Nanomaterials (NMs), both natural and synthetic, are produced, transformed, and exported into our environment daily. Natural NMs annual flux to the environment is around 97% of the total and is ...significantly higher than synthetic NMs. However, synthetic NMs are considered to have a detrimental effect on the environment. The extensive usage of synthetic NMs in different fields, including chemical, engineering, electronics, and medicine, makes them susceptible to be discharged into the atmosphere, various water sources, soil, and landfill waste. As ever-larger quantities of NMs end up in our environment and start interacting with the biota, it is crucial to understand their behavior under various environmental conditions, their exposure pathway, and their health effects on human beings. This review paper comprises a large portion of the latest research on NMs and the environment. The article describes the natural and synthetic NMs, covering both incidental and engineered NMs and their behavior in the natural environment. The review includes a brief discussion on sampling strategies and various analytical tools to study NMs in complex environmental matrices. The interaction of NMs in natural environments and their pathway to human exposure has been summarized. The potential of NMs to impact human health has been elaborated. The nanotoxicological effect of NMs based on their inherent properties concerning to human health is also reviewed. The knowledge gaps and future research needs on NMs are reported. The findings in this paper will be a resource for researchers working on NMs all over the world to understand better the challenges associated with NMs in the natural environment and their human health effects.
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•The ubiquitous presence of natural and synthetic nanomaterials in the environment•Nanomaterials influence on the natural ecosystem.•Exposure pathways and life cycle of nanomaterials in the human body•Nanotoxicity of nanomaterials on human health
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In the race to enhance agricultural productivity, irrigation will become more dependent on poorly characterized and virtually unmonitored sources of water. Increased use of irrigation water has led ...to impaired water and soil quality in many areas. Historically, soil salinization and reduced crop productivity have been the primary focus of irrigation water quality. Recently, there is increasing evidence for the occurrence of geogenic contaminants in water. The appearance of trace elements and an increase in the use of wastewater has highlighted the vulnerability and complexities of the composition of irrigation water and its role in ensuring proper crop growth, and long-term food quality. Analytical capabilities of measuring vanishingly small concentrations of biologically-active organic contaminants, including steroid hormones, plasticizers, pharmaceuticals, and personal care products, in a variety of irrigation water sources provide the means to evaluate uptake and occurrence in crops but do not resolve questions related to food safety or human health effects. Natural and synthetic nanoparticles are now known to occur in many water sources, potentially altering plant growth and food standard. The rapidly changing quality of irrigation water urgently needs closer attention to understand and predict long-term effects on soils and food crops in an increasingly fresh-water stressed world.
Riverbank filtration has the potential to supply water to numerous cities around the world. Many of these cities are currently using surface water that is often of poor quality. Gradual conversion of ...surface water intakes to bank filtration can help improve the water quality.
A four-decade dataset (1974–2013) of 107,823 nitrate samples in 25,993 wells from western and eastern parts of Nebraska was used to assess long-term trends of groundwater nitrate concentration and ...decadal changes in the extent of groundwater nitrate-contaminated areas (NO3-N ≥ 10 mg N/L) over the entire state. Spatial statistics and regressions were used to investigate the relationships between groundwater nitrate concentrations and several potential natural and anthropogenic factors, including soil drainage capacities, vadose zone characteristics, crop production areas, and irrigation systems. The results of this study show that there is no statistically significant trend in groundwater nitrate concentrations in western Nebraska, in contrast with the increasing trend (p < .05) to the east. The spatial extent and nitrate concentrations in contaminated groundwater in center pivot-irrigated areas was less than in gravity-irrigated areas. Areas with a thicker vadose zone and larger saturated thickness of the aquifer have relatively lower nitrate concentrations. The results of a classification and regression tree (CART) model indicate the difference in the influence of physical factors on groundwater nitrate concentrations between western and eastern Nebraska, namely that groundwater nitrate concentrations correspond with vadose zone thickness, effective hydraulic conductivity, and saturated thickness in the west, while in eastern Nebraska, concentrations are correlated with average percent sand in the topsoil (0–150 cm), well depth, and effective hydraulic conductivity.
•Groundwater nitrate concentrations in Nebraska have been increasing steadily over the last 40 years•Eastern Nebraska regions shows an increasing trend in groundwater nitrate contamination•Groundwater in western Nebraska lacks any definitive trend in nitrate contamination•Groundwater nitrate can be predicted by soil sand and organic matter, and is strongly correlated tovadose zone thickness
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Global demand for livestock products is rising, resulting in a growing demand for feed and potentially burdening freshwater resources to produce this feed. To offset this increased pressure on water ...resources, the environmental performance of livestock sector should continue to improve. Over the last few decades, product output per animal and feedstuff yields in the US have improved, but before now it was unclear to what extent these improvements influenced the water productivity (WP) of the livestock products. In this research, we estimate changes in WP of animal products from 1960 to 2016. We consider feed conversion ratios (dry matter intake per head divided by product output per head), feed composition per animal category, and estimated the water footprint of livestock production following the Water Footprint Network's Water Footprint Assessment methodology. The current WP of all livestock products appears to be much better than in 1960. The observed improvements in WPs are due to a number of factors, including increases in livestock productivity, feed conversion ratios and feed crop yields, the latter one reducing the water footprint of feed inputs. Monogastric animals (poultry and swine) have a high feed-use efficiency compared to ruminants (cattle), but ruminants consume relatively large portion of feed that is non-edible for humans. Per unit of energy content, milk has the largest WP followed by chicken and pork. Per gram of protein, poultry products (chicken meat, egg and turkey meat) have the largest WP, followed by cattle milk and pork. Beef has the smallest WP. These data provide important information that may aid the development of strategies to improve WP of the livestock sector.
•Over the last few decades, product output per head of animal have increased.•Increase in the yield of feedstuffs has helped to lower the water footprint of feeds.•Water productivity of livestock products has improved between 1960 and 2016.•Water footprint of livestock production has decreased by 36% between 1960 and 2016.•Choosing feed ingredients and sourcing wisely will improve water productivity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based ...on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO
). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM's predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil-plant-atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO
m
day
; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m
day
; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Water Productivity (WP) of a crop defines the relationship between the economic or physical yield of the crop and its water use. With this concept it is possible to identify disproportionate water ...use or water-limited yield gaps and thereby support improvements in agricultural water management. However, too often important qualitative and quantitative environmental factors are not part of a WP analysis and therefore neglect the aspect of maintaining a sustainable agricultural system. In this study, we examine both the physical and economic WP in perspective with temporally changing environmental conditions. The physical WP analysis was performed by comparing simulated maximum attainable corn yields per unit of water using the crop model Hybrid-Maize with observed data from 2005 through 2013 from 108 farm plots in the Central Platte and the Tri Basin Natural Resource Districts of Nebraska. In order to expand the WP analysis on external factors influencing yields, a second model, Maize-N, was used to estimate optimal nitrogen (N)-fertilizer rate for specific fields in the study area. Finally, a vadose zone flow and transport model, HYDRUS-1D for simulating vertical nutrient transport in the soil, was used to estimate locations of nitrogen pulses in the soil profile. The comparison of simulated and observed data revealed that WP was not on an optimal level, mainly due to large amounts of irrigation used in the study area. The further analysis illustrated year-to-year variations of WP during the nine consecutive years, as well as the need to improve fertilizer management to favor WP and environmental quality. In addition, we addressed the negative influence of groundwater depletion on the economic WP through increasing pumping costs. In summary, this study demonstrated that involving temporal variations of WP as well as associated environmental and economic issues can represent a bigger picture of WP that can help to create incentives to sustainably improve agricultural production.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
It is a known fact that large quantities of farm and meat products rot and are wasted if correct actions are not taken, which may lead to serious health issues if consumed. There is no proper system ...for tracking and communicating the status of the goods to their respective stakeholders in a secure way. Consumers have every right to know the quality of the products they consume. Using monitoring tools, such as the Internet of Agricultural Things (IoAT), and modern data protection techniques for storing and sharing, will help mitigate data integrity issues during the transmission of sensor records, increasing the data quality. The visibility state at the customer end is also improved, and they are aware of the agricultural product’s conditions throughout the real-time distribution process. In this paper, we developed and implemented a CorDapp application to manage the data for the supply chain, called “agroString”. We collected the temperature and humidity data using IoAT-Edge devices and various datasets from multiple sources. We then sent those readings to the CorDapp agroString and successfully shared them among the relevant parties. With the help of a Corda private blockchain, we attempted to increase data integrity, trust, visibility, provenance, and quality at each logistic step, while decreasing blockchain and central system limitations.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Groundwater overuse in different domains will eventually lead to global freshwater scarcity. To meet the anticipated demands, many governments worldwide are employing innovative and traditional ...techniques for forecasting groundwater availability by conducting research and studies. One challenging step for this type of study is collecting groundwater data from different sites and securely sending it to the nearby edges without exposure to hacking and data tampering. In the current paper, we send raw data formats from the Internet of Things to the Distributed Data Storage (DDS) and Blockchain (BC) edges. We use a distributed and decentralized architecture to store the statistics, perform double hashing, and implement access control through smart contracts. This work demonstrates a modern and innovative approach combining DDS and BC technologies to overcome traditional data sharing, and centralized storage, while addressing blockchain limitations. We have shown performance improvements with increased data quality and integrity.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The United States cotton industry is devoted to sustainable production strategies that reduce water, land, and energy consumption while enhancing soil health and cotton yield. Climate-smart ...agricultural solutions are being developed to increase yields and reduce operational costs. However, crop yield prediction is challenging because of the complex and nonlinear interactive effects of cultivar, soil type, management, pests and diseases, climate, and weather patterns on crops. To address this challenge, the machine learning (ML) method was used to predict yield, considering climatic change, soil diversity, cultivars, and fertilizer applications. Field data were collected over the southern US cotton belt in the 1980s and the 1990s. A second data source was generated from the process-based cotton model GOSSYM to reflect the most recent effects of climate change over the last six years (2017-2022). We focused on nine locations in three southern states: Texas, Mississippi, and Georgia. The accumulated heat for each set of experimental data was used as an analogue for the time-series weather data to reduce the number of computations. The Random Forest (RF) regressor, Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM) regressor, Multiple Linear Regression (MLR), and neural networks were evaluated. Cross-validation was performed to obtain an improved model that did not suffer from overfitting. The RF regressor achieved an accuracy of 97.75%, with an <inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula> of roughly 0.98 and a root mean square error of 55.05 kg/ha. The results demonstrate how a simple and robust model can be developed and utilized to help cotton climate-smart efforts.
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