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Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented. [ABSTRACT FROM AUTHOR]

Groundwater models serve the function of predicting and analyzing aquifer behavior. They require input information, such as hydrogeological parameters like hydraulic conductivity and storage coefficient, which are used to calibrate the model, and elementary actions that include recharge and extracted volumes. There are cases in which it is insufficient to know the homogeneous recharge entering through the surface basin, referred to as traditional recharge, since, in many instances, the distribution is altered by changes in land use. For this reason, based on the geomorphological characteristics of the basin, weighting is proposed for sites with greater recharge capacity. The present work shows a solution to the recharge distribution using the potential groundwater recharge (PGR) map, which is formed by weighting spatially distributed information: (i) drainage, (ii) precipitation, (iii) land use, (iv) geological faults, (v) soil type, (vi) slope, and (vii) hydrogeology. A comparison is made between groundwater modeling using traditional recharge and PGR recharge. It is noted that the modeling perform similarly for both recharges, and the errors do not exceed 5% absolute error, which validates the model's reliability. This manuscript demonstrates how to model and calibrate groundwater in aquifers with scarce information and variable recharge, making it reproducible. [ABSTRACT FROM AUTHOR]

Exposure to inorganic arsenic (InAs) through drinking water, even at low to moderate concentrations, is a global public health problem. The objectives of this study were to estimate the risk ratio (HQ), cancer risk (R), and DNA damage (comet assay) of children from three indigenous Yaqui populations located in southern Sonora, Mexico, who were exposed to InAs through drinking water. A cross-sectional study was employed, and analysis of InAs in water and urine was performed via HPLC/ICP-MS. InAs levels in drinking water from Pótam, Vícam, and Cócorit were 108.2, 36.0, and 6.2 µg/L-1 respectively. Children from Pótam had arsenic concentrations in urine of 107.1 µg As L-1 compared with 40.3 µg As L-1 for the children of Cócorit. The HQ values for the children of Pótam, Vícam, and Cócorit were 16.64, 6.02, and 0.94, while the R values were 9.4E-04, 3.5E-04, and 5.7E-05, respectively. Children with the highest arsenic exposure had significantly increased DNA damage (OTM = 14.4 vs. 4.3) [p < 0.0005] which positively correlated with urinary arsenic levels (r = 0.56, p < 0.0001). In conclusion, children of Pótam and Vícam are at significant risk of developing chronic diseases and cancers associated with chronic exposure to this metalloid. [ABSTRACT FROM AUTHOR]

The Monterrey Metropolitan Area (MMA) in Northeast Mexico has shown high PM2.5concentrations since 2003. The data shows that the annual average concentration exceeds from 2 to 3 times the Mexican PM2.5annual air quality standard of 12 µg/m3. In a previous work we studied the chemical characterization of PM2.5in two sites of the MMA during the winter season. Among the most important components we found ammonium sulfate and nitrate, elemental and organic carbon, and crustal matter. In this work we present the results of a second chemical characterization study performed during the summer time and the application of the chemical mass balance (CMB) model to determine the source apportionment of air pollutants in the region. The chemical analysis results show that the chemical composition of PM2.5is similar in both sites and periods of the year. The results of the chemical analysis and the CMB model show that industrial, traffic, and combustion activities in the area are the major sources of primary PM2.5and precursor gases of secondary inorganic and organic aerosol (SO2, NOx, NH3, and volatile organic compounds [VOCs]). We also found that black carbon and organic carbon are important components of PM2.5in the MMA. These results are consistent with the MMA emission inventory that reports as major sources of particles and SO2a refinery and fuel combustion, as well as nitrogen oxides and ammonium from transportation and industrial activities in the MMA and ammonium form agricultural activities in the state. The results of this work are important to identify and support effective actions to reduce direct emissions of PM2.5and its precursor gases to improve air quality in the MMA.Implications: The Monterrey Metropolitan Area (MMA) has been classified as the most air-polluted area in Mexico by the World Health Organization (WHO). Effective actions need to be taken to control primary sources of PM2.5and its precursors, reducing health risks on the population exposed and their associated costs. The results of this study identify the main sources and their estimated contribution to PM2.5mass concentration, providing valuable information to the local environmental authorities to take decisions on PM2.5control strategies in the MMA. [ABSTRACT FROM PUBLISHER]

The objective of this study is to investigate the drying kinetics of fruit and vegetable peel biowaste using a sustainable technique as a key-pretreatment for its conversion into useful feedstock. Biowaste represents a missed potential source of bioenergy and bioproducts, but moisture removal is required, and conventional drying methods are expensive since they require great quantity of energy supplied, almost always, by a non-renewable energy. In this study six batches with the same quantity of biowaste, and heterogeneous physical composition were dried under open-sun conditions. We evaluated the influence of the interaction between drying area and the initial moisture content on drying rate. Eight semi-theoretical models were fitted using Levenberg-Marquardt algorithm to predict drying rate, and their accuracy was assessed through goodness-of-fit tests. Maximum moisture content to preserve biomass (10%) was reached on 5 th day and the equilibrium on 16 th day of drying. According to goodness-of-fit test ( R 2  = 0.999, χ 2  = 4.666 × 10 -5 , RMSE = 0.00683) the best model to predict drying rate was Two-term model. The mathematical model obtained from Fick's second law is reliable to predict drying kinetics, R 2 (0.9648 ± 0.0106); despite the variation between drying area and initial moisture content. Kruskal-Wallis test showed that drying rates between batches are not significantly different ( p  = 0.639; 0.05); nor effective diffusion coefficient ( D eff  = 4.97 × 10 -11   ±  0.3491 × 10 -11 ), ( p  = 0.723; 0.05). The study of drying kinetics is crucial for selecting the optimal biowaste treatment based on its generation context. This could enable its use as feedstock for bioproduct or bioenergy production, thereby reducing waste accumulation in landfills and environmental impact., Competing Interests: The authors declare there are no competing interests., (©2024 Barajas Godoy et al.)