The composition of urban dust can affect the health of urban populations, making it necessary to study its elemental composition and its sources. Our objectives were: a) to identify the main emission sources of heavy metals, and b) to evaluate the influence of land use and road type on the elemental composition of urban dust. 100 samples of urban dust taken in San Luis Potosí were analyzed using FRX. Descriptive statistics, Pearson’s correlations coefficient, kriging interpolations, and analysis of variance were applied to the data. Cu and Zn were spatially associated with a metallurgical complex and, to a lesser extent, with an industrial park; Ca with fluorite and limestone industries, and Si with the surrounding soils. The highest concentrations of Cu, Zn, and Ca were found in mixed and industrial land uses. On the contrary, the highest concentrations of “natural elements” (Al, K, Si, Rb, and Y) were found in developable land and residential areas. The highest contents of Ca were found in secondary and primary roads. The conclusions were: a) the metallurgical complex is the main source of Cu and Zn, and b) the highest concentrations of Cu, Zn, and Ca were found in mixed and industrial land uses.

The global need to transition to renewable and decentralized systems entails agricultural systems as producers of residual biomass. Avocado trees (Persea americana Mill.) are one of the main woody crops cultivated in Mexico, with over 150,000 hectares grown in the country. The aim of the present study is to evaluate the use of avocado pruning residues as an energy input, focusing on the revalorization of biomass and the generation of economic benefits for small producers in the region. For that purpose, we developed allometric equations to calculate biomass availability from pruning residues, evaluated their thermochemical properties, and proposed technological alternatives for their energy use. Two allometric equations for pruning residues as a function of tree height and crown diameter were obtained: one for light and minor maintenance pruning (R² = 0.63) and one for rejuvenation pruning (R² = 0.86). From these equations, we estimate the mean amount of biomass generated from light and rejuvenation pruning to be 42.7 and 25.1 kg per tree and year, respectively, which amounts to 1324 and 780 kg ha⁻¹ DM (dry matter), with an energy potential of 26.2 and 15.4 GJ ha⁻¹. The thermochemical analysis shows that a higher proportion of leaves generates a higher ash content, which reduces the quality of the residues as a fuel. Avocado pruning residues have high potential for energy use and could be implemented in the industrial and residential sectors, generating a complementary source of income for producers.

Avocado orchards (Persea americana) in Mexico are constantly being expanded to meet the increasing demand for the fruit in the national and international markets. The land-use change (LUC) caused by this expansion has numerous negative impacts, including greenhouse gas (GHG) emissions due to the loss of forest cover and the burning of pruning residues. To generate a comprehensive evaluation of this complex environmental issue, we calculate emissions from LUC and from residue burning between 1974 and 2017 at a local scale (1:20,000), and the energy potential of pruning residues was estimated as an alternative to revalue a waste product and mitigate the negative impacts of avocado cultivation. Our results show that land-use conversions emitted 390.5 GgCO₂, of which 91% came from conversions to avocado orchards. Emissions of GHG from biomass burning amounted to an additional 20.68 GgCO₂e released per year. Given that around 12,600 tons of dry avocado pruning residues are generated annually in the study region, their use for energy generation could replace 240 TJ/year of fossil fuels in rural industries and could mitigate around 31 GgCO₂e per year. This study provides decision-makers with a concrete example of how to establish multiple-impact strategies at local scales.

Avocado farmworkers are potentially exposed to multiple pesticides, which may influence hematological parameters and detoxification enzyme activities. This study enrolled 105 avocado farmworkers from Michoacán, Mexico, during May-August 2021. We quantified 12 pesticide metabolites in urine samples collected at two visits (12-13 weeks apart) and estimated annual exposure intensity scores (EIS) using a questionnaire-based algorithm. Blood samples were analyzed for hemoglobin (Hb), hematocrit, platelet count, white blood cell (WBC) count, and enzymatic activities of acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and paraoxonase 1 (PON1). We assessed single-metabolite associations using multivariable linear regression with false discovery rate (FDR) correction, and mixture effects using Bayesian weighted quantile sum (BWQS) regression. In single-metabolite models, higher hydroxy-thiabendazole was associated with higher WBC count (β per 2-fold increase = 0.21; 95% CI: 0.07, 0.35), higher hydroxy-pyrimethanil was associated with lower WBC count (β per 2-fold increase = -0.14; 95% CI: -0.26, -0.02), and higher trans-DCCA (a pyrethroid metabolite) was associated with lower Hb (β per 2-fold increase = -0.24; 95% CI: -0.46, -0.01). These associations were attenuated after FDR correction. Annual EIS was not significantly associated with hematological parameters or AChE, BChE, or PON1 activities. BWQS analyses detected no significant mixture effects. Although no associations were robust to multiple-comparison correction, suggestive patterns for fungicide and pyrethroid metabolites warrant confirmation in larger studies with repeated exposure measurement and outcome monitoring.

Background: This technical note presents a methodological approach to agrichemical inventory documentation. It complements exposure assessments in field settings with time-restricted observational periods. Conducted in Michoacán, Mexico, this method leverages large language model (LLM) capabilities for categorizing agrichemicals from brief video footage. Method: Given time-limited access to a storage shed housing various agrichemicals, a short video was recorded and processed into 31 screenshots. Using OpenAI’s ChatGPT (model: GPT-4o^®), agrichemicals in each image were identified and categorized as fertilizers, herbicides, insecticides, fungicides, or other substances. Results: Human validation revealed that the LLM accurately identified 75% of agrichemicals, with human verification correcting entries. Conclusions: This rapid identification method builds upon behavioral methods of exposure assessment, facilitating initial data collection in contexts where researcher access to hazardous materials may be time limited and would benefit from the efficiency and cross-validation offered by this method. Further refinement of this LLM-assisted approach could optimize accuracy in the identification of agrichemical products and expand its application to complement exposure assessments in field-based research, particularly as LLM technologies rapidly evolve. Most importantly, this Technical Note illustrates how field researchers can strategically harness LLMs under real-world time constraints, opening new possibilities for rapid observational approaches to exposure assessment.