Distinctive adsorption and desorption behaviors of temporal and post-treatment heavy metals by iron nanoparticles in the presence of microplastics.

There are increasing concerns about microplastic (MP) pollution in the natural environment. Consequently, numerous physicochemical and toxicological studies have been conducted on the effects of MPs. However, few studies have concerned the potential impact of MPs on contaminated site remediation. We herein investigated the influence of MPs on the temporary and post heavy metal removal by iron nanoparticles, including pristine and sulfurized nano zero-valent irons (nZVI and S-nZVI). MPs inhibited adsorption of most heavy metals during the treatment of iron nanoparticles, and facilitated their desorption, such as Pb (II) from nZVI and Zn (II) from S-nZVI. However, such effects presented by MPs was usually less than those by dissolved oxygen (DO). Most desorption cases are irrelevant to the reduced formats of heavy metals involving redox reactions, such as Cu (I) or Cr (III), suggesting that the influence of MPs on metals are limited to those binding with iron nanoparticles through surface complexation or electrostatic interaction. As another common factor, natural organic matter (NOM) had almost no influence on the heavy metal desorption. These insights shed lights for enhanced remediation of heavy metals by nZVI/S-NZVI in the presence of MPs.

The Impact of Government-Led Farmland Construction on Market-Oriented Farmland Transfer-Evidence from Shandong, China.

This study explored the impact of government-led high-standard farmland construction (HSFC) on market-oriented farmland transfer using a unified analysis framework of HSFC and farmland transfers. We used a binary probit model based on 660 questionnaires from five counties in Shandong Province, China to empirically analyze this impact. The results show that HSFC can significantly promote farmland lease-in while inhibiting lease-out. We found that farmland fragmentation plays a significant role in moderating this impact, which is illustrated by the fact that improved farmland fragmentation does not promote HSFC in the context of farmland lease-in. Furthermore, it can effectively alleviate the inhibitory effect of HSFC on farmland lease-out. The impact of HSFC on farmland transfer has significant labor transfer heterogeneity. For households with a low degree of labor transfer, HSFC can significantly promote farmland lease-in and inhibit lease-out, while for households with a high degree of labor transfer, the above effect is not significant.

Application of the IoT in the Food Supply Chain─From the Perspective of Carbon Mitigation.

With the rising demands on supply chain transparency and food security, the rapid outspread of the Internet of Things (IoT) to improve logistical efficiency, and the rising penetration of sensor technology into daily life, the extensive integration of the IoT in the food sector is well anticipated. A perspective on potential life cycle trade-offs in regard to the type of integration is necessary. We conduct life cycle assessment (LCA) integrated with shelf life-food loss (SL-FL) models, showing an overall 5-fold leverage on carbon reduction, which is diet dependent and a function of income. Meat presents the highest leverage, 35 ± 11-times, owing to its high carbon footprint. Two-thirds (65%) of global sensors (1 billion) engaged in monitoring fruits and vegetables can mitigate less than 7% of the total reduced carbon emissions. Despite the expected carbon emission reductions, widespread adoption of the IoT faces multiple challenges such as high costs, difficulties in social acceptance, and regional variability in technological development. Furthermore, changes in the distribution of transportation resources and dealer service models, requirements regarding the accuracy of sensor data analysis, efficient and persistent operation of devices, development of agricultural infrastructure, and farmer education and training have all increased uncertainty. Nonetheless, the research trend in smart sensors toward smaller chips and the potential integration of machine learning or blockchain as further steps make it possible to leverage these advantages to facilitate market penetration. These insights facilitate the future optimization of the application of IoT sensors for sustainability.