Pub Date : 2024-05-15DOI: 10.3389/fenvs.2024.1389022
Mathias Stein, Daniel Puppe, D. Kaczorek, Christian Buhtz, Jörg Schaller
The growing interest in amorphous silica (ASi) within the fields of soil science and ecology underscores the necessity for a reliable protocol to estimate ASi contents in soil. Alkaline wet chemical extraction methods are commonly employed for silicon (Si) extraction from operationally defined (x-ray) amorphous Si phases or short-range ordered mineral phases in soils and marine sediments. In our study we conducted a comparative analysis of four alkaline extraction methods (1% sodium carbonate, 0.5 M sodium carbonate, 0.2 M sodium hydroxide, and 0.1 M Tiron), assessing their extraction selectivity as well as effectiveness using soils artificially enriched with varying, defined amounts of ASi. While extraction effectiveness was evaluated by determining the recovery rate of initially added ASi, extraction selectivity was determined by measuring aluminum (Al) and iron (Fe) concentrations as indicators of the dissolution of non-target mineral phases. Microwave plasma atom emission spectrometry was used to analyze Al, Fe, and Si concentrations in the extracts. Our results indicate that extraction with 0.2 M sodium hydroxide yields the best outcomes in terms of both extraction effectiveness and selectivity. This more recent extraction technique is conducted at the most alkaline pH (13.3) of all four methods tested, but at ambient temperature (21°C) decreasing the dissolution of non-target mineral phases. Though, no wet-chemical extraction used on heterogeneous samples like soil is precisely selective, and thus able to quantify the target analyte only. Hence, data obtained by such procedures still need to be interpreted with caution considering all their limitations.
土壤科学和生态学领域对无定形二氧化硅（ASi）的兴趣与日俱增，这突出表明有必要制定一个可靠的方案来估算土壤中的 ASi 含量。碱性湿化学萃取法通常用于从土壤和海洋沉积物中操作定义（X 射线）的无定形硅相或短程有序矿物相中提取硅（Si）。在我们的研究中，我们对四种碱性萃取方法（1% 碳酸钠、0.5 M 碳酸钠、0.2 M 氢氧化钠和 0.1 M 铁）进行了比较分析，使用人工添加了不同数量的 ASi 的土壤评估了它们的萃取选择性和有效性。萃取效果是通过测定初始添加的 ASi 的回收率来评估的，而萃取选择性则是通过测量铝（Al）和铁（Fe）的浓度来确定的，铝（Al）和铁（Fe）浓度是非目标矿物相溶解的指标。微波等离子体原子发射光谱法用于分析提取物中铝、铁和硅的浓度。我们的研究结果表明，使用 0.2 M 氢氧化钠进行萃取，在萃取效果和选择性方面都能获得最佳结果。这种最新的萃取技术是在所有四种测试方法中碱性最强的 pH 值（13.3）下进行的，但其环境温度（21°C）降低了非目标矿物相的溶解度。不过，在土壤等异质样品上使用的湿化学萃取法没有精确的选择性，因此只能对目标分析物进行定量。因此，考虑到这些程序的局限性，在解释这些程序获得的数据时仍需谨慎。
{"title":"Silicon extraction from x-ray amorphous soil constituents: a method comparison of alkaline extracting agents","authors":"Mathias Stein, Daniel Puppe, D. Kaczorek, Christian Buhtz, Jörg Schaller","doi":"10.3389/fenvs.2024.1389022","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1389022","url":null,"abstract":"The growing interest in amorphous silica (ASi) within the fields of soil science and ecology underscores the necessity for a reliable protocol to estimate ASi contents in soil. Alkaline wet chemical extraction methods are commonly employed for silicon (Si) extraction from operationally defined (x-ray) amorphous Si phases or short-range ordered mineral phases in soils and marine sediments. In our study we conducted a comparative analysis of four alkaline extraction methods (1% sodium carbonate, 0.5 M sodium carbonate, 0.2 M sodium hydroxide, and 0.1 M Tiron), assessing their extraction selectivity as well as effectiveness using soils artificially enriched with varying, defined amounts of ASi. While extraction effectiveness was evaluated by determining the recovery rate of initially added ASi, extraction selectivity was determined by measuring aluminum (Al) and iron (Fe) concentrations as indicators of the dissolution of non-target mineral phases. Microwave plasma atom emission spectrometry was used to analyze Al, Fe, and Si concentrations in the extracts. Our results indicate that extraction with 0.2 M sodium hydroxide yields the best outcomes in terms of both extraction effectiveness and selectivity. This more recent extraction technique is conducted at the most alkaline pH (13.3) of all four methods tested, but at ambient temperature (21°C) decreasing the dissolution of non-target mineral phases. Though, no wet-chemical extraction used on heterogeneous samples like soil is precisely selective, and thus able to quantify the target analyte only. Hence, data obtained by such procedures still need to be interpreted with caution considering all their limitations.","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.3389/fenvs.2024.1397825
Hanan F. Al-Harbi, Asma A. Alhuqail, Zubairul Islam, H. Ghrefat
This research analyses the long-term vegetation trends in Shada Mountain across six elevation zones, utilizing Landsat 5, 7, 8, and 9 imageries processed via Google Earth Engine and R. The study managed differences in images resolution through meticulous calibration and image processing techniques. The study is structured around two objectives: examining the relationship between vegetation and its proximity to streams and land surface temperature and analyzing trends in the Normalized Difference Vegetation Index (NDVI). Regression analysis revealed a negative correlation between vegetation and proximity to streams in lower zones (1–3), with no significant effect in higher zones (4–6). NDVI trend analysis indicated an overall increase in vegetation across most zones, with the exception of zone 5, which displayed a negative trend (slope −0.0025). The findings reveal that the decline is particularly pronounced among key tree species such as Ficus cordata subsp. salicifolia and Acacia asak, suggesting potential impacts from climate change and land use alterations. These zone-specific insights deepen our understanding of the dynamic ecological processes in semi-arid environments and guide targeted environmental management and conservation efforts.
{"title":"Vegetation trends and dynamics in Shada Mountain, Saudi Arabia, (1984–2023): insights from Google Earth Engine and R analysis","authors":"Hanan F. Al-Harbi, Asma A. Alhuqail, Zubairul Islam, H. Ghrefat","doi":"10.3389/fenvs.2024.1397825","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1397825","url":null,"abstract":"This research analyses the long-term vegetation trends in Shada Mountain across six elevation zones, utilizing Landsat 5, 7, 8, and 9 imageries processed via Google Earth Engine and R. The study managed differences in images resolution through meticulous calibration and image processing techniques. The study is structured around two objectives: examining the relationship between vegetation and its proximity to streams and land surface temperature and analyzing trends in the Normalized Difference Vegetation Index (NDVI). Regression analysis revealed a negative correlation between vegetation and proximity to streams in lower zones (1–3), with no significant effect in higher zones (4–6). NDVI trend analysis indicated an overall increase in vegetation across most zones, with the exception of zone 5, which displayed a negative trend (slope −0.0025). The findings reveal that the decline is particularly pronounced among key tree species such as Ficus cordata subsp. salicifolia and Acacia asak, suggesting potential impacts from climate change and land use alterations. These zone-specific insights deepen our understanding of the dynamic ecological processes in semi-arid environments and guide targeted environmental management and conservation efforts.","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.3389/fenvs.2024.1393878
Mona S. Ramadan, Abdelgadir Abuelgasim, Naeema Al Hosani
This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.
{"title":"Advancing air quality forecasting in Abu Dhabi, UAE using time series models","authors":"Mona S. Ramadan, Abdelgadir Abuelgasim, Naeema Al Hosani","doi":"10.3389/fenvs.2024.1393878","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1393878","url":null,"abstract":"This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.3389/fenvs.2024.1403248
Xin Ma, Jie Li, Guang Li
Introduction: The Gansu section of the Yellow River Basin is an important water resource conservation and replenishment area for the entire Yellow River Basin. With urbanization and socio-economic development, it is urgent to study the characteristics of land-use change and its future simulation in order to realize the coordinated ecological and economic development.Methods: Based on the patch-generating land-use simulation (PLUS) model, this paper investigated the main drivers of land-use type expansion with a comprehensive consideration of natural and socio-economic aspects; moreover, the study simulated land-use change in 2030 under the four scenarios of natural development, cultivated land protection, ecological priority, and economic construction.Results: The results showed the following: 1) the prediction of land-use types continued the historical evolution since 1980. Grassland, cultivated land, and forest land were still the dominant land types, accounting for more than 87% of the basin’s total area. Water bodies and wetlands remained relatively stable, and there was an obvious increase of approximately 20% in construction land. 2) Construction land and grassland were primarily driven by the social factor of the distance from the primary road and the distance from the secondary road, respectively. The cultivated land was greatly affected by the economic factor of population density. 3) The cultivated land protection scenario was the only one of the four scenarios that could make the cultivated land area increase positively, with an increase rate of 0.5%. This scenario also restricted effectively the conversion of cultivated land into construction land. The ecological priority scenario can expand grassland obviously with a proportion of 1.82% and slow down oasis desertion. The economic construction scenario can increase the construction land area the most by a rate of 25.5% to accelerate the economic development of specific regions in the study area.Discussion: Therefore, implementing policies on the basis of choosing suitable scenarios in different areas was significant for optimizing the land-use structure, promoting the efficient use of land resources and ecological environment in the Gansu section of the Yellow River Basin.
{"title":"Simulation and multi-scenario prediction of land-use change in the Gansu section of the Yellow River Basin, China","authors":"Xin Ma, Jie Li, Guang Li","doi":"10.3389/fenvs.2024.1403248","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1403248","url":null,"abstract":"Introduction: The Gansu section of the Yellow River Basin is an important water resource conservation and replenishment area for the entire Yellow River Basin. With urbanization and socio-economic development, it is urgent to study the characteristics of land-use change and its future simulation in order to realize the coordinated ecological and economic development.Methods: Based on the patch-generating land-use simulation (PLUS) model, this paper investigated the main drivers of land-use type expansion with a comprehensive consideration of natural and socio-economic aspects; moreover, the study simulated land-use change in 2030 under the four scenarios of natural development, cultivated land protection, ecological priority, and economic construction.Results: The results showed the following: 1) the prediction of land-use types continued the historical evolution since 1980. Grassland, cultivated land, and forest land were still the dominant land types, accounting for more than 87% of the basin’s total area. Water bodies and wetlands remained relatively stable, and there was an obvious increase of approximately 20% in construction land. 2) Construction land and grassland were primarily driven by the social factor of the distance from the primary road and the distance from the secondary road, respectively. The cultivated land was greatly affected by the economic factor of population density. 3) The cultivated land protection scenario was the only one of the four scenarios that could make the cultivated land area increase positively, with an increase rate of 0.5%. This scenario also restricted effectively the conversion of cultivated land into construction land. The ecological priority scenario can expand grassland obviously with a proportion of 1.82% and slow down oasis desertion. The economic construction scenario can increase the construction land area the most by a rate of 25.5% to accelerate the economic development of specific regions in the study area.Discussion: Therefore, implementing policies on the basis of choosing suitable scenarios in different areas was significant for optimizing the land-use structure, promoting the efficient use of land resources and ecological environment in the Gansu section of the Yellow River Basin.","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.3389/fenvs.2024.1323282
Pengzheng Lou, Xiaohui Zhou
Under the trend of synergistic development of digitalization and greening, this paper investigates the impact of enterprise digital transformation on audit fees and its mechanism, by using textual analysis and performing empirical tests on the data of Chinese listed companies from 2007 to 2021. It is found that enterprise digital transformation significantly increases audit fees, and green innovation partially mediates this process. The study results are robust, even after a series of robustness tests. When financing constraints and environmental regulations are low, the mediating role of green innovation between digital transformation and audit fees is more significant. In addition, green innovation has a stronger mediating role between the use of underlying technology and audit fees, while green substantive innovation has a stronger mediating role between digital transformation and audit fees. This study investigates the effect of enterprise digital transformation on audit fees from the standpoint of green innovation. It offers a new perspective on how accounting firms make audit pricing decisions, provides guidance for enterprise digital transformation and green innovation, and gives an opportunity for China to promote the synergistic transformation and development of digitalization and greening to achieve the dual-carbon goal.
{"title":"Digital transformation, green innovation, and audit fees","authors":"Pengzheng Lou, Xiaohui Zhou","doi":"10.3389/fenvs.2024.1323282","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1323282","url":null,"abstract":"Under the trend of synergistic development of digitalization and greening, this paper investigates the impact of enterprise digital transformation on audit fees and its mechanism, by using textual analysis and performing empirical tests on the data of Chinese listed companies from 2007 to 2021. It is found that enterprise digital transformation significantly increases audit fees, and green innovation partially mediates this process. The study results are robust, even after a series of robustness tests. When financing constraints and environmental regulations are low, the mediating role of green innovation between digital transformation and audit fees is more significant. In addition, green innovation has a stronger mediating role between the use of underlying technology and audit fees, while green substantive innovation has a stronger mediating role between digital transformation and audit fees. This study investigates the effect of enterprise digital transformation on audit fees from the standpoint of green innovation. It offers a new perspective on how accounting firms make audit pricing decisions, provides guidance for enterprise digital transformation and green innovation, and gives an opportunity for China to promote the synergistic transformation and development of digitalization and greening to achieve the dual-carbon goal.","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.3389/fenvs.2024.1421547
Ernest Czermański, Aneta Oniszczuk-Jastrząbek, Tomasz Laskowicz, Artur Badyda, Lara Aleluia Reis, Chris G. Tzanis
{"title":"Editorial: Air pollution as a risk factor affecting human health and economic costs","authors":"Ernest Czermański, Aneta Oniszczuk-Jastrząbek, Tomasz Laskowicz, Artur Badyda, Lara Aleluia Reis, Chris G. Tzanis","doi":"10.3389/fenvs.2024.1421547","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1421547","url":null,"abstract":"","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.3389/fenvs.2024.1412771
S. Sooktawee, T. Kanabkaew, Pichnaree Lalitaporn, Md Firoz Khan, D. A. Permadi, A. Limsakul
{"title":"Editorial: Crucial air quality, atmospheric environment, and climate change in low- and middle-income countries","authors":"S. Sooktawee, T. Kanabkaew, Pichnaree Lalitaporn, Md Firoz Khan, D. A. Permadi, A. Limsakul","doi":"10.3389/fenvs.2024.1412771","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1412771","url":null,"abstract":"","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141016483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.3389/fenvs.2024.1417773
L. Ghermandi, Sofía Gonzalez, Fermín J. Alcasena, António Bento-Gonçalves, J. R. Molina Martínez
{"title":"Editorial: Wildfires in the wildland-urban interface: applied research for fire prevention and hazard reduction","authors":"L. Ghermandi, Sofía Gonzalez, Fermín J. Alcasena, António Bento-Gonçalves, J. R. Molina Martínez","doi":"10.3389/fenvs.2024.1417773","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1417773","url":null,"abstract":"","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.3389/fenvs.2024.1370397
A. Onjia
{"title":"Concentration unit mistakes in health risk assessment of polycyclic aromatic hydrocarbons in soil, sediment, and indoor/road dust","authors":"A. Onjia","doi":"10.3389/fenvs.2024.1370397","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1370397","url":null,"abstract":"","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-26DOI: 10.3389/fenvs.2024.1400853
Mathieu Nsenga Kumwimba, F. O. Ajibade, Mawuli Dzakpasu, Elisa Soana
{"title":"Editorial: Advances in ecotechnologies for the control of non-point source pollution in agricultural and urban watersheds","authors":"Mathieu Nsenga Kumwimba, F. O. Ajibade, Mawuli Dzakpasu, Elisa Soana","doi":"10.3389/fenvs.2024.1400853","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1400853","url":null,"abstract":"","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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