Search

This research presents a time‐series study in one of the most polluted regions in Mexico, the southern part of the Mezquital Valley. Three mortality causes related to areas highly contaminated by industrial activities were considered to carry out this model, namely, ischemic cardiomyopathy, mesothelioma, and pneumoconiosis. The pollutant exposure factors used in the maximum entropy modeling were distance to rivers, distance to industries, particulate matter less than 2.5 microns (PM < 2.5 µm), and the digital elevation model (DEM). A model that expresses the presence of the disease by areas of exposure to pollutants was also obtained. In addition, the odds ratio was calculated to evaluate the level of association of ischemic cardiomyopathy (OR = 3.37 and 95% CI: 3.05–3.6) and mesothelioma (OR = 4.79 and 95% CI: 3.5–6.08) by areas of exposure. In the case of pneumoconiosis, only cases in the very high exposure category were recorded, so it was not comparable with the remaining areas. It is important to mention that particulate matter in the municipalities of the Mezquital Valley presented values above 20 μg/m3 and that in accordance with the provisions of the Norma Oficial Mexicana de Salud Ambiental or NOM (translated as Mexican Official Standard for Environmental Health) and the Agency for Toxic Substances and the Disease Registry (ATSDR), high concentrations of particulate matter can have a severe impact on the development of some diseases. In the studied area, ischemic cardiomyopathy and mesothelioma were attributed to pollution in 70.3% and 79.1%, respectively; therefore, pollution mitigation could prevent the occurrence of these two diseases. [ABSTRACT FROM AUTHOR]

Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed to evaluate different models for chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) in a surface water body, the J. A. Alzate dam, in the Mexican highland region (R2 ≥ 0.78 and RMSE ≤ 16.1 mg/L). The models were estimated using multivariate regressions, with a focus on identifying dilution and dragging effects in inter-annual flow rate estimations, including runoff from precipitation and municipal discharges. Second, the study sought to analyze the potential scope of application for these models in other water bodies by comparing mean WQP values. Several models exhibited similarities, with minimal differences in mean values (ranging from −9.5 to 0.57 mg/L) for TSS, TN, and TP. These findings suggest that certain water bodies may be compatible enough to warrant the exploration of joint modeling in future research endeavors. By addressing these objectives, this research contributes to a better understanding of the suitability of remote sensing-based models for characterizing surface water quality, both within specific locations and across different water bodies. [ABSTRACT FROM AUTHOR]

In recent years, the morbidity and mortality rates caused by stings and bites of poisonous species have been constant in Mexico; such a phenomenon has been emphasized due to the dominance or modification of the natural geosystem. The modification in the availability of water resources has caused changes in the climate, extreme droughts, and floods that influence the distribution of species, generating risks where they did not occur before. With the aforementioned, it is important to identify risky points through the development of new cartography in the country, which allows an analysis from a spatial and geostatistical perspective. Based on the number of victims of stings or bites, there will be a sharp increase in exposure to poisonous animals where the distribution of these species overlaps with areas of high vulnerability as well as social and natural contact in Mexico. The aim of this study is to model the anthropogenic risk of poisonous species in Mexico in a spatial way (data from 2010–2017). The spatial analyses of this study were carried out throughout the Mexican territory and focused on species such as coral snakes, rattlesnakes, scorpions, and centipedes. The variables of vulnerability, danger, and exposure were considered to create a generalized risk model using the core area alternative in the zonation program, allowing a spatial analysis. The methodology consisted of six stages: (1) the identification of threats and records collected from chosen poisonous animals; (2) obtaining risk models by using the Zonation software that summarized all the species distribution modeling (SDM); (3) the development of a general anthropogenic vulnerability indicator; (4) obtaining the general exposure model with the index of accessibility to medical services; (5) obtaining risk models; and (6) the validation of risk models with morbidity and mortality rates by obtaining geostatistical models. The highlighted risk areas are the Pacific Ocean coast from Southern Sinaloa to the border of Michoacán, a corridor from central Veracruz to northern Oaxaca, central Guerrero, northern Michoacán, and northwestern Nuevo León. [ABSTRACT FROM AUTHOR]

Mexico is among the countries that are facing the greatest water stress, where factors such as climate change, contamination of surface water, groundwater sources, and inefficient management have limited the availability of water resources. Consequently, new supply sources need to be implemented. Rainwater harvesting systems (RHS) are viable and sustainable alternatives, the implementation of which primarily depends on identifying suitable sites and applying technologies that are appropriate for different users. This research used the Analytical Hierarchy Process (AHP) technique in a GIS environment to select the optimal sites for designing RHS, taking into account hydrological, biophysical, and socioeconomic criteria. After determining the ideal sites, the study presents proposals and costs for the design of an urban and rural RHS based on the characteristics of the region and the needs of the community. The findings show that implementing RHS in the study area can be a practical, economical, and efficient alternative for water resource management, since these projects are aimed at sustainability. [ABSTRACT FROM AUTHOR]

The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results still need to be improved. In this study, linear, polynomial, and exponential models are developed to forecast rainfall in the Bani and Senegal River basins. All three models use Atlantic sea surface temperature (SST). A fourth algorithm using stepwise regression was also developed for the precipitation estimates over these two basins. The stepwise regression algorithm uses SST with covariates, mean sea level pressure (MSLP), relative humidity (RHUM), and five El Niño indices. The explanatory variables SST, RHUM, and MSLP were selected based on principal component analysis (PCA) and cluster analysis to find the homogeneous region of the Atlantic with the greatest predictive ability. PERSIANN-CDR rainfall data were used as the dependent variable. Models were developed for each pixel of 0.25° × 0.25° spatial resolution. The second-order polynomial model with a lag of about 11 months outperforms all other models and explains 87% of the variance in precipitation over the two watersheds. Nash–Sutcliffe efficiency (NSE) values were between 0.751 and 0.926 for the Bani River basin and from 0.175 to 0.915 for the Senegal River basin, for which the lowest values are found in the driest area (Sahara). Results showed that the North Atlantic SST shows a more robust teleconnection with precipitation dynamics in both basins. [ABSTRACT FROM AUTHOR]

Technical, economic, regulatory, environmental, and social and political interests make the process of selecting an appropriate wastewater treatment technology complex. Although this problem has already been addressed from the dimensioning approach, our proposal in this research, a model of decision making for conventional secondary treatment of municipal wastewater through continuous-discrete, non-parametric Bayesian networks was developed. The most suitable network was structured in unit processes, independent of each other. Validation, with data in a mostly Mexican context, provided a positive predictive power of 83.5%, an excellent kappa (0.77 > 0.75), and the criterion line was surpassed with the location of the model in a receiver operating characteristic (ROC) graph, so the model can be implemented in this region. The final configuration of the Bayesian network allows the methodology to be easily extended to other types of treatments, wastewater, and to other regions. [ABSTRACT FROM AUTHOR]

This paper shows the effects of changes in the spatial-temporal behavior and phase shift of climate variables on rainfed agriculture in the Lerma-Chapala-Santiago Basin in central Mexico. Specifically, changes in rainfall (R), maximum temperature (Tmax), and minimum temperature (Tmin) were analyzed over two 25-year periods (1960 to 1985 and 1986 to 2010). Climate surfaces were generated by interpolation using the thin-plate smoothing spline algorithm in the software ANUSPLIN. Climate data were Fourier-transformed and fitted to a sinusoidal curve model, and changes in amplitude (increase) and phase were analyzed. The temporal behavior (1960–2010) indicated that rainfall was the most stable variable at the monthly level and presented no significant changes. However, Tmax increased by 2°C in the final period, and Tmin increased by 0.7°C at the end of the final period. The basin was discretized into ten rainfed crop areas (RCAs) according to the extent of changes in the amplitude and phase of the climate variables. The central and southern portions (55% of the area) presented more significant changes in amplitude, mainly in Tmin and Tmax. The remaining RCAs were smaller (14.6%) but presented greater variation: the amplitude of the Tmin decreased in addition to showing a phase shift, whereas Tmax increased in addition to showing a phase shift. These results translate into a delay in the characteristic temperatures of the spring and summer seasons, which can impact the rainfed crop cycle. Additionally, rainfall showed an annual decrease of approximately 50 mm in all RCAs, which can affect the phenological development of crops during critical stages (emergence through flowering). These changes represent a significant threat to the regional economy and food security of Mexico. [ABSTRACT FROM AUTHOR]

We analyzed the behavior of forest fires for daily prediction purposes in one of the regions with the highest fire incidence in Mexico. The main objective was to build logistic regression models (LRMs) for daily prediction of forest fire ignition based on meteorological drought indices. We built 252 LRMs for seven types of vegetation cover of greater representativeness and interest for the study area. Three dynamic variables were considered to estimate daily dryness in combustible fuels based on the effective drought index and the standardized precipitation-evapotranspiration index. Additionally, two weather data sources were included in drought indices: conventional weather stations (CWS) and automatic weather stations (AWS). Prediction efficiency assessment for LRMs was done through the relative operating characteristic (ROC) and model precision efficiency (MPE). The results show that LRMs using data from CWS performed relatively better than those based on data from AWS, as the former data sources have higher spatial density and thus generate predictions with higher accuracy (ROC ≥ 0.700, MPE ≥ 0.934). For both data sources, the use of standardized precipitation-evapotranspiration index as a fuel dryness estimator is recommended, as it reflects an atmospheric moisture balance between precipitation and reference evapotranspiration (ROC ≥ 0.734, MPE = 1). Such predictive models can be used as inputs in early warning systems for forest fire prevention or mitigation. [ABSTRACT FROM AUTHOR]

Different components of sustainability have been measured in previous studies at national, regional, and local scales. However, few efforts have aimed to measure water sustainability at the basin level. In this work, a water management sustainability index (WMSI) for quantifying water sustainability at the sub-basin level in Mexico is proposed. Variables related to water management in the environmental, social, economic, and institutional sub-systems of each sub-basin system were selected. The variables were aggregated in four sub-indexes based on a factor analysis, the sub-indexes scores were codified to indicate the level reached of water sustainability. The WMSI classified the water sustainability of the sub-basins of Mexico between poor and deficient, and the code classification indicated the level of compliance of the 969 sub-basins proposed by the National Institute of Statistics and Geography with sustainability criteria. [ABSTRACT FROM AUTHOR]

Spatial assessments of historical climate change provide information that can be used by scientists to analyze climate variation over time and evaluate, for example, its effects on biodiversity, in order to focus their research and conservation efforts. Despite the fact that there are global climatic databases available at high spatial resolution, they represent a short temporal window that impedes evaluating historical changes of climate and their impacts on biodiversity. To fill this gap, we developed climate gridded surfaces for Mexico for three periods that cover most of the 20th and early 21st centuries: t1-1940 (1910–1949), t2-1970 (1950–1979) and t3-2000 (1980–2009), and used these interpolated surfaces to describe how climate has changed over time, both countrywide and in its 19 biogeographic provinces. Results from our characterization of climate change indicate that the mean annual temperature has increased by nearly 0.2°C on average across the whole country from t2-1970 to t3-2000. However, changes have not been spatially uniform: Nearctic provinces in the north have suffered higher temperature increases than southern tropical regions. Central and southern provinces cooled at the beginning of the 20th century but warmed consistently since the 1970s. Precipitation increased between t1-1940 and t2-1970 across the country, more notably in the northern provinces, and it decreased between t2-1970 and t3-2000 in most of the country. Results on the historical climate conditions in Mexico may be useful for climate change analyses for both environmental and social sciences. Nonetheless, our climatology was based on information from climate stations for which 9.4–36.2% presented inhomogeneities over time probably owing to non-climatic factors, and climate station density changed over time. Therefore, the estimated changes observed in our analysis need to be interpreted cautiously. [ABSTRACT FROM AUTHOR]

Humans greatly benefit from natural water resources, also known as hydrological ecosystem services. However, these services may be reduced by population growth, land use changes, and climate change. As these problems become more critical, the need to quantify water resources increases. The estimation of water yield and its distribution are of great importance for the management of water resources. In the present study, the average annual water yield of the hydrographic basins in the southern region of Ecuador was estimated for the 1970-2015 period using the InVEST water yield model based on the Budyko framework. The model estimates annual surface run-off at the pixel, sub- basin, and basin level considering the following variables: precipitation, actual evapotranspiration, land cover/use, soil depth, and available water content for plants. The model was calibrated by varying the ecohydrological parameter Z to reduce error between estimated and observed water yield. The results showed that the modeling of water yield in the majority of the hydrographic basins was satisfactory, allowing the basins to be ranked according to their importance for water production. The Mayo and Zamora basins had the highest water production, corresponding with 934 and 1218mm per year, respectively, while the Alamor and Catamayo basins had the lowest water production, corresponding with 206 and 291mm per year, respectively. The present study provides an initial estimate of water yield at the basin level in the southern region of Ecuador, and the results can be used to evaluate the impacts of land cover changes and climate change over time. [ABSTRACT FROM AUTHOR]

The current study presents a method for automating the Köppen–Garcia climate classification using a GIS module. This method was then applied in a case study of the Lerma-Chapala-Santiago watershed to compare time series data on climate from 1960 to 1989, 1981 to 2010, and 1960 to 2010. The kappa statistic indicated that the climate classifications of the generated model had a perfect degree of agreement with those of a prior nonautomated study. The climate data from the period 1960 to 2010 were used to create a climate map for the watershed. Overall, the dominant climates were dry, semiarid, temperate, and semiwarm temperate with a summer rainfall pattern. A comparative analysis of climate behavior between 1960 and 1989 and between 1981 and 2010 showed changes in temperature and extreme temperatures over 13.6% and 9.9%, respectively, of the watershed; the presence or absence of mid-summer drought also changed over 0.8% of the watershed. The module developed herein can be used to classify climates across all of Mexico, and data of varying spatial resolution and coverage can be inputted to the module. Finally, this module can be used to automate the creation of climate maps or to update climate maps at diverse spatial-temporal scales. [ABSTRACT FROM AUTHOR]

This paper analyzed the evolution of climate data in the Rio Bravo Basin in Mexico from 1980-2009 and projects future climate conditions in this region. Then, the potential impacts of climate change on water resources for crops in the nine irrigation districts (IDs) of the Rio Bravo Basin were evaluated. Specifically, climate data on precipitation, maximum and minimum temperatures, and evapotranspiration from the baseline period of 1980-2009 were compared with projected climate conditions for 2015-2039, 2045-2069, and 2075-2099. The projections were based on two representative concentration pathways (RCPs) of greenhouse gases in the atmosphere (RCP4.5 and RCP8.5). Patterns in the behavior of the analyzed climate variables over the past ten decades were examined and compared to the projected evolution of these variables through to the end of the century. Overall, in the future, temperatures, rates of evapotranspiration, and crop water demand are expected to increase. Also, the future precipitation patterns of all IDs were modified under the considered scenarios. Finally, the IDs of Acuña-Falcón and Delicias will be the most impacted by climate changes, while Palestina will be the least affected. [ABSTRACT FROM AUTHOR]

Mexico is a diverse country in terms of culture and natural environments. For this reason, the delimitation of homogeneous basins with similar environmental, social, and economic attributes is important in order to facilitate the elaboration of high-impact regional development strategies. However, this represents an ongoing challenge due to the complexity of the interactions that occur within socio-ecological systems at a regional scale. In the present study, the main objective was to identify the interrelationships among different aspects of the socio-ecological system located within basins, with the goal of utilizing this information to promote the region-specific sustainable development of an Integrated Water Resources Management (IWRM). Therefore, in this study, environmental, social, economic, and institutional variables, relevant to water management and with the capacity to be expressed spatially, were utilized to identify regions with similar characteristics and to regionalize the urban sub-basins of Mexico based on a principal component analysis (PCA) and the k-medoids clustering algorithm. The identification of the most adequate number of regions at the national level was determined by the silhouette method. As a result, five distinct regions for Mexico were generated, which forms the first step in the design of integrated water resources management strategies for these regions. [ABSTRACT FROM AUTHOR]

The Balsas River basin is an area with limited water availability; however, it annually exports 490 hm3 of water to the Valley of Mexico basin through the Cutzamala system. If the forests of this basin deteriorates, the water resources will also diminish and consequently its exported volume will be at risk. For these reasons it is necessary to identify factors that favor the likelihood of fire occurrence in the forest of this basin. In this way, actions can be taken in a preventive manner to manage these phenomena in order to minimize the vulnerability and deterioration of the resources in the region. In a balanced manner, forest fires are necessary for the natural restoration of the ecosystems, nevertheless their frequency has increased by anthropogenic causes and climate dynamics. The objective of this study was to build a logistic regression model for the daily detection of areas with forest fire hazard. This proposal took into account topographic, meteorological and anthropogenic variables for the period from 2006 to 2009. The results provided a spatial and temporal effectiveness larger than 86 %. A risk scale (low, moderate, high and very high) was proposed in accordance with its probability of occurrence. In the analysis, more than 85 % of the fires had a daily category of high or very high risk of ignition. The greatest danger was found in the dams catchment areas of the Valle de Bravo, Colorines-Chilesdo and Ixtapan del Oro, of the Cutzamala system. The results of this study suggests that the methodology can be used to improve early warning systems against fires and complement the design of the strategic location of command centers, camps, watchtowers, as well as priorities for equipment. [ABSTRACT FROM AUTHOR]

ABSTRACT Climate surfaces are digital representations of climatic variables from a region in the planet estimated via geographical interpolation techniques. Climate surfaces have multiple applications in research planning, experimental design, and technology transfer. Although high-resolution climatologies have been developed worldwide, Mexico is one of the few countries that have developed several climatic surfaces. Here, we present an updated high-resolution (30 arc sec) climatic surfaces for Mexico for the average monthly climate period 1910-2009, corresponding to monthly values of precipitation, daily maximum, and minimum temperature, as well as 19 bioclimatic variables derived from the monthly precipitation and temperature values. To produce these surfaces we applied the thin-plate smoothing spline interpolation algorithm implemented in the ANUSPLIN software to nearly 5000 climate weather stations countrywide. As an additional product and unlike the previous efforts, we generated monthly standard error surfaces for the three climate parameters, which can be used for error assessment when using these climate surfaces. Our climate surface predicted slightly drier and cooler conditions than the previous ones. ANUSPLIN diagnostic statistics indicated that model fit was adequate. We implemented a more recent error assessment, a set of withheld stations to perform an independent evaluation of the model surfaces. We estimate the mean absolute error and mean error, with the withheld data and all the available data. Average RTGCV for monthly temperatures was of 1.26-1.12 °C and 24.67% for monthly precipitation, and a RTMSE of 0.48-0.56 °C and 11.11%. The main advantage of the surfaces presented here regarding the other three developed for the country is that ours cover practically the entire 20th century and almost the entire first decade of the 21st century. It is the most up to date high-resolution climatology for the country. [ABSTRACT FROM AUTHOR]

Water is one of the primary resources for humans; however, clean water available for consumption is a constant problem worldwide. Nonetheless, environmental education is fundamental to promote social consciousness and orientate people to improve water management, and children are the main target as they are prone to adopt long-lasting habits. Therefore, this study aimed to implement an environmental education program on water conservation in 10-year-old students from two rural communities within the Nenetzingo River catchment in Mexico. The study was a three-step methodology that included diagnostics, education, and evaluation. It was found that the students that participated in the educational program enriched their knowledge, attitudes, and perception towards water. Therefore, implementing environmental education programs in young students can improve water use and conservation in Mexico. Additionally, the design used in this research allows identifying the target groups for immediate intervention. [ABSTRACT FROM AUTHOR]

This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash–Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R2). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R2 > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin. [ABSTRACT FROM AUTHOR]

This study explored climate variability in the Upper Lerma River Basin, State of Mexico, Mexico, at three timescales: annual (1960–2010), monthly (1980–2010) and seasonal (1980–2010). The effects of monthly and seasonal (2003–2010) variability on rainfed maize crops were also evaluated. The variables of rainfall, maximum temperature, minimum temperature and number of hailstorms were interpolated to generate monthly spatial-temporal series. Over a period of 51 years, the climate of the region shows an accumulative annual increase of 131 mm in rainfall and an increase of 0.8 and 0.74 °C in maximum and minimum temperature, respectively. In conclusion, significant changes in the climate variables were found at the three analyzed timescales. Seasonal climate changes were found to coincide with the most vulnerable stage or flowering period of maize; particularly, a shift in the rainfall pattern generates a water deficit that impacts production yield. Hailstorms have increased in frequency, yet their phase shift results in a lesser impact to maize during its most critical stage of development. [ABSTRACT FROM AUTHOR]

Dengue is the most important viral disease transmitted by mosquitoes, predominantly Aedes (Stegomyia) aegypti (L.) (Diptera:Culicidae). Forty percent of the world's population is at risk of contracting the disease, and a large area of Mexico presents suitable environmental conditions for the life cycle of Ae. aegypti. In particular, the Central Mexican Highlands have a high population density, increasing the risk of transmission and propagation of dengue. In the present study, the potential distribution of Ae. aegypti was modeled under an ecological niche approach using the maximum entropy technique with the aim of determining the spatial risk distribution of dengue. The final model of five variables (minimum temperature of the coldest month |Bio6|, precipitation of the wettest month |Bio13|, precipitation seasonality |Bio15|, the normalized difference vegetation index (NDVI), and relative humidity) contributed to more than 90% of the model's performance. The results of the potential distribution model were then compared with the number of dengue cases per locality during the 2009-2015 period considering four suitability of presence categories. Category 4 corresponded with the highest suitability of presence (0.747 to 1) and the greatest risk of dengue (odds ratio [OR] = 103.27; P < 0.001). In conclusion, the present ecological niche model represents an important tool for the monitoring of dengue and the identification of high-risk areas., (© The Author(s) 2019. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)