Biochar is gaining significant attention due to its potential for carbon (C) sequestration, improvement of soil health, fertility enhancement, and crop productivity and quality. In this review, we discuss the most common available techniques for biochar production, the main physiochemical properties of biochar, and its effects on soil health, including physical, chemical, and biological parameters of soil quality and fertility, nutrient leaching, salt stress, and crop productivity and quality. In addition, the impacts of biochar addition on salt-affected and heavy metal contaminated soils were also reviewed. An ample body of literature supports the idea that soil amended with biochar has a high potential to increase crop productivity due to the concomitant improvement in soil structure, high nutrient use efficiency (NUE), aeration, porosity, and water-holding capacity (WHC), among other soil amendments. However, the increases in crop productivity in biochar-amended soils are most frequently reported in the coarse-textured and sandy soils compared with the fine-textured and fertile soils. Biochar has a significant effect on soil microbial community composition and abundance. The negative impacts that salt-affected and heavy metal polluted soils have on plant growth and yield and on components of soil quality such as soil aggregation and stability can be ameliorated by the application of biochar. Moreover, most of the positive impacts of biochar application have been observed when biochar was applied with other organic and inorganic amendments and fertilizers. Biochar addition to the soil can decrease the nitrogen (N) leaching and volatilization as well as increase NUE. However, some potential negative effects of biochar on microbial biomass and activity have been reported. There is also evidence that biochar addition can sorb and retain pesticides for long periods of time, which may result in a high weed infestation and control cost.

There is a need for a more innovative fertilizer approach that can increase the productivity of agricultural systems and be more environmentally friendly than synthetic fertilizers. In this article, we reviewed the recent development and potential benefits derived from the use of nanofertilizers (NFs) in modern agriculture. NFs have the potential to promote sustainable agriculture and increase overall crop productivity, mainly by increasing the nutrient use efficiency (NUE) of field and greenhouse crops. NFs can release their nutrients at a slow and steady pace, either when applied alone or in combination with synthetic or organic fertilizers. They can release their nutrients in 40–50 days, while synthetic fertilizers do the same in 4–10 days. Moreover, NFs can increase the tolerance of plants against biotic and abiotic stresses. Here, the advantages of NFs over synthetic fertilizers, as well as the different types of macro and micro NFs, are discussed in detail. Furthermore, the application of NFs in smart sustainable agriculture and the role of NFs in the mitigation of biotic and abiotic stress on plants is presented. Though NF applications may have many benefits for sustainable agriculture, there are some concerns related to the release of nanoparticles (NPs) from NFs into the environment, with the subsequent detrimental effects that this could have on both human and animal health. Future research should explore green synthesized and biosynthesized NFs, their safe use, bioavailability, and toxicity concerns.

We employed the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006, and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+), and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km² and from 18.10 to 28.45 km² in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared toward the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study.

The present study is designed to monitor the spatio-temporal changes in forest cover using Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 to 2017. Landsat data from 1990 (Thematic mapper [TM]), 2000 and 2010 (Enhanced Thematic Mapper [ETM+]), and 2013 to 2017 (Operational Land Imager/Thermal Infrared Sensor [OLI/TIRS]) were classified into the classes termed snow, water, barren land, built-up area, forest, and vegetation. The method was built using multitemporal Landsat images and the machine learning techniques Support Vector Machine (SVM), Naive Bayes Tree (NBT) and Kernel Logistic Regression (KLR). According to the results, forest area was decreased from 19,360 km² (26.0%) to 18,784 km² (25.2%) from 1990 to 2010, while forest area was increased from 18,640 km² (25.0%) to 26,765 km² (35.9%) area from 2013 to 2017 due to "One billion tree Project". According to our findings, SVM performed better than KLR and NBT on all three accuracy metrics (recall, precision, and accuracy) and the F1 score was >0.89. The study demonstrated that concurrent reforestation in barren land areas improved methods of sustaining the forest and RS and GIS into everyday forestry organization practices in Khyber Pakhtun Khwa (KPK), Pakistan. The study results were beneficial, especially at the decision-making level for the local or provincial government of KPK and for understanding the global scenario for regional planning.

Groundwater contamination poses a severe public health risk in Lahore, Pakistan’s second-largest city, where over-exploited aquifers are the primary municipal and domestic water supply source. This study presents the first comprehensive district-wide assessment of groundwater quality across Lahore using an innovative integrated approach combining geographic information systems (GIS), multi-criteria decision analysis (MCDA), and water quality indexing techniques. The core objectives were to map the spatial distributions of critical pollutants like arsenic, model their impacts on overall potability, and evaluate targeted remediation scenarios. The analytic hierarchy process (AHP) methodology was applied to derive weights for the relative importance of diverse water quality parameters based on expert judgments. Arsenic received the highest priority weight (0.28), followed by total dissolved solids (0.22) and hardness (0.15), reflecting their significance as health hazards. Weighted overlay analysis in GIS delineated localized quality hotspots, unveiling severely degraded areas with very poor index values (>150) in urban industrial zones like Lahore Cantt, Model Town, and parts of Lahore City. This corroborates reports of unregulated industrial effluent discharges contributing to aquifer pollution. Prospective improvement scenarios projected that reducing heavy metals like arsenic by 30% could enhance quality indices by up to 20.71% in critically degraded localities like Shalimar. Simulating advanced multi-barrier water treatment processes showcased an over 95% potential reduction in arsenic levels, indicating the requirement for deploying advanced oxidation and filtration infrastructure aligned with local contaminant profiles. The integrated decision support tool enables the visualization of complex contamination patterns, evaluation of remediation options, and prioritizing risk-mitigation investments based on the spatial distribution of hazard exposures. This framework equips urban planners and utilities with critical insights for developing targeted groundwater quality restoration policies through strategic interventions encompassing treatment facilities, drainage infrastructure improvements, and pollutant discharge regulations. Its replicability across other regions allows for tackling widespread groundwater contamination challenges through robust data synthesis and quantitative scenario modeling capabilities.

Background: Arbuscular mycorrhizal fungi (AMF) have a positive effect on drought tolerance of plants after establishing reciprocal resymbiosis with roots, while the underlying mechanism is not deciphered. Metabolomics can explain the mechanism of plant response to environmental stress by analyzing the changes of all small molecular weight metabolites. The purpose of this study was to use Ultra High Performance Liquid Chromatography Q Exactive Mass Spectrometer to analyze changes in root metabolites of walnut (Juglans regia) after inoculation with an arbuscular mycorrhizal fungus Diversispora spurca under well-watered (WW) and drought stress (DS). Results: Sixty days of soil drought significantly inhibited root mycorrhizal colonization rate, shoot and root biomass production, and leaf water potential in walnut, while AMF inoculation significantly increased biomass production and leaf water potential, accompanied by a higher increase magnitude under DS versus under WW. A total of 3278 metabolites were identified. Under WW, AMF inoculation up-regulated 172 metabolites and down-regulated 61 metabolites, along with no changes in 1104 metabolites. However, under DS, AMF inoculation up-regulated 49 metabolites and down-regulated 116 metabolites, coupled with no changes in 1172 metabolites. Among them, juglone (a quinone found in walnuts) as the first ranked differential metabolite was up-regulated by AMF under WW but not under DS; 2,3,5-trihydroxy-5–7-dimethoxyflavanone as the first ranked differential metabolite was increased by AMF under DS but not under WW. The KEGG annotation showed a large number of metabolic pathways triggered by AMF, accompanied by different metabolic pathways under WW and DS. Among them, oxidative phosphorylation and phenylalanine metabolism and biosynthesis were triggered by AMF in response to WW and DS, where N-acetyl-L-phenylalanine was induced by AMF to increase under DS, while decreasing under WW. Conclusion: This study provides new insights into the metabolic mechanisms of mycorrhiza-enhanced drought tolerance in walnuts.