Earth, Volume 2, Issue 1 (March 2021) – 11 articles

Machine learning (ML), as an artificial intelligence tool, has acquired significant progress in data-driven research in Earth sciences. Land Surface Models (LSMs) are important components of the climate models, which help to capture the water, energy, and momentum exchange between the land surface and the atmosphere, providing lower boundary conditions to the atmospheric models. The objectives of this review paper are to highlight the areas of improvement in land modeling using ML and discuss the crucial ML techniques in detail. Literature searches were conducted using the relevant key words to obtain an extensive list of articles. The bibliographic lists of these articles were also considered. To date, ML-based techniques have been able to upgrade the performance of LSMs and reduce uncertainties by improving evapotranspiration and heat fluxes estimation, parameter optimization, better crop yield prediction, and model benchmarking. Widely used ML techniques used for these purposes include Artificial Neural Networks and Random Forests. We conclude that further improvements in land modeling are possible in terms of high-resolution data preparation, parameter calibration, uncertainty reduction, efficient model performance, and data assimilation using ML. In addition to the traditional techniques, convolutional neural networks, long short-term memory, and other deep learning methods can be implemented. Full article

In recent years, rainfall-induced waterlogging has become a common hazard in the highly urbanized coastal city of Chattogram, Bangladesh, resulting in a high magnitude of property damage and economic loss. Therefore, the primary objective of this research was to prepare a waterlogging inventory map and understand the spatial variations of the risk by means of hazard intensity, exposure, and vulnerability of waterlogging. In this research, the inventory map and factors influencing waterlogging hazards were determined from a participatory survey, and other spatial data, including land elevation, population, and structural data, were collected from secondary sources. The analytical hierarchy process was applied to measure the hazard intensity, and the exposure and vulnerability were estimated by overlaying the spatial data onto the hazard intensity map. A total of 58 locations were identified as waterlogging affected, which covered ~8.42% of the city area. We showed that ~3.03% of the city area was greatly vulnerable to waterlogging in terms of their social, infrastructure, critical facilities, economic, and environmental vulnerabilities. The obtained waterlogging risk index map suggested that ~2.71% of the study area was at very high risk, followed by moderate (~0.15%), low (~3.89%), and very low (~1.67%). The risk analysis presented in this study was a simple method that can be applied to assess the relative risk of waterlogging in different regions, and the results were applicable to the prevention and mitigation of waterlogging for Chattogram City. Full article

Alzheimer’s disease (AD) is a chronic neurodegenerative disease and the most common form of dementia in older adults. Treatment of AD symptoms is very challenging and expensive. Appropriate diet as well as mental and physical activity may delay or reduce the occurrence of AD. It is unknown whether environmental factors offer potentially protective effects against the development of AD. We explored the possible beneficial effects of greenspace (trees and herbaceous cover) on the rate of AD in the mid-Atlantic US. Data for initial AD medical claims during 2011–2013 were obtained from Medicare records for 2999 ZIP codes. The percentages of land cover classes in each ZIP code were calculated based on high-resolution land cover imagery. Associations between AD and greenspace, blue space (water), and other variables were examined using zero-inflated Poisson models. The rate of AD was negatively associated with greenspace (for a greenspace increase of 10%, risk ratio (RR) = 0.91, 95% confidence interval (CI): 0.89–0.94), and blue space (for a water area increase of 10%, RR = 0.85, 95% CI: 0.81–0.89). The inverse relationships between greenspace and the risk of AD held across season, gender, and race. The rate of AD was positively associated with the concentration of fine particulate matter (PM2.5) (RR = 1.03, 95% CI: 1.02–1.05 for an increase in PM2.5 of 1 µg/m³). Our results suggest that greenspace may have protective effects for AD, although potential mechanisms are unclear and require further investigation. Full article

Environmental pollution has a great impact on human health, ecosystems, and financial development. This paper depicts the recent studies on the severity of environmental pollution in developing countries. Its remedial measures were based on a questionnaire survey in the polluted sites, which collected data and information on the types, causes, effects, sources, and duration of environmental pollution, obtained from available publications and newspaper information reported in recent years. A total of 400 respondents from 10 zones of Dhaka City Corporation, Bangladesh, were interviewed as a case study via a semi-structured questionnaire survey. The results revealed that only 39.0% of respondents had explicit knowledge about environmental pollution. Air pollution was identified by 73.8%, noise pollution by 63.0%, water pollution by 55.2%, and soil pollution by only 6.5% of respondents in their surroundings. Automobiles, domestic activities, municipal garbage, and vehicle horns are significant sources of environmental pollutions. Around 49.0% of the respondents did not understand the effectiveness of currently conducted environmental programs. A discussion regarding the urgency of forming a local level environmental committee, the mass media’s active role, and monitoring the development activities was presented. Full article

The growing impact of CO₂ and other greenhouse-gas (GHG) emissions on the socio-climate system in the Western Cape, South Africa, urgently calls for the need for better climate adaptation and emissions-reduction strategies. While the consensus has been that there is a strong correlation between CO₂ emissions and the global climate system, few studies on climate change in the Western Cape have quantified the impact of climate change on local climate metrics such as precipitation and evaporation under different future climate scenarios. The present study investigates three different CO₂ emissions scenarios: Representative Concentration Pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, from moderate to severe, respectively. Specifically, we used climate metrics including precipitation, daily mean and maximum near-surface air temperature, and evaporation to evaluate the future climate in Western Cape under each different RCP climate scenario. The projected simulation results reveal that temperature-related metrics are more sensitive to CO₂ emissions than water-related metrics. Districts closer to the south coast are more resilient to severer GHG emissions scenarios compared to inland areas regarding temperature and rainfall; however, coastal regions are more likely to suffer from severe droughts such as the “Day-Zero” water crisis. As a result, a robust drying signal across the Western Cape region is likely to be seen in the second half of the 21st century, especially under the scenario of RCP 8.5 (business as usual) without efficient emissions reduction policies. Full article

Rapid Urbanization, and other anthropogenic activities, have amplified the change in land-use transition from green space to heat emission in built-up areas globally. As a result, there has been an increase in the land surface temperature (LST) causing the Urban Heat Island (UHI) effect, particularly in large cities. The UHI effect poses a serious risk to human health and well-being, magnified in large developing cities with limited resources to cope with such issues. This study focuses on understanding the UHI effect in Kathmandu Valley (KV), Delhi, and Dhaka, three growing cities in South Asia. The UHI effect was evaluated by analyzing the UHI intensity of the city with respect to the surroundings. We found that the central urban area, of all three cities, experienced more heat zones compared to the peri-urban areas. The estimated average surface temperature ranged from 21.1 ${}^{\circ }$C in March 2014 to 32.0 ${}^{\circ }$C in June 2015 in KV, while Delhi and Dhaka experienced surface temperature variation from 29.7 ${}^{\circ }$C in June 2017 to 40.2 ${}^{\circ }$C in June 2019 and 23.6 ${}^{\circ }$C in March 2017 to 33.2 ${}^{\circ }$C in March 2014, respectively. Based on magnitude and variation of LST, highly built-up central KV showed heat island characteristics. In both Delhi and Dhaka, the western regions showed the UHI effect. Overall, this study finds that the UHI zones are more concentrated near the urban business centers with high population density. The results suggest that most areas in these cities have a rising LST trend and are on the verge of being UHI regions. Therefore, it is essential that further detailed assessment is conducted to understand and abate the impact of the temperature variations. Full article

In this study, an inventory of storm-triggered debris flows performed in the area of the San Vicente volcano (El Salvador, CA) was used to calibrate predictive models and prepare a landslide susceptibility map. The storm event struck the area in November 2009 as the result of the simultaneous action of low-pressure system 96E and Hurricane Ida. Multivariate Adaptive Regression Splines (MARS) was employed to model the relationships between a set of environmental variables and the locations of the debris flows. Validation of the models was performed by splitting 100 random samples of event and non-event 10 m pixels into training and test subsets. The validation results revealed an excellent (area under the receiver operating characteristic (ROC) curve (AUC) = 0.80) and stable (AUC std. dev. = 0.01) ability of MARS to predict the locations of the debris flows which occurred in the study area. However, when using the Youden’s index as probability threshold to discriminate between pixels predicted as positives and negatives, MARS exhibits a moderate ability to identify stable cells (specificity = 0.66). The final debris flow susceptibility map, which was prepared by averaging for each pixel the score of the 100 MARS repetitions, shows where future debris flows are more likely to occur, and thus may help in mitigating the risk associated with these landslides. Full article

This study uses modern and historic spatial data to analyze land use around 13th Century AD Bulgarian fortified settlements to examine the current state of these features and how changes in land use over the past two centuries have affected these features. Historic maps from the late 18th to the early 19th centuries AD were used alongside Landsat 8 images from 2015–2018 as a source of information about land use. Based on the results of archival map analysis and the classification of satellite imagery, the interpretation of land use around Bulgarian fortified settlements was possible. This method generated new data about land use dynamics near cultural heritage sites in the Volga Region. The diachronic study of sequential map data allowed researchers to further understand how anthropogenic factors have impacted the survivability of Bulgarian sites in the region. Among these, arable farming, hydro-electric power generation, and urban growth have had the greatest impact on these features. Full article

Streamflow and sediment flux variations in a mountain river basin directly affect the downstream biodiversity and ecological processes. Precipitation is expected to be one of the main drivers of these variations in the Himalayas. However, such relations have not been explored for the mountain river basin, Nepal. This paper explores the variation in streamflow and sediment flux from 2006 to 2019 in central Nepal’s Kali Gandaki River basin and correlates them to precipitation indices computed from 77 stations across the basin. Nine precipitation indices and four other ratio-based indices are used for comparison. Percentage contributions of maximum 1-day, consecutive 3-day, 5-day and 7-day precipitation to the annual precipitation provide information on the severity of precipitation extremeness. We found that maximum suspended sediment concentration had a significant positive correlation with the maximum consecutive 3-day precipitation. In contrast, average suspended sediment concentration had significant positive correlations with all ratio-based precipitation indices. The existing sediment erosion trend, driven by the amount, intensity, and frequency of extreme precipitation, demands urgency in sediment source management on the Nepal Himalaya’s mountain slopes. The increment in extreme sediment transports partially resulted from anthropogenic interventions, especially landslides triggered by poorly-constructed roads, and the changing nature of extreme precipitation driven by climate variability. Full article

Surface sediments from Khnifiss lagoon (Morocco) were analyzed to evaluate the contamination degree of the area. Concentrations of V, Cr, Co, Ni, Cu, Zn, As, Cd, Hg, and Pb were determined on samples taken during the summer and the autumn of 2016. On the whole, higher concentrations were found in the summer season. The results revealed the following average concentrations (mg/kg), reported in descending order: Zn (51.7 ± 31.3) > V (38.8 ± 24.7) > Cr (26.6 ± 17.8) > Ni (16.5 ± 5.47) > As (8.50 ± 2.00) > Cu (6.60 ± 3.81) > Pb (6.13 ± 3.46) > Co (3.57 ± 2.09) > Cd (0.16 ± 0.11) > Hg (0.006 ± 0.001). Organic matter showed a positive significant correlation with some trace metals (mainly V, Cr, Co, Zn, Cd, Pb). Three pollution indices were calculated: Enrichment Factor (EF), Index of Geo−accumulation (Igeo), and Pollution Load Index (PLI). Minimal enrichments (for Zn, As, and Cd) were detected at some sampling points. Overall indices showed that the Khnifiss sediments can be classified as not contaminated, and that the trace metals amounts found are ascribable to the geogenic origin. The results of this work can be used as a starting point for further evaluations of trace metals distribution in Moroccan lagoons. Full article

The transportation sector of Senegal is dominated by the road subsector, which relies on fossil fuels: gasoline and diesel. Their combustion generates substances such as carbon dioxide, methane, nitrous oxide, and many others responsible for climate change, which has negative impacts on the environment, human health, and activities. This study is based on data collected from Senegal’s official reports on transport and energy, and Intergovernmental Panel on Climate Change (IPCC) greenhouse gases’ analysis methods. In the period 2000–2013, growing emissions were experienced, reaching up to 2.38 × 10⁶ tCO₂-eq in 2013. The aging vehicle fleet (~20 years old on average), made up of light-duty vehicles (around 85%), a fast-growing number of imported cars, and the predominance of diesel engines (around 59%) are the aggravating factors. Beyond climate change, other gaseous substances resulting from the combustion of fuels such as carbon monoxide (CO), sulfur oxide (SO₂), and particle matters (PMs) contribute to the deterioration of the outdoor air quality. Therefore, it is becoming urgent to monitor the evolution of these emissions and take appropriate measures to reduce their concentrations in the atmosphere. The Government of Senegal has taken a step forward through the modernization of transport infrastructure, and the creation of a center dedicated to the monitoring of outdoor air quality (Centre de Gestion de la Qualité de l’Air—CGQA) and a center in charge of the technical control of vehicles (Centre de Contrôle Technique des Véhicules Automobiles—CCTVA) in Dakar, but much remains to be done. Full article