The high concentrations of particulate matter (PM) in the atmosphere have adverse effects on both the environment and human health, as well as on urban and faunal biodiversity. Short-term events, such as the burning of fireworks, attributable to human activity, result in a significant and rapid increase in PM concentration levels within the atmosphere. We present findings derived from observations made using low-cost PM sensors deployed as part of the Environmental Monitoring Network in Querétaro, Mexico. These sensors utilize the Environmental Geo Monitoring Stations (EMGA) device, developed by the Institute of Geosciences at UNAM. The observation period spanned from 10 December 2023 to 10 January 2024, encompassed the Christmas and New Year holidays, and focused on PM_2.5 concentrations, and a comparison with a similar period from 10 February 2024 to 10 March 2024 considered a typical activity period in the area. The results reveal two substantial increases in PM_2.5 concentrations (from less than 50 µg/m³ to more than 340 µg/m³) over time and spatial distribution during this period, with notable increases observed during the festive season coincident with the Christmas and New Year celebrations. Specifically, prolonged periods exceeding up to 6 h were noted on festivity days. This research offers insight into the effects, trends, and spatial–temporal distribution of pollutants within the city of Queretaro, which may be used as a reference for other cities around the world.

Airborne magnetic particles may be harmful because of their composition, morphology, and association with potentially toxic elements that may be observed through relationships between magnetic parameters and pollution indices, such as the Tomlinson pollution load index (PLI). We present a fuzzy-based analysis of magnetic biomonitoring data from four Latin American cities, which allows us to construct a magnetic index of contamination (IMC). This IMC uses four magnetic parameters, i.e., magnetic susceptibility χ, saturation isothermal remanent magnetization SIRM, coercivity of remanence H_cr, and SIRM/χ, and proposes summarizing the information to assess an area based exclusively on magnetic parameters more easily. The fuzzy inference system membership functions are built from the standardization of the data to become independent of the values. The proposed IMC is calculated using the baseline values for each case study, similar to the PLI. The highest IMC values were obtained in sites close to industrial areas, and in contrast, the lowest ones were observed in residential areas far from avenues or highways. The linear regression model between modeled IMC and PLI data yielded robust correlations of R² > 0.85. The IMC is proposed as a complementary tool for air particle pollution and is a cost-effective magnetic approach for monitoring areas.

Paenibacillus larvae is the causative agent of American Foulbrood (AFB), a devastating disease that affects honey bee larvae. Plant-based antimicrobials that do not harm bees or hive products represent a natural alternative, particularly for diseases related to food provisioning, such as AFB. This study evaluated the antibacterial activity of Humulus lupulus extracts against Paenibacillus larvae and assessed their toxicity on honey bee larvae and adults. Additionally, total polyphenol and flavonoid contents, antioxidant activity, and volatile organic compound profiles were analyzed across 24 samples obtained by Soxhlet extraction. The extracts obtained using dichloromethane as a solvent exhibited the highest antimicrobial activity and showed no toxicity to adult bees or A. mellifera larvae. In these extracts, we identified a diverse array of volatile compounds with antibacterial properties, whose synergistic effects likely contributed to the enhanced antimicrobial efficacy observed.

Airborne magnetic particles (AMPs) are associated with potentially toxic elements, and their size, mineralogy, and concentration can significantly impact both the environment and human health. However, their spatial analysis is often limited by small datasets, non-normality, and pronounced local variability. In this work, two sites with distinct demographic and geographic characteristics, the city of Mar del Plata (Argentina) and the Aburrá Valley region (Colombia), were analyzed using the fuzzy Magnetic Pollution Index (IMC) as an indicator of the concentration of AMPs. Moreover, an original methodological framework that explicitly incorporates measurement uncertainty through fuzzy numbers, combined with an approach modeling fuzzy semivariances via α-cuts, performs spatial prediction via ordinary kriging. This study produces maps that simultaneously reflect the magnitude of IMC and its associated uncertainty. Unlike classical geostatistics, the fuzzy-based model captures the inherent imprecision of magnetic measurements and reveals spatial patterns where uncertainty becomes informative about the type and origin of pollution. In particular, this approach demonstrates that areas with higher IMC levels are associated with high anthropic activity (near industrial zones, main avenues, slow traffic). In contrast, lower values were found in residential areas. Overall, the fuzzy-driven approach provides an additional layer of information not accessible through traditional methods, improving spatial interpretation and supporting the identification of priority areas for environmental monitoring.