivar (SC10) decreased by 4.1, 17.2, and 55.9% for the three time slices of 2030s, 2050s, and 2080s, respectively, compared to baseline yield (1981-2010). Such reduction increased with early maturity cultivar (TW24), recording 12.4, 40.6, and 71.3% for near (2030s), mid (2050s), and late century (2080s) respectively relative to baseline yield. The most suitable adaptation options included choosing a stress-resistant genotype, changing the planting date to plus or minus 30 days from baseline planting date, and raising the sowing density to 9 m-2 plants. These insights could minimize the potential reduction of climate change-induced yields by 39% by late century.