Urban Honey: A Review of Its Physical, Chemical, and Biological Parameters That Connect It to the Environment

Abstract

Humans mainly use the land for agriculture and housing, resulting in the loss of natural habitat and a decrease in the number of species, including wild bees. The reduction of wild bees generates several negative consequences for the agricultural and ecosystem contexts, although sometimes the farming reduces the probability of abandonment of the land. In parallel, urban beekeeping has emerged and consolidated as one of the current trends, while the consumption of honey from urban environments is also growing. Current scientific literature establishes different physical, chemical, and biological parameters which evaluate the quality of honey products and its environment. The review carried out here collects the various compounds contained in this source-dependent food matrix from anthropogenic activities in the sampling area. Using gas and liquid chromatography and spectrometry, the main physicochemical parameters have been detected, 27 chemical elements, of which 6 are heavy elements, 16 polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and pesticides (organochlorine pesticides and neonicotinoids). Additionally, their total phenolic and microbiological content has been typified. This analysis can help to frame the main characteristics to evaluate this universal product, whose consumption began with the first settlers of the world, and its properties have been evolving as well as the characteristics of the production systems.

Sustainable Water Monitoring via Analytical Techniques and Protocols Applied in the Assessment of Organochlorine Pesticides

“…Despite OCPs being banned for many years, they are still found in various products and environments: water [1,6,14,16,29,[36][37][38][39]45,[48][49][50]62,70,75,78,87], soil [92,[94][95][96], vegetables [97,98], meat [99,100], dairy products [101], and honey-bees [102]. This is related to their long life and insolubility in water.…”

Research on Real-Time Prediction of Hydrogen Sulfide Leakage Diffusion Concentration of New Energy Based on Machine Learning

“…Later, after decades of development and improvement, researchers established mathematical models applicable to the study of heavy gas diffusion simulations. In the 1970s, Blackmore and Wheatly [14,15] conducted a review and analysis of most of the heavy gas [16][17][18][19] diffusion models established at that time. The ones that are the most applied currently include the BM model [20], box model [21], shallow model [22], and hydrodynamic approach [23], but there are great limitations in the use of these models.…”