Environmental Sciences Proceedings doi: 10.3390/ECRS2023029084

Authors: Alexander Kokhanovsky

In submitting conference proceedings to Environmental Sciences Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...]

Environmental Sciences Proceedings doi: 10.3390/ecas2023027039

Authors: Anthony R. Lupo

In submitting conference proceedings to Environmental Sciences Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...]

Environmental Sciences Proceedings doi: 10.3390/ECRS2023029085

Authors: Alexander Kokhanovsky

The 5th International Electronic Conference on Remote Sensing, with a focus on “Advances in experimental and theoretical studies of the terrestrial atmosphere and underlying surface” was held on 7–21 November 2023 [...]

Environmental Sciences Proceedings doi: 10.3390/ecas2023027040

Authors: Anthony R. Lupo

The sixth International Electronic Conference on Atmospheric Sciences, the range of topics will remain more general, but we are open to subject areas with a thematic topic of importance, especially interdisciplinary or transdisciplinary science [...]

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16860

Authors: Anatoli Chaikovsky Andrey Bril Philippe Goloub Zhengqiang Li Vladislav Peshcherenkov Fiodar Asipenka Luc Blarel Gael Dubois Mikhail Korol Aliaksandr Lapionak Aleksey Malinka Natallia Miatselskaya Thierry Podvin Ying Zhang

The statistical characteristics of combined lidar and radiometric measurements obtained from satellite lidar CALIOP and ground-based sun-radiometer stations were used as input datasets to retrieve the altitude profiles of aerosol parameters (LRS-C technique). The signal-to-noise ratio of the input satellite lidar signals increased when averaging over a large array of measured data. An algorithm and software package for processing the input dataset of the LRS-C sounding of atmospheric aerosol in regions with medium and low aerosol loads was developed. This paper presents the results of studying long-term changes in the concentration profiles of aerosol modes in regions of East Europe (AERONET site Minsk, 53.92° N, 27.60° E) and East Antarctic (AERONET site Vechernaya Hill, 67.66° S, 46.16° E).

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-17345

Authors: Ehsan Rostami Rasool Vahid Arastou Zarei Meisam Amani

Wetlands are valuable natural resources that provide many services to both the environment and humans. Over the past several decades, climatic change and human activities have had a considerable impact on the water level of wetlands. Zarivar Lake, located in the northwestern region of Iran, represents a significant ecological unit and aquatic ecosystem. In this study, from 2015 to 2022, the relationship between seasonal changes in Zarivar Lake’s waterbody (LWB) area and weather factors like precipitation, evapotranspiration, and the temperature of the lake’s surface water (LSWT) were examined. For this purpose, the Google Earth Engine (GEE) cloud platform, a powerful and fast tool for processing the time series of images, was used. The LWB was extracted by utilizing the average images of the dual-polarized SAR Sentinel-1 imagery for each season. Furthermore, meteorological parameters encompass the utilization of the Landsat-8 satellite’s thermal band to determine LSWT by using statistical mono-window (SMW), the CHIRPS rainfall model data for assessing precipitation levels, and the employment of MODIS evapotranspiration products in the form of 8-day data. The study revealed significant correlations between variations in Zarivar Lake’s waterbody area and meteorological factors. Correlation coefficients indicated a positive relationship between LWB area and precipitation during the winter (r = 0.67) and spring (r = 0.73), while weaker positive correlations were observed in the summer (r = 0.29) and fall (r = 0.30). Conversely, the LWB area showed a relative relationship with LSWT, with positive correlations in winter (r = 0.10) and spring (r = 0.26), and negative correlations in summer (r = −0.30) and fall (r = −0.07). Additionally, evapotranspiration parameters aligned with precipitation changes throughout the seasons, highlighting the significant influence of climate on Zarivar Lake.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16889

Authors: Mohammad Reza Zargar Mahdi Hasanlou

Access to high spatial resolution satellite images enables more accurate and detailed analysis of these images. Furthermore, it facilitates easier decision-making on a wide range of issues. Nevertheless, there are commercial satellites such as Worldview that have provided a spatial resolution of fewer than 2.0 m, but using them for large areas or multi-temporal analysis of an area brings huge costs. Thus, to tackle these limitations and access free satellite images with a higher spatial resolution, there are challenges that are known as single-image super-resolution (SISR). The Sentinel-2 satellites were launched by the European Space Agency (ESA) to monitor the Earth, which has enabled access to free multi-spectral images, five-day time coverage, and global spatial coverage to be among the achievements of this launch. Also, it led to the creation of a new flow in the field of space businesses. These satellites have provided bands with various spatial resolutions, and the Red, Green, Blue, and NIR bands have the highest spatial resolution by 10 m. In this study, therefore, to recover high-frequency details, increase the spatial resolution, and cut down costs, Sentinel-2 images have been considered. Additionally, a model based on Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) has been introduced to increase the resolution of 10 m RGB bands to 2.5 m. In the proposed model, several spatial features were extracted to prevent pixelation in the super-resolved image and were utilized in the model computations. Also, since there is no way to obtain higher-resolution (HR) images in the conditions of the Sentinel-2 acquisition image, we preferred to simulate data instead, using a sensor with a higher spatial resolution that is similar in spectral bands to Sentinel-2 as a reference and HR image. Hence, Sentinel-Worldview image pairs were prepared, and the network was trained. Finally, the evaluation of the results obtained showed that while maintaining the visual appearance, it was able to maintain some spectral features of the image as well. The average Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Spectral Angle Mapper (SAM) metrics of the proposed model from the test dataset were 37.23 dB, 0.92, and 0.10 radians, respectively.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16888

Authors: Soufiane Hajaj Abderrazak El Harti Amine Jellouli Saloua Mnissar Himyari Abderrazak Hamzaoui

Hydrothermal alteration mapping is considered as a widely adopted step in the mineral exploration of numerous ore deposits. In this work, the wavelength mapping and relative absorption band depth (RBD) method were applied to map hydrothermal alterations in a site from the abandoned mine of Idikel, western Anti-Atlas, Morocco. Fe2+/Fe3+, Al-OH, and Mg-Fe-OH/CO3 hydrothermal alteration minerals were targeted based on HyMap airborne imaging spectroscopy data. Using the wavelength mapping approach, the 900 to 1205 nm, 2094 to 2217 nm, and 2264 to 2318 nm ranges were selected to map Fe2+/Fe3+, Al-OH, and Mg-Fe-OH/CO3 absorption features, respectively. By carefully selecting these spectral ranges, the study aimed to achieve the accurate and reliable mapping of hydrothermal alteration features. The highest interpolated depth of Al-OH features was matched with a major cluster of pixels at 2200 nm. The highest interpolated depth of Mg-Fe-OH/CO3 was depicted at 2300 nm. The highest interpolated depth of Fe2+/Fe3+ was depicted between 900 and 1000. The relative absorption band depth method was also applied to enhance the detectability of hydrothermal alteration minerals. This method involves assessing the depth of the absorption bands associated with the target minerals, allowing for a detailed characterization of the alteration features. The combination of both wavelength mapping and enhancement methods contributed to a comprehensive and robust hydrothermal alteration mapping process. The identification of Fe2+/Fe3+, Al-OH, and Mg-Fe-OH/CO3 manifestations provided valuable insights into potential mineralization zones within the study area. Overall, this research contributes to the advancement of hydrothermal alteration mapping using hyperspectral data by selecting the required HyMap bands for mapping targeted alterations. The combination of wavelength mapping and enhancement methods proves to be a powerful approach for accurately identifying and characterizing hydrothermal alteration features using specific hyperspectral channels. The findings from this study can aid future mineral exploration endeavors in similar geological settings, providing valuable guidance for locating potential mineral resources in mountainous and challenging terrains.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16887

Authors: Ashutosh Bhardwaj Hari Shanker Srivastava Raghavendra Pratap Singh

The CartoDEM Version 3 Release 1 openly accessible datasets are currently the most reliable datasets for relatively plain regions in India specifically. The aim of the presented study is to evaluate CartoDEM with respect to two openly accessible spaceborne LiDAR datasets from two LiDAR sensors: the Advanced Topographic Laser Altimeter System (ATLAS) on board the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI) over the International Space Station (ISS). The differences and deviations were computed for CartoDEM and LiDAR footprint elevations for the two datasets, namely, ICESat-2 and GEDI. The difference values were filtered for footprints with differences between 0 and 2.5 in the DEM and LiDAR elevation values. Besides this, an overall estimate was also calculated for the elevation values obtained over the surface, i.e., the ground, as well as objects such as the trees or buildings. The RMSEs were observed to be 1.16 m and 1.74 m for the ICESat-2 and GEDI datasets for the points/footprints on the terrain, whereas when considering similar parameters for the two datasets, the RMSEs were found to be 1.78 m and 5.48 m for the ICESat-2 and GEDI footprints on the surface (terrain/object), respectively. This study reveals that CartoDEM is highly accurate in the plain regions when validated with respect to the ICESat-2 datasets, which work via the photon counting technique. Further, it was observed that ICESat-2’s performance is better than that of the GEDI mission for terrain height. Thus, it was observed that the spaceborne LiDAR datasets from ICESat-2 can be utilized for the validation of DEMs and can be useful for applications where an input to a DEM is required for engineering or modeling applications.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023026213

Authors: Konstantinos Moustris Nastos Panagiotis

In submitting conference proceedings to Environment Sciences Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...]

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028032

Authors: Nohemy Lizeth Rivera Gutiérrez

Los Naranjos is an archaeological site inhabited since approximately 800 BC. The objective is to analyze the landscape of this site to understand the territorial, social, and cultural dynamics, along with its natural environment, since pre-Hispanic times. The methodology involves a documentary review of investigations, and a search for mappings and reconstructions of previous studies, historical sources, and fieldwork. Preliminary results include a review of existing sources for model generation.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028031

Authors: Raquel Perdiguer-López José Luis Berné Valero Natalia Garrido-Villen

The delay of GNSS signals in the neutral atmosphere allow the determination of atmospheric water vapor. The conversion factor of the delay in the water vapor uses the water vapor-weighted mean temperature, Tm, which is a crucial parameter to improve the quality of conversion. This study analyzed two different types of models: linear models such as Bevis, Mendes and Ortiz de Galisteo, and empirical models such as GPT2w, GPT3 and GWMT_D. The performance of the models was analyzed using the models as the source of Tm to obtain the precipitable water vapor (PWV), which was compared to a reference set of PWV obtained from a matched radiosonde site. The results show a better performance of the linear models, with the Bevis model achieving the best performance.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028030

Authors: María Belén Benito Oterino José Fernández Torres Rosa María García Blanco Jorge Miguel Gaspar Escribano Miguel Ángel Manso Callejo Antonio Vázquez Hoehne

In submitting conference proceedings to Environment Sciences Proceedings, the volume editors of these proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...]

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028029

Authors: Itziar Irakulis-Loitxate Javier Roger Javier Gorroño Adriana Valverde Luis Guanter

Methane is the second most important anthropogenic greenhouse gas, whose emissions need to be mitigated to curb global warming. There is a large uncertainty about its point source, but thanks to a new generation of high-spatial-resolution satellites, this situation is changing drastically, revealing thousands of emission point sources worldwide. In this paper, several hotspot areas are mapped, looking for methane emission point sources with different types of high-resolution satellites. Our results demonstrate the potential of satellite remote sensing to reveal methane emission point sources in different scenarios.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028028

Authors: Ausiàs Roch-Talens Josep Eliseu Pardo-Pascual Jaime Almonacid-Caballer

This article presents the preliminary results of a study on satellite-derived bathymetry. The purpose of this research is to explore the use of remote sensing and optical imagery for mapping the depth of coastal waters. This study uses empirical models to estimate the water depth based on the optical properties of the water column. To carry this out, it employs atmospheric correction algorithms to remove the influence of atmospheric scattering and absorption on the optical signals. The authors then apply the empirical models to the corrected imagery to obtain the bathymetric maps. The study shows promising results (RMSE ranging between 0.49 and 0.96m using the Lyzenga methodology), with the estimated depths generally consistent with the available ground-truth data. However, the accuracy of the estimated depths varies depending on the water conditions, such as the presence of waves and bottom type. The authors conclude that satellite-derived bathymetry has great potential for coastal applications, such as environmental monitoring and coastal management.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16864

Authors: Nilufar Makky Khalil Valizadeh Kamran Sadra Karimzadeh

Over the past few years, the effects of global warming have become more pronounced, particularly with the melting of the polar ice caps. This has led to an increase in sea levels, which poses a threat of flooding to coastal cities and islands. Furthermore, monitoring and analyzing changes in water levels has proven effective for predicting natural disasters caused by the rising sea levels. One vital factor in understanding the impact of global warming is the sea surface height (SSH). Measuring the SSH can provide valuable information about changes in ocean levels. This study used data from the Jason 2 altimetry radar satellite, which provided 36 cycle periods per year, to investigate the water heights around the Hawaiian Islands in 2019. To accurately evaluate the water height variations, a specific area near the Pacific Ocean close to the Hawaiian Islands was selected. By analyzing the collected satellite data, a chart of water heights was generated, which showed an overall increase in the height over one year. This analysis provided evidence of changing ocean levels in the region, highlighting the urgency of addressing the potential threats faced by coastal communities. This study also explored several factors that contribute to water height variations, such as the sea surface temperature, precipitation, and sea surface pressure in the Google Earth Engine cloud-based platform. Algorithms, including MLP and XGBOOST, were used to model the water height within the specified range. The results showed that the XGBOOST algorithm was superior in accurately predicting the water height, with an impressive R-squared value of 0.95. In comparison, the MLP algorithm achieved an R-squared value of 0.92. This study shows that advanced machine learning techniques are effective in understanding and modeling the complex changes in the water height due to climate change. This information can help policymakers and local authorities make informed decisions and create strategies to protect coastal cities and islands from the growing threats of rising sea levels. Taking proactive measures is crucial in reducing the risks posed by more frequent and severe natural disasters caused by global warming.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16863

Authors: Amir Sharifi Reza Shah-Hosseini

Super-resolution (SR) is a well-established technique used to enhance the resolution of low-resolution images. In this paper, we introduce a novel approach for the super-resolution of Sentinel-2 10 m RGB images using higher-resolution Venus 5 m RGB images. The proposed method takes advantage of a modified SRResNet network, integrates perceptual loss based on the VGG network, and incorporates a learning rate decay strategy for improved performance. By leveraging higher-resolution VENµS 5 m RGB images as reference images, this approach aims to generate high-quality super-resolved images of Sentinel-2 10 m RGB images. The modified SRResNet network was designed to capture and learn underlying patterns and details present in Venus images, enabling it to effectively enhance the resolution of Sentinel-2 images. In addition, the inclusion of perceptual loss based on the VGG network helps preserve important visual features and maintain the overall image quality. The learning rate decay strategy ensures the network converges to an optimal solution by gradually reducing the learning rate during the training process. Our research contributes to the field of super-resolution by offering a novel approach specifically tailored for enhancing the resolution of Sentinel-2 10 m RGB images using Venus 5 m RGB images. The proposed methodology has the potential to benefit various applications, such as remote sensing, land cover analysis, and environmental monitoring, where high-resolution imagery is crucial for accurate and detailed analysis. In summary, our approach presents a promising solution for the super-resolution of Sentinel-2 10 m RGB images, providing an effective means to obtain higher-resolution imagery by leveraging the complementary information from Venus 5 m RGB images. We used the SEN2VENµS dataset for this research. The SEN2VENµS dataset comprises cloud-free surface reflectance patches obtained from Sentinel-2 imagery. Notably, these patches are accompanied by corresponding reference surface reflectance patches captured at a remarkable 5 m resolution by the VENµS Micro-Satellite on the same acquisition day. To assess the effectiveness of the proposed approach, we evaluated it using widely used metrics such as the mean squared error (MSE), the peak signal-to-noise ratio (PSNR), and the structural similarity index (SSIM). These metrics provided quantitative measurements of the quality and fidelity of the super-resolved images. Experimental results demonstrate the effectiveness of our proposed approach in achieving improved super-resolution performance compared to existing methods. As an example, our method achieved a PSNR of 35.70 and a SSIM of 0.94 on the training dataset, outperforming the bicubic interpolation method, which yielded a PSNR of 29.53 and a SSIM of 0.92. On the validation dataset, our approach achieved a PSNR of 40.3809 and a SSIM of 0.98, while the bicubic interpolation method achieved a PSNR of 34.26 and a SSIM of 0.94. Finally, on the test dataset, our approach achieved a PSNR of 29.8231 and a SSIM of 0.90, whereas the bicubic interpolation method yielded a PSNR of 26.99 and a SSIM of 0.85. The evaluation based on MSE, PSNR, and SSIM metrics showcases the enhanced visual quality, increased image resolution, and improved similarity to the reference Venus images.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16862

Authors: Khim Cathleen Saddi Leni Yap-Dejeto

This study focuses on the quantification and forecasting of the biological pump potential in the Philippine seas, specifically inside the Exclusive Economic Zone (EEZ). Variabilities and disturbances that might potentially influence ocean productivity such as increased sea surface temperature (SST), and the high frequency of typhoons in the Philippines were investigated. CHL and SST spatio-temporal maps were used to provide visualization for the trends and phenomena before, during, and after typhoon occurrence for the years 2019–2021. Integrating the NASA Ocean Color data of CHL and SST with typhoon tracks, the biological pump potential annual estimate was generated.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16861

Authors: Mojtaba Atar Reza Shah-Hosseini Omid Ghaffari

Soil moisture (SM) is an important variable related to the health of terrestrial ecosystems, agriculture, the continental water cycle, etc. It also provides an opportunity for drought monitoring, flood forecasting, weather forecasting, and the calibration of hydrological models. This study aims to estimate the surface soil moisture at a high spatial resolution (10 m) by combining radar and optical remote sensing data and improving the spatial resolution and accuracy. Synthetic aperture radar (SAR) operates with the competence to acquire data in any weather condition. The SAR images were acquired by C-band SAR sensors in the VV polarization boarded on Sentinel-1 satellites and the optical images were obtained from a Sentinel-2 multispectral instrument. The main algorithm involves the retrieval of soil moisture using radar data through a change detection (CD) method that is somehow combined with the WCM (parameters include vegetation descriptors and model coefficients) to estimate the SM and reduce the effect of vegetation cover. The method is applied to 13 months of time-series satellite data, from 7 November 2019 to 20 October 2020, over Salamanca (western Spain) and is validated using field data acquired at a study site with the use of a TDR sensor. The results showed good accuracy between the retrieved and ground measurement soil moisture data (Root Mean Square Error (RMSE) of 0.053 m3/m3) and the obtained accuracy is promising compared to recent similar works.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16859

Authors: Anxin Ding Han Ma Ping Zhao Shenglian Ren Kaijian Xu Hailan Jiang

The Hapke model has been widely used in the field of soil remote sensing. However, the latest development of the Hapke model (i.e., Hapke-HSR model) adopted a simple hypothesis to consider the influence of the soil moisture content (SMC), which brought great difficulties to SMC parameter inversion. This paper presents a method to improve the Hapke model using the improved multilayer radiative transfer model of soil reflectance (MARMIT-2), which can effectively improve the ability of the Hapke-HSR model to characterize the variation in the SMC. Finally, we used the soil database to comprehensively verify the ability of the improved Hapke model. The results show that the improved Hapke can effectively characterize the spectral characteristics of soil and show a higher fitting accuracy (RMSE = 0.009) compared with the Hapke-HSR model (RMSE = 0.031), especially at a high SMC (≥30%). Therefore, the improved Hapke model can better understand soil physical properties and improve the inversion accuracy of soil–vegetation physical parameters, which can be used to enhance agricultural water use efficiency.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16841

Authors: K. V. Suresh Babu Swati Singh Kabdulova Gulzhiyan Gulnara Kabzhanova GR Baktybekov

Forest fires are common occurrences in Kazakhstan, particularly from June until September, and damage the country’s forest resources extensively. The mapping of burned areas is crucial for fire management, to implement the proper mitigation strategies and restoration actions following the fire season. The mapping of burned areas enables a thorough evaluation of the damage caused by fires to forests. The unique characteristics of forest plants and soil are dramatically altered by the fire’s destruction, leading to a dramatic shift in reflectance. The destruction caused by fires can be mitigated, and vegetation can be replanted, with the use of maps depicting the affected areas. The accurate and timely mapping of burned areas is critical for fire prevention methods such as planning, mitigation, and vegetation regeneration. The country Kazakhstan launched two satellites, KazEOSat 1 and KazEOSat 2, as part of the Earth Remote Sensing Satellite System (ERSSS) for the management of natural resources and monitoring. The KazEOSat 1 is a high-resolution observation satellite, launched in a Sun-synchronous orbit at an altitude of about 630 km, consisting of four spectral bands (4 m) and a very high panchromatic (1 m) band. In this study, KazEOsat 1 satellite datasets were used to map the burned areas in various parts of Kazakhstan. Three different spectral indices, viz., the Global Environmental Monitoring Index (GEMI), Ashburn Vegetation Index (AVI), and Burn Area Index (BAI), are used and the findings are compared to the best burnt area discrimination index using the KazEOsat 1 satellite datasets. The results show that the BAI shows a higher accuracy than the other indices at mapping the burnt area using the KazEOsat 1 satellite datasets.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16840

Authors: Ali Fadel Ghaleb Faour Raed Halawi Ghosn Kamal Slim

Cyanobacterial blooms impact aquatic environments and human health. Cyanobacterial biomass is usually estimated using traditional time-consuming and costly field sampling techniques. A remote sensing approach is time and cost efficient and feasible for repetitive monitoring. In this work, we test the potential of various algorithms to retrieve phycocyanin concentration in a Mediterranean lake. Field Spectro radiometric measurements and sampling were performed during 2016 and 2017. The results obtained prove that various ratio models can be used for the estimation of phycocyanin, with the model “R(700)/R(600)” being the best (R2 = 0.716). This research highlights the potential of cyanobacteria mapping using various available satellites.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16839

Authors: Tommaso Orusa Annalisa Viani Enrico Borgogno-Mondino

Recently, the Italian government has announced IRIDE, a new Earth observation program. IRIDE will likely be completed by 2026 under the management of the European Space Agency (ESA) and with the support of the Italian Space Agency (ASI). IRIDE is an end-to-end system made up of a set of sub-constellations (with radar and optical sensors) and services intended for the Italian Public Administration. The aims of this work are twofold: firstly, to disseminate information within the scientific community regarding the IRIDE program by highlighting key constellation characteristics as outlined in the latest ASI technical communications; secondly, to put forth valuable recommendations for the global applicability of this data, adopting a bottom-up perspective.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028027

Authors: Martha Patricia Valbuena Gaona Cindy Carolina Ferrucho Parra María Angélica Prieto Arenas Germán Alberto Muñoz Bravo

The Colombian environmental policies in the 2022–2026 Development Plan align with international guidelines such as the Escazú Agreement, the Rio Declaration, and the Sustainable Development Goals. In concordance with these ongoing efforts, a specialized tool has been developed to effectively identify potential cases of deforestation and illegal mining linked to forest cover loss and the presence of mercury-contaminated water bodies using planet imagery. The workflow used the ArcGIS Pro Task module, with geo-processes integrated into the Python library, arcpy, and fundamental concepts of object-based image analysis (OBIA) and pixel-based analysis. A methodology known as tip and cue was also implemented to detect illegal mining zones. The tool provides efficient means to study environmental crimes and prevent ecosystem damages.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-15144

Authors: Karan Nayak Charbeth López Urias Rosendo Romero Andrade Gopal Sharma Manuel Edwiges Trejo Soto

The occurrence of earthquakes, which can strike suddenly without any warning, has always posed a potential threat to humanity. However, researchers worldwide have been diligently studying the mechanisms and patterns of these events in order to develop warning systems and improve detection methods. One of the most reliable indicators for predicting large earthquakes has been the examination of electron availability in the ionosphere. This study focuses on analyzing the behavior of the Total Electron Content (TEC) in the ionosphere during the 30-day period leading up to the three most devastating earthquakes of the past decade. Specifically, the data were examined from the cGPS stations closest to the epicenters: MERS for the Turkey earthquake with 7.8 Mw on 6 February 2023, CHLM for the Nepal earthquake with 7.8 Mw on 25 April 2015, and MIZU for the Japan earthquake with 9.1 Mw on 11 March 2011. Notable positive and negative anomalies were observed for each earthquake, and the vertical Total Electron Content (vTEC) for each PRN (pseudo-random number) was plotted to determine the specific time of the TEC anomaly. The spatial distribution of vTEC for the anomalous specific time revealed that the anomalies were in close proximity to the earthquake epicenters, particularly within denser fault zones.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028026

Authors: Vicente Bayarri Alfredo Prada

Caves are open ecosystems with natural microbiota, and they are generally stable if environmental conditions are stable. Some have rock art, which is generally characterized as fragile, especially when the equilibrium conditions of the hypogeum are changed. This article shows how high-resolution microphotogrammetry, supported by other geomatic techniques, allows the objective and quantifiable control of the alterations suffered by the pigment and its variation over time regarding earlier campaigns. This method, applied periodically, makes it possible to prevent and/or detect possible alterations at an early stage and improve the conditions of the conservation of the cave.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028025

Authors: María Joaquina Porres Edgar Lorenzo-Sáez Javier Solá Eloína Coll

Air pollution control and air quality monitoring are global priority, which also applies to local scales. Ground-based monitoring stations provide high quality values, but their number and cost make them insufficient for use at certain scales and for air monitoring in urban areas. Satellite imagery provides indicators directly related to air quality. Aerosol optical thickness (AOD), used in atmospheric corrections of images, can be used as an indicator of air quality. This product is present in images obtained by satellites of medium spatial resolution, so it is necessary to develop methodologies to obtain it at higher resolution. This work aims to compare methodologies for obtaining AOD and its use in high spatial resolution satellites.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028024

Authors: Ángel Morales Sánchez Serafín López-Cuervo Juan F. Prieto

This study analyzes repercussions for the morphology, talweg, riverbanks and surrounding structures of several aquatic systems transformed by multipurpose reservoirs located within the Ecuadorian territory of South America. For this purpose, several geomatics techniques were used simultaneously, minimizing the temporal error in the reservoir water level in order to measure the impact of partial or total emptying operations on these reservoirs. High precision geodetic networks were designed to synchronously use geospatial data-capturing equipment, namely UASs/drones with INS/GNSS systems, LiDAR sensors, RGB optical sensors, USVs/aquatic drones equipped with GNSS systems, and single-beam sensors. Photogrammetric, LiDAR and underwater results were contrasted with topographic techniques used in the monitoring and control of structures. Environmental changes in the surroundings, soil movements due to sedimentary and erosive effects, and possible displacements in existing structures were analyzed.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15849

Authors: Constantinos Nefros Constantinos Loupasakis Stavroula Alatza Charalampos Kontoes

Landslides are natural disasters with a high socio-economic impact on human societies due to the considerable number of fatalities and the destruction of infrastructure that they cause. A comprehensive landslides inventory is vital for reducing this impact as it can be used in landslides susceptibility studies for the identification of the subregions of an area that are most prone to landslides for the evaluation of the landslide precipitation activation thresholds, and subsequently for the determination of the most suitable precautionary measures. Nowadays, remote sensing techniques are widely used by scientists for creating landslide inventories as they can be rapidly applied to identify landslides along with their spatial characteristics. Nevertheless, besides these characteristics, a comprehensive inventory must also include the time of their activation and the factors that led to their activation. These elements can be quite difficult to specify, especially in areas where official landslide data do not exist, such as in countries that do not have a published national landslides inventory. The objective of this research study is to provide a framework for the creation of a comprehensive landslides inventory by combining open access or publicly available data with remote sensing data and techniques. The Chania regional unit in the western part of Crete Island, Greece, was selected as the study area. Our study presents how a complete landslides inventory, consisting of 236 landslides, was established based on differential interferometric synthetic aperture radar (DInSAR) techniques and open access or publicly available data. This framework can significantly contribute to scientific research on landslide susceptibility in countries that lack a comprehensive landslides inventory. Moreover, it highlights the potential of remote sensing techniques and open access data in improving our understanding of landslide activation mechanisms.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16704

Authors: Rukea Al-hasn

Vegetation degradation is correlated with drought. The more drought intensifies, the more degraded vegetation increases. Therefore, this study aimed to assess the correlation between rainfall and changes in the Normalized Difference Vegetation Index (NDVI) under arid and semi-arid conditions in Syria. This study was carried out using annual rainfall data for 2012–2022, obtained from the Agricultural cloud seeding Project, to determine the average rainfall in the study area and to link it to the NDVI of MODIS image data processed using the Google Earth Engine (GEE) for April of each year for the same time series. The results showed that the lowest NDVI value (0.098) was in 2016, representing the driest year during the studied series, while the highest NDVI value (0.24) was in 2019, which coincided with the highest rainfall rate of 206.67 mm, thus representing the least arid year during the same series. It also found a strong correlation (R = 0.7) between the overall average rainfall and the overall NDVI values of the studied time series. This study shows that changes in the NDVI are associated with changes in rainfall, indicating that they can be used to estimate and study drought as a simple method derived from satellite data in isolation from ground data.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16644

Authors: Aamir Raza Muhammad Adnan Shahid Muhammad Safdar Muhammad Zaman Rehan Mehmood Sabir Hafsa Muzammal Mian Muhammad Ahmed

Land use and land cover (LULC) change is a major driver of environmental change in District Bahawalnagar, Punjab. LULC change can lead to changes in soil quality, water availability, and climate, all of which can affect crop yields. LULC change can also lead to the loss of agricultural land, forest land, water bodies, and an increment in urban land that causes climate change and affects the agricultural sector. The study area showed that in the last thirty years, the population increased, built-up land increased, and agricultural land dropped by 30%. The present status of knowledge is reviewed in this paper on the impact of LULC on agricultural production in District Bahawalnagar. The conversion of agricultural land to urban development in District Bahawalnagar has led to a decline in crop yields of an average of 10%. The production of wheat and rice, the two major crops grown in District Bahawalnagar, is influenced by LULC changes. This study also found that the loss of agricultural land has resulted in an increase in soil salinity, which has further reduced crop yields. The detrimental effects of LULC change on agricultural output in District Bahawalnagar can be mitigated by adopting sustainable land management practices. These practices include reforestation, conservation agriculture, and water conservation. The government of Pakistan can also play a role in mitigating the negative impacts of LULC change on agricultural production by developing and implementing land use plans that protect agricultural land from conversion to other uses. More research is required to fully comprehend the effects of LULC and develop effective management strategies. However, LULC is a major challenge that must be addressed if we are to ensure food security in the future.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15832

Authors: Safae Ahsissene Cristina Peña Ortiz Naoufal Raissouni

As urban areas expand, the focus on improving outdoor thermal comfort intensifies. This study generated Summer Discomfort Index (SDI) maps for Seville and Barcelona (Spain), as well as Tetuan (Morocco). SDI integrates temperature and humidity for an accurate comfort assessment. Calculations involved substituting air temperature with land surface data from MODIS and incorporating humidity from weather stations, then comparing it to Thermal Sensation Votes (TSV) gathered through surveys. The objective was to assess thermal comfort levels and explore the relationship between remotely sensed SDI and residents’ reported perception. These detailed SDI maps offer crucial insights into summer thermal conditions, advancing urban climate studies and influencing urban planning, design, and well-being strategies.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15831

Authors: Virginia Estévez Stefan Mattbäck Anton Boman

Acid sulfate soil mapping is the first step to avoid possible environmental damages created by one of the most problematic soils existing in nature. One of the problems in acid-sulfate soil mapping is the lack of soil samples in some regions. This prevents the creation of occurrence maps. For the first recognition of these regions, a possible solution could be the use of soil samples from other areas with similar characteristics. In this study, we analyze if a machine learning method is able to correctly classify the soil samples in an area where it has not been trained. For this, Random Forest and two different regions located in southern Finland with a similar composition of soils are considered.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028023

Authors: Mónica Zabala Haro Ángel Martín Ana Anquela María Jesús Jiménez

Indoor navigation is the most challenging environment regarding precise positioning service for a smartphone’s physical quality limitations and interferences for high buildings, trees and multipath fading in the GNSS signal received. A GPS by itself cannot offer a solution; the A-GNSS from a network mobile provided through telecommunication infrastructure provides information that is useful to counteract these issues. A smartphone has full connectivity to the mobile network 24/7 and has access to the GNSS database when required, and the assisted information is sent over an Internet Protocol (IP) and processed by the GNSS chip, increasing the accuracy, TTFF, and availability of data even in harsh environments. The outdoor, light indoor, and urban canyon scenarios are experienced when driving in some places in the city, and they are recorded with Geo++ and processed with RTKlib using a single frequency in a standalone and multi-constellation double-frequency smartphone, Xiaomi Mi 8, with A-GNSS. The results show good accuracy in the SPS for over 10 (m) and in assisted positioning over 50 (m); the TTFF in assisted positioning is always 5 (s), and in the SPS, it reaches 20 (s). Finally, during the trajectory, only the assisted positioning can compute the position; this is because of the data availability from a mobile network.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028022

Authors: Adriana Valverde Itziar Irakulis-Loitxate Javier Roger Javier Gorroño Luis Guanter

Reducing methane, which is the second most important anthropogenic greenhouse gas after carbon dioxide, has been shown to be a good opportunity to mitigate global warming in the short to medium time. Remote sensing is nowadays a useful tool for the identification of anthropogenic emission from methane point sources. In this work, we will demonstrate the capability of high-resolution satellites to detect point sources of methane. Specifically, this study focuses on emissions from offshore oil and gas platforms using sun-glint mode acquisitions, as these platforms represent a significant fraction of total emissions and pose a challenging issue due to the low radiation from water.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028021

Authors: Jose Manuel Tordesillas Francisco Javier Pavón-Carrasco Ana Belén Anquela

The agonic line, characterized by zero values of geomagnetic declination, has had a westward drift during the last centuries, crossing the location of the Real Observatorio de Madrid at the end of the year 2021. This fact, which was monitored by the Instituto Geográfico Nacional, moves us to study the evolution of the magnetic declination in this emblematic emplacement between the last two crosses of the agonic line. Our results point out that the current westward drift started around the year 1810 and, before this period, the agonic line moved from west to east, crossing the location of the Real Observatorio around 1650–1675.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028019

Authors: Edier Fernando Ávila Bibiana Royero Benavides Gelberth Efren Amarillo

The municipality of Fusagasugá is located 50 kilometers from the city of Bogotá, Colombia, in the eastern cordillera of the Andes in South America. Due to its geographical location, a mountainous area with heights between 1000 and 2000 meters above sea level and two rainy seasons a year, it is affected by processes of instability and surface deformations. The objective of the present investigation was to identify and quantify the displacement speeds of the zones affected by processes of instability and superficial deformation. In this study, 20 radar satellite images from the Sentinel-1 program were used in the SLC format between 30 January 2020 and 19 April 2022 in descending orbit, applying the Small Base Line (SBAS) technique. On the other hand, 21 SAR images were also used in descending orbit between 6 January 2020 and 14 December 2021, applying the persistent scatterers (PS) technique. With the above information, it was possible to map and update the data of the municipality of Fusagasugá in order to include them in the monitoring processes at the regional level.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028020

Authors: Javier Roger Itziar Irakulis-Loitxate Javier Gorroño Adriana Valverde Luis Guanter

The detection and monitoring of methane anthropogenic emissions is of vital importance in order to curb global warming. Satellite-based imaging spectrometers, such as PRISMA and EnMAP, have proven instrumental in this task. Methane absorption features from the shortwave infrared spectral range (1000–2400 nm) are exploited by algorithms such as the matched-filter. This method can correctly characterize methane plumes, but retrieval artifacts disturb methane plume detection when using only those spectral channels related to the methane absorption features. Retrievals from simulated plumes and real emission cases from PRISMA and EnMAP data cubes are used to demonstrate that using the whole shortwave infrared region in the matched-filter method results in a better plume detection.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028018

Authors: Ángel Luis Navarro Jesús Velasco Serafín Lopez-Cuervo

To achieve safety, accessibility, and technical compatibility in the operation of rail systems, technical specifications for interoperability are adopted to be complied with. This paper presents a study of the essential interoperability requirement ʺStructure Gaugeʺ within the framework of the regulations applicable to new, upgraded, or renewed infrastructure in the rail system within the European Union.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028017

Authors: Saeedeh Shahbazi José A. Navarro Anna Barra

The European Ground Motion Service (EGMS) monitors and measures land displacement on a European scale using Sentinel-1 data, providing reliable and consistent data on natural ground motion phenomena. The Geomatics Research Unit of the Center Tecnològic de Telecomunicacions de Catalunya (CTTC) is working on a project to generate wide-area differential deformation maps from EGMS basic products and make this information available to the public through a web server. The project involves configuring a self-hosted, low-cost web server using open-source tools; adapting the ADAfinder application to identify active deformation areas (ADAs); developing software pipelines to compute and convert deformation data; and developing a tailored web visor to display the results. Automation is crucial to the project’s success since it must handle a significant volume of data with millions of PS points and long processing durations.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028016

Authors: Javier A. Quille-Mamani Luis A. Ruiz Lía Ramos-Fernández

Crop yield prediction at plot scale is a vitally important magnitude for farmers at the socio-economic level. This study aims to quantify rice yield using phenological metrics from a normalized difference vegetation index (NDVI) time series derived from Sentinel-2 imagery, with yield data collected from 32 plots with an area of 36 ha in the Ferreñafe District of the Lambayeque region, Peru. Three different rice yield models were obtained, the best linear regression models were obtained for the SVM classification, with R2 of 0.69, MAE = 1.01 and RMSE = 1.23 t ha−1; and MRL with R2 of 0.61, MAE = 1.10 and RMSE = 1.38 t ha−1; RF with R2 of 0.44, MAE = 1.23 and RMSE = 1.66 t ha−1. The models obtained open the possibility to generate more robust models using a larger number of samples, which would be useful for farmers as well as for management and planning decisions for food and economic security.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028015

Authors: Jhonatan Rivera-Rivera Marta Béjar-Pizarro Héctor Aguilera Carolina Guardiola-Albert César Husillos Pablo Ezquerro Anna Barra Rosa María Mateos María Cuevas-González Roberto Sarro Oriol Monserrat Mónica Martínez-Corbella Michele Crosetto Juan López-Vinielles

Deformation processes, both natural (e.g., subsidence, landslides, active tectonics) and induced (e.g., associated with mining, construction. groundwater exploitation), result in significant socioeconomic losses worldwide. Accurate detection and classification of these processes are crucial for effective risk management. In this study, we present a novel approach for the automatic classification of deformation processes using Interferometric Synthetic Aperture Radar (InSAR) data and machine learning techniques. Specifically, we use a decision tree-based classification algorithm to train a model capable of recognizing and distinguishing different types of deformation processes using time series of displacements, grouped into Active Deformation Areas (ADAs). We test this methodology in a large area in SE Spain. Our results demonstrate promising performance, with an Area Under the Curve (AUC) > 0.95, identifying several covariates of morphometric, geological, hydrogeological, and geotechnical nature as key factors. This automatic classification of InSAR data holds significant implications for risk management associated with ground deformation, providing a potentially valuable tool for decision makers in urban planning and land management officials.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028013

Authors: Henry Leonel Carcamo María José Viñals

This research analyzed a sample of agricultural cultural landscapes recognized by United Nations agencies on the American continent, with the objective of study being the geographical delimitation established for each of the cultural properties. The results show the lack of “general considerations” that provide guidelines to carry out this activity, which consequently enables the protection of a territory and its management. It was identified that the perimeters of these cultural landscapes may have limits based on the following; geomorphological features, linear infrastructures, political–administrative limits, and if none of these limits are present then they are physically catalogued as vague or unclear. The establishment of general delimitation guidelines will allow the development of public policies for the organizing of such territory and a sustainable, dynamic, efficient, and resilient management for this type of living landscape.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028014

Authors: Alejandra Benavides-Ocampo Jorge Gaspar-Escribano Carlos Ocampo-León

The present investigation addresses the context in which the heritage buildings of the city of Guaranda (Ecuador) are exposed, in the face of natural or anthropic hazards. The interest lies in the degree of sensitivity towards the correct assessment of cultural heritage, given that, for several years, this issue has been scarcely analyzed or documented. Consequently, field work was carried out in conjunction with the authorities of Guaranda and the Bolívar State University, in order to update the inventory of goods. The exposure and vulnerability measures of the National Institute of Cultural Heritage were used to analyze hazards and vulnerabilities in a GIS environment.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028012

Authors: Kenneth Pachacama-Vallejo Ángel Balaguer-Beser

Live Fuel Moisture Content (LFMC) describes the amount of water present in any type of vegetation and helps quantify the amount of fuel available in a wildfire. In this paper, a multivariate linear regression model was built to estimate the LFMC of the weighted average of all shrub-type species present, using the fraction of canopy cover (FCC) of each forest species as weights. Sample training was conducted with field data obtained during the fire season of the years 2019, 2020 and 2021 in 15 plots of a Mediterranean area where vegetation composed of the shrub-type species dominates. Different spectral indices extracted from Sentinel-2 together with the mean surface temperature, the accumulated precipitation and the seasonal parameters were considered as predictors. The results were compared with the extrapolation of another model trained with field data collected in the year 2019.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028011

Authors: Raquel Luján Luis García-Asenjo Sergio Baselga

Refraction has to be eliminated or mitigated for medium- or long-range applications requiring high accuracy (such as deformation monitoring). The high variability of meteorological parameters, and thus refraction, makes mitigation complicated. This study explores the possibility of direct refractivity interpolation with different algorithms for a nine-station meteorological sensor network in Cortes de Pallás (Spain). Our Multiple Linear Regression (MLR) model can potentially contribute to improving the refraction correction of the monitored area. MLR provides, on average, an RMSE of 0.6 (dimensionless) compared to 1.5 obtained with Inverse Distance Weighting (IDW). For future improvements, the previous smoothing of meteorological data will be considered, and the possibility of using GNSS for vertical atmospheric information will be studied.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16656

Authors: James G. Acker R. Jude Wilber

In the days following the passage of Hurricane Ian over the West Florida Shelf, a large plume of calcium carbonate (CaCO3) mud slurry was observed extending from west of the Dry Tortugas and curving to the east into the Straits of Florida. This discreet target offered a unique opportunity to quantify the suspended mass of CaCO3 in the slurry. Estimating the concentration of sediment in a plume of suspended CaCO3 by satellite sensor observations has been stymied up to now owing to a lack of in situ suspended sediment measurements during storm events, as “sea truth” data for such events is difficult to acquire. However, the Particulate Inorganic Carbon (PIC) standard product provided by the NASA Ocean Biology Distributed Active Archive Center (OBDAAC) is based on Moderate Resolution Imaging Spectroradiometer (MODIS) observations of a plume of coccolith chalk released from a ship in the “Chalk-Ex” experiment. Due to the similarities (particle size, mineralogy, and reflectance properties) of the suspended chalk features and the Ian-induced slurry, we utilized this data product to make initial estimates of the concentration of suspended sediment in the plume.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16315

Authors: Mohammed Yasser Ouis Moulay Akhloufi

In this work, we present a comprehensive study on fish detection in underwater environments using sonar images from the Caltech Fish Counting Dataset (CFC). We use the CFC dataset, initially designed for tracking purposes, to optimize and evaluate the performance of YOLO v7 and YOLO v8 models in fish detection. Our findings demonstrate the high performance of these deep learning models in accurately detecting fish species in sonar images. In our evaluation, YOLO v7 achieved an average precision of 68.3% (AP50) and 62.15% (AP75), while YOLO v8 demonstrated an even better performance with an average precision of 72.47% (AP50) and 66.21% (AP75) across the test dataset of 334,017 images. These high-precision results underscore the effectiveness of these models in fish detection tasks under various underwater conditions. With a dataset of 162,680 training images and 334,017 test images, our evaluation provides valuable insights into the models performance and generalization across diverse underwater conditions. This study contributes to the advancement of underwater fish detection by showcasing the suitability of the CFC dataset and the efficacy of YOLO v7 and YOLO v8 models. These insights can pave the way for further advancements in fish detection, supporting conservation efforts and sustainable fisheries management.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028010

Authors: Rafael Enrique Corrales Juan Gregorio Rejas Mercedes Farjas

The present area of investigation is located in the core zone of the Cerro Azul Meámbar National Park, Honduras. The objective was to demonstrate the vegetation cover and its biomass conditions in the core zone, using geomatics techniques. The methodology is based on Principal Component Analysis, using the image p18r50 (year 2018) from the Landsat 8 Program as data. This returned the following results: (A) Percentages of the total variation for each of the five components; (B) Eigenvalues and percentages of total variation; and (C) The correlation between the principal components and the five bands of the sensor. By using these components, we achieved a separability between the vegetation cover and the bare soil in the image.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028008

Authors: Ramiro González Jorge Gaspar-Escribano

This paper describes all the characteristics of the Guatemalan earthquake catalog and how it has evolved. Over 64,483 earthquakes are included in this paper distributed in some areas of El Salvador, Mexico, Honduras, and Belize, but mainly in Guatemala. Regularly, the earthquake catalogs improve their characteristics over time, however, this is not the case for the catalog of Guatemala. Although earthquake detection improved with the establishment of the national seismic network operated by the National Institute of Seismology, Vulcanology, Meteorology and Hydrology (INSIVUMEH) in 1977, the catalog has not kept a favorable evolution over time. This has led to problems with earthquake detection, large location errors, increasing magnitude of completeness, and others that are going to be discussed later in this paper.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028009

Authors: Angel Collado Fernando Buchón-Moragues David Hernández-López

Keeping the database of a country's land administration system up to date is essential for territorial and social development. In the dual Spanish case, public policies should be applied in order to coordinate and improve land management. This article aims to highlight the need to update the outdated cadastral ownership component as a result of its scarce updating. The comparison of cadastral ownership with population and genealogical information will be fundamental to show the current situation and justify the need to improve the rural cadastre.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028006

Authors: Daniel Flores-Rozas Miguel-Ángel Manso-Callejo Sandra Martínez-Cuevas

In many governments, a digital transformation is gradually taking place in their municipal governance, not without many difficulties due to the lack of resources and qualified professionals. This is where the cadastre, understood as a multifunctional tool, provides a strategic vision of the socio-economic situation of the territory through geospatial data. If environmental conditions are added to this information, a diagnostic tool can be used by citizens under the concept of “Smart Communities” for the application of public policies to ensure that the administration of the territory is more efficient and to strengthen decision making.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028007

Authors: Carlos Gamboa-Canté Belén Benito Alicia Rivas-Medina Ligia Quiros Mario Arroyo-Solórzano Conrad Lindholm

This study compares methods that address two key aspects: how to quantify geological information and transfer it to recurrence models, and how to distribute the seismic potential between two types of sources. These methods are as follows: 1) the mom-rate method, 2) the mom-slip method, 3) the Hybrid Method Proposed (MHP), and 4) the method to build hazard models including earthquake ruptures involving several faults named Seismic hazard and earthquake rate in fault systems (SHERIFS). The results show that the peak ground acceleration (PGA) values increase significantly in the vicinity of the faults, when these are modeled as independent sources in the hybrid methods, reaching, in some cases, to be multiplied by a factor of 2.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16615

Authors: Nima Ahmadian Amin Sedaghat Nazila Mohammadi Mohammad Aghdami-Nia

Buildings are objects of great importance that need to be observed continuously. Satellite and aerial images provide valuable resources nowadays for building footprint extraction. Since these images cover large areas, manually detecting buildings will be a time-consuming task. Recent studies have proven the capability of deep learning algorithms in building footprint extraction automatically. But these algorithms need vast amounts of data for training and they may not perform well under the low-data conditions. Digital surface models provide height information, which helps discriminate buildings from their surrounding objects. However, they may suffer from noises, especially on the edges of buildings, which may result in low boundary resolution. In this research, we aim to address this problem by using edge bands detected by a deep learning model alongside the digital surface models to improve the building footprint extraction when training data are low. Since satellite images have complex backgrounds, using conventional edge detection methods like Canny or Sobel filter will produce a lot of noisy edges, which can deteriorate the model performance. For this purpose, first, we train a U-Net model for building edge detection with the WHU dataset and fine-tune the model with our target training dataset, which contains a low quantity of satellite images. Then, the building edges of the target test images are predicted using this fine-tuned U-Net and concatenated with our RGB-DSM test images to form 5-band RGB-DSM-Edge images. Finally, we train a U-Net with 5-band training images of our target dataset, which contain precise building edges in their fifth band. Then, we use this model for building footprint extraction from 5-band test images, which contain building edges in their fifth band that are predicted by a deep learning model in the first stage. We compared the results of our proposed method with 4-band RGB-DSM and 3-band RGB images. Our method obtained 82.88% in IoU and 90.45% in F1-score metrics, which indicates that, by using edge bands alongside the digital surface models, the performance of the model improved 2.57% and 1.59% in IoU and F1-score metrics, respectively. Also, the predictions made by 5-band images have sharper building boundaries than RGB-DSM images.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15848

Authors: Yusuf Ibrahim Umar Yusuf Bagaye Abubakar Ibrahim Muhammad

This paper presents a meticulous exploration of advanced machine learning techniques for precise forest type classification using multi-temporal remote sensing data within a woodland environment. The study comprehensively evaluates a diverse range of models, ranging from advanced (ensemble) machine learning (ML) methods to several finely tuned support vector machine (SVM) variants, with a specific focus on Bayesian-optimized SVM with a radial basis function (RBF) kernel. Our findings highlight the robust performance of the Bayesian-optimized SVM, achieving a high accuracy of up to 94.27% and average precision and recall of 94.46% and 94.27%, respectively. Notably, this accuracy aligns with the levels attained by acclaimed ensemble techniques such as random forest and CatBoost while also surpassing those of XGBoost and LightGBM. These results highlight the potential of these methodologies to significantly enhance forest type mapping accuracy compared to traditional (linear) SVM and black-box neural networks. This, in turn, can enable the reliable identification and quantification of key services, including carbon storage and erosion protection, intrinsic to the forest ecosystem. The findings of our comparative study emphasize the profound impact of employing and fine-tuning ML approaches in the realm of remote sensing-based environmental analysis.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028005

Authors: Llinet Benavides Cesar Miguel Ángel Manso Callejo Calimanut-Ionut Cira

The connection between solar irradiance and wind is a topic of interest in the field of renewable energies, as wind data have proven to be effective predictors of solar energy, being indicators of cloud movement and atmospheric conditions. This study focuses the use of decision tree-based algorithms (random forest, XGBoost and light gradient boosting machine, and LightGBM) to analyse the impact of the meridional and zonal wind components as input variables. In the study, past observations of neighbours were included as predictors to include a spatiotemporal analysis. The studied models were trained on the open, well-established OIH dataset (containing data from Oahu Island, Hawaii, located in the United States of America) featuring predominantly northeasterly winds. In the post-training analysis, it was found that the inclusion of the wind components resulted in a mean improvement of approximately 1% in the forecast skill (FS) score for all models, with the XGBoost model being the best performing model (with a 27.63% FS score).

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028003

Authors: Juan L. Cabanillas Zavala Manuel E. Trejo Soto Xóchitl G. Torres Carrillo

Mexico is surrounded by a highly dynamic tectonic environment, where the area of greatest influence is in the west, since it is where large earthquakes occur and tectonic blocks are generated due to the subduction of two oceanic plates in the North American plate. In the present study, the horizontal velocities of 15 GNSS stations of continuous operation are calculated, over a period of 11 years, which are located within the Jalisco Block, Mexico with the objective of analyzing the current dynamics of this tectonic block, which is mainly influenced by the oblique subduction of the Rivera plate.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028002

Authors: Paulo Escandón-Panchana Gricelda Herrera-Franco Sandra Martínez Cuevas

Agricultural management guarantees food security and economic development in various countries by applying new technologies to improve management practices. This study aims to identify the geomatic tools and their relationship with the agricultural activities used in cartography by reviewing scientific publications that contribute to improving agricultural management practices. The methodology consists of (i) a data source search strategy related to geomatics and agricultural management; (ii) data analysis; and (iii) a literary review of the contribution of geomatics in agricultural management. The results show that a large part of the studies orients to agricultural cartography and a smaller number to the use and cover of land (LULC) by agricultural activity, cadastre and precision agriculture. The studies focus on improving agricultural management practices to contribute to food security and combat the impacts of climate change (Sustainable Development Goals (SDGs) 2, 12 and 13).

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028001

Authors: Jorge Hernández Olcina Ana B. Anquela Julián Ángel E. Martín Furones

High-precision GNSS algorithms have a complexity that requires a high computational load, so not all mobile devices will be capable of calculating position through their use, and without compromising their efficiency and wasting battery. The development of an app will provide the opportunity to research the possibility of controlling 100% of the raw data from the GNSS sensor from smartphones. Oriented to “cloud computing”, since the main objective is to provide a real-time computing tool to achieve a navigation solution, we use GNSS algorithms for this purpose, and thus avoid the computational load on the smartphone.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023028004

Authors: Angel Collado David Hernández-López José Fernando Ortega

The application of geomatics to the agroforestry field is acquiring greater prominence in recent times in a world that is increasingly digital and aware of sustainability and food security. The use of geomatics tools for 3D documentation and visualisation is becoming essential in so-called precision agriculture. This article describes the methodology used to obtain the wood volume of a set of vines in the town of Tarazona de la Mancha (Albacete, Spain).

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15846

Authors: Boris Boiarskii Iurii Vaitekhovich Shigefumi Tanaka Doğan Güneş Tsubasa Sato Hideo Hasegawa

The use of drones to gather remote data and soil sensors to collect ground information has become a powerful method for agricultural monitoring and analysis. However, integrating data from drone remote sensing and soil sensors in agricultural contexts can be problematic due to variations in spatial and temporal resolutions. Ensuring precise synchronization and calibration is crucial for accurate comparative analysis. The objective of this study was to investigate the strengths and limitations of drone-based remote sensing and on-the-go Veris U3 sensor in agricultural contexts and explore the potential for data fusion. Through a series of field trials, data from drone-based remote sensing and ground-based soil sensing were collected in parallel. These data encompassed a range of factors, including vegetation health (vegetation indices), soil properties such as EC, pH, and optical measurements. The study delves into the challenges of data synchronization, calibration, and validation between the two methodologies. We discuss the potential for synergy in building a more holistic understanding of agriculture by fusing data from drones and in situ soil sensors. The findings of this research have implications for environmental monitoring, agriculture, and ecosystem management, suggesting that the combination of aerial and ground sensing offers a multi-dimensional perspective that can enhance decision-making processes and our grasp of intricate environmental processes.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16366

Authors: Antoine Collin Dorothée James Eric Feunteun

Satellite-derived bathymetry is increasingly attracting stakeholders’ attention tasked with remote and/or shallow depths, given its affordability compared to airborne lidar and waterborne sonar surveys. The 6-band 1.2 m Pléiades Neo (PNEO) multispectral imagery has not yet been evaluated for such a purpose. The contribution of the novel PNEO bands to the depth retrieval was assessed over unclear coastal seawaters (0.2 m−1 of vertical light attenuation in the bay of Saint-Malo, France). The relevance of the radiometric level was also tested: top-of-atmosphere (TOA) digital number (DN), TOA radiance, TOA reflectance, bottom-of-atmosphere (BOA) maritime-modeled reflectance, and BOA tropospheric-modeled reflectance. The lidar response, ranging from 0 to 20 m depth, was stratified by 90 random samples per bathymetric slice of 1 m. The model was based on an easy-to-transfer neural network (one hidden layer and three neurons). The best predictions, reaching R2test of 0.81, were equally obtained for the full PNEO dataset at TOA DN, radiance, and reflectance. For both BOA full-dataset products, the results were slightly less satisfactory: R2test of 0.75 (maritime) and 0.76 (tropospheric).

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16189

Authors: Shruti Pancholi A. Abhinav Sandeep Maithani Ashutosh Bhardwaj

The digital elevation model (DEM) is a three-dimensional digital representation of the terrain or the Earth’s surface. For determining topography, DEMs are the most used and ideal method with (i.e., the digital surface model) or without the objects (i.e., the digital terrain model). Various techniques are used to create DEMs, including traditional surveying methods, photogrammetry, InSAR, lidar, clinometry, and radargrammetry. DEMs generated by LiDAR tend to be the most accurate except for the VHR datasets acquired from UAVs with spatial resolution of a few centimeters. In many parts of the region, LiDAR data are not available, which limits researchers’ access to high-resolution and accurate DEMs. With a beam footprint of 13 m and a pulse interval of 0.7 m, ICESat-2 promises high orbital precision and high accuracy. ICESat-2 can produce high-accuracy DEMs in complex topographies with an accuracy of a few centimeters. The Earth’s surface elevations are provided by discrete photon data from ICESat-2. It is difficult to justify the continuity of the topographical data using traditional interpolation techniques since they over-smooth the estimated space. Geospatial data can be analyzed with machine learning algorithms to extract patterns and spatial extents. To estimate a DEM from LiDAR point data from ICESat-2 using CartoDEM, machine learning regression algorithms are used in this study V3 R1. This study was conducted over a hilly terrain of the Dehradun region in the foothills of the Himalayas in India. The applicability and robustness of these algorithms has been tested for a plain region of Ghaziabad, India, in an earlier study. The interpolation of DEM from ICESat-2 data was analyzed using regression-based machine learning techniques. Interpolated DEMs were evaluated against the TANDEM-X DEM of the same region with RMSEs of 7.13 m, 7.01 m, 7.15 m, and 3.76 m respectively, using gradient boosting regressors, random forest regressors, decision tree regressors, and multi-layer perceptron (MLP) regressors. Based on the four algorithms tested, the MLP regressor shows the best performance in the previous study. The accuracy of the simulated ICESat-2 DEM using the MLP regressor was assessed in this study using the DGPS points over the Dehradun region. The RMSE was of the order of 6.58 m for the DGPS reference data.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16702

Authors: Sonia Sharma Banjade Nitant Rai Bipana Subedi

Hyperspectral imaging is becoming popular in land use/land cover classification because of its ability to capture detailed information through higher spatial resolution and contagious spectral bands. Using the hyperspectral image from G-LiHT (Goddard’s LiDAR, Hyperspectral, and Thermal) Airborne Imager covering a study area in Tennessee, Knoxville, we compared the performance of Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Support Vector Machine (SVM) for land use/land cover classification. We used a confusion matrix for the accuracy assessment of the classifiers. Among the three classifiers, SVM showed the highest accuracy with 92.03%. Our results also show that some classes, such as water and forests, are consistently distinguishable across all classification methods, while others, such as built-up areas, vary depending on the technique used.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15841

Authors: Jona Brügmann Dmitry Efremenko Thomas Trautmann

Photon transport within Earth’s atmosphere is a vital aspect of atmospheric science. The accurate modeling of radiative transfer is crucial for remote sensing data analysis. Yet, simulating the photon transport in multi-dimensional models poses a significant computational challenge. Monte Carlo simulations are a common approach, but they demand a large number of photons for reliable results. Parallelization techniques can be employed to accelerate Monte Carlo computations by using multi-core CPUs and GPUs. This research delves into a comparative analysis of different parallelization techniques for the Python version of the Monte Carlo model. We consider conventional photon transport simulations that rely on iterative loops, the multithreading technique, NumPy’s vectorization, and GPU acceleration via the CuPy library. It is shown that CuPy, harnessing GPU parallelism, significantly accelerates simulations, making them suitable for large-scale scenarios. It is shown that as the number of photons grows, the overhead from reading and retrieving data to the GPU decreases, making the CuPy library an effective and easy-to-use option for Monte Carlo simulations.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16610

Authors: Antoine Collin Dorothée James Régis Gallon Emmanuel Poizot Eric Feunteun

Coastal areas gather increasing hazards, exposures, and vulnerabilities in the context of anthropogenic changes. Understanding their spatial responses to acute and chronic drivers requires ultra-high spatial resolution that can only be achieved by UAV-based sensors. UAV lasergrammetry constitutes, to date, the best observation of the xyz variables in terms of resolution, precision, and accuracy, allowing coastal areas to be reliably mapped. However, the use of lidar reflectivity (or intensity) remains poorly examined for mapping purposes. The added value of the lidar-derived near-infrared (NIR) was estimated by comparing the classification results of nine coastal habitats based on the blue–green–red (BGR) passive and BGR-NIR passive–active datasets. A gain of 4.14% was found at the landscape level, while habitat-scaled improvements were highlighted for the “salt marsh” and “soil” habitats (4 and 4.56% for producer’s accuracy, PA, and user’s accuracy, UA; and 8.95 and 9.48% for PA and UA, respectively).

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15866

Authors: Alejandro Corbea-Pérez Carmen Recondo Javier F. Calleja

It is known that changes in temperature could cause changes in the Antarctic Ice Sheet, which would have an immediate and long-term impact on the global mean sea level. For this reason, the monitoring of air temperature (Ta) is of great interest to the scientific community. On the other hand, Antarctica constitutes an area of difficult access, which makes it difficult to obtain in situ data. Because of this, Land Surface Temperature (LST) remote sensing data have become an important alternative for estimating Ta. In this work, we estimated Ta from daytime and nighttime LST data at maritime Antarctic sites in the South Shetland Archipelago using empirical models, based on the addition of spatiotemporal variables. We used Ta data from the Spanish Antarctic stations and from the PERMASNOW project stations. MOD11A1 and MYD11A1 (Collection 6) Moderate Resolution Imaging Spectroradiometer (MODIS) LST products were downloaded from the Google Earth Engine platform and only the highest quality data were selected. Outliers associated with clouds were removed with filters. Two different multilinear regression models were tested: models for each individual station and global models based on the data from all the stations. The simple regression analysis LST against Ta showed that a better fit is always achieved with daytime LST data (R2 average = 0.73) than with nighttime LST data (R2 average = 0.56). The performance of the models was improved with the addition of spatiotemporal variables as predictive variables, with which we obtained an average R2 = 0.75 for daytime data and an average R2 = 0.60 for nighttime data. The global models allowed for improving the correlation and reducing the errors with respect to the models obtained using individual stations. Global models provide a precise description of the behavior of the temperature in maritime Antarctica, where it is not possible to install and maintain a dense network of weather stations.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15840

Authors: Shridhar D. Jawak Sagar F. Wankhede Prashant H. Pandit Keshava Balakrishna

Glacier surface facies (GSFs) are visible glaciological regions that can be distinguished and mapped at the end of summer using optical satellite data. GSF maps act as visual metrics of glacier health when assessed independently or correlated with in situ mass balance measurements. The literature suggests that the spatiotemporal distribution of all accumulation and ablation facies are important inputs to 3D mass balance models because the GSF trends enhance the precision of models. For example, the progressive increase in the area and distribution of melting ice and decrease in the area and distribution of glacier ice, as estimated by satellite data, may signal potential mass loss without significant change in the overall area of the ablation zone. Tracking the evolution of GSFs in Svalbard is important for the predictive assessment of the cryosphere in the Arctic. This will further facilitate robust methods for monitoring GSFs on a planetary scale. In this context, we present a regional spatiotemporal analysis of GSFs of Midtre Lovénbreen, Ny Ålesund, Svalbard. We used openly available Landsat 8 Operational Land imager (OLI) and Sentinel 2A imagery taken in 2017–2022 to track the occurrence and variations of GSFs via machine learning. The current results suggest that ablation facies such as melting ice and dirty ice are increasing over time. Sentinel 2A provides finer resolution but is limited by its temporal coverage. Although Landsat is suitable for long-term trend analysis, its coarser resolution can lead to errors such as over/underestimation of smaller patches of facies on relatively smaller glaciers. As the spectral properties of GSFs are consistent over time, a robust set of spectra depicting variations in physical appearance of facies may be used to train machine learning algorithms, thereby improving efficacy. In forthcoming studies, our objective is to expand the temporal scope spanning decades and to trace facies evolution over longer time series.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15856

Authors: Jorge Veiras-Yanes Laura Martín-García Enrique Casas Manuel Arbelo

This research evaluates the capability of Sentinel-2 satellite imagery for mapping Cymodocea nodosa meadows in El Médano (Tenerife, Canary Islands, Spain). A Level-1C image from 27 October 2022 was used. Atmospheric correction was addressed using the Sen2Cor tool, while Lyzenga’s method was employed to account for the water column effect. Three supervised classifications were performed using Random Forest, K-Nearest Neighbors (KNN) and KDTree-KNN algorithms. These classifications were complemented by an unsupervised classification and in situ data. Additionally, the amount of blue carbon sequestered by the C. nodosa in the study area was also estimated. Among the classifiers, the Random Forest algorithm produced the highest F1 scores, ranging from 0.96 to 0.99. The results revealed an average area of 237 ± 5 ha occupied by C. nodosa in the study region, translating to an average sequestration of 111,000 ± 2000 Mg CO2. Notably, the seagrass meadows in this study area have the potential to offset the CO2 emissions produced by the industrial combustion plant sector throughout the Canary Islands. This research represents a significant step forward in the protection and understanding of these invaluable ecosystems. It effectively underlines the potential of Sentinel-2 satellite data to map seagrass meadows and highlights their crucial role in achieving net zero carbon emissions on our planet.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15845

Authors: Mahsa Bozorgi Jordi Cristóbal

Satellite remote sensing has become an important tool for monitoring and evaluating the impacts of drought. In this study, a modeling framework aimed at estimating the time series of evapotranspiration (ET), a key variable for drought monitoring, at a regional scale is presented. A two-source energy balance (TSEB) model was used concurrently with Terra/Aqua MODIS data and the ERA5 atmospheric reanalysis dataset. The modeling framework is based on the SEN-ET scheme to calculate the surface energy balance of the soil-canopy-atmosphere continuum and estimate ET at 1 km spatial resolution. The model was applied for the whole Iberian Peninsula, and it was evaluated with a pistachio orchard flux tower data in Lleida (NE Iberian Peninsula). Preliminary daily ET evaluation results for the Terra dataset showed an RMSE, MBE, and R2 of around 1.43 W·m−2, −1.27 W·m−2, and 0.56, respectively, and for the Aqua dataset were 1.05 W·m−2, −0.84 W·m−2 and 0.48, respectively within 100 days in 2022. Ongoing evaluation is being carried out on two forested watersheds as well as mountain meadows and semi-arid vegetation flux towers.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16317

Authors: Ventsislav Danchovski Danko Ivanov

Cities may have local weather and climates that are significantly different from their surrounding rural areas due to the different physical characteristics of urban surfaces and emissions of substances, with the latter being modulated by the rhythm of the urban ecosystem. Radiative, thermal, moisture and aerodynamic properties of the urban surface influence cloud formation as well as their characteristics. By using in situ measurements as well as data from remote sensing instruments (ceilometers) located in the city center and its outskirts, urban impact on cloudiness over the city of Sofia is evaluated. It is found that the cloud base height over the city center reaches more than 200 m higher than that over the rural area. It is shown that clouds over the rural area are more persistent in cold months as well as in the afternoon in spring.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16368

Authors: Miao Gu

The Amery Ice Shelf is a major source of sea ice, whose production is linked to the global climate. In 2019, a collapse event occurred in the Amery Ice Shelf; sea ice production before and during this collapse needs to be studied. In this study, polynyas in the Amery Ice Shelf were identified according to ice thickness, and sea ice production was obtained by calculating the heat flux during winter (March–October) in 2013–2020. It was found that the sea ice production in the polynyas fluctuated greatly, and the maximum annual ice production occurred in 2018, which reached 225.4 km3. As for the collapse event in 2019, it is assumed that it may have exacerbated the volatility and instability of sea ice production.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16186

Authors: Sahand Tahermanesh Behnam Asghari Beirami Mehdi Mokhtarzade

Climate change has directly impacted Earth’s habitats, resulting in various adverse effects, such as the desiccation of water bodies. The process of identifying such changes through field observations is time-consuming and costly. By using remote sensing techniques, it has become easier than ever to monitor changes in the environment. Radar satellites, unlike optics, can acquire data in all weather conditions, regardless of the time of day. These data can provide valuable information about the environment and surface roughness. Various methods have been proposed for detecting changes, which can be divided into classic and deep learning methods. Classic methods only use image information, such as radar backscatter, which cannot extract spatial information. Sentinel-1 (S1) is an Earth observation radar sensor that provides free access to SAR (Synthetic Aperture Radar) images. This study aims to compare the performance of two classic methods, a ratio index (RI) and Markov random field (MRF), with deep learning networks in detecting changes. As a deep network, Inception CNN (convolutional neural network) is presented as an enhancement of the original CNN to detect the changes. To evaluate methods, two instances of S1 images from Lake Poopó, located in the Altiplano Mountains in Oruro Department, Bolivia, are used as a primary dataset. The results of the comparison models were assessed using three evaluation metrics: Overall Accuracy (O.A), Missed Error (M.E), and Kappa Coefficient (K). Based on the evaluations, the Inception CNN performed exceptionally in all metrics, with O.A, K, and M.E rates of 97.35%, 90.28%, and 9%, respectively. Meanwhile, the ratio index had poor performance, with 83.27%, 29.05%, and 75.03%, respectively, for O.A, K, and M.E. These results indicated that the Inception CNN could provide better performance in detecting changes from S1 images.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15836

Authors: Gulam Mohiuddin Jan-Peter Mund

Rapid urbanization in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes. Such abrupt and significant land cover changes considerably affect the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential for effective urban planning and environmental management in agglomerations, particularly in the face of escalating climate change. This study aims to elucidate the spatiotemporal variations in LST in urban areas compared to LULC changes by applying remote sensing techniques. The study focused on a peripheral urban area of Phnom Penh (Cambodia) undergoing rapid urban development, using Landsat images from 2000 to 2021. The analysis employed an exploratory time series analysis of LST and examined areas with consistently higher LSTs (hotspots) regarding their specific LULC changes. The study revealed noticeable variability in LST (20 to 69 °C), predominantly influenced by seasonal variability and LULC changes. The hotspots provided insights into how LST varies within different LULCs at the exact spatial locations. These changes in LST did not manifest uniformly but displayed site-specific responses to LULC changes, warranting the attention of urban planners and policymakers. This study contributes to understanding the spatial relationship between LST and LULC changes, demonstrating the potential for developing new empirically rooted urban climate models that account for this complex physical interplay of changing land surfaces over time. While the study focused on a specific urban area, the methodology provides a replicable model for other similarly structured regions, potentially inspiring future research in various urban planning and monitoring contexts.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15842

Authors: Saulo de Oliveira Folharini Ana Maria Heuminski de Avila

The calculation of the R-factor (rainfall erosivity) for implementation in soil erosion models such as USLE (Universal Soil Loss Equation) and RUSLE (Revised Universal Soil Loss Equation) encounters substantial difficulties due to the scarcity of spatial databases with adequate resolution for territorial planning actions at the local level. Otherwise, there is a spatial database available with a coarse resolution of themes that can be used to calculate the R-factor. We apply the spatial downscaling—based on the following regression models: linear (LN), general additive model (GAM), random forest (RF), cubist (CU)—to erosivity data (target variable) prepared for the State of São Paulo, Brazil, with a spatial resolution of 2500 m. We used DEM and slope data with 30 m fine resolution from the Atibaia watershed, located between the metropolitan regions of São Paulo (RMSP) and Campinas (RMC), to apply the downscaling. This framework improved the spatial resolution of the R-factor, which is necessary to calculate soil loss in the USLE and RUSLE equations in a territory where data with a fine resolution are still limited to the development of territorial planning projects at the local level. The RF model was better with R2 0.94.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-16345

Authors: Ameh J. Adah Taaji Daniel Deborah U. Akpaso

Household air pollution was responsible for an estimated 3.2 million deaths per year in 2020, including over 237,000 deaths of children under the age of 5. A large number of these death cases was particularly recorded in developing countries where many people rely heavily on biomass for energy. Burning biomass emits carbon monoxide and other pollutants resulting in indoor air pollution, exacerbations of asthma, hospitalizations for heart attacks and respiratory illness, birth defects, neurological diseases, and even mortality, which are all brought on by indoor air pollution. Because women and children typically do most of the cooking, they are most affected by indoor air pollution. In this research, an active sampling technique was adopted in estimating the amount of three major criteria gaseous pollutants (CO, H2S, and SO2) in the air in rural household kitchens within the Jos metropolis. The Attair 5X gas detector was used. The power button was pressed and the equipment was allowed to initialize for few minutes while the readings were taken downwind in-situ at a distance of 1 m, 2 m, 3 m, 4 m, and 5 m respectively from the emission source at the expiration of one (1) minute for each distance to check the impact of emissions on the environment and people in such areas. The results obtained shows that CO, H2S, and SO2 were higher from firewood emission sources when compared with charcoal emission sources from the 14 different rural kitchens in the Bauchi ring road, Jos, Plateau State, Nigeria. Hence, this study serves as a ready reference for environmentalists to make target decisions on air pollution reduction.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-16347

Authors: Xin Nie

The optical and radiative properties of aerosols are governed by their types. In this paper, the optical and radiative properties of different aerosol types in Wuhan, China, have been inverted and investigated using the collected PM2.5 samples. The results show that PM2.5 (average mass concentration about 31.25 μg/m3) is mainly contributed by sulfate (SO4) and organic carbon (OC) (22% and 52%, respectively), while aerosol optical depth (AOD, average about 0.28) is mainly contributed by SO4 and black carbon (EC) (22% and 19%, respectively). SO4 and nitrate (NO3) have a negative radiative forcing at the top of the atmosphere (TOA) and a cooling effect, while OC and EC have a positive radiative forcing and a heating effect. Moreover, EC has the most significant effect on the radiative forcing in Wuhan, contributing up to 61% and 73% at the bottom of the atmosphere (BOA) and atmosphere (ATM), respectively, while contributing up to 75% to the atmospheric heating rate (about 1~2 K/day).

Environmental Sciences Proceedings doi: 10.3390/ecas2023-16342

Authors: Kyriakos-Stavros Malisovas Chris G. Tzanis Kostas Philippopoulos

Wildfires threaten human lives and ecosystems and have a significant impact on the economy. Greece is one of the most vulnerable countries in the world with respect to wildfires. The purpose of this article is to assess the climate change impact of wildfires on the ecosystems of Greece and to determine areas where prevention measures should be utilized. To achieve this, the variability of the Fire Weather Index (FWI) is examined under the RCP4.5 and RCP8.5 scenarios from 2022 to 2098. Under both scenarios, a significant intensification of fire weather is observed, which increases the likelihood of severe wildfires occurring in various ecosystems in Greece. The worst affected areas are Southern and Eastern Greece, provided that they have sufficient fuel. The results are more pronounced for RCP8.5, especially after the mid-century. By the end of the century, most ecosystems will be prone to intense fire activity under RCP8.5. Even under the milder RCP4.5 scenario, high-intensity wildfires are projected to occur with increasing frequency in places where they are currently rare. This project highlights the necessity of climate change mitigation and the employment of more effective and widespread prevention and firefighting methods. The management of the current fire-prone areas should be emphasized, but the state must be prepared to face extreme fire incidents in a broader range of ecosystems, including mid-altitude and high-altitude forests.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-16350

Authors: Letizia Cremonini Federico Carotenuto Daniela Famulari Edoardo Fiorillo Marianna Nardino Luisa Neri Teodoro Georgiadis

Urban regeneration is not only an opportunity for the city to adapt according to criteria of resilience to climate change, but also a significant opportunity to build a city based on an approach to health that places the human person at the center of the whole system. According to the World Health Organization, health is not only the absence of disease, but the broader well-being understood as a complex of socio-economic, biological, and environmental relationships. We want to present some results of applying this human-centered approach where the urban fabric’s shape, texture, and materials are essential to building the boundary conditions for developing a healthy city.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-16346

Authors: Krishna Neeti Mohammad Minhaj Faisal Reena Singh

Air pollution is a serious issue in most parts of Bihar, especially in its capital city, Patna. The air quality in Patna has significantly worsened due to factors like rapid urbanization, increased traffic, and various natural and human-related causes. This decline in air quality has led to several negative health effects. In light of this, the aim of this study was to examine how air pollution affects the long-term health of Patna’s residents, taking into account age and exposure time as important factors. We gathered data from one busy intersection in Patna, specifically Danapur. Health effects from air pollution were collected from the residents via a formatted questionnaire. To analyze the relationship between age, exposure time, and the health effects reported by the participants, we used a statistical test called the chi square test of independence. The findings of the study revealed a clear link between age, exposure time, and the health status of the participants. We concluded that older individuals and those with longer exposure times faced a higher risk associated with the increasing air pollution levels. This study provides a foundation for raising awareness among both authorities and the general public of the adverse health impacts associated with declining air quality, emphasizing the urgency in taking appropriate measures to counter this challenge.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-1634

Authors: Wells Utembe Arox Wadson Kamng’ona

Exposure to chemicals in many occupational and environmental settings has the capacity to significantly disturb the commensal microbiota that symbiotically reside in humans. However, much more is known about gut microbiota (GM) than lung microbiota (LM) due to the challenges of collecting LM samples. The advent of culture-independent methodologies has revealed the complex and dynamic community of microbes harbored by the respiratory tract. It is now being recognized that LM can directly impact immunity in a manner that can result in disease. Significant differences in community composition and diversity have been shown between the LM of diseased lungs and those of healthy subjects. Studies have linked LM dysbiosis with human diseases such as idiopathic pulmonary fibrosis, lung inflammation, chronic obstructive pulmonary disease (COPD), asthma, and lung cancer. However, it is not known whether LM dysbiosis initiates/promotes disease pathogenesis or is merely a biomarker of disease. Many chronic lung diseases often occur together with chronic GIT diseases in what is termed as the gut–lung axis. The LM also affects the CNS, in the bidirectional lung–brain axis, through a number of potential mechanisms that include the direct translocation of micro-organisms. Chemically induced LM dysbiosis appears to play a significant part in human diseases as has been shown to arise due to air pollution, cigarette smoking, and the use of chemical antibiotics.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-16341

Authors: Bassma El Gourch Bouchaib Ihssane Mounia Tahri Fatiha Zahry Ghassan Acil Taoufik Saffaj Abdelfettah Benchrif

A comprehensive study was executed within the urban vicinity of Kenitra city, covering the period from 2020 to 2021. During this study, 60 effective PM2.5 samples were collected in a period of 24 h using a dichotomous sampler and Nuclepore track-etched polycarbonate filters with a diameter of 37 mm. Ion chromatography was employed to identify the composition of our samples, including Cl−, SO42−, F−, NO3−, NH4+, Na+, Ca2+, and K+. The results showed that the average mass concentration (± standard deviation) of the seven ions in PM2.5 was 3.2 ± 1.3 µg/m3, constituting approximately 18% of the total mass concentration. Among the ions, the concentrations followed the order of Na+ > SO42−> Cl− > NO3−> K+ > NH4+ > F−. The predominant constituents of water-soluble ions in PM2.5 were detected to be secondary inorganic species (NH4+, SO42−, and NO3−), contributing an average of 44% to the total PM2.5 ions. Throughout the four seasons, the concentrations of these three ions exhibited variability, with the greatest levels observed in spring, followed by summer, fall, and winter. The ratio of [NO3−]/[SO42−] was found to be almost equal to unity, indicating that the primary sources of nitrogen and sulfur in the Kenitra atmosphere were prioritized from stationary sources (typically associated with power plants, industrial and commercial activities, and other large-scale facilities).

Environmental Sciences Proceedings doi: 10.3390/ecas2023-16344

Authors: Charbeth Lopez Urias Karan Nayak Guadalupe Esteban Vazquez Becerra Rebeca Lopez Montes

The layer of the Earth’s atmosphere known as the ionosphere presents a significant obstacle to global satellite navigation systems (GNSS) due to its ability to introduce errors. To address this challenge, various navigation systems have introduced new signals designed to minimize the errors caused by the ionosphere. These signals not only aid in error reduction but also facilitate the examination of electron content behavior. This study focuses on the analysis of vTEC plots obtained from RINEX data collected at the INEG station in Aguascalientes, Mexico, from 2011 to 2018, with a particular emphasis on highly intense geomagnetic storms characterized by values below −100 nT. Our analysis of these plots employed the Probability Density Function (PDF), which allows for the graphical representation of data distribution. This distribution is then examined in conjunction with the station’s Total Electron Content (TEC) values and the Dst index during the corresponding geomagnetic storm events. The findings establish the correlation between each of these parameters during such events.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-16351

Authors: Marianna V. Kharlamova

In this work, the single-walled carbon nanotubes (SWCNTs) were filled with cobaltocene. The growth properties of individual chirality nanotubes were studied with Raman spectroscopy. It was shown that the larger nanotubes grow slower. The growth of inner nanotubes becomes faster with increasing annealing temperature. These results are of high importance as they stimulate research on carbon nanotubes, and bring ideas from laboratories into factories.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-16343

Authors: Hung-Cheng Chen

This study performs a sensitivity analysis of strong cyclone track deflection over isolated topography, exploring the impacts of vortex impinging direction and strength. A dynamic model investigates track adjustments of cyclonic vortices on a β-plane. The study derives a meridional adjustment velocity (MAV) for vortex motion and examines variations in track patterns under different flow conditions. Results reveal an S-shaped pattern in most tracks and significant deflections when the vortex passes over high-rise terrain. Larger direction angles of the vortex result in more pronounced deflections attributed to the terrain loop effect induced by the strong topographic β effect. Adjacent vortex paths impinging from the south converge on the leeward side, improving prediction accuracy, while vortices crossing from the north diverge, reducing prediction accuracy. This study offers valuable insights into uncertainties associated with path prediction.

Environmental Sciences Proceedings doi: 10.3390/environsciproc2023026212

Authors: Chrysoula Chrysakopoulou Dimitrios Vogiatzis Alexandros Drakoulis Lambrini Papadopoulou Nikolaos Kantiranis

Magnetic particles were separated from soil and sediment samples from the Sarigiol basin. The sources of their highest values are ophiolite complexes and fly ash dispersion while magnetite is the dominant mineral. Ιron is the dominant element of magnetic particles and minor amounts of Mn, Ti and Cr are presented in anthropogenic magnetic particles. The highest values of iron load are shown near to a power station and close to the locations of the ophiolite complexes. Anthropogenic activities in the research area are responsible for the presence of anthropogenic magnetic particles in the upper horizons of the Sarigiol basin.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16300

Authors: Idania Briceño de Urbaneja Waldo Pérez-Martínez Carolina Martínez Josep Pardo-Pascual Jesús Palomar-Vázquez Catalina Aguirre Raimundo Donoso-Garcés

Coastal erosion has become one of the many natural hazards affecting Chile’s sandy coastlines. Currently, more than 90% of the sandy coasts of Valparaíso show high erosion rates. Cartagena Bay is one of the coastal areas with the greatest transformations caused by extreme events and anthropogenic activities. Satellite imagery is seen as an invaluable resource for following these coastal changes. This study combines optical satellite imagery, a simulation-derived wave climate, in situ data, the SHOREX system developed in Python, and GIS-based tools such as DSAS to quantify rates of change in the Bay from 1986 to 2022. Satellite-derived shorelines were used to identify erosion hotspot areas in the Bay, differentiating the impact of erosive processes associated with ENSO hydrometeorological phenomena, the 27-F 2010 earthquake, and tidal waves from 2015–2022, which led to major transformations in the morphodynamics of the beach. The results show that the Bay is currently undergoing high erosional processes in 20% of the coastline with values <− 1.5 m/year and 60% with erosion rates ranging from [−0.2 to −1.5 m/year]. Since 2015, these processes have been accentuated, due to increased swells throughout the year.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16316

Authors: Federico Filipponi Alessandro Mercatini

Thermal anomalies detected by Earth observation satellites have been widely used to identify active fires, even though there has been a high percentage of misclassified fire pixels. A total of about 75,000 Fire Radiative Power (FRP) pixels have been spatially and temporally combined with the EFFIS Burned Areas Database, distributed under the Copernicus Emergency Management Service, in order to identify thermal anomaly hotspots misclassified as fire pixels. The proposed approach uses a cluster analysis to partition the FRP pixels dataset into discrete subsets, based on defined distance measures like the spatial distance of the pixel centroids and the temporal frequencies. Later, zonal statistics were performed in order to evaluate fractional land cover within each identified hotspot. Results demonstrate that misclassified large surfaces, like industrial areas, can be identified from both spatial and temporal patterns, while other FRP false alarms are smaller in size.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15984

Authors: Natalia C. Wiederkehr Fábio F. Gama Polyanna da C. Bispo

This study assesses ALOS-2/PALSAR-2 (ALOS2) polarimetric images for detecting forest volume losses due to selective logging in a region in the Brazilian Amazon. Two logging-intensive areas, APU 2016, and APU 2017, were studied. ALOS2 imagery attributes, including backscatter and phase data, were analyzed for differences between logged and unlogged regions using Wilcoxon’s nonparametric test at a 95% confidence level. The Radar Normalized Difference Vegetation Index proved effective in detecting selective logging-induced forest volume losses, with consistent results (p-values of 0.003 for APU 2016 and 0.037 for APU 2017). These findings provide insights for monitoring and mitigating ecological impacts of logging in complex forest ecosystems.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15860

Authors: Lwandile Nduku Cilence Munghemezulu Zinhle Mashaba-Munghemezulu Wonga Masiza Phathutshedzo Eugene Ratshiedana Ahmed Mukalazi Kalumba Johannes George Chirima

Crop growth and yield often vary, not only between farms, but also at the sub-field level. These variations can stem from sub-field heterogeneities of soil and plant biophysical parameters. This means that soil and plant biophysical data can be used to predict intra-field crop growth and yield variability. This study used soil properties and vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery as predictor variables, and monthly measurements of crop height (cm) as a response variable to predict crop growth rate in two winter wheat farms in South Africa. These datasets were analyzed using two regression models including Gaussian process regression (GPR) and ensemble learning that uses least-squares boosting (LSboost) and bagging (Bag) in MATLAB. The results showed that soil properties, particularly Ca, Mg, K and clay, were more important than VIs in predicting actual crop growth. Furthermore, GPR (R2 = 0.68 to 0.75, RMSE = 15.85 to 18.38 cm) performed slightly better than LSboost-Bag-ER (R2 = 0.64 to 0.70 and RMSE = 17.26 to 19.34 cm) in predicting crop growth. These findings are useful for crop agronomic management.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16318

Authors: Vijayasurya Krishnan Manimaran Asaithambi

Plant growth and health are affected by 0.06–0.09% of crustal fluoride. A semi-empirical model estimated wet soil fluoride using Sentinel-1 5.405 GHz data as dependent on dielectric components and loss angles. Mineral surface charges and electrical potential limited clay soil ion mobility via moisture and permeability. Real and imaginary dielectric components approximated a 3° to 4° loss angle in lab soil samples with high and low fluoride electrical conductivity. An estimated percentage of dielectric component loss over wide areas could have implied fluoride. Finally, linear regression between field fluoride value and conductance loss was used to estimate fluoride. The statistical differences (R2 = 0.86, RMSE = 1.90, and Bias = 0.35) between predicted and simulated fluoride levels over clay soil and soil with different vegetation development suggest that C-band SAR data may detect fluoride.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15838

Authors: Peri Subrahmanya Hari Prasad A. N. V. Satyanarayana

Due to rapid urban growth and population increase, accurately tracking land use and cover changes (LULC) is vital for predicting outdoor thermal comfort. We used high-res Landsat 8 imagery and on-site weather data, employing a Support Vector Machine (SVM) with PCA to estimate thermal comfort. The PCA addressed variable multicollinearity, and LULC was classified using decision trees. Notable LULC trends emerged. In Hyderabad, built-up areas rose from 37% to 48% (2009–2019) and barren lands fell from 42% to 18%. In Bangalore, built-up areas surged from 25% to 80%, causing vegetation loss (25% to 2%) and reduced barren land (50% to 18%). Jaipur saw a 12% built-up area increase with a slight vegetation uptick. The thermal comfort analysis highlighted Bangalore’s intense urbanization and Jaipur’s limited expansion. Discomfort ranked highest in barren lands, followed by urban, vegetation, and water areas.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-16294

Authors: Mohammad Aghdami-Nia Reza Shah-Hosseini Saeid Homayouni Amirhossein Rostami Nima Ahmadian

Radiative Transfer Models (RTMs) are one of the major building blocks of remote-sensing data analysis that are widely used for various tasks such as atmospheric correction of satellite imagery. Although high-fidelity physical RTMs such as MODTRAN are considered to offer the best possible modeling of atmospheric procedures, they are computationally demanding and require a lot of parameters that should be tuned by an expert. Therefore, there is a need for surrogate models for the physical RTM codes that can mitigate these drawbacks while offering an acceptable performance. This study aimed to suggest surrogate models for the MODTRAN RTM using deep-learning models. For this purpose, the top of atmosphere (TOA) spectra calculated by the MODTRAN code as well as the bottom of atmosphere (BOA) input spectra and other atmospheric parameters such as temperature and water vapor content observations were collected and used as the training dataset. Two deep-learning regression models, including a fully connected network (FCN) and an auto-encoder (AE), as well as a random forest (RF) machine-learning regression model were trained. The results of these models were assessed using the three evaluation metrics root mean squared error (RMSE), regression coefficient (R2), and spectral angle mapper (SAM). The evaluations indicated that the AE offered the best performance in all the metrics, with RMSE, R2, and SAM scores of 0.0087, 0.9906, and 1.4295 degrees, respectively, in the best-case scenarios. These results showed that deep-learning models can better reproduce results via high-fidelity physical RTMs.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15830

Authors: Yangyang Chen

The latest laser altimetry technology employed by NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) enables the capture of denser and more precise spatial details. Here, we utilize ICESat-2 data from September 2018 to July 2022 to replicate and analyze the dynamics of the recovery ice stream’s subglacial lake system. To investigate the pathways of subglacial water transfer and determine the outline of subglacial lakes, we employ the differential digital elevation model (DEM) method to depict the surface elevation changes of each subglacial lake at monthly intervals. Our findings indicate significant migration in the activity location of 4 lakes. Notably, Rec1, previously regarded as a single lake, performed as two distinct lakes during the study. Furthermore, we identify two large-scale lakes with subglacial water flux reaching 0.5 km3.

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15837

Authors: Niki Papavasileiou Stavros Kolios

The scope of this study is to analyze the variations in the mixing layer height (MLH) under different cloud conditions on a daily and monthly basis. For this scope, the data of the first five months from the Campbell ceilometer CS135 were analyzed. The instrument is operating in a rural place on Euboea Island (Greece), and the study presents preliminary results about the atmospheric profile of this area, which is also related to the air transport of the largest airport in Greece (Athen’s airport).

Environmental Sciences Proceedings doi: 10.3390/ECRS2023-15854

Authors: Mehdi Khoshboresh-Masouleh Reza Shah-Hosseini

The most efficient tool for practical uses, like weed monitoring in smart farming, is presently small object localization from drone images. While most object detection models indicate competency in localization when trained on large datasets, applying a few-shot learning technique can enhance scene comprehension, even when provided with limited training data. This investigation introduces a few-shot model for localizing weed grasses in multispectral drone images. The model encompasses a reflectance calibration factor, enabling it to perform well on tasks that it has yet to be specifically trained. An inductive transfer system enhances the model’s ability to generalize and accurately localize weeds. The research results demonstrate the potential of the suggested approach to detect weed grasses in drone-based multispectral images and calibration reflectance factor with a mIoU score of 71.45% and an accuracy of 84.3%, despite several difficulties in practical implementation.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-15127

Authors: Sudhir Kumar Sharma Sakshi Gupta Rubiya Banoo Akansha Rai Martina Rani

On the basis of a long-term analysis (2013–2021), we report the inter-annual and seasonal concentrations and possible sources of trace elements (TEs) in PM2.5 over Delhi, India. In all the PM2.5 samples, 19 major and trace elements were extracted: Na, Al, Fe, Ti, Mg, Cu, Zn, Cr, Mn, Ni, As, Mo, Cl, P, S, Ca, K, Pb, and Br. The total annual mean concentration (∑El in PM2.5) of major and trace elements was 17.4 ± 3.1 µg m−3, accounting for 13.9% of PM2.5. The enrichment factor (EF) and IMPROVE model analysis indicate the seasonal abundance of mineral/soil dust (Fe, Al, Ti, Na, Ca, and Mg) at the sampling location of Delhi. During the sampling period, the highest loading of trace elements was recorded in 2015 (19% of PM2.5) and the lowest in 2020 (9% of PM2.5), possibly due to limited activity during COVID-19 lockdown/unlock times. The major sources of elements (in PM2.5) were extracted by a principal component analysis (PCA) as crustal/soil/road dust, vehicular traffic/industrial emissions, combustion (solid + fossil fuels), and sodium magnesium salts in Delhi.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-15922

Authors: Muhammad Shehzaib Ali Viney Aneja Indrila Ganguly Swarnali Sanyal Srijan Sengupta

Wildfires emit large quantities of air pollutants into the atmosphere. As wildfires increase in frequency, intensity, duration, and coverage area, the emissions from these fires have become a significant control issue and health hazard for residential populations, especially vulnerable groups. A critical barrier to addressing the health impacts of air pollution caused by wildfires lies in our limited understanding of its true extent. This problem is expected to be exacerbated by additional factors such as the anticipated increase in wildfire intensity due to climate change, and the associated rise in fine particulate matter (PM2.5) in wildfire smoke, which, according to recent toxicological studies, could be more harmful than typical ambient PM2.5. The primary goal of our study is to develop a novel statistical framework that enables the forecasting of future emissions from active wildfires. This research aims to address the unquantified impacts of wildfire emissions and is a priority research area for many US federal agencies, e.g., NIEHS, US EPA, and NOAA. The framework integrates physicochemical models of emissions and satellite observations with forecasting models based on spatial statistics and machine learning models. Through the incorporation of these diverse datasets, we aim to improve the accuracy and reliability of our predictions regarding the spatio-temporal distribution of wildfire emissions. The potential human health impacts resulting from poor air quality during wildfires are also explored. By modeling the relationship between environmental exposures and disease risk, the burden of disease attributed to both short- and long-term impacts of exposure to wildfire events will be assessed.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-15921

Authors: Sergey Beresnev Maria Vasileva Elvira Ganieva

Volcanoes are not traditionally considered to be significant sources of black carbon particles for the stratosphere. The main reason for this well-established view is the absence of appreciable traces of black carbon in volcanic emissions. Recently, a new hypothesis of the formation and injection of nanodisperse carbon into the stratosphere during explosive volcanic eruptions due to the transformation of carbon-containing volcanic gases into black carbon particles was proposed. Critical analysis of this hypothesis and new observational data have shown that it does not contradict the existing ideas about the principal possibility of the process but can and should be substantially supplemented and corrected. The data on the detection of carbon particles in the stratosphere and in volcanic ash confirm the possibility of the formation of the predicted particles and their identity with particles formed by known technological processes and found after powerful volcanic eruptions in Kamchatka (Russia). The main limiting factors determining both the possibility and the lower boundary of the conditions for the formation of particles of different types of black carbon have been identified: temperature and concentration of carbon-bearing gases in the volcanic column. For Plinian-type eruptions, these parameters appear to be insufficient for the formation of black carbon particles in appreciable amounts and their accumulation in the stratosphere, which contradicts the previously mentioned hypothesis. Virtually, all of the black carbon produced must remain in volcanic ash and volcanic sediments.

Environmental Sciences Proceedings doi: 10.3390/ecas2023-15923

Authors: Sakshi Gupta Priyanka Srivastava Manish Naja Nikki Choudhary Sudhir Kumar Sharma

The air quality of the Himalayan region of India is deteriorating due to the increasing load of particulate matter that is emitted from various local and regional sources, as well as to the transit of dust-related pollutants from the Indo-Gangetic Plain (IGP) and surrounding areas. In this study, the mineralogical characteristics of coarse mode particulate matter (PM10) was analyzed using the X-ray diffraction (XRD) technique from January to December 2019 over Nainital (29.39° N, 79.45° E; altitude: 1958 m above mean sea level), a central Himalayan region of India. XRD analysis of PM10 samples showed the presence of clay minerals, crystalline silicate minerals, carbonate minerals, and asbestiform minerals. It was shown that quartz minerals with significant levels of crystallinity were present in all the samples. Other minerals that are contributing to the soil dust were also observed in the analysis (CaFe2O4, CaCO3, CaMg(CO3)2, calcium ammonium silicate hydrate (C-A-S-H), gypsum, kaolinite, illite, augite, and montmorillonite). The minerals ammonium sulphate, hematite, and magnetite were also found in the samples and are suggested to be from biogenic and anthropogenic activities, including biomass burning, fuel combustion, vehicle exhaust, construction activities, etc. This study indicated that the majority of the minerals in PM10 that were present in this Himalayan region are from soil/crustal dust.