Delineating spring protection zones is key to managing groundwater. This work presents a proposal for delineating spring protection zones (SPZs) that uses hydrogeological, topographical, land use, ...and climate characteristics as a basis to protect springs located in fractured volcanic media from potential contamination processes. This was accomplished through five stages: (1) identification of hydrogeological characteristics of the environments in which springs are located and physicochemical properties of water, (2) delineation of spring potential catchment zones, (3) estimation of spring recharge zones in the potential catchment zones, (4) SPZ proposal based on annual recharge analysis for each spring, and (5) projection of future land use and climate change scenarios. The result was a proposal of three typically established zones for protecting springs: SPZ1 was defined by a 50-m radius around springs, SPZ2 was delineated based on spring annual recharge zone estimate, and SPZ3 was considered the remainder of potential catchment zone. By delineating these zones, more suitable protection measures can be identified based on trends in land use and climate changes, measures which would thereby aid in sustainable use of these types of springs.
Fire suppression and climate change have increased the frequency and severity of wildfires, but the responses of many organisms to wildfire are still largely unknown. In this study, we assessed the ...risk of habitat loss for amphibians, mammals, and reptiles caused by wildfires in central Mexico. We accomplished this by: (1) determining the likelihood of wildfire occurrence over a 12-year period using historical records and the Poisson probability mass function to pinpoint the most susceptible areas to wildfire; (2) evaluating species exposure by identifying natural land use that aligns with the potential distribution areas of biodiversity; (3) assessing species vulnerability based on the classifications established by the IUCN and CONABIO. Our findings have unveiled three regions exhibiting a concentration of high-risk values. Among these, two are positioned near major urban centers, while the third lies in the southeastern sector of the Nevado de Toluca protection area. Amphibians emerged as the taxonomic group most severely impacted, with a substantial number of species falling within the Critically Endangered and Endangered categories, closely followed by mammals and reptiles. Furthermore, we have identified a correlation between the location of risk zones and agricultural areas. This study revealed hotspots that can offer valuable guidance for strategic initiatives in fire-prone regions associated to the potential distribution of amphibians, mammals, and reptiles. Moreover, future studies should contemplate integrating field data to enhance our comprehension of the actual effects of wildfires on the spatial distribution of these animal groups.
•An objective function is needed to evaluate the suitability of the near-optimum location of light pollution sensors.•The representativeness of a set of locations are related not only to the light ...intensity over a satellite image, but also to the environmental vulnerability of each location.•Measurements in the most vulnerable areas of a city may optimally represent a wide region of the studied territory.
Light Pollution is an environmental problem that needs to be retrieved by experimental means. However, to the best of our knowledge, there is no methodology nor a quantitative procedure to determine an optimal light pollution monitoring network. In this work, we propose a methodology for locating sensors in a light pollution monitoring network by formulating an optimization problem. We introduce an objective function that measures the representativeness of a set of locations using the spatial semi-variance over an image, and different levels of monitoring needs according to the environmental vulnerability of each location. To apply the methodology to a region of interest, we consider three inputs: a Nighttime-Light Image NTLI, an Environmental Vulnerability Map, and a constrained number of sensors. The output is a set of coordinates to locate sensors that consider the intensity of luminosity in nighttime images and its environmental impact. A case study shows that the methodology locates sensors in the most vulnerable areas in which measurements may optimally represent a wide region of the studied territory.
Light pollution is a global environmental issue that affects photosensitive organisms. For instance, several researchers have recognized melatonin suppression in humans as a direct cause of long-term ...exposure to high artificial light levels at night. Others have identified low melatonin levels as a risk factor for a higher prevalence of hormone-sensitive cancer. This paper analyzes the association between light pollution, estimated as the emission analysis of satellite worldwide nighttime light collections from 1999 to 2012, and 25,025 breast and 16,119 prostate cancer events from 2003 to 2012. Both types of cancer increased during the study period, but light pollution increased in urban and peri-urban areas and decreased in rural areas. Cumulative light pollution during 5 years showed a positive association with breast cancer but not with prostate cancer. The association between light pollution and breast cancer persisted when adjusted to age-standardized rates with a mean increase of 10.9 events per 100,000 population-year (95% confidence interval 7.0 to 14.8). We conclude that exposure to elevated light pollution levels could be a risk factor for breast cancer in Slovakia. This work can interest researchers who study relationships between atmospheric pollutants and the growing cancer epidemic. The results and the methodology can be extrapolated to any country in the world if data is available.
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•Light pollution is a global environmental issue that affects photosensitive organisms.•Long-term exposure to light pollution can be a risk factor for breast cancer.•Breast cancer incidence in Slovakia showed a positive relation with light pollution.•Prostate cancer did not relate to light pollution.
Geomorphological classification serves as a valuable tool for comprehending the origin and evolution of landscapes, as well as for making informed decisions regarding environmental hazard mitigation ...and sustainable development. However, the process of classifying landforms is typically time-consuming and necessitates specialized expertise. This research article presents a novel approach that utilizes a convolutional neural network (CNN) to classify valleys. The methodology involves employing an initial classification generated by an unsupervised geomorphons classifier as input data, which is subsequently refined using human-generated ground truth. In contrast with the original geomorphons method, this novel method enhances spatial coherence by effectively connecting pixels classified as valleys. The results show that the proposed CNN-based method significantly enhances the accuracy of the classification. We are confident our approach is competitive according to the Total Operating Characteristic (TOC) curve as well as classification metrics.
Landform classification is the basis for understanding and describing the processes and evolution of landscape. This process usually requires elevation information from different sources, expertise ...and time. Automatic geomorphological classification, via the geomorphons algorithm, supports expert classification by using local ternary patterns for labeling landform elements, significantly reducing the computation time. Nevertheless, it presents issues such as a noisy output, valleys that are not classified as continuous forms, valleys that are classified as peaks at low altitude, flat zones inside the valley that are not classified as a part of it, and other similar issues. In this proposal, we tackle the mentioned issues for valley classification by binarizing the geomorphons output and applying it binary-image operators. The proposal's performance is measured by using binary classification metrics and expert-made groundtruth images. The results show that the accuracy, balanced accuracy, and F1 metrics are greater than those delivered by the geomorphons classifier for all the instances in the testing data.
One of the most common problems related to meteorological information is the missing registers. This lack of data generates uncertainties in the analysis of climate, hydrology, and natural disasters. ...In Mexico, very often, this problem is present in all the meteorological stations of the country. In this study, we apply two well-established spatial interpolation methods that have report competitive performance in the specialized literature: the Inverse Distance Weighting (IDW) and Modified Inverse Distance Weighting (MIDW); and they are compared with a proposal of spatio-temporal regression using an artificial neural network of the kind of multilayer perceptron (MLP). The results show that using a combination of spatial and temporal data with a low number of predictors is competitive with the comparing methods using a high number of predictors. We compare the methods through statistical measures of the error for 31 meteorological stations of the Jalisco state in the period of 2002-2006.
